International Journal of Medical Engineering and Informatics (118 papers in press)

Regular Issues

• Developments in technology assisted gait analysis in post-knee arthroplasty  
  by Sumit Raghav, Anshika Singh, Shashwat Pathak, Suresh Mani, Mukul Kumar 
  Abstract: Early monitoring in knee arthroplasty is a critical issue to tackle deviation from expected healing, patient satisfaction and ensuring quality of life. There are various methods suggested and implemented over years with varied degree of performance. This paper presents a relevant review of technology assisted gait analysis in knee arthroplasty. The systematic search revealed 272 studies, of which 13 were added retrospectively through reference screening of the included articles. After title and abstract screening, only 20 studies were included in this review. This review paper provides a comprehensive overview of applications of technology assisted gait analysis to monitor and quantify the status of waking. There is moderate-quality of evidence showed technology-assisted; in particular, sensor-based technology, motion sensors and motion analysis results in a statistically significant improvement in monitoring of gait parameters.
  Keywords: gait analysis; knee arthroplasty; gait parameters.
  DOI: 10.1504/IJMEI.2022.10051736
   
  
• Implementing machine learning techniques to predict bipolar disorder  
  by Nisha Agnihotri, Sanjeev Kumar Prasad 
  Abstract: Bipolar disorder (BD) is a mental and psychiatric disorder which is characterised by alternate mode swings between mania and depression is very common these days. The classification, modelling and characterisation and diagnosis of these mental disorders are important in medical research. An unexpected and unexplored area in BD is to judge the non-verbal behaviour of person accurately. Therefore, this paper address the challenges of detecting BD state by machine learning (ML) techniques to test the non-verbal behaviours activities like various facial expressions, voice recordings and body gestures of mentally ill and controlled persons in a whole spectrum. ML techniques can potentially provide new horizons in diagnosing and treating in mental healthcare. Further, this paper aims to present commonly used algorithms such as decision trees (DT), support vector mechanism (SVM), logistics regression (LR), K-nearest neighbours (KNN), etc. and describe their properties and performances which could act as a guide to select appropriate models. The study shows that people with controlled state behaves significantly different as compared to BD patients in their interpersonal accuracy (IPA). This develops a new training program to improve better understanding and psychosocial functionality in their rehabilitation.
  Keywords: machine learning; mood disorder; anxiety depression; bipolar disorder-I and II; Python; interpersonal accuracy.
  DOI: 10.1504/IJMEI.2022.10052205
   
  
• Segmentation of retinal features in colour fundus images  
  by N. Bino, P.A. Haris, O. Sheeba 
  Abstract: Retinal diseases could be diagnosed by the variations in size, shape and texture of features in retinal images. Colour fundus images are widely used for the diagnosis of retinal diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), etc. Image processing enhances the input image, which segments and highlights regions of interest and quantify it. In this paper, we have segmented optic disc and blood vessels in colour fundus images using an algorithm coded in MATLAB. The input images and their corresponding ground truth images were obtained from public datasets available in Kaggle. The validation of the segmented images is done for parameters like accuracy, sensitivity, precision, F-measure, Mathews correlation coefficient, Dice coefficient, Jaccard index and specificity. The algorithm could effectively segment images, with an average accuracy of 99.74% in segmenting optic disc and 94.74% that of blood vessels.
  Keywords: retinal image segmentation; colour fundus imaging; optic disc; retinal blood vessels.
  DOI: 10.1504/IJMEI.2022.10052912
   
  
• Hybrid multi-kernel SVM algorithm for microaneurysm recognition in colour fundus images  
  by S.B. Mohan, B. Kannan, D. Ravikumar, C. Sivakumaran 
  Abstract: Diabetic retinopathy (DR) is a condition that results in impaired vision and is triggered by diabetic. These alterations in the retinal vessels may be traced back to hyperglycemia. Microaneurysms (MAs) seem to be the initial disease symptoms of DR, and a prompt identification of microaneurysms may assist in the detection of DR in the preclinical phase. Optical coherence imaging system, often known as OCT, is a non-invasive imaging technology that offers a cross-sectional picture of the retinal. We construct new probability foundations for support vector machines (SVMs) using informational differences and the Fisher score. The findings that were achieved were as follows: the accuracy criterion had a score of 96.32%, the sensitivity criterion had a score of 97.34%, the specificity criterion had a score of 95.42%, and the precision criterion had a score of 95.27%.
  Keywords: retinal pictures; scaled Dirichlet combination; support vector machines; SVMs; microaneurysm images.
  DOI: 10.1504/IJMEI.2023.10053229
   
  
• Application of deep learning to segment pelvis bones  
  by N. Thamaraikannan, M. Saravanan, N.K. Anushkannan, S. Ramesh, C. Sivakumaran 
  Abstract: The proper identification and localisation of pelvic bone metastases begins with precise segmentation of the pelvic bones. Existing pelvic bone segmentation algorithms are generally manual or semi-automatic, and they exhibit low accuracy when dealing with picture appearance changes caused by multi-site domain shifts, etc. This paper presents a strategy for segmenting normal pelvic bone characteristics in multiparametric magnetic resonance imaging (mpMRI) using a 3D U-Net based on deep learning. Extensive testing on our dataset indicates the usefulness of our automated technique. The 3D U-Net network, based on deep learning, offers patients accurate identification and segmentation of pelvic bone metastases.
  Keywords: U-Net; pelvis bone; segmentation; biomedical signal processing.
  DOI: 10.1504/IJMEI.2023.10053901
   
  
• Psychological distress and its association with socioeconomic and health behaviour factors  
  by Xiangdong An, Hui-chuan Chen, Monty Taylor 
  Abstract: The authors assess the relationships among psychological distress, socioeconomic status, and health behaviour factors. Data from the Canadian Community Health Survey (CCHS) dataset was analysed for this study using hierarchical regression and ANOVA. Results show that daily energy expenditure is positively related with psychological distress. The authors also find that daily fruit consumption, daily green salad consumption, and monthly frequency of leisure activities are negatively associated with psychological distress. Furthermore, the results indicate that people with personal incomes of less than $20,000 perceive significantly higher distress than people with higher incomes. Among five occupation groups, the sales and services group perceives significantly higher distress compared to the other four groups. The four other occupation groups as categorised by CCHS do not perceive significantly different distress levels.
  Keywords: psychological distress; gender; occupation; income; health behaviour; fruit consumption; green salad consumption; leisure activities; energy expenditure.
  DOI: 10.1504/IJMEI.2023.10053931
   
  
• A novel method to predict stroke using deep learning approach  
  by Swagata Sarkar, A. Jayashree, R. Thirumurugan, C. Sivakumaran 
  Abstract: Strokes remain the main cause of major impairment that lasts for lengthy period of time and ranks as the third greatest cause of mortality overall in United States. The ability to accurately anticipate a stroke is very useful for facilitating earlier intervention as well as treatments. Several studies have concentrated on determining the chance of a heart attack; on the other hand, only few have investigated the possibility of a brain attack. Various machine learning techniques are being created to estimate the chance of a cerebral infarction. We present a data-driven classifiers deep neural Networks (ResNet) for diagnosing strokes predicated on 12-leads ECG information. The quality of the model, which has been fine-tuned to perfection, allows us to achieve a training correctness of 99.99% and an accuracy rate of 85.82%. The findings imply that ECG is a viable adjunct tool for diagnosing stroke.
  Keywords: machine learning; stroke prediction; CNN; deep learning; ResNet.
  DOI: 10.1504/IJMEI.2023.10053942
   
  
• Detection and classification of lung cancer using deep neural network  
  by S. Babu Kumar, M. Vinoth Kumar 
  Abstract: Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%.
  Keywords: lung cancer; histopathology; deep convolution neural network; DCNN; EfficientNetB7.
  DOI: 10.1504/IJMEI.2023.10054175
   
  
• A review of the relationship between flow-volume curve and obstructive sleep apnea  
  by Seval Bulut Eris, Cahit Bilgin, Ömer Eriş, Mehmet Recep Bozkurt 
  Abstract: This study presents a systematic review of studies in the literature evaluating the relationship between the flow-volume curve and obstructive sleep apnea (OSA). When the literature was examined, it was observed that there are few studies in this field, and only statistical analyses were made using the ready parameters given by the pulmonary function test (PFT). New biomarkers can be discovered with characteristic and statistical features extracted from the flow-volume curve to diagnose OSA. Machine learning-based systems can be developed using biomarkers. Rules-based automatic diagnosis systems can enable faster OSA diagnosis. In addition, it can eliminate the disadvantages arising from using a sleep laboratory to diagnose OSA.
  Keywords: obstructive sleep apnea; OSA; flow-volume curve; flow-volume loop; saw-tooth pattern; pulmonary function test; PFT; machine learning.
  DOI: 10.1504/IJMEI.2023.10054791
   
  
• Design and analysis of microfluidic channel with W-shaped electrodes for separation of bio-particles using dielectrophoresis technique  
  by Muktimani Brahma, R. Kumar, Trigunesh Narzary 
  Abstract: A unique microfluidic device with W-shaped electrodes is designed to separate the micro-bio-particles. The microfluidic device efficiently separates the red blood cells (RBCs) and platelets using the dielectrophoresis technique. The separation process has been analysed by using COMSOL Multiphysics software and found that the electrode with the minimum gap of 25 um acquired optimum results at an applied voltage of 8 V. The proposed device gave a high separation efficiency of 89.5% and a separation purity of 99.63%. The enhanced microfluidic device gave better results compared to other recent works and is exceptionally suitable for separation of micro-bio-particles.
  Keywords: dielectrophoresis; red blood cells; RBCs; platelets; separation; microchannel; electrodes; flow rate; electric field; non-uniform; force.
  DOI: 10.1504/IJMEI.2023.10054875
   
  
• Diffusion tensor imaging for Alzheimer’s disease classification using a bag of features and majority voting  
  by Latifa Houria, Noureddine Belkhamsa, Assia Cherfa, Yazid Cherfa 
  Abstract: Alzheimer’s disease (AD) is a neurodegenerative disease and the most common cause of dementia. Thus, various neuroimaging-based methods were proposed to detect this disease at its early stage, called mild cognitive impairment (MCI). We developed a novel approach combining Diffusion tensor imaging (DTI)-indices and ensemble learning to classify AD. A bag of features (BoF) is used to retrieve the locale features, and a support vector machine (SVM) is applied for classification. The majority voting technique is used to combine the final predicted labels. The proposed method achieves an accuracy of 94.0%, 97.0%, and 95.9% to classify CN vs. MCI, CN vs. AD, and MCI vs. AD, respectively.
  Keywords: diffusion tensor imaging; Alzheimer’s disease; bag of features; BoF; support vector machine; SVM; majority voting.
  DOI: 10.1504/IJMEI.2023.10054939
   
  
• An intelligent method for predicting cardiac disease based on PSO-convolutional neural network  
  by M. Balamurugan, P. Blessed Prince 
  Abstract: Cardiovascular disease (also known as CVD) is one of the primary contributors to both morbidity and death. The present state of the art in artificial intelligence plays a significant part in the process of aiding medical professionals in the diagnosis of a variety of disorders. A hybrid framework is proposed for the diagnosis of cardiovascular illnesses by analysing medical voice data. Eleven datasets comprising 14,416 numerical characteristics may be produced by using the method that has been suggested. From the datasets that are produced as a consequence, numerical and graphical characteristics are extracted. In the third layer, numerical data is provided to five separate machine learning (ML) techniques and graphical characteristics are transmitted to convolutional neural networks (CNNs), with transfer learning utilised to choose the best suited configurations. From a benchmark dataset, the PSO-CNN technique that was presented obtains an accuracy of 96.78%.
  Keywords: cardiovascular; PSO-CNN; optimisation; machine learning.
  DOI: 10.1504/IJMEI.2023.10054940
   
  
• Data mining approach for nutrition score calculation of geriatric patients  
  by Vaishali P. Suryawanshi, Rashmi S. Phalnikar 
  Abstract: Due to lack of uniformity in terms of understanding nutrition status, comorbidity types, patient mobility and BMI readings mal-nutrition screening forms cannot be used to correctly comprehend each patients nutritional status. The goal of this study is to use a data mining approach to understand nutrition screening forms in order to determine the malnutrition status of hospitalised geriatric patients. The evaluation measures are used to indicate the accuracy of the classification algorithms. The research work on nutrition screening form, which is designed using classification algorithms, will help physicians to understand their patients nutritional status and help in avoiding malnutrition in them.
  Keywords: malnutrition screening forms; classification; data mining; evaluation measures.
  DOI: 10.1504/IJMEI.2023.10055068
   
  
• Integer wavelet transform based data hiding scheme for medical image protection  
  by Moad Med Sayad, Zermi Narima, Khaldi Amine, Kafi Med Redouane 
  Abstract: A crucial security and protection challenge today in telemedicine is how to ensure the confidentiality of sensitive data sent over the internet and limit access to specified designated information. To keep such details private, we propose in this work a robust and blind medical image watermarking approach that combines integer wavelet transform (IWT) and singular value decomposition (SVD). Low embedding capacity is a potential drawback of modern integer wavelet transform (IWT)-based watermarking systems. A secure high capacity watermarking scheme based on IWT is proposed in this work to overcome this specific limitation. According to experiment results on imperceptibility and robustness, the proposed solution efficiently maintains a significant quality of watermarked images and the watermark is resistant to the most commonly used attacks in watermarking. The integration of information about the patient in his medical image will avoid any confusion between two images.
  Keywords: medical image; digital watermarking; blind watermarking; integer wavelet transform; IWT; singular value decomposition; SVD; QR code.
  DOI: 10.1504/IJMEI.2023.10055240
   
  
• Segnet and U-Net based brain tumour segmentation  
  by R. Ashwini, Swagata Sarkar, C. Pandi, S. Rajalakshmi 
  Abstract: The process of separating individual brain tumours in diagnostic pictures is an essential component of therapeutic therapy. The manual segmentation process takes a lot of time and requires a lot of work, while the current automated segmentation techniques have problems such as a large number of parameters and a lack of accuracy. We created a completely automated technique for the segmentation of brain tumours by applying deep learning. The system was tested on 285 examples of brain tumours using multi-parametric magnet resonant images taken from either the BraTS2018 data set. The quantitative study of brain tumours is helpful in gaining a better knowledge of the features of the tumour as well as in developing more effective treatment strategies. Through the use of this technology, it was possible to get mean dice values of 0.9213 for the overall tumour and 0.8729 for the tumour core.
  Keywords: brain tumour; BraTS database; computer intelligence; Densenet; U-Net.
  DOI: 10.1504/IJMEI.2023.10055242
   
  
• Myocardial infarction detection in late gadolinium enhancement cardiac MRI  
  by Sarra Dali Youcef, Mahammed Messadi 
  Abstract: Cardiac magnetic resonance imaging (MRI) has become the most used technique for assessing myocardial viability. Myocardial segmentation is a fundamental step in the detection of myocardial infarction (MI) on late gadolinium enhancement (LGE) images. In this paper, we provide a system for automated myocardial infarct detection. The myocardial segmentation is applied to cine images and then transferred to LGE images, to subsequently detect myocardial infarction. We tested our approach on the sunnybrook cardiac database. The proposed method shows remarkable accuracy. We obtained a dice similarity coefficient of 0.92 and an average perpendicular distance of 1.75 (mm) between automated and manual segmentation.
  Keywords: left ventricle; cine MR images; late gadolinium enhanced MR images; myocardial infarct; viability.
  DOI: 10.1504/IJMEI.2023.10055269
   
  
• Medical image registration and automatic hippocampus segmentation through convolutional neural network  
  by S. Durga Prasad, K.S.N. Murthy, B. Kannan, C. Sivakumaran 
  Abstract: Alzheimer’s disease (AD) is a brain degenerative ailment that progresses and is irreversible. Mild cognitive impairment, known as MCI, is a clinical indicator that AD may eventually develop. In order to effectively treat and prevent AD, an accurate diagnosis of the illness’s early stages is required. AD often manifests its symptoms first in the hippocampus. Deep machine learning was used in this project with the intention of achieving its aim of segmenting a specific region. The suggested method’s performance was compared to manual segmentation using similarity measures. The performance of a CNN that segments the hippocampus directly is inferior to that of any contouring technique and the findings were 96% accurate. The quantitative results are improved by the application of stringent corrections to the data, although the gap is still rather large. The suggested technique is promising and may be expanded in AD prognosis by predicting hippocampal volume changes in the early stages of the illness.
  Keywords: Alzheimer’s disease; hippocampus; magnetic resonance imaging; convolutional neural network images; TBI; U-net.
  DOI: 10.1504/IJMEI.2023.10055511
   
  
• CNN-based detection of cervical spinal cord injury  
  by G. Chandraiah, K. Mekala Devi, A. Mohamed Abbas, S. Rajalakshmi 
  Abstract: Magnetic resonance imaging (MRI) has the ability to infer alterations on a microscopic level in lesions that are present on the spinal cord. For the purpose of locating lesions brought on by cervical illnesses, our deep neural network using MRI was suggested. The segmentation of spine pictures, as well as their identification, diagnosis, as well as quantitative assessment, have all seen significant applications of the deep learning technology. The most cutting-edge approach to machine learning using medical imaging data is called convolutional neural networks (CNNs), which are powered by deep learning. The proposed network produces segmentation results that are in high degree of agreement with the real world situation. The suggested network produces outstanding results on testing. These findings are based upon that pixel level. The machine learning network that was suggested is both efficient and reliable for doing completely autonomous segmentation of the problematic area.
  Keywords: analysis of the spine province; magnetic resonance imaging; MRI; spinal cord; convolutional neural networks; CNNs.
  DOI: 10.1504/IJMEI.2023.10055578
   
  
• Pneumonia detection using modified VGG 19 net architecture: application of deep convolutional neural network  
  by K. Sivakumar, P. Vinitha Baby, Lourdu Jennifer J.R., C. Sivakumaran 
  Abstract: Pneumonia is a somewhat common condition that may be brought on by a variety of micro-organisms, including bacteria, viruses, and fungus. The use of a deep convolutional neural network fed with X-ray images to detect lung pneumonia has been proposed. Collecting data, enhancing images, performing an adaptive and precise ROI evaluation, extracting features, and diagnosing diseases are all part of the framework’s scope. The suggested deep CNN models were trained using 12,000 pictures from the Pneumonia chest X-ray Dataset, which contained both infected and uninfected chest X-rays. The publicly available new CNN models were trained, and the results were compared with ensemble approach. On the validation set, the suggested technique demonstrated its superiority with an AUC of 95.21 and a sensitivity of 97.76. The proposed technique had a 90.71% success rate in properly identifying images of a patient’s chest as normal, viral pneumonia, or bacterial pneumonia.
  Keywords: pneumonia; deep learning; machine learning; CNN.
  DOI: 10.1504/IJMEI.2023.10055646
   
  
• Effects of various phases and errors on partial volume estimation in the analysis of PV correction in ASL MRI  
  by A. Shyna, Amma C. Ushadevi, Ansamma John, C. Kesavadas, Bejoy Thomas 
  Abstract: Arterial spin labelling (ASL) MRI is widely used to measure cerebral blood flow (CBF). The low resolution of ASL images causes partial volume (PV) effect that causes an erroneous estimation of CBF values. A two-phase study is conducted to investigate the impact of various factors on the PV correction technique using linear regression. The effects of different stages in the PV estimation process are examined in the first phase, and the impact of noise and blurring on PV estimates on the second phase. Experiments are conducted on simulated PCASL images generated from ADNI and the results are validated using RMSE score and ROI analysis.
  Keywords: arterial spin labelling MRI; partial volume effect; cerebral blood flow; CBF; linear regression.
  DOI: 10.1504/IJMEI.2023.10055831
   
  
• Motor imagery classification based upon wavelet packet decomposition and binary particle swarm optimisation  
  by Rohtash Dhiman, Pawan 
  Abstract: Motor imagery (MI)-electroencephalogram (EEG) signals are most convenient basis for brain computer interface (BCI) systems. This investigation aims to develop a novel method for feature extraction and selection that improves classification accuracy in the BCI system. Wavelet packet decomposition (WPD) and approximate entropy (ApEn) are used for feature extraction for EEG signals. Binary particle swarm optimisation (BPSO) technique is used for feature selection. Artificial neural network (ANN) is employed for classifying MI activities with mean accuracy of 87.29%. The proposed methodology can be useful in clinical applications for motor-disabled subjects connecting outside environment with sequences of MI activities.
  Keywords: approximate entropy; ApEn; artificial neural network; ANN; binary particle swarm optimisation; BPSO; brain computer interface; BCI; electroencephalogram; EEG; motor imagery; MI; wavelet packet decomposition; WPD.
  DOI: 10.1504/IJMEI.2023.10056105
   
  
• Radial basis function network equipped with an ensemble-based Lasso ridge model in diagnosis of breast cancer  
  by Pooja J. Shah, Trupti P. Shah 
  Abstract: The information that is kept in the form of medical records is of tremendous assistance when it comes to the process of putting together medical decision support systems. The goal of this study is to provide a description of two distinct methods for the development of a medical diagnostic tool that is driven by data in the detection of breast cancer. The radial basis function network (RBFN) architecture with Lasso and ridge regularisation, as well as ensemble learning, are both methods that have been proposed as potential solutions. Following the implementation of the proposed networks on the Wisconsin Breast Cancer (WBC) dataset, comparative analysis is carried out.
  Keywords: radial basis function network; RBFN; Lasso and ridge regularisation; LR; ensemble learning; breast cancer; BC.
  DOI: 10.1504/IJMEI.2023.10056107
   
  
• A new hybrid approach for automated leakage detection in fundus fluorescein angiography images  
  by Bikesh K. Singh, Suraj Chandrakar, Ankur Khare, Ravi Khatri, Deepak Chandravanshi, Sumit Kumar Banchhor 
  Abstract: The existing methods for quantitative investigation of leakage in fluorescein angiography (FA) images involve manual delineation and lack annotated datasets and high accuracy. The proposed hybrid approach can automatically detect leakage in FA images with high accuracy. The hybrid approach combines the fuzzy c-means (FCM) with distance regularised level set evolution (DRLSE). Two expert radiologists test the accuracy of the proposed system. We have also tested inter-and intra-observer variability between the two experts. The study observed high accuracy of 0.98 +- 0.02 and 0.98 +- 0.03 with experts 1 and 2, respectively. Further, the accuracy of manual inter-observer (expert 1 and expert 2) and intra-observer (expert 1) were observed as 0.95 += 0.05 and 0.98 +- 0.02, respectively. The proposed fully automated hybrid approach can accurately detect and quantify the leakage in FA images and thus has great potential value in diabetic retinopathy (DR).
  Keywords: diabetic retinopathy; DR; fluorescein angiography; FA; leakage; segmentation; inter- and intra-observer variability; fuzzy c-means; FCM; distance regularised level set evolution; DRLSE.
  DOI: 10.1504/IJMEI.2023.10056108
   
  
• Remotely operated infant incubator  
  by Maham Sarvat, Suhaib Masroor, Jawaid Shabbir, Zohra Jabeen, Bilal Ahmad 
  Abstract: A neonatal incubator is a sealed enclosure where a child may be placed for treatment in a safe and healthy environment. The system can provide a heater, a fan, a water container for applying moisture, a control valve to regulate oxygen supply, and access ports for nursing care. In this paper, a novel approach is presented to create a cost effective wireless portable baby incubator. It is shown that the control and operation of the incubator is performed wirelessly from the nursing counter, located within the range of 30 m via an Android device. The device will provide information of all the installed functions on a single screen. Moreover, it requires only a single staff member to monitor them, and give them necessary instructions via the same Android device. To validate the efficiency of the proposed design, the incubator is simulated 30 times, and the acquired results are tested statistically by ANOVA.
  Keywords: infant incubator; remote monitoring and control; wireless communication; smart electro-medical devices.
  DOI: 10.1504/IJMEI.2023.10056665
   
  
• Efficacy and bioequivalence study of Zithromax and Azithral a drug for the treatment COVID-19  
  by Hattab Youcef 
  Abstract: Generic drugs are increasingly marketed around the world to the detriment of their quality. The objective of this work was therefore to carry out a bioequivalence study between the originator Zithromax a drug for the treatment COVID-19 and its generic Azithral, this bioequivalence study is based on the quality control of generic and originator azithromycin tablets, which is carried out after validation of the azithromycin assay method, and essentially on pharmaco-technical tests (mass uniformity, breakability, disintegration, dissolution in vitro). By referring mainly to different pharmacopoeias, the tests have shown that the quality of generic tablets is acceptable but seems to be less satisfactory than that of originator tablets.
  Keywords: bioequivalence study; generic; azithromycin; COVID-19.
  DOI: 10.1504/IJMEI.2023.10056861
   
  
• Multi perceptron network based model to classify the multi stages of Alzheimer’s disease using clinical data  
  by Chevvenahalli Rangegowda Nagarathna, Mohanchandra Kusuma 
  Abstract: Alzheimer’s is a neurodegenerative disease that diminishes the capability of individuals to perform their daily activities. It is an irreversible disease. Once it is started it cannot be cured, but early detection of the disease can help to slow down the progression of the disease by starting the treatment early. In this experiment, we use clinical study data available at the Alzheimer’s disease neuroimaging initiative (ADNI) dataset to detect the different stages of Alzheimer’s and forecast the duration required for conversion from mild cognitive impairment to Alzheimer’s disease (AD) and also classification of the disease is done. The clinical signs of AD are rendered by age, patient education details, the progression rate of disease, and cognitive information. Various machine learning techniques like multi perceptron networks, random forest, SVM, and decision tree classifiers are used to do binary and multi-classification of AD, late mild cognitive impairment (LMCI), early cognitive impairment (EMCI), and cognitive control (CN). The multi perceptron network shows the best performance by giving an average accuracy of 99.24% for binary classification and 93% for multi class classification. The proposed model enables early detection and also predicts the different stages of Alzheimer’s using the clinical dataset.
  Keywords: Alzheimer’s; dementia; MMSE; multi-layer perceptron; random forest; SVM; late mild cognitive impairment; LMCI; early cognitive impairment; EMCI.
  DOI: 10.1504/IJMEI.2023.10056957
   
  
• Diagnosis of the COVID-19 with deep learning  
  by Moulud Demouche, Abderrahmane Baadache, Djamil Aissani, Taha Zerrouki 
  Abstract: Since the beginning of 2020, the COVID-19 virus has spread widely throughout the world. RT-PCR is used to diagnose the disease, but radiologists studied advances in chest CT scans to produce high-quality images. The purpose of this research is to develop a deep learning system for detecting the COVID-19 virus using chest computed tomography. We proposed three convolutional neural network (CNN) approaches to developed three models, and applied them to a publicly available COVID-19 screening chest CT scan dataset of 2,482 images. Our models have shown that they work by getting a high F1-score of 98.5% for the InceptionResNetV2 model.
  Keywords: COVID-19; convolutional neural network; CNN; deep learning; Xception; InceptionResNetV.
  DOI: 10.1504/IJMEI.2023.10057090
   
  
• Deep learning for smart home security systems  
  by N. Ashokkumar 
  Abstract: Smart home security systems helps to monitor the homes from remote place. These systems were created with the goal of mitigating risks and, eventually, leading to the consolidation of a sense of security. We get off with an overview of the deep learning approach and an analysis of its significance in the evolution of machine learning. The most basic prerequisite for choosing the most appropriate way to build a smart home is face recognition. Kinect is used to generate the point cloud image of a human body in this technique. The data that has been received will be processed by the Arduino microcontroller, which will ultimately result in the intelligent management of various domestic electrical equipments. The experimental findings demonstrate that by analysing and processing human point cloud photos, the proposed algorithm is able to effectively recognise human attitude and operate domestic appliances and other operations.
  Keywords: smart home; machine learning; deep learning; DL; machine learning; ML; denial of service; DoS; intrusion detection system; IDS; internet of things; IoT.
  DOI: 10.1504/IJMEI.2023.10057160
   
  
• Information domain approach to investigate the cardio-vascular, cardio-respiratory and vasculo-respiratory causal coupling to study gender and age-based changes  
  by Kirti Singh, Indu Saini, Neetu Sood 
  Abstract: This study presents a framework to quantify the coupling changes in cardiovascular, cardiorespiratory, and vasculorespiratory complexity using information domain approaches based on compensated transfer entropy (cTE). The dataset used for this research consists of the group of healthy young and old subjects. The proposed technique delivers significant coupling changes in healthy dataset based on age and gender in contribution of heart rate (HR), blood pressure (BP), and respiration (RESP). For validation of statistically significant values, the paired t-test is used. This study provides additional value to the prognostic and diagnostic approach in biomedical science.
  Keywords: heart rate variability; HRV; respiration; RESP; blood pressure; BP; RR interval; transfer entropy; TE.
  DOI: 10.1504/IJMEI.2023.10057240
   
  
• Parkinson disease detection using ResNet-50: a CNN architecture  
  by N.K. Anushkannan, S. Nirmalkumar, L. Chithra, K. Manoharan 
  Abstract: A person’s motor and cognitive symptoms from Parkinson’s disease (PD) can change dramatically over time. Some of the signs of Parkinson’s disease are shared with more common illnesses including ageing and essential tremor, making diagnosis challenging. Lots of study has gone into figuring out the best ways to diagnose this illness. Using deep learning, recursive neural networks (RNNs), and convolutional neural networks (CNNs) that can differentiate between healthy and PD patients, this research hopes to automate the Parkinson’s disease (PD) diagnostic procedure. As such, this study intends to analyse numerous imaging and movement datasets to determine which Parkinson’s disease test is best in terms of its capacity to differentiate between individuals, as different datasets may emphasise different elements of the illness (notably cube and spiral pentagon datasets). Furthermore, this study will be utilised to compare imaging and time series datasets for their diagnostic utility in Parkinson’s disease.
  Keywords: Parkinson disease; deep learning; signal processing; convolutional neural network; CNN; ResNet-50.
  DOI: 10.1504/IJMEI.2023.10057278
   
  
• Parkinsons detection based on combined ResNet architecture and LSTM  
  by M. Gokuldhev, Anjani Kumar, K. Kiruthika, B.R. Tapas Bapu 
  Abstract: In this research, a novel approach to the clinical condition of neurodegenerative disorders like Parkinson’s is presented. The proposed method uses a mix of deep networks that have been pre-trained as well as long-term and short-term memory (LSTM). A new model called PD-ResNet is constructed and based on the residual network (ResNet) architecture to understand the variations between people with Parkinson disease and healthy controls. In order to execute adoption of the obtained learnt representations across data originating from various medical contexts, a new loss functionality is presented as well as used in the development of the deep neural networks (DNNs). Experiments conducted on the clinic gait dataset demonstrate that our suggested model has good performance, with a correctness of 95.51%, an accuracy of 94.44%, a recalls of 96.59%, a sensitivity of 94.44%, as well as a F1 measure of 95.50%.
  Keywords: Parkinson illness; ResNet architecture; deep neural networks; DNNs; long short-term memory.
  DOI: 10.1504/IJMEI.2023.10057280
   
  
• Forward neck posture on cervical pain among university students: effect of smartphone addiction  
  by Selvaraj Antoniraj, Hafizah Che Hassan, K. Baleswamy 
  Abstract: These days, smartphones are used for much more than just making and receiving phone calls and text messages; they can also be used to view and share media, surf the web, and send and receive electronic mail. A cross-sectional analysis of college students between the ages of 17 and 30, who were using smartphones for prolonged period of time. The survey was conducted through an online questionnaire. Simple random sampling was used to get 404 samples, 216 of which were men and 188 of which were women. There is a positive and highly significant (p < 0.01) association between forward neck posture and cervical pain in college students. Using a smartphone excessively is associated with increased risk of cervical pain (p < 0.01). The findings of this study reveal that the extended and extensive use of smartphones by university undergraduates is a key contributing cause of their neck pain.
  Keywords: cervical pain; neck posture; smartphone addiction; students.
  DOI: 10.1504/IJMEI.2023.10057778
   
  
• An optimised machine learning approach using Intel oneAPI for detecting cardiovascular system failure  
  by Akshay Bhuvaneswari Ramakrishnan, A. Srilakshmi, Shriram K. Vasudevan 
  Abstract: Heart failure is a frequent illness that might result in circumstances that could be fatal. Early heart illness identification is essential for prompt treatment and better patient outcomes. In this study, we offer a multi-machine learning model, trained with Intel oneAPI, technique for the early diagnosis of heart illness. To choose the most pertinent patient characteristics that will be used as input for our machine learning models, we employ a genetic algorithm (GA). To forecast the risk of cardiac disease, we optimise the models using a genetic algorithm and Intel oneAPI. Our findings demonstrate the high accuracy of our approach, with linear discriminant analysis optimised with GA producing the most accurate model with a 92.34% accuracy rate.
  Keywords: heart failure; machine learning; genetic algorithm; optimisation; oneAPI.
  DOI: 10.1504/IJMEI.2023.10057866
   
  
• Increasing the accuracy of oscillometric blood pressure measurement  
  by Y.M. Blessy, K. Rajalakshmi, R. Raj Anandh, Vajravelu Ashok 
  Abstract: The presence of unregulated high blood pressure (BP) is linked to an elevated chance of health problems, some of which may cause harm to a range of organs across the body. Among the most dangerous as well as widespread conditions is hypertension, which refers to increased blood pressures. Unfortunately, correct blood pressure readings need the use of many medical instruments. The proposed system compares the systolic and diastolic objectives of blood-pressure readings, which are obtained using analogue devices and those obtained using an electronically controlled blood pressure reading tool. The systolic measurement alone had a mean variance of 3,265 mmHg, while the diastolic measurement had a mean variance of 6,165 mmHg. The discrepancy is then included into these processes of calibrating the automated blood pressure measuring device. The accuracy of the output of the calibration reading tool is between 99.47% and 99.82%.
  Keywords: blood pressure measurement; non-invasive; micro controller.
  DOI: 10.1504/IJMEI.2023.10057971
   
  
• Integrating dialysis machine functions using system design approach  
  by Vicky B. Sardar, Atul Sajgure, Neela R. Rajhans 
  Abstract: In new product development (NPD) process, system design thinking contributes significantly towards the effectiveness of the product. For effective system design solution, incremental improvements to the existing system at each element level should meet the user expectations. A detailed view of the inputs and outputs occurring at each component level of a hemodialysis machine is presented in this study. This research work provides suggestions to address the functions of existing dialysis machine for home use. The suggestions are based on systems approach. Risk mitigation plan is devised for preventing the critical accidents while designing the machine for home hemodialysis using Swiss Cheese approach.
  Keywords: new product development; NPD; system design; home hemodialysis; HHD; Swiss Cheese approach.
  DOI: 10.1504/IJMEI.2023.10057977
   
  
• Classification of cardiac arrhythmia disease using deep learning auto encoder algorithm  
  by B. Nithyasundari 
  Abstract: An electrocardiogram (ECG) is a very important diagnostic tool for figuring out what is wrong with a person’s heartbeat. Arrhythmia has been put into groups in many different ways. Due to the fact that electrocardiogram (ECG) data are in a state of continual change, it might be challenging to employ standard handmade approaches. We offer a framework for deep learning that does not need a great deal of supervision and is geared toward the detection of arrhythmias (WSDL-AD). Three distinct ECG waveforms are selected from the arrhythmia database maintained by MIT and BIH in order to evaluate the proposed methodology. The primary objective of this research is to discover a deep learning method, with the end aim of classifying the three distinct heart ailments that have been selected. The highest rate of correct recognition that can be achieved is 98.51%, while the accuracy of testing is around 92%.
  Keywords: MIT-BIH dataset; electrocardiogram; ECG; arrythmia disease; deep learning.
  DOI: 10.1504/IJMEI.2023.10058004
   
  
• Breast cancer prediction using whale optimisation algorithm and ANFIS classifier  
  by M. Sri Geetha, S. Navaneethan, G. Divya, B. Geethavani 
  Abstract: Breast cancer is one of the most prevalent forms of the disease that affects women and is responsible for billions of deaths globally. The adaptive clustering is for the purpose of image segmentation, followed by an adaptive neuro-fuzzy inference system (ANFIS) for the categorisation of images. In order to optimise the parameters, a deep learning-based method known as the whale optimisation algorithm (WOA) has been suggested. The breast cancer database for the state of Wisconsin comprised information of patients who had a known diagnosis. The ANFIS classifiers were provided with a training set of records of this kind in order to learn how to discriminate between new cases in the domain. In order to identify breast cancer, the ANFIS classifier was fed nine characteristics that define breast cancer indications. The suggested ANFIS model included both the adaptability of neural networks and the qualitative approach of fuzzy logic.
  Keywords: adaptive neuro-fuzzy inference system; ANFIS; whale optimisation algorithm; WOA.
  DOI: 10.1504/IJMEI.2023.10058260
   
  
• Human emotion classification enabled by EEG signal analysis and machine learning  
  by Dattaprasad A. Torse, Mahadev M. Bagade 
  Abstract: In recent years, automated human emotion recognition system poses numerous challenges with large and complex data and its vast computation. In this work, we consider a three-dimensional continuous valence-arousal-dominance framework for data representation in space using the DEAP dataset of 32 participants. An experiment was carried out to test practicality of the proposed system using the EMOTIV Insight headset electroencephalogram (EEG) data from four channels. We utilise a tunable-Q wavelet transform (TQWT) algorithm to extract frequency domain characteristics of the signals and classify using time windows of two seconds. The extracted power of the signals was identified as features from different frequency bands and the K-nearest neighbour (KNN) and random forest (RF) classifiers resulted in an accuracy of 97.8%.
  Keywords: emotion recognition; tunable-Q wavelet transform; TQWT; K-nearest neighbour; KNN; random forest; RF.
  DOI: 10.1504/IJMEI.2023.10058261
   
  
• Brain tumour segmentation using deep learning method - inception U-net  
  by S.B. Mohan, B. Ramesh, G. Gurumoorthy, B. Kannan 
  Abstract: The objective of the procedure of segmentation for brain tumours is to provide an accurate delineation of the regions that are affected by the tumour. In recent years, deep learning algorithms have shown performance that is seen to be promising in the field of addressing a variety of difficulties pertaining to computer vision. With the assistance of a deep learning model that is supported by DenseNet and commencement, a highly autonomous process for managing the work of glioma division in pre-usable X-ray examinations has been developed. This method can handle the task of handling the task of segmentation of gliomas. The incorporation of inception modules resulted in a substantial improvement (p < 0.001) in the segmentation performance across the board including all glioblastoma sub-districts. The algorithm attained a high degree of accuracy while maintaining a high level of performance throughout all three of the BraTS 2018 datasets.
  Keywords: segmentation; brain tumour; computer vision; inception; DenseNet architecture.
  DOI: 10.1504/IJMEI.2023.10058262
   
  
• Hybrid brain computer interface structures to control drone in 3D  
  by B. Divya, S. Bharathi, G.R. Mahendra Babu, K. Rajalakshmi, Nalini Mohan 
  Abstract: In order to overcome the shortcomings of traditional single BCI systems, a novel method of brain-computer interfacing (BCI) has been proposed; this method is known as a hybrid BCI. Although several tests using hybrid BCIs have yielded promising results, much more research and development is still needed in this area. As a corollary, we also did a thorough analysis of the most recent studies focusing on the usefulness of BCIs. To determine the weight each factor should have in the overall evaluation of BCI usability, we zeroed in on the tasks and measurements involved. We reported on the satisfaction-based usability features of BCI and mixed BCI systems efficacy, and efficiency, and made some suggestions for further research. Usability testing can reveal problems with the HCI and ergonomics of BCI and hybrid BCI systems, as well as provide suggestions for future study in the area.
  Keywords: brain-computer interfacing; BCI; drone control; 3D images.
  DOI: 10.1504/IJMEI.2023.10058455
   
  
• Integration of matching smart phone data with long-term continuous glucose monitoring data pictures of food intake  
  by A. Rajasekar, J. Megala, Swagata Sarkar, C. Sivakumaran 
  Abstract: The treatment of diabetes relies heavily on careful glucose monitoring as an important component. Nevertheless, further information on the context is required in order to completely comprehend and interpret glucose levels in a meaningful manner. Continuous glucose monitoring (CGM), automated analysis of new glucose variables, and visualisation of CGM data via the ambulatory glucose profile have all contributed to significant advancements in precise glucose monitoring over the past five years. This made significant improvements in glucose monitoring. There are considerable challenges to its adoption, such as the fact that it is inconvenient and does not give immediate and frequent feedback. However, intermittent self-monitored blood glucose (SMBG) can offer extra information that may be used to make choices about therapy. Systems that indicate greater results compared to standard SMBG alone include those that give instant feedback to patients as well as decision support tools for both patients and physicians.
  Keywords: glucose monitoring; biomedical; smartphone; diabetes.
  DOI: 10.1504/IJMEI.2023.10058522
   
  
• Revolutionising organ donation and transplantation for a better future: a blockchain based approach  
  by Rachana Y. Patil, Yogesh H. Patil 
  Abstract: Prolonged monitoring of life-sustaining organs and their supply chain is challenging. Patients have to wait for a longer time due to numerous lacunas in the organ donation and transplant system. The objectives of this article are manifold, first to address the major challenges in successful organ donation and transplantation systems worldwide. Second, to focus opportunities in workforce expansion, developing modern infrastructure and creating social awareness for organ donation. Third is to regulate the legal, ethical and administrative management among government authorities and medico-legal firms. To overcome these irregularities, we have proposed a secure, distributed and immutable blockchain based approach to improve and streamline organ donation and transplantation procedures. This ensures the availability of organs for the needy patents. This blockchain-specific approach helps to prevent and monitor organ trafficking by auditable medical transactions. This opens up blockchain-based smart health services to defend doctors and patients rights.
  Keywords: organ donation; transplantation; blockchain; challenges; opportunities.
  DOI: 10.1504/IJMEI.2023.10058768
   
  
• Anomaly detection architecture for smart hospitals based on machine learning, time series, and image recognition analysis: survey  
  by Somaya Haiba, Tomader Mazri 
  Abstract: Smart hospital networks are considered the most sensitive networks for anomalies; any tiny existence might produce very different dangerous scales. The usual anomaly detections dedicated to this kind of network are not able to analyse all the different categories and proprieties of the generated data, because the majority of them rely only on time series analysis which is not able to cover all the circulated pieces of information. For that, in this paper, we will survey a proposed anomaly detection architecture that can dominate all the data categories that exist inside the e-health network using image recognition as well as time-series analysis.
  Keywords: E-healthcare monitoring network; IoMT; smart hospitals; E-health anomaly; anomaly detection; machine learning; time-series analysis; IoT security; ImageGray analysis; medical data; Cybersecurity.
  DOI: 10.1504/IJMEI.2023.10058832
   
  
• A novel deep learning approach for b-value optimisation in intravoxel incoherent motion magnetic resonance imaging on simulated data  
  by Abin Shoby, Jerome Francis, Jini Raju, C. Ushadevi Amma, Ansamma John 
  Abstract: Intravoxel incoherent motion magnetic resonance imaging (IVIM MRI) is a non-invasive technique which measures the perfusion and diffusion effects present in a tissue. One of the major challenges in IVIM imaging is the prolonged scan time since multiple b-value images are required for estimating IVIM parameters. The proposed work introduces a novel approach for reducing the number of b-values required for the generation of IVIM signals from unknown b-values using long short-term memory (LSTM) network. Experimental results show that LSTM network has the capability of accurately estimating IVIM parameters even with 4 b-values, with the estimated values are in agreement with the literature. If an IVIM machine takes 37.5 seconds for the acquisition of a b-value signal, then it will take only 2.5 minutes for acquiring IVIM signals for 4 b-values. This will reduce the patient discomfort and increases the clinical acceptance of IVIM imaging.
  Keywords: intravoxel incoherent motion imaging; long short-term memory; LSTM; area error; optimal b-values.
  DOI: 10.1504/IJMEI.2023.10058833
   
  
• Tri-partition-based b-value optimisation for intravoxel incoherent motion magnetic resonance imaging of brain  
  by Jini Raju, C. Ushadevi Amma, Ansamma John, V. Jineesh 
  Abstract: Intravoxel incoherent motion (IVIM)-based magnetic resonance imaging (MRI) technique allows the simultaneous estimation of perfusion and diffusion without the use of contrast agents. As the number of b-values increases, the scan time also increases, which in turn causes patient discomfort. This necessitates the reduction in the number of b-values (b-value count) and the optimisation of absolute b-values that quantify both the perfusion and diffusion effects accurately. The two partition approach of biexponential model fails to consider the b-value regions where both the perfusion parameters have significance. The proposed work explores the possibility of finding minimal and optimal set of b-values using the images corresponding to a set of 21 b-values, using random sampling-based tri-partition method, by varying b-value counts from 10 to 4. Experimental results demonstrate that appropriate selection of b-values from the three partitions generate quality parametric maps.
  Keywords: intravoxel incoherent motion; IVIM; diffusion weighted imaging; b-value optimisation; random subsampling.
  DOI: 10.1504/IJMEI.2023.10058916
   
  
• Early-stage leukaemia detection using sophisticated machine learning algorithms  
  by Pawan Whig, Anant Aggarwal, Dhaya Sindhu Battina, Srinivas Venkata, Shama Kouser, Ashima Bhatnagar Bhatia 
  Abstract: This paper investigates the efficacy of convolutional neural networks (CNNs), a deep learning technique, in early-stage leukaemia detection - a crucial task for improving outcomes. Comparing support vector machines, random forests, artificial neural networks, and CNNs, we assess performance on a dataset of blood samples from leukaemia patients and healthy subjects. Results reveal high accuracy across models, with CNN outperforming other methods in both accuracy and efficiency. CNNs capacity to learn complex patterns from raw data, such as blood samples, sets it apart from traditional algorithms. This study underscores CNNs potential to revolutionise early-stage leukaemia detection, demonstrating its significance in advancing cancer diagnosis.
  Keywords: early-stage leukaemia; machine learning; deep learning; convolutional neural network; CNN; classification; feature extraction; diagnosis.
  DOI: 10.1504/IJMEI.2023.10060130
   
  
• Solution for I-RFID-based smart infrastructure health monitorings security and privacy  
  by A. Rajasekar, K. Vidya, G. Adharsh, C. Sivakumaran 
  Abstract: The idea of a smart city and smart infrastructure is a relatively recent one, and it refers to the management and control of the various infrastructures of a city via the integration of internet and cellular networks. The addition of internet of things functionality to cellular networks results in an increase in both the scalability and dependability of the whole system. The most current innovation in integrated radio frequency identification (I-RFID) sensor technology allows for the data collected by the sensor to be sent over greater distances in a more secure manner using access point and base station. We constructed a network of intelligent nodes, each of which will consist of a radio-frequency identification tag, a reduced function RFID reader and sensors. The proposed method exhibits minimal data delivery losses and a considerable reduction in the amount of time spent on transmission delays.
  Keywords: radio frequency identification; RFID; healthcare monitor; security; smart infrastructure.
  DOI: 10.1504/IJMEI.2023.10060421
   
  
• A novel hybrid-based approach for detection of skin lesion using machine learning techniques  
  by Nikhil Singh, Sachin Kumar, Shriram K. Vasudevan 
  Abstract: As a result of medical sector treatment strategies, the incidence of skin cancer has increased globally over the past few decades. It is essential to develop automatic detection systems to aid doctors in the early diagnosis of skin cancer. The automated identification of skin lesions using dermoscopic pictures remains a difficult and complex endeavour. This proposal describes a unique method for detecting skin cancer using dermoscopy pictures. In order to enhance the performance, we combined the segmentation and classification techniques with the innovative hybrid methodology fuzzy brain storming optimisation (FBSO). In two steps, we performed lesion segmentation, noise reduction, and feature extraction before submitting our dataset to machine learning algorithms for optimisation and analysis. Many characteristics, including accuracy, sensitivity, specificity, precision, F1-score, and AUROC, have been used to validate the performance of the suggested model. The experiment demonstrates that random forest’s accuracy [91.0% for dataset 1 (ISIC) and 92.5% for dataset 2 (HAM10000)] and AUROC [96.1% for dataset 1 (ISIC) and 97% for dataset 2 (HAM10000)] are superior to those of traditional models.
  Keywords: machine learning; AI; healthcare; skin cancer; medicine; melanoma.
  DOI: 10.1504/IJMEI.2023.10060493
   
  
• A machine learning-based methodology for stratifying patients into obstructive sleep apnea risk  
  by Christos Bellos, Konstantinos Stefanou, Georgios Stergios, Dafni Patelou, Thomas Katsantas, Konstantinos P. Exarchos, Apostolis Nikolopoulos, Agni Sioutkou, Georgios Siopis, Konstantinos Kostikas, Chara Tselepi, Athanasios Konstantinidis 
  Abstract: Obstructive sleep apnoea (OSA) is a common and chronic disorder that leads to increased day-time sleepiness, is associated with accidents, emerging of cardiovascular and metabolic disorders as well as depression. Almost 20% of the population suffers from OSA while a large portion of people are undiagnosed. The objectives of the current work are: 1) the development of a platform to keep a record of home sleep studies and monitor patients; 2) its use for screening tool for the detection of undiagnosed cases in the general population; 3) the design of a machine learning-based methodology for stratifying patients into high and low risk of OSA based on a series of clinical findings and questionnaires. The proposed methodology showed overall accuracy 87.4%, sensitivity 92.1% and specificity 77.1%.
  Keywords: obstructive sleep apnea; machine learning; web-based system; visualisation platform; data analysis.
  DOI: 10.1504/IJMEI.2023.10060654
   
  
• Prediction of COVID-19 cases with epidemiological and time series models  
  by Aman Shakya, Anita Sharma, Sanjeeb Prasad Panday, Rom Kant Pandey 
  Abstract: This work analyses the official data of coronavirus and predicts the evolution of the epidemic in Nepal. The generalised SEIR model has been applied with hybrid of ETS-ARIMA time series model for the time series analysis and predictions of evolution of COVID-19 cases. The prediction has been made for 30 days using the past data of thirteen months. The prediction made by generalised SEIR model has been corrected using two time series models, ETS and ARIMA model. The predicted error by ARIMA model is added to the prediction made by generalised SEIR model. Use of generalised SEIR model along with ETS and ARIMA model improves the time series prediction of coronavirus spread in case of Nepal as compared to the generalised SEIR model. Also, the SEIR-ETS-ARIMA model reduces the estimation error as compared to SEIRD-ARIMA model. Improvement in all quality measures, MAE, MSE, RMSE and MAPE has been observed.
  Keywords: corona; SEIR; ETS; ARIMA; SEIRD-ARIMA.
  DOI: 10.1504/IJMEI.2023.10060974
   
  
• Sentiment analysis in medication related texts  
  by Rabia Bounaama, Mohammed El Amine Abderrahim 
  Abstract: This study deals with the sentiment analysis of medication related texts improvement by using classical and state-of-the-art approaches like long-short-term memory (LSTM) and bidirectional encoder representation from transformers (BERT) model. The models evaluation demonstrates that transformer model outperformed other models like Na?ve Bayes (NB), logistic regression (LR) and support vector machine (SVM). Despite the superiority of state-of-the-art models, we show that the use of classical approaches remains a possible choice to avoid the complexity problem of the state-of-the-art approaches. The texts used in our experiment come from the Social Media Mining for Health (SMM4H) corpus and the coronavirus tweets NLP-text classification corpus.
  Keywords: sentiment analysis; machine learning; transformer; long-short-term memory; LSTM; bidirectional encoder representation from transformers; BERT.
  DOI: 10.1504/IJMEI.2023.10061044
   
  
• Predicting low back pain symptoms with machine learning  
  by T. Senthilkumar, A. Kanmani, P. Senthil, A. Niranjil Kumar, S. Narasimha Prasad 
  Abstract: Chronic low back pain (LBP), often known as low back syndrome or LBP, is a condition that may be brought on by a number of different disorders. It is also the biggest cause of disability on a global scale. Because there are now more digital pictures available in orthopaedics, researchers have been able to create techniques that are connected to artificial intelligence. The objective of this research was to assess the usefulness of a variety of machine learning (ML) algorithms and sample entropy (SampEn), a metric that evaluates the degree to which motion variability is complicated, in determining the presence of the ailment known as LBP. The Gaussian naive Bayes ML algorithm had the highest level of performance out of all the other algorithms that were evaluated, with an accuracy rate of 79% when it came to identifying patients suffering from CLBP.
  Keywords: low back pain; LBP; machine learning; artificial intelligence.
  DOI: 10.1504/IJMEI.2024.10061273
   
  
• A real-time deep learning-based system for the blood glucose prediction of the diabetic patients  
  by Abhay Kumar Tripathi, Sumita Mishra, Shriram Kris Vasudevan 
  Abstract: Diabetes is a prevalent and long-term condition. It is possible to prevent diabetes if it is detected early enough. Insulin risk and severity may be considerably decreased if an accurate early prediction could be made. Diabetes datasets are challenging to predict due to the small amount of data that has been categorised and the potential for outliers (or missing values). The purpose of this study is to predict diabetes in a human or a patient with greater accuracy using various machine learning techniques, such as neural networks. Techniques for machine learning may increase the accuracy of predictions with patient-generated data. We begin by classifying the database and separating it into training and testing sets of information, which we then analyse separately. The CNN-LSTM combination is seen to yield the best results, with an accuracy rate of 96.5%. An accuracy of 91.56% is achieved in the testing data. Improved accuracy of the categorisation is achieved by imposing an LSTM model for diabetes prediction.
  Keywords: smart healthcare system; diabetes prediction; long short-term memory; LSTM; machine learning; deep learning.
  DOI: 10.1504/IJMEI.2024.10061586
   
  
• Telemedicine system for healthcare monitoring: heart rate and body temperature vital signs  
  by Ashok Vajravelu, Sreeja Vijay, R. Ramamoorthi, S. Ramesh 
  Abstract: The phrase remote health monitoring systems (RHMS) is used in the area of telemedicine to consult with, diagnose, and treat patients while working remotely. The RHMS was developed with the intention of using several types of communication technology in order to provide timely medical care to populations who are geographically separated. This article provides a detailed examination of the non-invasive capture of crucial data as well as the internet of things in the context of the discipline of healthcare informatics. It discusses the difficulties encountered in the area of healthcare informatics and offers recommendations for further research. In particular, the investigation that was carried out indicated that there has been a difficult obstacle to overcome in the process of developing multi-frequency essential IoT systems. If such technologies are successfully implemented, the resulting healthcare system will be one that is not only strong but also environmentally friendly, self-sufficient, and intelligent.
  Keywords: healthcare system; non-invasive data acquisition; internet of things; IoT; wireless sensor network; WSN.
  DOI: 10.1504/IJMEI.2024.10061587
   
  
• Deep transfer learning based two-stage multi-modal depressive tendency recognition using physiological and psychophysiological signals  
  by Gaurav Kumar Gupta, Dilip Kumar Sharma 
  Abstract: Depressive tendency recognition is challenging due to the limitations of labelled depressive knowledge and diverse variations in behaviour. This paper proposes a deep transfer learning-assisted two-stage multi-modal depressive tendency recognition system (DTL-TMD) by utilising physiological signals such as audio, transcript texts, and images and EEG as psychophysiological signals. The proposed system leverages the deep transfer learning (DTL) model to address and enhance the insufficient annotated physiological and Psychophysiological signals. Finally, it fuses the outcome of two stages with the assistance of a depression level-aware attention model in the adaptive neuro-fuzzy interface system (ANFIS) to detect depressed and non-depressed subjects effectively. Experimental results demonstrate superior performance and improve the depression recognition rate.
  Keywords: depression; electroencephalogram; EEG; convolutional neural network; CNN; adaptive neuro-fuzzy interface system; ANFIS; grow net; radial basis function neural network; RBFNN; deep Boltzmann machine; DBM; deep transfer learning; DTL; whale optimisation algorithm.
  DOI: 10.1504/IJMEI.2024.10061938
   
  
• Multiclass epilepsy seizure classification using deep learning  
  by Pankaj Kunekar, Mukesh Kumar Gupta, Pramod Gaur 
  Abstract: The neurosurgeon must be able to identify the type of epileptic seizure for the purpose to comprehend the cortical connectivity of the brain. Recurrent seizures, often known as epilepsy, are symptoms of the central nervous system and last either a few seconds or, in rare cases, a few minutes. EEG is one of the methods for recording seizures. The majority of EEG devices are composed of scalp electrodes that capture electrical activity. These signals are complex and often difficult to classify. Despite the existence of automated premature seizure identification from a usual electroencephalogram, little effort at multiple classes classification of seizures have been made done. Therefore, utilising the Bonn University dataset, a deep learning models has been developed using RNN, LSTM, and bi-directional-long short-term memory also known as Bi-LSTM, to solve this challenge. Bi-LSTM is found to be the most effective model for multi-class categorisation of epilepsy episodes in this research, with an accuracy of nearly 99% for three classes.
  Keywords: epilepsy seizures; electroencephalogram; EEG; Bi-LSTM; deep learning.
  DOI: 10.1504/IJMEI.2024.10061939
   
  
• Automated adaptive gamma improvement with weighting distribution in wireless capsule endoscopy  
  by Kanika Pasrija, Kavita Mittal 
  Abstract: This study focuses on enhancing the wireless capsule endoscopy (WCE) systems capabilities in disease localisation and bleeding detection within the gastrointestinal (GI) tract. By leveraging direction of arrival (DOA)-based localisation methods, the research proposes a novel approach involving the identification of regions of interest (ROIs) along the GI tract, streamlining feature extraction and bleeding image classification while reducing computational complexity. Additionally, a new bleeding detection method is introduced, utilising the Y.I/Q colour space as an efficient alternative to the conventional YIQ model. Experimental results demonstrate the effectiveness of the proposed computer-assisted diagnostic algorithm in accurately identifying gastritis and gastrointestinal bleeding, underscoring its potential to significantly enhance WCE technology for improved disease localisation and diagnostic precision. Our experiments yielded compelling results, with our computer-assisted diagnostic algorithm achieving an accuracy rate of over 90% in accurately identifying gastritis and gastrointestinal bleeding. This significant level of accuracy underscores the potential of our approach to greatly enhance the capabilities of wireless capsule endoscopy (WCE) technology, improving disease localisation and diagnostic precision within the gastrointestinal tract.
  Keywords: gastrointestinal; computer-assisted diagnostic-procedure; wireless capsule endoscope; luminance-in phase-quadrature; region of interest; deep neural network; disease localisation; bleeding detection; patient care; medical practices; early disease detection; telemedicine.
  DOI: 10.1504/IJMEI.2024.10062053
   
  
• Disease detection system based on haemogram report using random forest algorithm  
  by Deepali K. Gaikwad 
  Abstract: The healthcare systems require vast amount of medical data that is gathered post performing numerous medical tests. Medical data provides the essential information in order to diagnose diseases early or even before they occur. The classifiers use the characteristics of the complete blood count to forecast information about potential blood illnesses in early stages, which may improve the likelihood of a cure. Machine learning builds predictive models based on prior data. The illness prediction procedure may decrease as many as fatalities and improves the condition of living for those who tract with these diseases. In this study we applied random forest classifier on blood test report dataset to identify various blood diseases like anemia, leukemia, lymphoma, sickle-cell, etc.
  Keywords: machine learning; random forest; blood components; blood diseases.
  DOI: 10.1504/IJMEI.2024.10062222
   
  
• Analysis of type-2 diabetes datasets using sampling techniques  
  by Puneeth N. Thotad, Geeta R. Bharamagoudar, Basavaraj S. Anami 
  Abstract: Diabetes is one of the significant health disorders causing mortality in a developing country like India. Using technology to treat disease can lead to insights using data mining and machine learning techniques. Patient information plays a significant role in the diagnosis and prognosis of diabetes disease for decision-making. A comparative study is carried out using three imbalanced diabetes datasets. Synthetic minority oversampling technique is applied, and datasets are balanced. Balanced datasets gave higher accuracy, recall, precision, F1-score, and area under the curve compared to imbalanced datasets. Random forest has performed well on the balanced datasets. This work is appropriate for use in the medical field to manage diabetes efficiently.
  Keywords: data mining; machine learning; sampling techniques; random forest; decision tree; diabetic complications.
  DOI: 10.1504/IJMEI.2024.10062368
   
  
• Enhanced brain structure segmentation in schizophrenia MRI using bias correction and optimisation  
  by N. Swathi, J.M. Mathana, K. Sakthidasan 
  Abstract: Exploring the structural details of schizophrenia through neuroimaging, particularly MRI, has been essential in unravelling the disorder’s underlying pathophysiology. This research addresses the challenge of accurately segmenting MR images in schizophrenia studies, contending with issues like magnetic field inhomogeneity and noise. The central objective is to refine segmentation precision by effectively estimating bias fields. Employing multiplicative intrinsic component optimisation (MICO) and non-uniformity correction via brain SUITE for bias correction, and utilising firefly and partial swarm optimisation (PSO) algorithms for gray matter and ventricle segmentation, the study identifies the optimal combination through a comparative analysis. Evaluation metrics, including structural similarity measures (SSIM), feature similarity measures (FSIM) and Accuracy, highlight the superior performance of the MICO and PSO pairing in this proposed framework.
  Keywords: schizophrenia; SZ; image segmentation; bias correction; multiplicative intrinsic component optimisation; MICO; non-uniformity correction; brain suite; firefly; PSO.
  DOI: 10.1504/IJMEI.2024.10062369
   
  
• A survey on the automated computer-aided diagnosis of epilepsy seizure  
  by Pankaj Kunekar, Mukesh Kumar Gupta, Pramod Gaur 
  Abstract: Epilepsy, a life-threatening neurological disorder, necessitates early diagnosis and treatment to mitigate its risks. Neurologists detect epileptic seizures based on manual analysis of electroencephalography (EEG) signals. However, this manual approach is time-consuming, prone to errors due to human fatigue, and challenging for interpreting non-linear, non-stationary data. This paper presents a comprehensive review of state-of-the-art automated computer-aided diagnosis techniques for early epilepsy seizure detection, focusing on advancements made to overcome the limitations. By synthesising existing research, this survey aims to pinpoint the current state of the field, shed light on its strengths and weaknesses, and identify promising directions for future research.
  Keywords: computer-aided diagnosis; CAD; epilepsy; computerised tomography; CT; magnetic resonance imaging; MRI; positron emission tomography; PET.
  DOI: 10.1504/IJMEI.2024.10063066
   
  
• Breast cancer diagnosis based on thermography images using deep learning and forest optimisation algorithm  
  by M. Sri Geetha, A. Grace Selvarani, R. Vinodhini, M. Murugan 
  Abstract: Breast cancer is one of the most lethal types of cancer affecting women. The method of obtaining mammograms is a painful and unpleasant procedure for women since it requires compression of the breasts. This article would analyse thermal breast images for symptoms of the disease using image-processing techniques and algorithms. With this procedure, breast cancer could be discovered at an earlier stage of development. We describe a novel approach for extracting breast differentiating features utilising bio-data, image analysis, and image statistics. These features were obtained from thermal images acquired by a camera, and they will be classified by convolutional neural networks (CNNs) as normal or suspicious. The proposed method gives an accuracy rate of 98.95% for the thermal image dataset.
  Keywords: breast cancer; breast thermal image; convolutional neural network; CNN; image analysis; thermography.
  DOI: 10.1504/IJMEI.2024.10063877
   
  
• COPD: assessment of COPD prediction through machine learning techniques  
  by Mrinal Goswami, Arpita Nath Boruah 
  Abstract: Chronic obstructive pulmonary disease (COPD) is a progressive and debilitating respiratory condition characterised by persistent airflow limitation, typically associated with chronic bronchitis and emphysema. COPD represents a significant global health burden, affecting millions of individuals worldwide. In recent years, there has been growing interest in applying machine learning techniques to various aspects of COPD management, including diagnosis, treatment optimisation, etc. This work investigates the performance of different machine learning classifiers used in COPD prediction, especially in single and ensemble classification. A detailed performance comparison among all the classifiers is also done, considering accuracy, precision, recall, and F1 score.
  Keywords: chronic obstructive pulmonary disease; COPD; machine learning; ML; classification; ensemble learning; confusion matrix.
  DOI: 10.1504/IJMEI.2024.10064464
   
  
• Monitoring liver fibrosis in chronic liver disease patients using parametric electrical impedance tomography  
  by Shimon Hury, Shimon Abboud, Oranit Cohen Ezra, Maria Lichter, Davidov Yana, Ziv Ben Ari 
  Abstract: Liver fibrosis stage is the major factor that impacts liver morbidity and mortality. Previous studies suggest that liver dielectric properties, namely its bioimpedance, can be helpful in fibrosis stage classification. In this study, the feasibility of liver fibrosis classification using parametric electrical impedance tomography (pEIT) coupled with demographic & geometric clinical data is investigated. Data from chronic liver patients was collected and used to fit a machine learning model on the task of screening healthy subjects. An accuracy score of 85% was achieved. The exclusion of pEIT measurements, resulted in drop in accuracy to 72% (p-value < 0.005), sensitivity (85% vs. 70%), specificity (84% vs. 74%) and area under the receiver operating characteristic curve (AUC) (0.86 vs. 0.83).
  Keywords: electrical impedance tomography; liver fibrosis; machine learning; logistic regression.
  DOI: 10.1504/IJMEI.2024.10068351
   
  
• Clinical applications of magnetic resonance imaging and spectroscopy technology  
  by G.S. Uthayakumar, J. Jeneetha Jebanazer, A. Prabha, T.J. Nagalakshmi 
  Abstract: Magnetic resonance imaging has been refined as a non-invasive technique due to the superior contrasts it presents in soft tissues. Recent advances in instrumentation have allowed for measurements at ultra-high field strengths, leading to improved signal-to-noise ratios and increased resolution. This study discusses the magnet and gradient subsystems of MRI systems, as well as a number of complications that can occur from using a magnet. Furthermore, it illustrates the RF coils and transceiver’s finer characteristics and numerous constraints. It also showed the idea behind the data processing technology and the difficulties that come with it. In conclusion, the many artefacts inherent to MRI were elucidated. It also gives a quick rundown of the various problems that MRI have to deal with.
  Keywords: MRI image; spectroscopy; clinical application; medical image processing.
  DOI: 10.1504/IJMEI.2024.10064609
   
  
• Proposed novel approach for detecting Alzheimer’s disease in early stages  
  by Nadish Ayub, Syed Zubair Ahmad Shah, Rayees Ahmad Dar, Assif Assad, Abdullah Shah 
  Abstract: Alzheimers disease (AD), an incurable brain condition leading to memory loss, requires early detection for effective management. Utilising deep learning (DL) and computer vision (CV) in medical image analysis (MIA) shows promise, but acquiring annotated medical data is costly. Analysing 3D magnetic resonance images (MRIs) demands resource-intensive 3D convolutional neural networks (CNNs). This study introduces a 2D MRI slice-based transfer learning framework, employing a majority voting mechanism during testing. Experimentation reveals the optimal balance between data volume and accuracy is achieved with approximately 10 middle slices. The proposed approach attains a notable 93.91% accuracy, surpassing the state-of-the-art by 12.61% in distinguishing AD from cognitively normal (CN) cases.
  Keywords: deep learning; machine learning; Alzheimer’s disease; 3D MRI; transfer learning; 2D slices; convolutional neural networks; CNNs.
  DOI: 10.1504/IJMEI.2024.10064680
   
  
• Blood disease prediction system based on haemogram report using XGBoost algorithm  
  by Deepali K. Gaikwad, Ashok T. Gaikwad 
  Abstract: Healthcare systems gather extensive medical data from diverse tests spanning various fields. Unearthing concealed insights within this data remains critical for early disease detection or preventive measures. Classifiers utilise comprehensive blood count characteristics to forecast potential blood disorders in their initial phases, potentially amplifying the likelihood of effective treatment. Machine learning assumes a pivotal role in crafting predictive models based on historical data. This predictive procedure holds potential in curbing fatalities and enriching the lives of individuals managing these conditions. As an illustration, in this investigation, an XGBoost classifier was deployed on a blood test report dataset, successfully identifying a range of blood-related ailments like anemia, leukemia, lymphoma, sickle-cell anemia, and others.
  Keywords: machine learning algorithms; supervised learning; unsupervised learning; blood disease; XGBoost algorithm.
  DOI: 10.1504/IJMEI.2024.10064883
   
  
• Simulation of heart beat interval series - a neurophysiology based model in comparison with a nonlinear network oscillator model  
  by Sajitha Somasundaran Nair, Mini Maniyelil Govindankutty, Minimol Balakrishnan, Remya George 
  Abstract: Heart rate variability (HRV) analysis is a non-invasive method of autonomic nervous system (ANS) function assessment. In the present study a quantitative physiological model of the ANS is compared with a nonlinear network oscillator model in terms of power spectrum and poincare plots across three autonomic states. The physiological model more accurately reproduces HRV features compared to the nonlinear oscillator model. Rooted in actual physiology, the physiologic model allows extensive parameter tuning for various autonomic states, offering multiple modulable parameters. In contrast, the nonlinear oscillator model lacks physiological roots and permits only limited adjustments through coupling coefficients.
  Keywords: autonomic nervous system; ANS; heart rate variability; HRV; quantitative physiological model; neurotransmitter kinetics; nonlinear oscillator model; poincare plot; power spectrum.
  DOI: 10.1504/IJMEI.2024.10064942
   
  
• Data analysis, early diagnosis and management of COVID-19  
  by Rani Samyuktha Devabhaktuni, Simisola Olawoye, Dinesh P. Mital, Shankar Srinivasan 
  Abstract: The COVID-19 pandemic is one of the deadliest infectious diseases in recent history. Our research goals are to study the mode of spread of COVID-19 on various counties in New York and New Jersey, its effect on various, races, gender, age groups and to develop a clinical decision support system (CDSS) for early detection of COVID-19. The Bronx borough in New York state, the Bergen and Essex counties in the neighbouring New Jersey state, and men and people over 65 had higher hospital admissions, and fatalities, according to our research, where we used SAS and SPSS for data analysis.
  Keywords: clinical decision support system; CDSS; Corvid; COVID-19; SARSCoV-2; RT-PCR; systemic disease; fatality rate.
  
  
• Automatic blood disease detection system based on haemogram report using decision tree algorithm  
  by Deepali K. Gaikwad, Ashok T. Gaikwad 
  Abstract: In healthcare systems, a vast quantity of medical data is collected from multiple medical tests conducted in various sectors. It remains required to extract hidden information from medical data to diagnose diseases at an early stage or even before they manifest. In order to predict information about possible blood disorders in their early stages and potentially increase the chance of a cure, the classifiers make use of the features of the full blood count. The development of predictive models based on historical data is the responsibility of machine learning. The process for predicting illness may reduce the number of deaths and enhance the lives of those who suffer from these conditions. Using a decision tree classifier applied to a dataset of blood test reports, this study identified several blood illnesses, including sickle cell disease, leukaemia, anaemia, and lymphoma.
  Keywords: blood disease; decision tree; machine learning; supervised; unsupervised.
  DOI: 10.1504/IJMEI.2024.10065459
   
  
• Cervical cancer detection and segmentation using ANFIS classifier and prediction by ensemble deep learning network  
  by D. Baskar, K. Manivanan 
  Abstract: In underdeveloped nations, the incidence frequencies of cervical cancer have been sharply rising while the health services for prevention, diagnosis, as well as therapy are still relatively few. Cancer screening procedures may lead to an early diagnosis, which increases the likelihood of successful treatment and, ultimately, the protection of cervical cancer. An approach for the identification, as well as fragmentation of cervical cancer depending on the adaptive neuro-fuzzy inference system (ANFIS), is provided here. A novel model has been built and given the name the colposcopy ensemble network (CYENET) to automatically diagnose cervical malignancies from colposcopy pictures. The cell classification feature is dependent on a compact vision geometry grouping (VGG) network termed compact VGG. The overall accuracy of the classification for VGG19 was 73.31%. The findings of the experiments indicate that the suggested CYENET displayed high levels of sensitivity (92.42%), specificity (96.22%), and kappa scores (88%).
  Keywords: ANFIS; machine learning; VGG19; cervical cancer.
  DOI: 10.1504/IJMEI.2024.10065831
   
  
• Detection of early biomarkers for Parkinson disease  
  by M.Z. Shaikh, R. Harini, Megala Jambulingam, M. Nalini 
  Abstract: The neuro-degenerative disorder known as Parkinsons disease (PD) is characterised by the gradual death of dopaminergic neurons. Individuals with Parkinsons disease often display a number of neurological as well as non-motor abnormalities. It is of the utmost importance to locate responsive as well as specific indications in order to meet the pressing requirement for improved earlier detection as well as subsequently disease-modifying therapy for Parkinsons disease (PD) individuals. There are four different types of markers that may be distinguished from one another. We extracted certain properties using a deeper artificial neuronal system, and then we categorised them as either belonging to Parkinsons disorder or not belonging to Parkinsons disorder. Utilising a method called ten-fold crossing validity, we were able to get extremely positive findings and the accuracy rate was ranging from 9293.5% for the input images used.
  Keywords: bio-marker; Parkinson’s disorder; deep learning; machine intelligence.
  DOI: 10.1504/IJMEI.2024.10066539
   
  
• Infrared hand joint dataset: a custom rheumatic hand thermal image dataset creation and its medically approved validation for extending research in diagnosing arthritis  
  by Rozina Naz, Gopal Sakarkar, Mohtashim Ahmad, Amina Vali, Noorul Amin 
  Abstract: Osteoarthritis (OA) and rheumatoid arthritis (RA) are distinct types of arthritis with different causes; OA results from joint wear and tear, while RA is an autoimmune disease. This research focuses on hand joints affected by arthritis, a leading cause of disability globally. Estimating the number of arthritis patients is challenging as many seek treatment only in severe stages. Conservative estimates suggest over 54 million adults and nearly 300,000 children have arthritis, with elderly individuals being more susceptible. However, 64% of affected adults are younger than 65. Arthritis can range from mild to severe, leading to chronic pain and disability. Severe arthritis can result in permanent joint damage and may affect other organs. The research aims to create a standardized thermal image dataset of hand joints, crucial for identifying inflamed areas. This dataset is well labelled and validated, addressing issues in previous datasets lacking proper annotation and validation.
  Keywords: osteoarthritis; infrared camera; thermal imaging; rheumatoid arthritis; thermal dataset.
  DOI: 10.1504/IJMEI.2024.10066772
   
  
• Prediction of cardiac disease based on whale optimisation algorithm and residual U-Net  
  by M. Balamurugan, P. Blessed Prince, M. Umapathy, S.P. Jeno Lovesum 
  Abstract: Due to the prevalence of the disease and the high death rate it is connected with, heart disease has become a significant public health problem in recent years. Therefore, predicting heart disease using certain simple physical indications that may be gained through having frequent physical examinations at an early stage has emerged as an important topic of study. UWOA is a deep neural network (U-Net) and whale optimisation (WOA) optimised technique created and applied to pick the ideal features that may boost the accuracy of heart disease prediction. The method that has been suggested makes use of a hybrid algorithm that will be broken up into two stages. The algorithm above can extract features, and the values that it generates show a high accuracy range of 97.34%, with improved accuracy by an average of 3.77 percentage points for heart attack prediction.
  Keywords: cardiovascular disease; whale optimisation algorithm; residual U-Net architecture.
  DOI: 10.1504/IJMEI.2024.10066862
   
  
• Image segmentation through advanced distance regularised level set evolution in chest X-ray analysis  
  by Ruchika Arora, Indu Saini, Neetu Sood 
  Abstract: This paper introduces a pioneering solution for the problem of weak edges and intensity inhomogeneities in medical images. Improved distance regularised level set evolution (I-DRLSE) model harnesses the power of local edge gradient features, local fitting variances, and meticulously weighted regional intensity information to orchestrate a seamless evolution of image contours. The variational level set method optimises diffusion rate through a refined quad-well potential function with range-based adaptability. It is validated on publicly available Japanese society of radiological technology (JSRT) dataset by achieving remarkable average dice similarity coefficient (DSC) and intersection over union (IoU) scores of 0.996 and 0.985, respectively.
  Keywords: image segmentation; synthetic images; CXR images; level set method; LSM; DRLSE; CV; adaptive bilateral filter; ABF.
  DOI: 10.1504/IJMEI.2024.10066938
   
  
• The role of thyroid medicine in relieve discomfort and health sustainability a study of patient health behaviour  
  by Yan Xu 
  Abstract: The objective of research is to determine the role of thyroid medicine in relieving discomfort and health sustainability from the perspective of patient health behaviour. This research collected 594 responses from thyroid patients as the sample size, and data analysis for this research is taken by Smart PLS 4. The study has confirmed that the impact of thyroid medicine is significant on relieve discomfort and health sustainability. This research also highlighted the importance of patients’ health behaviour as a moderating variable between thyroid medicine, relieving discomfort, and health sustainability. The research asserts that patients to get thyroid medicine for advancing their health sustainability.
  Keywords: patient health behaviour; thyroid medicine; health sustainability; relieve discomfort; health improvement.
  DOI: 10.1504/IJMEI.2024.10067004
   
  
• BiLSTM-net: a simple and efficient model for foetal compromise detection during intrapartum and antepartum phase  
  by Vidya Sujit Kurtadikar, Himangi Milind Pande 
  Abstract: Cardiotocography (CTG) is a well-known device used to assess foetus health by monitoring foetal heart rate (FHR) and uterine contraction (UC) simultaneously during the antepartum and intrapartum phases. The visual interpretation of CTG signals has associated high false positive and false negative rates due to its complex structure. Automatic detection of foetal state can be done using machine learning methods, thereby reducing undesirable adverse perinatal outcomes and unnecessary caesarean section. We propose a deep learning model suitable for time series FHR signals based on a bidirectional long short-term memory (BiLSTM) network. We used two open datasets and one private dataset, which are diverse enough to test the robustness of the deep learning model. The experiments on all three datasets achieved excellent and consistent results for all parameters. The results are encouraging enough to demonstrate the proposed models good predictive capacity.
  Keywords: cardiotocography; CTG; foetal monitoring; foetal compromise; bidirectional long short-term memory; BiLSTM; deep learning.
  DOI: 10.1504/IJMEI.2024.10067030
   
  
• SMART2C - a healthcare digital platform based on process mining for managing COVID-19  
  by Nesrine Missaoui, Asma Mejri, Wissem Hachfi, Amel Letaief, Asma Daassa, Emna Ammar, Hajer Chackroun, Haifa Boudriga, Marwa Seghaier, Ricardo Martinho, Sahar Toumia, Yemna Sayeb, Henda Hajjami Ben Ghezala, Sonia Ayachi Ghannouchi 
  Abstract: This paper addresses the difficulties and challenges that affected the Tunisian healthcare system during the spread of the COVID-19 virus. It proposes a digital platform to improve healthcare COVID-19 related processes, by applying process management methodologies and integrating process mining and decision mining techniques to analyse process execution traces. This integration assists stakeholders in making informed decisions and optimising resource utilisation, offering recommendations for healthcare providers to enhance their decision making. Additionally, it highlights emerging healthcare solutions with potential to mitigate the impact of pandemic-related events and safeguard against pandemics and other natural disasters.
  Keywords: COVID-19; digital platform; process mining; security; process implementation.
  DOI: 10.1504/IJMEI.2024.10067213
   
  
• Comprehensive assessment of enhanced transfer learning-based MRI classification for accurate brain tumour detection and grading  
  by S. Amsavalli, K. Saminathan, M.Chithra Devi 
  Abstract: Brain tumour identification and classification improve early detection and therapy planning. Brain cancer diagnosis with MRI scans is essential for fast and effective treatment. This study improves transfer learning-based MRI categorisation to improve brain tumour diagnosis and grading. Our method uses a varied collection of normal brain MRIs and glioma tumour pictures from different reliable databases. The preprocessing pipeline captures unique image features using Z-score normalisation and SURF model feature extraction. The work uses pre-trained weights and the VGG19 transfer learning model, fine-tuned on the MRI dataset, to classify brain tumours. Attention mechanisms in the VGG19 architecture prioritise informative regions in input images to improve discrimination. The Python technique uses TensorFlow and Keras deep learning libraries. Our brain tumour categorisation method achieved 99.2% accuracy in experiments. Our method surpasses Neural Networks, ANN, CNN, and CRNN by 2.1% in brain tumour detection and grading from MRI scans. According to extensive investigation and evaluation, our framework’s robustness and reproducibility could change neuroimaging diagnosis and improve clinical outcomes.
  Keywords: brain tumour detection; MRI images; transfer learning; speeded-up robust features; SURF; VGG19; Z-score normalisation; Keras deep learning libraries.
  DOI: 10.1504/IJMEI.2024.10067243
   
  
• Eye ball sensor controlled wheel chairs for disabled patients  
  by J. Gowrishankar, S.B. Mohan, Sreeja Vijay, P. Sivakumar 
  Abstract: This article proposes a system for eye ball motion controlled wheelchair, a device designed to help the elderly and those with mobility issues get around. An eye frame equipped with three proximity infrared (IR) sensor modules monitors the iris. If an infrared camera detects something white, it will generate a new string of binary digits. Arduino boards will these signals to operate the wheelchairs motors. Individuals experiencing difficulties with their motor nerves are the focus of this research. Even though they are disabled, this method will allow them to steer an electric wheelchair using their eyes alone. The user can steer and accelerate the wheelchair by looking left or right, and can also initiate and halt movement with their gaze. The proposed method runs well with an average reaction time of 3.2 seconds for eye control.
  Keywords: wheelchair; IR sensor; ultrasonic sensor; accelerometer.
  DOI: 10.1504/IJMEI.2024.10067333
   
  
• Age-based comparison of resting-state brain activity in autism spectrum disorder: an fMRI study  
  by B. Divya, A. Kavitha, R. Sowmya, B.S. Sneha, T.V. Santhoshiya 
  Abstract: Atypical alterations in brain development is a defining feature of autism spectrum disorder (ASD), but age-related changes in regional brain function and spontaneous activity may be more significant. ASD is linked to irregularities in neurological connections. Current rs-fMRI diagnosis lacks age differentiation, causing inconsistent findings. This study compares functional connectivity in adolescent and adult ASD patients. An age-based comparison shows connectivity declines with age in most brain regions but increases in the insula. Ring connectivity representation reveals better connections in adolescents, particularly in language regions. Connectivity matrices results are validated using fractional amplitude of low-frequency fluctuations (fALFF), which reduces physiological noise and increases accuracy in detecting brain activity. fALFF may serve as an early ASD detection tool or predict sibling risk, potentially through machine learning models. This research broadens the understanding of ASD brain connectivity development across age groups and opens ways for more focused, individualised interventions.
  Keywords: autism spectrum disorder; ASD; connectivity matrix; ring connectivity; amplitude of low-frequency fluctuations; ALFF; fALFF.
  DOI: 10.1504/IJMEI.2024.10067379
   
  
• A research based study of quantitative analysis and segmentation of thermal images for assessing rheumatoid arthritis in the hand joints  
  by Rozina Naz, Gopal Sakarkar, Mohtashim Ahmad, Noorul Amin, Amina Vali 
  Abstract: To investigate rheumatoid arthritis and observing bodys heat flow in the body and monitoring the changes in temperature of the skin. We will compare the difference in skin temperature between people with rheumatoid arthritis and people without it, in individuals with joint pain, the algorithms called the fuzzy c-means algorithm and expectation maximisation (EM) algorithm is used to pinpoint unusual areas on the hand (Backhaus et al., 1999; Tipa and Baltag, 2006; Kuruganti and Qi, 2002; Selvarasu et al., 2009; Snekhalatha et al., 2012). This study reveals how to examine thermogram measurement with heat distribution index (HDI) and estimate skin temperature. The temperature examination demonstrated a temperature increment of 0.970 C within the hand region of those patients with RA as compared to the hands of regular individual. Analysis of the patients data showed that there was a strong connection between HDI and skin temperature.
  Keywords: rheumatoid arthritis; thermograms; thermal segmentation; HDI; infrared images; hand joints.
  DOI: 10.1504/IJMEI.2024.10067688
   
  
• Osteoporosis diagnosis by femur bone modelling using COMSOL multi-physics  
  by Ammu Anna Mathew, Aastha Kapoor, Vivekanandan Sundaram 
  Abstract: Analysing bone behaviour requires a mathematical understanding of the functional history of bone and modelling since bone tissue can change shape to fit its function, much like a structural material. This study aims to model a femur bone using three-dimensional scanning by utilising the physical characteristics of a healthy and diseased one to detect osteoporosis as a pre-clinical evaluation tool. The Von Mises stress and Tresca stress values, which are the resulting point-load responses, were then recorded as part of the expanded inquiry, and a comparative analysis was conducted to get a thorough conclusion with the simulated model by calculating the bone reaction to mechanical changes.
  Keywords: COMSOL multi-physics; femur bone; stress distribution; osteoporosis; SolidWorks; Wolff’s law of remodelling.
  DOI: 10.1504/IJMEI.2024.10067727
   
  
• Genetic algorithm with ensemble feature ranking for feature selection in dengue prediction using convolutional neural networks  
  by K. Saraswathi, K. Rohini 
  Abstract: Early intervention and successful treatment depend on accurate disease diagnosis in medical diagnostics. With its high frequency and severe complications, dengue fever, a mosquito-borne viral infection in tropical and subtropical climates, is a major public health problem. Enhancing predictive models for dengue detection requires effective feature selection, especially when dealing with high-dimensional datasets. This study introduces a novel approach for feature selection in dengue prediction by integrating CNNs with GA-eFR. The GA-eFR algorithm leverages the feature ranking capabilities and the search efficiency of genetic algorithms to identify the most relevant features in the dataset. The methodology involves pre-processing the dataset, designing the CNN architecture, and incorporating GA-eFR into the feature selection process. Experimental results demonstrate that the proposed method significantly enhances the performance of CNN classifiers in detecting dengue, achieving an F-score of 0.92, precision of 0.90, recall of 0.88, and 92.85% accuracy on the OpenDengue dataset. The effectiveness of the proposed feature selection method is further validated by comparing it with other techniques, such as particle swarm optimisation (metaheuristic/swarm-based), recursive feature elimination (wrapper-based), and correlation-based feature selection (filter-based).
  Keywords: genetic algorithm; GA; feature ranking; feature selection; convolutional neural networks; CNN; dengue detection; medical diagnostics; optimisation algorithms; GA combined with ensemble feature ranking; GA-eFR.
  DOI: 10.1504/IJMEI.2024.10067742
   
  
• AI-based radiodiagnosis: a comprehensive review  
  by Jay Sawant, Niti Chikhale, Ayushi Tanna, Gajanan Nagare 
  Abstract: AI plays a critical role in the interpretation and diagnosis of medical conditions based on radiographic images. This review explores the symbiotic relationship between artificial intelligence (AI) and medical imaging, focusing on conventional and emerging modalities. Beginning with an overview of imaging modalities and AI fundamentals, a dive into diverse AI applications in the medical field has been made, emphasising advancements in image recognition and segmentation. Dedicated sections review the role of AI integrated with conventional as well as emerging radiology techniques, showcasing its adaptive potential for rapid diagnostics. Concurrently, the challenges in implementing AI in medical settings, including ethical considerations and validation frameworks have been addressed. By consolidating insights into the transformative impact of AI on diagnostic accuracy and efficiency, this review underscores its pivotal role in shaping the future of medical imaging.
  Keywords: artificial intelligence; healthcare.
  DOI: 10.1504/IJMEI.2024.10067812
   
  
• Cranial abnormalities: a deep learning-based approach for classification and measurement, enhanced treatment sensitivity through 3D printing  
  by Osman Nuri Uçan, Incilay Yıldız, Mehmet Ateş, Fulya Oduncu, Emir Kaan Cengiz 
  Abstract: This article emphasises that technological advances have a significant impact on the treatment process and are essential for the detection and correction of cranial abnormalities. Using the MUG500+ dataset, a deep learning-based classification was used. The aim of the research is to increase measurement precision and accuracy by simplifying the processes involved in helmet therapy measurements. The artificial intelligence solution focusing on deep learning makes a significant contribution to the identification of skull anomalies. The research provides a mathematical model for scaling the abnormal segment using the ResNet-18 algorithm for classification, which further improves the processes involved in 3D printing of the abnormal segment. Examining the results, it appears that the accuracy measure performed successfully in the testing phase (95% success rate). The accuracy and reliability of the proposed model is demonstrated by the sensitivity measure reaching 100%.
  Keywords: artificial intelligence; deep learning; cranial abnormalities; classification; 3D printing.
  DOI: 10.1504/IJMEI.2025.10070941
   
  
• Detection of sleeping disorders in Parkinson's affected patients using multi-head attention-based BiLSTM model  
  by Sk.Wasim Akram, A.P. Siva Kumar 
  Abstract: The proposed study aims to introduce a novel technique for identifying sleeping difficulties in PD patients by overcoming the existing limitations. Initially, the input samples are collected from the available dataset, and to remove unwanted noises in the input signals, pre-processing is carried out by using improved finite impulse response filter (FIRF), Bessel filter (BF), and continuous wavelet transform (CWT). After pre-processing, the complexity problem is avoided by extracting the most significant features by introducing an adaptive residual dense network (AResDenseNET) model. Based on the extracted features, a novel multi-head attention-based BiLSTM (MultiHeadAtt _BiLSTM) is proposed for detecting sleeping disorders. Finally, to improve the efficiency of the proposed model, its parameters are fine-tuned by an adaptive aquila optimiser (AAO) approach. As compared with existing approaches, the proposed model achieves a better accuracy result of 98.7%, and hence, it proves the efficacy of the proposed model.
  Keywords: time domain; linear shift-invariant system; scale factor; skip connections; backward information; candidate solution; forward information.
  DOI: 10.1504/IJMEI.2024.10068369
   
  
• Framework for sentimental analysis in healthcare data using deep Biaffine attention enabled CNN classifier  
  by Rushali Patil, Arunkumar Molapalayam Sekar 
  Abstract: Sentiment analysis associated with the COVID-19 pandemic tweets are paramount for informed decision-making and effective public health interventions. Hence, this research presents an effective deep Biaffine attention enabled CNN classifier model to handle the nuances and subjectivity inherent in COVID-19 tweets. This innovative model combines CNN with deep Biaffine attention mechanisms to comprehensively capture intricate word relationships within the COVID-19 tweets. Specifically, the attention mechanism enables the model to identify subtle emotional cues often ignored by traditional approaches, thereby enhancing the precision of sentiment analysis. Our framework demonstrated remarkable performance with an accuracy of 96.16%.
  Keywords: sentiment analysis; Biaffine attention enabled CNN; Aurelia swarm search optimisation; healthcare data; COVID-19 tweets.
  DOI: 10.1504/IJMEI.2024.10068395
   
  
• Challenges and issues in detection of seizure in early stages using artificial intelligence approach  
  by Pratima Ranjit Patil, Deepa Sachin Deshpande 
  Abstract: This study finds the challenges and issues in the early-stage detection of seizures using the artificial intelligence (AI) approach. This study examines the advances made in detection, calculation, and tracking of epileptic seizures utilising EEG technology. So, primary contribution of this study is to analyse numerous EEG-based seizure detection studies and how they address some of the challenges in seizure detection. The research investigated a number of methodologies, such as the internet of things framework, deep learning, and machine learning. This review focuses on: 1) detailed survey on elliptic seizure detection systems employing AI techniques; 2) survey conducted on AI approaches; 3) survey on the basis of EEG signal-based seizure detection in children; 4) survey on importance of thermal images in seizure detection; 5) dataset utilisation in seizure detection; 6) challenges and advantages in seizure detection; 7) the overall conclusion of this review is described in detail.
  Keywords: seizure detection; artificial intelligence; electroencephalography; epileptic seizures; early stages; thermal images; issues in seizure detection.
  DOI: 10.1504/IJMEI.2024.10068483
   
  
• Optimising heart disease prediction model parameters using pelican optimisation algorithm and Talos hyperparameter tuning  
  by R. Vinitha, Viji Vinod 
  Abstract: Heart disease (HD) is a major global health issue requiring reliable and efficient prediction models. Recent advances in machine learning and optimisation methods promise early identification and diagnosis. This systematic study uses data preparation, several optimisation algorithms, and Talos hyperparameter adjustment to enhance heart disease prediction. The goal Is to create a reliable prediction model with high sensitivity and specificity. Starting with heart disease data collection and preparation, the research is systematic. The processed data is then optimised using three algorithms: POA, CO, and Incomprehensible but Intelligent-in-time (ILAIBI) logic. After optimisation, statistical analysis evaluates sensitivity and specificity. Talos hyperparameter tuning optimises kernel parameters to refine model parameters. The POA method with optimised parameters improves prediction accuracy to 99% in the final prediction model. With such great accuracy, the model may be useful in early heart disease identification. Sensitivity and specificity analyses verify model reliability and robustness. The suggested cardiac disease prediction model uses advanced optimisation algorithms and hyperparameter tuning to be very accurate. The POA with Talos works well for clinical practice, the findings suggest. Future research should investigate model validation and optimisation strategies for different patient populations.
  Keywords: heart disease; HD; Talos; optimisation; pelican optimisation algorithm; POA; cheetah optimiser algorithm; CO; hyperparameter tuning; pre-processing and enhancement.
  DOI: 10.1504/IJMEI.2024.10068484
   
  
• Polynomial regressive projection-based pruning extreme learning classifier prediction of autism spectrum disorder by age group  
  by S. Sreevidya, Lipsa Nayak 
  Abstract: Autism spectrum illness (ASD) is a neurodevelopmental disorder that affects social interaction, communication, and behaviour. Early ASD discovery allows for prompt therapy. Multiple machine learning methods have been developed for autism identification; however, reliable prediction is difficult and time-consuming. PRCDP-PELC is a new method that improves ASD prediction accuracy and reduces time. Initially, data on toddlers, children, adolescents, and adults are collected from four databases. In the preprocessing phase, missing values are handled with polynomial regression interpolation, Tukeys range statistical test identifies noisy data, and generalised Blomqvists correlative Sammon projection reduces dimensionality. Finally, Tversky indexive pruning extreme learning classifier is used to classify data samples as ASD or not. In this way, the ASD prediction accuracy is improved, and prediction time is minimised. The results of the PRCDP-PELC technique provide better performance with higher accuracy, precision, and sensitivity and lesser prediction time when compared to existing methods.
  Keywords: autism spectrum disorder prediction; different age groups; data preprocessing; polynomial regression interpolation; generalised Blomqvists correlative Sammon projection; Tversky indexing pruning extreme learning classifier.
  DOI: 10.1504/IJMEI.2025.10068627
   
  
• Brain-computer interface and eye tracking for smart homes  
  by Prabhu, Nithya Jenev, Kamalakannan Ramachandran, Ashok Vajravelu 
  Abstract: The term brain-computer interface (BCI) refers to a sophisticated and diverse active research topic that is based on neurology, signal processing, biomedical sensors, hardware, and other related fields of study. Individuals who are paralysed may regain some of their freedom with the use of brain-computer interfaces (BCIs), which use brain impulses to operate prostheses or activate functional electrical stimulation. We examine the possibility of controlling a smart home environment using an augmented reality system that incorporates eyetracking as well as a brain-computer interface. We show controls that are depending on the context to the user through a head-mounted display, and the user chooses which controls to utilise by focusing their attention on those controls. In an average of 86% of the instances, the desired meaning was accurately inferred using a combination of EEG and eye tracking data.
  Keywords: electroencephalography; brain-computer interface; BCI applications; smart home; BCI challenges.
  DOI: 10.1504/IJMEI.2025.10068831
   
  
• Efficient clinical decision-making in nephrology: predictive analytics for chronic kidney disease management  
  by S. Vairachilai, Shreyash Jaiswal, Thota Chandan, S. Periyanayagi, S.P. Raja 
  Abstract: Chronic kidney disease (CKD) significantly impacts global health, making early detection and management crucial. This study explores the use of machine learning (ML), ensemble learning (EL), and deep learning (DL) models to enhance clinical decision-making in nephrology. We processed a comprehensive dataset, identifying feature importance using SHAP and splitting it into training and testing sets. Among the models, support vector machine (SVM) achieved 97.5% accuracy, while XGboost and Adaboost scored 98.7%. Deep learning models, especially the multilayer perceptron, also performed well. Our findings underscore predictive analytics potential to personalise CKD management and improve clinical outcomes in nephrology.
  Keywords: chronic kidney disease; CKD; nephrology; clinical decision-making: predictive analytics; machine learning; ML; ensemble learning; EL; deep learning; DL; SHapley additive explanation; SHAP; feature selection; evaluation metrics.
  DOI: 10.1504/IJMEI.2025.10068832
   
  
• Real-time visual quality enhancement and low-complexity image reconstruction via GPU-based deep learning  
  by Yogesh Mahadev Kamble, Raj B. Kulkarni 
  Abstract: This article presents a novel approach for reconstructing human figures from skeletal images captured at two different angles, utilising NVIDIAs CUDA framework for parallel image processing. The study enhances image quality and processing speed, particularly for optical-fibre endoscopic optical coherence tomography (OCT) systems, where high image quality is crucial. The research introduces an improved van Herk/Gill-Werman morphology algorithm for image component extraction and optimises operations with NVMe SSDs for better storage efficiency. Validation results demonstrate the methods superior performance, with a high dice coefficient (0.964), accuracy (0.993), correlation (0.97), and Structural Similarity Index (0.985), outperforming existing techniques.
  Keywords: image reconstruction; visual quality; NVIDIA GPU; parallel image processing; temporal making mechanism; generalisation.
  DOI: 10.1504/IJMEI.2025.10068833
   
  
• Time-resolved high-resolution imaging with enhanced contrast and SNR via multithreaded GPU-based autoregressive interpolation  
  by Yogesh Mahadev Kamble, Raj B. Kulkarni 
  Abstract: This study introduces a novel image reconstruction approach using a piece-wise autoregressive (PAR) model for predicting unknown pixels in high-resolution images. The method employs a block-based prediction strategy to enhance reconstruction accuracy. To optimise performance, a parallel processing strategy leveraging graphics processing units (GPUs) is implemented. CUDA threads are used to launch each particle in the optimisation framework, while CUDA streams divide the swarm into smaller groups for efficient task offloading. The particle swarm optimisation (PSO) technique integrates a particle coding strategy and a gradient-based local search to improve accuracy and speed up convergence. The approach is evaluated using the DIV2K and APTOS datasets, demonstrating its ability to reconstruct high-resolution images with minimal errors. Experimental results show that the CUDA-accelerated PSO method significantly enhances reconstruction performance, highlighting its effectiveness in handling noisy or incomplete data, making it a promising solution for high-quality image reconstruction tasks.
  Keywords: image reconstruction; time-resolved high resolution; contrast-enhanced imaging; autoregressive image interpolation; residual filtering; and conjugate gradient method; CG.
  DOI: 10.1504/IJMEI.2025.10068834
   
  
• High-resolution magnetic resonance image reconstruction model for early epilepsy detection using hybrid optimisation approach  
  by D. Chandraprakash, Kapil Gupta, Sagarkumar S. Badhiye, Senthil Kumar Chandrasekaran, Pavan Kumar Varma Kothapalli 
  Abstract: Epilepsy, a complex neurological disorder, requires early detection and intervention to reduce its impact. In MRI-based diagnostic imaging, reconstructing images from sparse data is essential for accurate diagnosis. This study introduces a novel approach for high-resolution MRI reconstruction aimed at early epilepsy detection. Initially, a multi-scale adaptive mean-Wiener filtering technique is used to reduce noise in collected MRI data. The processed images are then reconstructed using a generative adversarial network (GAN) to enhance high-level feature extraction. Subsequently, a hybrid model integrating a deep convolutional spiking neural network with the artificial gorilla troops optimiser algorithm is proposed, combining traditional iterative methods with machine learning for optimised reconstruction. The model, implemented using MATLAB, demonstrates superior performance with a remarkable accuracy of 99.57%, surpassing existing methods such as MCC-TL, HANQL, and MBINet-SONN. This advancement promises enhanced diagnostic accuracy and improved early intervention for epilepsy and other neurological disorders.
  Keywords: magnetic resonance image; reconstruction; epilepsy seizure detection; generative adversarial network; GAN; deep convolutional spiking neural network; DCSNN.
  DOI: 10.1504/IJMEI.2025.10068873
   
  
• Efficient skin disease detection using optimised AlexNet50 and feature correlation subspace vector modulation  
  by D. Ushanandini, T. Kamala Kannan 
  Abstract: In identifying skin cancer, image processing algorithms have increasingly relied on object recognition in isolated regions. However, most current approaches ignore crucial parameters like feature dimension and disease-specific impact, resulting in large classification mistakes, especially during early identification. These errors lower precision, recall, and F1 scores. This research proposes a feature-constrained support vector machine with optimised ALEX-CNN to overcome these restrictions and identify skin cancer. Image pre-processing and normalisation are done in the algorithm. We identify sick regions using the former and Canny edge detection. To segment the impacted region, we use limited feature subset selection. The optimised ALEX-CNN evaluates the strongest disease-relevant feature margins ranked by FCSVM. The experimental results demonstrate that our suggested method outperforms all other comparable studies in skin cancer diagnosis accuracy (97.2%), precision (96.4%), recall (97.1%), and false detection rate. This shows that the strategy may improve categorisation and early cancer diagnosis.
  Keywords: skin disease detection; feature selection and classification; AlexNet50; cross-fold slice segmentation; CFSS; AlexNet50 convolutional neural network; ALEX-CNN; Canny edge morphing; CEM; feature correlation subspace vector modulation; FCSVM.
  DOI: 10.1504/IJMEI.2025.10070978
   
  
• Deep learning techniques-based prediction of chronic insomnia  
  by Rajalakshmi Ramanathan, K. Nithiya, S. Gayathri, C. Sivakumaran 
  Abstract: The potential of technologies has resulted in significant changes to the globe during the course of that steadily for the past decades. Insomnia is one of the many health problems that are on the rise as a result of persons increased participation in digital activities, decreased engagement in physically active pursuits, and increased reliance on electronic gadgets that emit radio waves. This may happen on its own or as a consequence of another issue. Both scenarios are possible. Insomnia that persists over a long period of time, often known as severe insomnia, might cause significant and irreparable harm to a persons brain. Sleeplessness is a condition that may be identified by a variety of clinical testing in accordance with the many internal elements that influence sleep. This method, on the other hand, is not only costly but seems to be equally time-consuming as well as labour-intensive.
  Keywords: deep learning; artificial intelligence; machine learning; insomnia.
  
  
• Optimised palm-print recognition system using deep learning models with image processing approaches  
  by J. Sheela Mercy, S.Silvia Priscila 
  Abstract: Palm print recognition enables biometric authentication in forensics, access management, and security. Traditional palm print identification systems sometimes have segmentation errors, noise interference, and limited feature extraction. New deep learning (DL) and image processing approaches can improve the accuracy and reliability of palm print recognition systems. This paper presents a revolutionary palm print identification method using cutting-edge DL and image processing approaches. After removing noise with an AWF, marker-controlled watershed (MCW) segmentation is used. ResNet, GoogLeNet, and AlexNet are DCNN frameworks for feature extraction and categorisation. The squirrel search algorithm optimises results. ResNet with adaptive Weiner filtering, marker-controlled watershed segmentation and SSA perform well in accuracy (95.10%), sensitivity (0.93), specificity (0.91), and F1-score (0.95). This integrated system improved palm print recognition technology. This study uses DL and image processing to present a palm print identification approach with high performance and reliability.
  Keywords: palm-print recognition; deep learning; ResNet; image processing; segmentation; marker-controlled watershed; MCW; classification; convolutional neural network; CNN; squirrel search algorithm; SSA.
  DOI: 10.1504/IJMEI.2025.10069107
   
  
• An intellectual framework for brain tumour detection and survival prediction using deep learning methods  
  by K. Shanmuga Priya, G. Rosline Nesa Kumari 
  Abstract: This paper aims to design a deep learning-based brain tumour detection and survival prediction method. Initially, the process starts with standard database and then the images are processed through a segmentation phase to segment the abnormality by multiscale MobileUNet++ (MMUnet++). Next, the feature extraction process is performed by using segmented images. Finally, the resultant features are given to vision transformer-based residual bidirectional long short-term memory (ViT-ResBiLSTM) for tumour detection. The diverse metrics are considered for the validating model efficiency. This integration facilitates the detection process, allowing for the identification of brain tumours with better accuracy and provides performance.
  Keywords: tumour segmentation; brain tumour detection; survival prediction; multiscale MobileUNet++; MMUnet++; vision transformer-based residual bidirectional LSTM; ViT-ResBiLSTM.
  DOI: 10.1504/IJMEI.2025.10069116
   
  
• An Intelligent model of brain tumour detection and survival prediction using adaptive MobileNet with multihead cross attention module  
  by K. Shanmuga Priya, G. Rosline Nesa Kumari 
  Abstract: A new deep learning method is developed for identifying brain tumours and forecasting patient survival outcomes is developed. The requisite images are garnered from standard sources. Thereafter, the aggregated images are forwarded to the segmentation phase, which is carried out by the multi-dilated MobileUnet3+ (MDMUnet3+). The obtained segmented image is fed into the adaptive MobileNet with multihead cross attention module (AM-MCAM) for getting the classified outcome; here the parameters are optimised by employing enhanced garter snake optimisation (EGSO). Finally, the experimental analysis is carried out on the designed brain tumour identification and survival-prediction approach to confirm its effectiveness.
  Keywords: brain tumour detection; survival prediction; segmentation; multi-dilated MobileUnet3+; MDMUnet3+; adaptive mobilenet with multihead cross attention module; enhanced garter snake optimisation.
  DOI: 10.1504/IJMEI.2025.10069117
   
  
• Enhancing maize disease detection and classification using optimised hyper-tuned EfficientNet architecture  
  by Bhawna Kumawat, Reeba Korah, Ashok Kandipati 
  Abstract: Climate variability exacerbates diseases that pose serious production difficulties for maize, a staple crop for global food security. Manual inspection is the foundation of traditional disease detection techniques, which are beset by scaling issues, errors and inefficiencies. This study presents a state-of-the-art convolutional neural network (CNN) tailored for the detection of maize disease: the hyper-tuned EfficientNet (HTENet) architecture. The unique spatial-channel attention method of the proposed model reduces the influence of complicated image backgrounds and improves disease-specific feature extraction by separating important visual characteristics. The study emphasises the significance of hyperparameter optimisation, which includes adaptive pooling techniques, learning rate adjustments, and data augmentation. The study uses criteria like precision, recall, F1-score and mean average precision (mAP) to show how much better HTENet is at detecting and categorising maize leaf diseases. With the help of sophisticated transfer learning and hyperparameter tuning, HTENet outperforms benchmark models like YOLOv5, ResNet50 and VGG16 in terms of efficiency and precision.
  Keywords: maize disease detection; deep learning; hyper-tuned EfficientNet; HTENet; convolutional neural networks; CNN; transfer learning; agricultural technology.
  DOI: 10.1504/IJMEI.2025.10069118
   
  
• A comprehensive approach to integrating multimodal data in healthcare AI  
  by Zunzarrao Vilasrao Thorat, Amina Kotwal, Mahesh M. Bulhe 
  Abstract: Integrating multimodal data in healthcare AI such as clinical notes, lab results, imaging, genetic profiles, and wearable data presents challenges like data silos, interoperability, privacy concerns, algorithmic bias, and interpretability. To optimise patient care, standardised frameworks for data integration are needed, along with enhanced interoperability, improved AI accuracy, and stronger privacy measures. Techniques like multi-criteria collaborative filtering (MCCF) enhance personalised treatment recommendations, while the synthetic minority over-sampling technique (SMOTE) addresses class imbalances and improves predictive accuracy. Network access control (NAC) ensures security. Future advancements should focus on real-time integration, AI-driven analytics, patient engagement, and ethical standards.
  Keywords: healthcare AI; multimodal data; multi-criteria collaborative filtering; MCCF; synthetic minority over-sampling technique; SMOTE; network access control; NAC; clinical decision making.
  
  
• Projection of chronic disease using machine algorithms  
  by P. Nithya, C.D. Nandakumar, S. Srinivasan 
  Abstract: The study aims to project the future prevalence of chronic diseases in India using data from the World Health Organisations 2024 report, focusing on nine key factors related to non-communicable diseases (NCDs). The study analysed data from 2002 to 2023, using Holt-Winters exponential smoothing for missing values and Gaussian hidden Markov models to predict NCD occurrences from 2024 to 2030. Key areas for intervention identified include obesity, physical inactivity, and elevated fasting glucose. Four machine learning models were evaluated for forecasting NCD mortality, with linear regression yielding the best accuracy (R2 of 0.989), outperforming Random Forest and Gradient Boosting models.
  Keywords: chronic diseases; Hidden Markov models; machine learning; mortality; forecasting.
  DOI: 10.1504/IJMEI.2025.10069647
   
  
• Breast cancer forecast using enhanced support vector machine model  
  by M.M. Agarwal 
  Abstract: Breast cancer (BC) significantly affects womens health worldwide. Timely identification and precise risk evaluation are essential for successful treatment and improved patient outcomes. Among women globally, breast cancer is the second deadliest cancer, with only lung cancer causing more fatalities. Contemporary advancements have allowed specialists to utilise various innovative approaches alongside conventional methods for breast cancer diagnosis in women. Machine learning techniques have been successfully applied in the realm of computational cancer treatment. Studies have demonstrated that diagnostic precision can be enhanced using machine-learning techniques. The breast cancer risk predictor was developed through a dedicated study using the Breast Cancer Wisconsin (diagnostic) dataset and a refined support vector machine (SVM) learning algorithm. SVMs provide a novel method for transforming nonlinear data, enabling the use of a linear algorithm to fit a linear model to data. The resulting model exhibited an accuracy of approximately 95% when applied to the training data.
  Keywords: breast cancer; supervised learning; support vector machine; SVM; cancer risk predictor; breast cancer forecast.
  
  
• Impact of demographic, temporal, and socio-economic factors on Lyme disease incidence in the Northeastern USA  
  by Ali Ben Khalil, Dinesh P. Mital, Shankar Srinivasan 
  Abstract: Lyme disease ranks as the leading vector-transmitted illness across the USA, with its incidence demonstrating significant variations across different demographics. This paper examines the impact of demographic (age, gender, race/ethnicity), temporal (seasonality), and socio-economic factors on the incidence of Lyme disease in the Northeastern USA. Data from the Healthcare Cost and Utilization Project (HCUP) from 2017 to 2020 were analysed to identify disparities in disease incidence and aid in the development of targeted public health interventions. The results indicated peak Lyme disease admissions during July (18.7%) and the least in January (4.4%). Mortality during hospitalisation was low (1.2%), with the disease predominantly affecting males (56.1%) and individuals identified as White (88.8%). 2 A.B. Khalil et al. Socio-economic analysis revealed a higher incidence in the highest income quartile (42.9%) and the lowest (8.8%). Age-wise distribution showed the highest incidence in older adults 65 years and above (42.0%).
  Keywords: Lyme disease; Lyme borreliosis; demographic factors; temporal factors; socio-economic status; epidemiology; geographic variability; disease surveillance; the northern USA.
  
  
• On the reduction of pulsations of piezoelectric micropump using a microneedle valve for drug delivery applications  
  by Sachin R. Gavali, Prashant M. Pawar 
  Abstract: Piezoelectric (PZT) diaphragm-type mechanical micropumps produce pulsing fluid flow. However, continuous fluid flow devoid of any flow pulsation is necessary for applications such as drug delivery systems. To reduce the pulsations, the micropump is paired with microneedle valves for medication applications. This study incorporated a microneedle valve at the delivery side to investigate the flow pulsations produced by the piezoelectric micro pump. The study reveals that by incorporating a microneedle valve into the micropump pulsations can be significantly reduced. Inculcating a microneedle valve to the piezoelectric pump utilised in the study reduced the overall pulsation by 93.67%.
  Keywords: pulsations in flow; degree of pulsation; piezoelectric micropump; microneedle valve; drug delivery.
  DOI: 10.1504/IJMEI.2025.10069816
   
  
• Multimodal authentication for healthcare data protection using gated recurrent networks  
  by Maddila Suresh Kumar, Satyanarayana Botsa 
  Abstract: Protecting biometric data in healthcare is critical. This study introduces a multimodal authentication model utilising gated recurrent networks (GRNs) to enhance security. This study presents a multimodal authentication model utilising gated recurrent networks (GRNs) to enhance the security of biometric data in healthcare. The examination presents the usage of a squeeze-and-excitation network (SE-Net) consideration instrument, combined with an example taken on the wavelet method, for productive element extraction. The gated recurrent unit (GRU) addresses long-range dependencies in the verification process. Tested in MATLAB, the model achieved a classification accuracy of 99.12%. Future work aims to improve real-time authentication efficiency, incorporate additional biometric modalities, and develop adaptive security measures against evolving cyber threats in healthcare.
  Keywords: healthcare; biometric data; multimodal authentication; gated recurrent network; iCanClean algorithm; squeeze-and-excitation network; SE-Net.
  DOI: 10.1504/IJMEI.2025.10069817
   
  
• Retinal structure segmentation for glaucoma classification using fundus and OCT images  
  by Esra'a Mahmoud Jamil Al Sariera, M.C. Padma, Thamer Mitib Ahmad Al Sariera 
  Abstract: Glaucoma is a frequent chronic condition that causes irreversible vision loss. We propose a new technique to build an automated feature-learning system for detecting glaucoma in colour retinal fundus images and optical coherence tomography (OCT) images. Three phases are described in this paper. First, fundus and OCT image enhancement are performed. Secondly, the ROI is segmented and glaucoma features are extracted. Finally, a fuzzy logic classifier is used to classify the features as either glaucoma or normal. We were able to reach 96% accuracy, which was significantly less expensive to process than the state-of-the-art. The proposed system is evaluated using the data on OCT, fundus images and DRISHTI-GS for glaucoma detection.
  Keywords: glaucoma; optical coherence tomography images; OCT; fuzzy logic classifier; DRISHTI-GS dataset.
  DOI: 10.1504/IJMEI.2025.10069956
   
  
• Smart IoT-based system for efficient neonatal respiration monitoring and control  
  by Sukhwinder Sharma, L. Maria Michael Visuwasam, R. Deeptha, T. Shynu, S. Suman Rajest 
  Abstract: This work presents a system that enables the monitoring of infants in incubators, collecting relevant data for diagnosis and research at neonatal intensive care units (NICU). It comprises the incubator with breath, heartbeat, heat detectors, and a MEMS detector that tracks an infants heart monitor. The receiver part sends this information (respiration rate, pulse, and MEMS sensor levels) to a mobile device with an Arduino microcontroller. When there is an abnormal heart rate, a DC motor triggers to provide oxygen by giving commands through the microcontroller. Doctors could even use a local area network to check the babys status before it goes home for remote telehealth monitoring. The data comes to a mobile device via IoT, and an app manages therapy progress. We have developed this framework to illustrate a passive sensing application designed to enhance future remote health monitoring solutions that can benefit care and outcomes for infants in the NICU. This early persuasive evidence also suggests that the system integration of real-time transduction and automated responses represents a credible path to facilitate more efficient neonatal care with higher performance objectives.
  Keywords: automated respiration control; IoT-based sensors; neonatal health tracking; oxygen supply regulation; real-time data analysis; remote health monitoring; smart infant care system.
  DOI: 10.1504/IJMEI.2025.10070120
   
  
• A review: liposomal formulations in the management of idiopathic pulmonary fibrosis  
  by Nehaba Natwarsinh Solanki, Vijaykumar K. Parmar 
  Abstract: Idiopathic pulmonary fibrosis (IPF) is progressive lung illness that affects lung function and is characterised by fibrosis. New strategies are needed since traditional treatments frequently have severe side effects and little effectiveness. Drug distribution, bioavailability, and toxicity reduction in the treatment of IPF may all be enhanced by liposomal formulations, which are made up of lipid bilayers encasing medicinal substances. Liposomal drug research, including anti-fibrotic medicines, their physical characteristics (size, charge), and encapsulation efficiency are all included in this review. In addition, issues like stability and repeatability are discussed, emphasising how liposomal formulations may improve the outcomes for IPF patients.
  Keywords: idiopathic pulmonary fibrosis; IPF; liposomal formulation; aerosol characterisation; anti-fibrotic agents; pulmonary drug delivery technologies.
  DOI: 10.1504/IJMEI.2025.10070227
   
  
• Healthy eating followed by circadian rhythm help to reverse diabetes  
  by Madhavi Vijaykumar Chekatla, Amiya Bhaumik, Mohammad Gousuddin, Vijaykumar Chekatla 
  Abstract: The transformative potential of coordinating lifestyle treatments with circadian rhythms for the management and possible reversal of type 2 diabetes mellitus (T2DM) is investigated in this study. 36 people, ages 30 to 60, participated in the 180-day experiment, which focused on individualised dietary modifications, exercise, intermittent fasting, and meal scheduling based on circadian rhythms. There were notable glycemic improvements: postprandial glucose levels plummeted by 60%, fasting glucose levels dropped by 52.5%, and HbA1c levels dropped by 26%. As a result, diabetic drugs were completely stopped. Among the supplementary advantages were a 10% decrease in body mass index, increased vigour, and glowing skin; participants reported feeling 1015 years younger. The results highlight the importance of circadian rhythms in promoting metabolic health, with time-restricted meals improving insulin sensitivity and regulating blood pressure. By combining exercise and detoxification techniques, the studys integrative approach emphasises the comprehensive advantages of managing diabetes that go beyond glucose control to include general health and well-being. The findings support a paradigm change in diabetes care by highlighting individualised, circadian-aligned therapies.
  Keywords: circadian rhythms; type 2 diabetes mellitus; T2DM; glycemic control; lifestyle interventions; intermittent fasting; circadian-aligned therapies.
  DOI: 10.1504/IJMEI.2025.10070298
   
  
• Depression prediction based on socio-economic factors: a comparative study  
  by C. Gunavathi, Aadya Ranjan 
  Abstract: This article addresses the impact of the COVID-19 pandemic on mental health and evaluates various feature selection methods with supervised classification algorithms to predict mental health conditions using socio-economic data. Results show information gain and Gaussian Naive Bayes as the most effective combination with 84.75% accuracy. The study emphasises the importance of feature selection for improved predictive accuracy and sheds light on algorithm performances when paired with different feature selection methods. Notably, the decision tree algorithm with fisher score yields the lowest accuracy at 71.98%. Insights gained from this comprehensive comparison include identifying common attributes significantly influencing model predictions.
  Keywords: classification methods; supervised learning; machine learning; mental health; feature selection methods; classification models; Chi square test; information gain; Gaussian Naïve Bayes; random forest.
  DOI: 10.1504/IJMEI.2025.10070323
   
  
• Hybrid classifier of adaptive potential in biotechnical systems of rehabilitation type  
  by Sergey Filist, Riad Taha Al-Kasasbeh, Alexey Kiselev, Olga Vladimirovna Shatalova, Nikolay Korenevskiy, Osama M. Al-Habahbeh, Ashraf Shaqadan, Maxim Ilyash 
  Abstract: For the rehabilitation of persons with disabilities, a method for classifying adaptation potential is proposed. It allows you to monitor and manage the functional state of patients during therapy or a rehabilitation session. A hybrid classifier of adaptive potential has been developed, based on the aggregation of heterogeneous data characterising the functioning of various organs and subsystems of the body. It is used to predict the risk of recurrent myocardial infarction. The hybrid classifier was tested on an experimental group of post-infarction patients. The quality indicators of the medical risk classification of the synthesised hybrid classifier allow us to recommend it for biotechnical rehabilitation systems that carry out therapeutic and restorative procedures in post-infarction patients. Diagnostic efficiency by 11%.
  Keywords: adaptive potential; hybrid classifier; HC; virtual model; algorithm; recurrent myocardial infarction; cumulative survival.
  DOI: 10.1504/IJMEI.2025.10070409
   
  
• EEG-driven mental state detection using residual networks  
  by Soumen Ghosh, Tanmay Sinha Roy, Prem Prakash, Raja Gupta 
  Abstract: The study explores the use of EEG, a method for measuring electrical brain activity, in recognising emotions. EEGs role in emotion recognition is crucial for enhancing human-computer interaction and brain-computer interfaces (BCI). The dataset includes EEG data from two participants (one male and one female), observed for three minutes per emotional state (positive, neutral, negative). The research employs a residual neural network (ResNet) architecture, achieving a 95.12% accuracy in classifying emotions. ResNet models, typically used in image recognition, prove effective in this context as well. The study concludes that this deep learning approach outperforms traditional machine learning models in emotion classification.
  Keywords: residual neural network; human emotions; emotional classification; deep learning; ResNet architecture.
  DOI: 10.1504/IJMEI.2025.10070763
   
  
• The internet of things-based platform for real-time classifications with autism spectrum disorder detection using modified SVM algorithm  
  by Subair Ali Liayakath Ali Khan, K. Saravanan 
  Abstract: There are many challenges for the children who are diagnosed with autism spectrum disorder (ASD) in every aspect of their lives, including social engagement, interpersonal communication, and so on. The symptoms of ASD differ from one individual to another and can range from mild to severe. Autistic children use fewer communication signals than typically developing children. Parents may be able to identify and recognise the activities of their kids and comprehend their ideas with practice. Others, on the other hand, have difficulties in interpreting their gestures. An internet of things (IoT) based platform is proposed to identify the posture and signals of ASD children using multiple classification approaches. The data set is created by performance of ten children execute every movement roughly ten times to build a dataset. The primary objective of this research is to develop an IoT-based system for recognising gestures of kids with ASD.
  Keywords: autism spectrum disorder; ASD; machine learning; ML; random forest; SVM; XGB; internet of things; IoT.
  DOI: 10.1504/IJMEI.2025.10070768
   
  
• Endoscopic ultrasonography in differential diagnostics of benign and malignant pathology of the common bile ducts utilising fuzzy mathematical technologies  
  by Etab Taha Al-Kasasbeh, Nikolay A. Korenevskiy, Vladimir Anatolievich Belozerov, Riad Taha Al-Kasasbeh, Sofia Nikolaevna Rodionova, Sergey Filist, Ksenia V. Razumova, Emad Saleh Al-Tarawneh, Osama M. Al-Habahbeh, Moath Musa Al Smady, Mahdi Salman Alshamasin, Elena Shalimova, Vladislav Krutskikh, Maxim Ilyash 
  Abstract: The study aims to enhance the outcomes of differential diagnosis of obstructive pathology of the common bile duct by leveraging the results of endoscopic ultrasonography (EUS) using hybrid fuzzy decision-making technologies. The main diagnostic methods are endosonography and the hybrid fuzzy decision rule synthesis methodology, developed at Southwest State University in Kursk, Russia. Based on expert assessments of endosonograms depicting common bile duct pathology, and the hybrid fuzzy decision rule synthesis methodology, a hybrid fuzzy model for differential diagnosis of the studied pathology was developed. Mathematical modelling and statistical calculations demonstrated the effectiveness of this differential diagnostic model, providing a confidence level of 0.93 in the desired diagnosis which is an acceptable result when working with poorly formalised initial data.
  Keywords: fuzzy mathematical models; common bile duct obstruction; reference images.
  DOI: 10.1504/IJMEI.2025.10070769
   
  
• Clinical decision support system for the early diagnosis and management of COVID-19  
  by Pravalika Nyalapatla, Rodly Gaillard, Dinesh P. Mital 
  Abstract: Having a tool that can figure out the likelihood of contracted severe acute respiratory syndrome coronavirus 19 to combat its spread will be a considerable step to slow the speed of the virus. Therefore, we describe a series of steps to analyse data from the New York State Government Health and New Jersey using SAS and Python. First, focusing on counties bordering each state using variables such as individual social-economic, gender, age group to determine the likelihood of getting the severe acute respiratory syndrome coronavirus (SARS-CoV-2) COVID-19. Furthermore, we created a clinical decision support system (CDSS) interface to collect data and analyse them better to understand this virus.
  Keywords: severe acute respiratory syndrome coronavirus; SARS-CoV-2; COVID-19; clinical decision support system; CDSS; SAS; Python.
  DOI: 10.1504/IJMEI.2025.10070822