International Journal of Computational Biology and Drug Design (5 papers in press)

Regular Issues

• Importance of safety maintenance of the survived with recent former infection experience during a pandemic syndrome episode: A Study by Difference Equation Approach  
  by Subhasis Bhattacharya, Suman Paul, Sudip Mukherjee 
  Abstract: During the outbreak of a highly infectious disease conceded by a virus, handling of healthcare catastrophe is the most momentous part. Any type of known or unknown relaxation may generate enormous loss in terms of population. Present study consider the concern that survived one who has some fresh former infection history can be fingered with appropriate care throughout the syndrome period otherwise a huge harm can be advent by the state. The study follow difference equation modelling considering two aspects where the survived with former infection history handled with care and not reckoned as a part of sustained population and the other is they encompassed with the general population category. The study considers an example of a hypothetical state with some give infection rate, death rate and quarantine rate. By using R- programme language the study observes that proper care for such group of population is very significant to reduce the situation like human loss.
  Keywords: Infectious disease; SARS-CoV-2; 2019-nCov; Difference Equation; Survived from the infected; Quarantine rate; Death Rate.
  
  
• Strengthening IoT Security: Assessing Ensemble Machine Learning for Cloud DDoS Attack Protection  
  by Bijay Kumar Paikaray, Lalmohan Pattnaik 
  Abstract: The vulnerability to Distributed Denial of Service attacks has significantly increased due to the simultaneous advancement of cloud services and the Internet of Things. This has facilitated the ability of unscrupulous individuals to interrupt cloud services and harm the reputation of organisations. Due to the unique characteristics and constraints of IoT devices, traditional approaches to identifying distributed denial of service attacks can prove inadequate within an IoT setting. The performance of the five supervised learning models Logistic Regression (LR), Ridge Classifier (RC), AdaBoostClassifier (ADB), and ExtraTreesClassifier (ETC) are evaluated in the accurate identification of IoT-based network activities. The evaluation of the learning models is done on a subclass of CIC DoS, CICIDS-2017, and CSECICIDS-2018 datasets, with a special focus on 2017 data. The feature engineering approach is employed to improve the learning model's accuracy. The experimental results revealed the highest level of accuracy rate of 99.97% for ExtraTreeClassifier.
  Keywords: DDoS; IoT; Machine Learning; IDS.
  DOI: 10.1504/IJCBDD.2025.10069480
   
  
• Advancing Surgical Instrument Recognition Through Shape Recognition Techniques in the Medical Industry  
  by Bijaya Paikray, Sonia Rathee, Shalu Mehta, Amita Yadav, Tiruveeduula Gopikirishna, Bijay Kishor Shishir Sekhar Pattanaik 
  Abstract: Computer assisted intervention (CAI) system is anticipating surgical workflow. Its goal is to use of instruments, supporting the intraoperative clinical decision support. The shape recognition of surgical instruments enables the identification of the different surgical instruments which are almost similar in shape and size and also helps to understand the category of each instrument. The proposed method is where one can extract the feature set of an object and compare it with the feature set of the universal collection of objects. The patchbased segmentation algorithm that is being suggested can get an F-score of 0.90. With a variety of instrument layouts, on average, the recommended force based grasping protocol achieves a 92% picking success rate, and the recommended attention based instrument recognition module achieves a 95.6% recognition accuracy. The end result consists of the name of the object, along with the percent classification and percent recognition with the universal object collection.
  Keywords: Universal object collection; Euclidean distance; percent classification; percent recognition; object pixels.
  DOI: 10.1504/IJCBDD.2025.10069727
   
  
• Improving Multiple Sclerosis Identification with an Advanced U-Net Architecture Featuring Dilated Convolutions  
  by M. Divya, Dhilipan J, A. Saravanan 
  Abstract: Multiple Sclerosis (MS) is a disease that impacts the CNS, which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Every five minutes, a new case of MS is reported globally. Numerous deep neural network models have been developed using different types of MS data, including MRI and clinical data. However, there is no standard approach available for the identification of abnormalities (lesions) in the DGM and MTL of the brain using MRI. This research proposed a U-Net-based modified architecture with dilated convolution operation for detecting MS. In this, the images from the BioGPS dataset are trained using the proposed U-Net model for extracting image features automatically. These outcome features are fed into softmax classification for performing pixel-wise probabilities of class labels. This network learns special features effectively, requiring much less computation than the traditional U-Net. This work gained 97.26% accuracy in predicting the abnormalities in MS, which is comparatively higher than the existing methods.
  Keywords: Multiple Sclerosis lesions; Modified U-Net; Dilated Convolution; Pixel-wise Classification; BioGPS dataset; Deep Grey Matter (DGM) and Mesial Temporal Lobe (MTL); Magnetic Resonance Imaging (MRI); Cen.
  DOI: 10.1504/IJCBDD.2025.10070187
   
  
• Mathematical Analysis of an Alcohol Drinking Model for Pregnant Women and its Influence on Her Unborn Baby in Fuzzy Environment  
  by Payal Singh, Neharani Wadhva, Nilam Kumhar, Kamal Hossain Gazi, Soheil Salahshour, Aditi Biswas, Sankar Prasad Mondal 
  Abstract: Alcohol consumption during pregnancy poses significant risks to both maternal and fetal health. Understanding the dynamics of alcohol consumption during pregnancy and its absorption within the womb is crucial for assessing potential risks to fetal development. This research discusses two crucial mathematical models, first Compartment model of different stages, potential drinker to moderate drinker, moderate drinker to heavy drinker (in pregnant women) of alcohol consumers and second dynamical system is on absorption of alcohol in blood in fetus through placenta. The mathematical modelling is considered completely in fuzzy environment due to the uncertainty. All the results like existence and uniqueness of solution, stability of system are proposed and proved in fuzzy environment. Ultimately, this research contributes to the ongoing efforts to protect the health and wellbeing of both mothers and their unborn children by providing a quantitative tool for understanding and mitigating prenatal alcohol exposure with uncertainty.
  Keywords: Nonlinear fuzzy differential equations; Fetal alcohol syndrome (FAS); Numerical Simulation; Stability Analysis.
  DOI: 10.1504/IJCBDD.2024.10070737