International Journal of Reasoning-based Intelligent Systems (38 papers in press)

Regular Issues

• Personalized Recommendation Method of Japanese Teaching Resources Based on Collaborative Filtering  
  by Pan Zhao, Yang Wang 
  Abstract: In the existing personalized recommendation methods of Japanese teaching resources, the confidence of resource recommendation is low, and the personalized recommendation effect is poor. Therefore, a personalized recommendation method for Japanese teaching resources based on collaborative filtering is designed. Firstly, the multi-modal interest similarity model and hidden factor depth are used to extract the features of Japanese teaching resources. Then, the scoring matrix and label matrix are constructed, and TF-IDF algorithm is introduced to calculate user preferences, so as to realize user preference mining of Japanese teaching resources. Finally, the Japanese teaching resources recommendation scoring matrix is constructed, similar neighbors with preferences are sought, and personalized recommendation rules are set to realize personalized recommendation of Japanese teaching resources. The experimental results show that the proposed method can improve the confidence of Japanese teaching resource recommendation and improve the poor performance of personalized recommendation.
  Keywords: Collaborative filtering; Japanese language teaching resources; Preference; Scoring matrix; Label matrix; Penalty factor.
  DOI: 10.1504/IJRIS.2023.10059841
   
  
• Multi channel user interface generation method based on conflict degree and collaborative filtering  
  by Ting Zhang 
  Abstract: Aiming at the problems of low recommendation accuracy, low success rate of interactive control and long time of traditional recommendation methods, a multi-channel user interface generation method based on conflict degree and collaborative filtering is proposed. Firstly, the evidence optimality and binary comparison matrix of user behaviour are determined, and the evidence conflict degree of user behaviour is calculated using the weight calculation results, and user behaviour recognition is realised according to the evidence characteristics. Secondly, collaborative filtering is adopted to implement user interface pattern recommendation. Finally, according to the recommended user interface model, a multi-channel user interface generation framework is constructed, including user task decomposition, multi-channel interaction control and interface code determination. Experimental test results show that the maximum accuracy of user interface pattern recommendation using proposed method is 98.36%, the average success rate of multi-channel interaction control is 97.35%, and the minimum time for multi-channel user interface generation is 1.8 s.
  Keywords: conflict degree; collaborative filtering; multi-channel; user interface generation; evidence optimality; multi-channel interaction control.
  DOI: 10.1504/IJRIS.2024.10061315
   
  
• An Abnormal Detection method of Enterprise Financial Accounting Data Based on Bayesian network  
  by Baoyuan Liu 
  Abstract: To improve the effectiveness of anomaly detection in enterprise financial accounting data and reduce the error probability of anomaly detection, this paper proposes a Bayesian network-based anomaly detection method for enterprise financial accounting data to ensure the accuracy and reliability of financial reports. By introducing the nearest neighbour rule and KNN algorithm to calculate the distance between different data attributes, the XGBoot algorithm is used to obtain the optimal balance point and achieve the classification of enterprise financial accounting data; According to the topological structure, the prior knowledge and accounting data characteristics are fitted, the accounting data characteristics are extracted, the interference items of abnormal features are removed by Markov blanket elimination method, the conditional probability of Bayesian network is calculated, and the data anomaly detection is realised to realise the final research. The test results indicate that the false positive rate of abnormal data detected by this method is low, and the recall rate is high, which has certain feasibility.
  Keywords: Bayesian network; enterprise financial accounting data; abnormal detection; nearest neighbour rule; parameter learning.
  DOI: 10.1504/IJRIS.2024.10061316
   
  
• A Modified Algorithm to Solve Minimum Spanning Tree Problem  
  by Siva Behera, Prasanta Kumar Raut 
  Abstract: Here, we give a modified and improved algorithm to find an MST from a given weighted undirected graph. Our algorithm works in two stages. In the first stage, it finds a forest formation involving the edges with minimum weights, and in the second stage, the algorithm converts the forest obtained in the first stage into a spanning tree. Further, we demonstrate our algorithm with one example, discuss the complexity of our algorithm, give its implementation using the Java Applet program, and compare the output with Kruskal’s algorithm. This study demonstrates that the suggested approach is superior to conventional algorithms for handling MST.
  Keywords: array; binary heap; Kruskal’s algorithm; minimum spanning tree; forest; stack.
  DOI: 10.1504/IJRIS.2024.10061376
   
  
• Color Offset Compensation Method of 3D Animation Scene Image Based on Color Difference Interpolation  
  by Xu Lan, Lizhen Jiang 
  Abstract: A colour offset compensation method for 3D animation scene images based on colour difference interpolation is proposed to address the issues of unsatisfactory colour offset compensation and long time in traditional 3D animation scene images. Adopting high-dimensional convolutional fast bilateral filtering algorithm for denoising 3D animation scene images, using an improved gain coefficient greyscale world correction method for correction, converting the image from RGB space to HIS colour space, linearly stretching the saturation component of the image, and achieving colour offset compensation for 3D animation scene images. The simulation results show that after applying the method proposed in this paper for image processing, the highest signal-to-noise ratio is 23.85db, the highest peak signal-to-noise ratio is 28.69db, the denoising running time is always kept below 125ms, the maximum information entropy is 8.30, the maximum contrast is 72.80, the minimum colour difference is 0.521, and the colour offset compensation time is always kept below 90ms. The colour offset compensation effect is good.
  Keywords: colour difference interpolation; 3D animation scene image; colour offset compensation.
  DOI: 10.1504/IJRIS.2024.10061377
   
  
• Speech recognition method of English translation robot based on HMM algorithm  
  by Xiaolin Zhang, Tao Wang, Ling Jiang 
  Abstract: To solve the problems of poor speech feature extraction and high false recognition rate in English translation robot speech recognition, a speech recognition method for English translation robot based on HMM algorithm is proposed. Firstly, the speech signal is collected and processed by noise reduction, reverberation removal, speech segmentation, volume normalisation and speech feature extraction. Then, HMM algorithm is introduced to extract the optimal speech features. Finally, the speech recognition function is constructed, and the speech features are regarded as discrepancy, and the recognition function is optimised through the error term to realise the speech recognition of the English translation robot. The experimental results show that this method can effectively extract the voice features of voice command signals. The average error rate is 1.5%, and the average recognition rate is 98.47%. The identification results of valid information and invalid information obtained are consistent with the actual results.
  Keywords: HMM algorithm; English translation robot; speech recognition; noise reduction; de-reverberation; speech segmentation; volume normalisation; speech feature extraction.
  DOI: 10.1504/IJRIS.2024.10061378
   
  
• A Multi object Tracking Method for Complex Scenes Based on Edge Feature Extraction of Video Images  
  by Haidi Yuan, Wei Li 
  Abstract: In order to improve the accuracy of complex scene tracking and shorten tracking time, this paper proposes a multi object tracking method for complex scenes based on video image edge feature extraction. Firstly, capture video images from complex scenes. Secondly, obtain the edge feature components of video images and extract complex edge features of video images. Then, a single target tracker is designed to construct an affinity matrix and filter out elements that exceed the threshold. Finally, an undirected graph is constructed to match the previously tracked trajectory with the detection results. For trajectory updates on the matching, multi target tracking is achieved by solving the maximum weight clique graph to achieve complex scene multi target tracking. The experimental results show that the accuracy of our method can reach 99.89%, and the tracking time is only 2.6 seconds, indicating that our method can effectively improve the tracking effect.
  Keywords: template matching; sports affinity; maximum weight clique graph; affinity function.
  DOI: 10.1504/IJRIS.2024.10061379
   
  
• Intelligent Management Platform for Enterprise Dynamic Accounting Information Based on Data Mining  
  by Huiying Kang 
  Abstract: In order to promote the improvement of enterprise financial management level, a data mining-based intelligent management platform for enterprise dynamic accounting information is proposed. Firstly, the SOM algorithm is used to fully collect dynamic accounting information of enterprises, and the collected data is used as the management object. Secondly, the association rule method is used to calculate the support and confidence levels, completing the normalisation of accounting information. Finally, based on the normalised accounting information, a quantitative management function for accounting information is constructed to achieve intelligent management of accounting information. The experimental results show that the average accounting information mining time of the platform in this article is around 8 minutes, the maximum access latency is not more than 10 ms, and the accounting information reading time is always less than 2.5 s. The performance of the intelligent management platform has been significantly improved.
  Keywords: data mining; dynamic accounting information; intelligent management platform; SOM algorithm; normalisation processing.
  DOI: 10.1504/IJRIS.2024.10061380
   
  
• Enterprise financial anomaly data detection method based on improved support vector machine  
  by Hao Wang, Huan Wang 
  Abstract: In this paper, a method for detecting financial anomaly data in enterprise based on improved support vector machine is proposed. By analyzing the abnormal financial data of enterprises, the distribution of abnormal financial data is determined, and a multi-channel Text-CNN neural network model is constructed to extract initial abnormal data features. The nonlinear features of abnormal data are adjusted using the least squares method to achieve feature extraction of abnormal financial data of enterprises. Optimize the support vector machine algorithm through differential evolution algorithm to determine the optimal classification population for enterprise financial anomaly data features for global optimization; By constructing a global selection model, achieve the detection of abnormal financial data in enterprises. The test results indicate that the fitting degree of the method proposed in this paper is good, and the detection error is low, indicating a certain degree of feasibility.
  Keywords: Improving support vector machines; Point anomaly; Context exception; Collection anomaly; Least squares method; Differential Evolution Algorithm.
  DOI: 10.1504/IJRIS.2024.10061381
   
  
• Fog Computing Approaches for Sustainable Smart Cities  
  by Parimal Giri, Sanjoy Choudhury, Diptendu Sinha Roy, Bijay Paikaray 
  Abstract: Reverse auction resource allocation and utilisation in a fog environment is an exciting yet challenging problem due to the numerous constraints and needs. The end user presents the resource needed and exposes it to a notable set of qualified expert suppliers in a reverse auction mechanism. This paper provides a combinatorial reserve auction for the fog environment that considers both cost and non-cost attributes, such as the type of QoS criteria, reputation, and other factors, to ensure the success of winning stakeholder cooperatives. To solve this problem, estimate calculation is used, and a polynomial-time solution that comes near to being exact is obtained. In order to increase their benefit, the auction process allows suppliers to disclose accurate data. This strengthens the system since it can keep up with the client’s utility. According to the execution assessment and a relative report utilising various widely used models, the suggested method performs better.
  Keywords: fog computing; CRA; smart cities.
  DOI: 10.1504/IJRIS.2024.10062540
   
  
• AMAA-GMM: Adaptive Mexican Axolotl Algorithm based Enhanced Gaussian Mixture Model to Segment the Cervigram Images  
  by Lalasa Mukku, Jyothi Thomas 
  Abstract: Colposcopy is a crucial imaging technique for finding cervical abnormalities. Colposcopic image evaluation, particularly the accurate delineation of the cervix region, has considerable medical significance. Before segmenting the cervical region, specular reflection removal is an efficient approach. Because, cervical cancer can be found using a visual check with acetic acid, that turns precancerous and cancerous areas white and these could be viewed as signs of abnormalities. Similarly, bright white regions known as specular reflections obstruct the identification of aceto-white areas and should therefore be removed. So, in this paper, specular reflection removal with segmenting the cervix region in a colposcopy image is proposed. The proposed approach consists of two main stages, namely, pre-processing and segmentation. In the pre-processing stage, specular reflections are detected and removed using a swin transformer. After that, cervical regions are segmented using an enhanced Gaussian mixture model (EGMM). For better segmentation accuracy, the best parameters of GMM are chosen via the adaptive Mexican axolotl optimisation (AMAO) algorithm. The performance of the proposed approach is analysed based on accuracy, sensitivity, specificity, Jaccard index, and dice coefficient, and the efficiency of the suggested strategy is compared with various methods.
  Keywords: Gaussian mixture models; machine learning; segmentation; metaheuristics; deep learning; enhanced Gaussian mixture model; EGMM; adaptive Mexican axolotl optimisation; AMAO.
  DOI: 10.1504/IJRIS.2024.10063302
   
  
• Modified VGG19 Transfer Learning Model for Breast Cancer Classification  
  by Sashikanta Prusty, Srikanta Patnaik, Sujit Kumar Dash 
  Abstract: Breast cancer (BC) seems to have become a sign of great concern in everyday life. There have been a lot of research and methods already designed in the last few years but continue to be prone worldwide. To address this issue, a modified version of the visual geometric group-19 (VGG19) model, namely BCNet21 has been proposed here to classify the malignant class from breast mammogram images collected from the MIAS dataset. Furthermore, the performance of our proposed BCNet21 model has been compared with the two most common predefined VGG16 and VGG19 models using the performance metrics and Cohen-Kappa test (k). The result shows that the proposed BCNet21 model outperforms with a higher accuracy of 98.96 % and a kappa score of 86%, compared to the VGG16 and VGG19 models. This concludes that the BCNet21 model is much closer to the near-perfect agreement between actual and predicted breast cancer instances.
  Keywords: breast cancer; BC; deep learning; DL; transfer learning; TL; VGG19; VGG16; kappa score.
  DOI: 10.1504/IJRIS.2024.10063303
   
  
• Unsupervised English-Chinese word translation using various retrieval methods  
  by Cuiping Zou 
  Abstract: Because it is essential for improving the user experience, controlling styles in neural machine translation (NMT) has garnered a lot of interest in recent years. The majority of the earlier research on this subject focused on managing the amount of formality, and it was successful in making some headway in this particular area. The purpose of this study is to tackle each of these difficulties by presenting a new benchmark and strategy. A benchmark for multiway stylistic machine translation (MSMT) is presented, which incorporates a wide variety of styles that span four different language domains. Following that, we offer an approach that we call style activation prompt (StyleAP), which involves extracting prompts from a styled monolingual corpus and does not need any more fine-tuning alterations. Experiments demonstrate that StyleAP is capable of exerting a significant amount of control on the translation style and achieving extraordinary levels of performance.
  Keywords: unsupervised English-Chinese; neural machine translation; NMT; translation induction for Chinese.
  DOI: 10.1504/IJRIS.2024.10067116
   
  
• A Hybrid Model of Fuzzy Logic to Enhance Data Mining Accuracy Incorporating Intra-Concentration and Inter-Separability (I2CS) Loss into Neighborhood Component Analysis  
  by Hemangini Mohanty, Santilata Champati 
  Abstract: Data mining is crucial to discovering meaningful insights and patterns from massive datasets. However, the accuracy and efficiency of data mining algorithms are often challenged by the curse of dimensionality and the complexity of real-world data. In this article, we propose a novel approach to enhance the accuracy of data mining by enriching the concept of intra-concentration and inter-separability (I2CS) loss into neighbourhood component analysis (NCA). NCA is a dimensionality reduction technique that focuses on preserving local neighbourhood information, thus improving classification accuracy. Fuzzy logic, on the other hand, provides a flexible framework to handle uncertainty and vagueness in data, enabling more nuanced decision-making. By integrating fuzzy C-means clustering with I2CS-NCA, we aim to leverage the complementary strengths of both approaches to enhance the accuracy and robustness of data mining algorithms. Also, the experimental results show that the proposed model gives the highest accuracy.
  Keywords: I2CS loss; neighbourhood component analysis; NCA; fuzzy C-means clustering; random forest.
  DOI: 10.1504/IJRIS.2024.10067117
   
  
• Ensemble of Transfer Learning With Convolutional Neural Networks for Writer Recognition in Historical Documents  
  by Radmila Jankovic Babic, Alessia Amelio, Ivo R. Draganov, Marijana Cosovic 
  Abstract: In the cultural heritage domain, writer recognition has become a challenging classification task still explored for historical documents, due to the presence of different types of noise in the documents, i.e. ink bleed-through, ink corrosion, stains on paper or parchment, difficulty in the character discrimination, elements different from the text, such as images, etc. that limit the effectiveness of existing techniques. To further advance in terms of robustness of classification and experimental setting, we propose a new deep learning model which ensembles pre-trained Convolutional Neural Networks for writer recognition. Specifically, the ensemble is composed of three pre-trained Inception-ResNet-v2 models with different hyperparameter values. Results obtained on the benchmark ICDAR 2019 dataset of handwritten historical documents prove that the proposed approach is very promising in recognizing the handwritten characters of different writers, also when compared with other deep learning models.
  Keywords: Convolutional Neural Networks; Writer recognition; Cultural heritage; Historical documents; Ensemble learning; Artificial neural networks; Document analysis; Deep learning; Transfer learning.
  DOI: 10.1504/IJRIS.2024.10067482
   
  
• Information fusion method on hexagonal fuzzy number based Multi-Criteria Decision Making problems  
  by Lakshmana Gomathi Nayagam Velu, Bharanidharan R 
  Abstract: Recieving the information from the experts are crucial stage in fuzzy multicriteria decision making (MCDM) problems. Different types of fuzzy numbers are used in fuzzy MCDM problems. Moreover, Hexagonal fuzzy numbers is widely used in fuzzy MCDM problems because of its convenience on piecewise linearity. The major drawback of fuzzy MCDM problems is non-availability of information for some alternatives with respect to some criteria while collecting information from the experts. To overcome this, researchers found some methodologies which are known as information fusion/infusion methods. In this paper, we have proposed two infusion methods based on score functions and similarity measures and studied infusion fusion algorithms by giving illustrative numerical examples. Further, due to the needfulness, a new similarity measure on Hexagonal fuzzy numbers have been introduced and used in the infusion method.
  Keywords: Hexagonal fuzzy numbers; Information Fusion; Missing data MCDM; Similarity measure on HXFN.
  DOI: 10.1504/IJRIS.2024.10068105
   
  
• Rice Plant Nutrient Deficiency Classification Using Deep Learning Techniques  
  by D. Sindhujah, R. Shoba Rani 
  Abstract: Every day, half of the world’s population eats rice. The World Bank predicts that by 2025, the demand for rice consumption will have increased by 51%. Mineral deficiency is one of the variables that impact rice yield. Plants need a variety of minerals and nutrients to flourish, especially while they are in the process of blooming or developing fruit. Critical plant growth disorders, which impact agricultural productivity, are caused by nutrient deficiencies. As soon as farmers see signs of nutrient inadequacy in their plants, they may use effective nutrient management measures to remedy the situation. New possibilities in non-destructive field-based analysis for nutritional deficiencies have emerged with computer vision and deep learning algorithms. In this research, we presented a ResNet50 model that has been fine-tuned to identify nutritional deficits in rice images. Our suggested model is combined with the ADAM optimiser and the softmax classifier to get the best possible outcome. Using our model, we will determine whether the rice plant is deficient in nitrogen, phosphorus, and potassium. Our findings show that our model outperforms the competition with an accuracy of 94.34%.
  Keywords: image augmentation; ResNet50; ADAM optimiser; softmax classifier; critical plant growth disorders; deep learning algorithms; nutrient inadequacy; agricultural productivity.
  DOI: 10.1504/IJRIS.2024.10068106
   
  
• Optimizing Feature Selection in Educational Data Sets Using an Enhanced Teaching-Learning Based Optimization Algorithm  
  by George Amalarethinam, A. Emima 
  Abstract: Educational data mining (EDM) is an emerging study topic that helps schools improve student performance. Selecting only relevant data reduces model input parameters with feature selection. It reduces dimensionality by selecting a subset of features and removing incorrect, superfluous, or noisy ones. It improves learning accuracy, computational cost, and model interpretability. This impacts the accuracy of performance models used to assess student outcomes. Most optimisation methods, including the genetic algorithm, must optimise many governing parameters for greater performance. Optimisation approaches using wrapper feature selection (WFS) improve classifier prediction. The proposed ETLBO algorithm with WFS techniques uses the Euclidean distance formula to assess fitness value and popular control parameters to select the optimal feature subset. The algorithm above is used on the educational dataset. Classification algorithms evaluate the best features from TLBO ETLBO algorithms: 4 algorithms classify performance metrics: GNB, LR, SVM, and K-nearest neighbour. Experimental results suggest that the ELTBO algorithm’s best feature subset improves classification accuracy for GNB, LR, SVM, and KNN compared to TLBO.
  Keywords: classification algorithms; feature selection; FS; optimisation technique; Euclidean distance; enhanced teacher learner based optimisation; ETLBO; teacher learner based optimisation.
  DOI: 10.1504/IJRIS.2024.10068107
   
  
• Advancing Healthcare Intelligent Systems: The Critical Role of Paternity Benefits in Modern Caregiving  
  by Swapna Ashmi, P.R.L. Rajavenkatesan 
  Abstract: The Maternity Benefit Act of 1961 ensures that women are entitled to receive payment for maternity leave and leave in the event of a miscarriage. It is also important to note that Indian law has not appropriately recognised paternity leave. Fathers can take paternity leave after the birth of their child or after miscarriage, adoption, or similar circumstances. The legislation regarding paternity leave in India was officially passed in 2017. The execution of this law needs improvement, as dads’ paternity leave rights are not regulated. Gender-neutral policy guidelines matter in a global economy. Fathers’ contributions to their spouses and children’s well-being make paid parental leave crucial. The study examines how paternity benefits affect children’s development and growth. The study also compared India’s paternity leave policy to many others. Healthcare analysis and kid well-being are also examined. It was given to 317 people from diverse fields. The study evaluates the importance of paternity benefit enforcement in India based on 250-member replies. MS Office was used to draft and organise the research, while Python was used to process and compare data.
  Keywords: childcare; advancing healthcare; intelligent systems; equality and fatherhood; gender-neutral; maternity leave; miscarriage and paternity leave.
  DOI: 10.1504/IJRIS.2024.10068108
   
  
• Leveraging Social Capital and SIoT for Sustainable Entrepreneurship Development  
  by K.M. Ashifa, Mehdi Safaei, HINA Zahoor, Rehab El Gamil, NASIR MUSTAFA 
  Abstract: The current research examines the combined effect of integrating social internet of things technology in entrepreneurial skill development programs for the Irula tribal community, Tamil Nadu, toward socio-economic upliftment. LAS and SCAM were adopted to collect data at the household level of 538 households, besides gathering qualitative information through purposive collection through focused group discussion and an in-depth interview of 60 participants. Quantitative results, as shown by paired t-tests and CR analyses, recorded significant increases in social capital and entrepreneurial skills following intervention. In-depth interviews, FGDs, and workshops brought rich qualitative insights into improved networking, innovation, and decision-making. Increasing communities’ cohesion and resilience resulted in enhanced livelihood
  Keywords: social internet of things; SioT; tribal development; indigenous knowledge; community health; entrepreneurial skills; government interventions; livelihood assessment schedule; LAS.
  DOI: 10.1504/IJRIS.2024.10068109
   
  
• Ancient Epigraphical Monuments' Convolution Neural Network-Based Skeletonized Structural Angularmorphing Character Identification Intelligent Systems  
  by P. Selvakumar 
  Abstract: Tamil is one of the oldest languages, and it is based on several proofs from ancient Kiladhi epigraphic monuments. Tamil texts have various structural styles and projections identified from monuments like palm lead characters, vattezhuthu, and stone inscriptions. By projecting Tamil characters in various angles, the text style may vary due to structural representation, leading the actual character style to differentiate from the old style. Thus, recognition of the specific projection of the old character leads to more features on the dimension level to get the Tamil character and classification. Consider skeletonized structural angular morphing (S2AM) based on a CNN-identified Tamil character from ancient epigraphic monuments for optimum identification. Epigrammatic images will be pre-processed using Gaussian filters, then SMS will glide the character region using CED. Use the skeletonized angular projection to discover text structural components and extract angular information. The selected features will be trained with a DFCNN to
  Keywords: Script Systems Identifying; Tamil character detection; edge detection skeletonization; Character Identification Intelligent Systems; Canny Edge Detection (CED); Deep Features Convolution Neural Networ.
  DOI: 10.1504/IJRIS.2025.10068690
   
  
• Embracing Creativity and Encouraging Teacher Satisfaction at Intelligent Systems  
  by Neenet Baby Manjaly, S.A.Vignesh Karthik, H. Lekha, V. Ameena Babu, Gayathri Joshi 
  Abstract: Many firms can now achieve high employee performance thanks to self-motivated working cultures. Employee behavior and job satisfaction at the organizational climate level have been extensively studied. Academics need job dedication and satisfaction to boost productivity, student advancement, retention, and cognitive and personal growth. Academic independence, creativity, professional commitment, and job joy are examined in this study. This research will examine the relationship between these factors. The study tested work-life balance theories for Chennai's private professional teachers. Data was collected using a self-administered questionnaire, and 353 were analyzed. The model's validity and reliability were assessed using multivariate statistics. Data was analyzed using structural equation modeling for normalcy, reliability, and discriminant validity. Results demonstrated that employment independence boosts creativity, dedication, and satisfaction. All components boost job happiness. Freedom at work and job commitment facilitated creativity, supporting the mediation hypothesis. The results also showed that job dedication mediates flexibility at work and
  Keywords: Embracing Creativity; Encouraging Teacher; Freedom at Work; Job Commitment; Intelligent Systems; Job Satisfaction; Employment Independence; Teaching Profession.
  DOI: 10.1504/IJRIS.2025.10068691
   
  
• Enhancing Critical Thinking Skills through Generative AI Models: Mechanisms and Educational Impacts  
  by Vincent Raj, Eronimus Jeslin Renjith, S.Silvia Priscila, C.Sathish Kumar, S. Suman Rajest 
  Abstract: Enhancing Critical Thinking Skills has even been considered to revolutionize the future of artificial intelligence and has such huge impacts across sectors, especially education. This study looks at the way generative AI models enhance critical thinking in learners based on recent studies; it debates applying them to actual education and their influence. The study adopted a mixed-method approach. One would be to carry out an analysis of learners’ performance using a quantitative method and also obtain a subjective assessment of the development of learners’ critical thinking skills. The results show that generative AI improves scholastic performance; personalised learning tools raise critical thinking scores from 50-80 to 70-100. The conclusion shows that models increase engagement and positive attitudes towards enhanced learning outcomes. AI integration into higher education faces various hurdles, including privacy issues over higher education data and educator training. The study also discusses how educators and governments might use focused
  Keywords: Generative AI; Critical Thinking; Educational Technology; Personalized Learning; Interactive Learning; AI in Education; Pedagogical Strategies; Cognitive Development.
  DOI: 10.1504/IJRIS.2025.10068827
   
  
• Intelligent Techniques for Evaluating Organizational Agility via Contingency Theory in Dynamic Environments  
  by Sivakoti Reddy Manukonda, Seema Bhakuni, Vinayak Anil Bhat, Rameshwaran Byloppilly, Rishi Shukla, Jayesh Solanki 
  Abstract: The contingency approach to management holds that effective management depends on the context of a situation. This paper discusses how the contingency approach works in different managerial settings, focussing on environmental variables like organisational size, task structure, and leadership style that may affect manager effectiveness. This study will combine an in-depth literature review, interview and survey data, and statistical analysis to understand the contingency strategy in practice. Management approaches combined with these environmental variables improved organisational performance, adaptation, and resilience. With organised contingency practices, larger organisations perform better, adaptive management tactics help difficult tasks, and democratic leadership styles work better in varied situations. Regression models and the coefficient of correlation show strong positive correlations supporting these links. Since the contingency technique usually requires ongoing adjustment and integration of various factors into a system, it also highlights its obstacles and limitations from the study. Long-term contingency practices and their effects or
  Keywords: Contingency Approach; Managerial Effectiveness; Organizational Performance; Leadership Style; Environmental Variables; Task Structure; Organizational Size; Adaptability.
  DOI: 10.1504/IJRIS.2025.10068931
   
  
• Reinforcement Learning-Driven Collective Intelligence for Prioritized Spectrum Reservation in Cognitive Radio Network  
  by Meetu Nag, Bhanu Pratap 
  Abstract: In the realm of cognitive radio networks, research aims to enhance spectrum usage by enabling access for more users through different spectrum allocation policies. The dynamic and rapid changes in the communication environment pose challenges in making correct decision for spectrum allocation. To facilitate dynamic spectrum allocation, intelligence is integrated into the cognitive system to analyze environmental parameters, various known parameters have to be analyzed to know about the nature of the radio node. In this paper a novel method is discussed for spectrum allocation by involving a multiple decision system that works on priority-based allocation approach. This system collects environmental data for decision-making, ensuring efficient service in this adaptive communication scenario.
  Keywords: Reinforcement Learning; Collective Intelligence; Spectrum Reservation in Cognitive Radio Network; Spectrum Sensing; Cognitive Radio Network.
  DOI: 10.1504/IJRIS.2025.10069324
   
  
• Predicting Chronic Obstructive Pulmonary Disease (COPD) using Machine Learning with Bio-Inspired Hyperparameter Optimization  
  by Yalin Song 
  Abstract: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory condition for which early detection is crucial to effective patient management. With LGBM and DTC as the foundational models, this study explores the predictive capability of ML approaches for COPD. Two bio-inspired optimizers, the TSA and ROA, were employed to enhance their performance. These optimizers mimic the collective behavior of biological systems, such as tunicates’ foraging patterns and jellyfish’s pulsating movements, to achieve optimal solutions within the model training process. Relevant features are extracted from patient data, potentially including demographics, medical history, lung function tests, and lifestyle factors. Among the metrics used to evaluate the performance of the optimized models are their accuracy and precision. The DTTS model’s excellent performance shows how well the DTC model predicts COPD. The greatest accuracy and precision scores of 0.907 and 0.911 support its COPD prediction accuracy. These findings demonstrate the DTTS model’s reliability and
  Keywords: Chronic Obstructive Pulmonary; Decision Tree Classification (DTC); Light Gradient Boosting Classification (LGBM); Rhizostoma Optimization Algorithm (ROA); Tunicate Swarm Algorithm (TSA); Machine Learn.
  DOI: 10.1504/IJRIS.2025.10070424
   
  
• Efficient deep mood-based Hindustani raga music recommendation using facial emotion expressions  
  by Yogesh Prabhakar Pingle, Lakshmappa K. Ragha 
  Abstract: Music recommendation is considered as a solution, and the performance is degraded with prediction error. A novel approach for music recommendation based on facial emotions with the objective of extracting better feature information without loss is required. In this paper, an efficient cross-dense network model with multi-pooling is used to detect basic emotions from the face image. The complex cross-dense connections are provided for the extraction of most discriminate feature information. After recognising the emotion from the face, a new attention-based deep collaborative filtering recommendation system is proposed, with a list of Hindustani raga music to improve users moods. The proposed framework is invoked with the Facial Expression Recognition 2013 (FER-2013) dataset, and the recommendation is provided for happy and sad emotions from the ragas. The performance is compared with existing deep learning-based approaches. The proposed approach improves accuracy, precision and recall by 0.9972, 0.9896, and 0.9906.
  Keywords: facial emotion recognition; CrossDenseNet; multi-pooling layer; AttentionNet; collaborative recommendation; Hindustani music.
  DOI: 10.1504/IJRIS.2025.10070903
   
  
• Enhancing academic success: a deep dive into students' performance prediction using decision tree classification models  
  by Tingting Du, Linglanxuan Kong 
  Abstract: Education, a fundamental human right, plays a pivotal role in personal and societal advancement, cultivating critical thinking and problem-solving skills, fostering social integration, and contributing to global progress, with a focus on innovative strategies to elevate education standards and prioritise students' performance. Educational data mining (EDM) is a burgeoning field within DM that investigates patterns in education, covering analysis of student knowledge and behaviour, teacher curriculum planning, and course scheduling, all with the primary goal of enhancing student learning performance and achieving efficiency in education systems. This paper addresses the task of predicting and categorising students' performance in the Portuguese language, emphasising decision tree classification (DTC) models, along with 2 hybrid models optimised using aquila optimiser (AO) and honey badger algorithm (HBA), for a cohort of 649 students. The results underscore the exceptional predictive capabilities of the DTHB model, outperforming the DTAO model in G2 prediction with an impressive F1-score of 0.9428 compared to 0.9381. Additionally, the DTHB model continues to excel in G3 prediction, boasting the best performance at an F1-score of 0.9275.
  Keywords: Student performance; decision tree; aquila optimiser; AO; honey badger algorithm; HBA; teacher curriculum planning; educational data mining; EDM; course scheduling; decision tree classification; DTC.
  DOI: 10.1504/IJRIS.2025.10072219
   
  
• Comprehensive study on digital image encryption using magic square  
  by Vybhavi. Balasundar, K. Mani, Uma Devi, S.Kumar Chandar 
  Abstract: Digital image encryption plays a vital role in safeguarding sensitive images from unauthorised access. Among the emerging methodologies, magic square-based encryption has gained significant attention due to its simplicity, flexibility, and capacity to generate diverse encryption keys. This review provides a detailed analysis of magic square-based techniques for image encryption, emphasising their unique properties and applications. The paper examines several recent algorithms, exploring their design, strengths, and limitations. Furthermore, it highlights the potential of hybrid encryption approaches that integrate magic square techniques with other cryptographic methods to enhance security and efficiency. Finally, the review discusses the current advancements in magic square-based image encryption and identifies key challenges, such as scalability, robustness, and adaptability, clearing the path for additional study and advancement in this area.
  Keywords: digital image encryption; magic square methodology; hybrid encryption; algorithms; techniques.
  DOI: 10.1504/IJRIS.2025.10072799
   
  
• A novel method for solving probabilistic programming problem in interval type-2 fuzzy environment  
  by Babita Chaini, Narmada Ranarahu 
  Abstract: This paper introduces a novel mathematical model for stochastic programming in a type-2 fuzzy environment, addressing the dual uncertainties of fuzziness and randomness through fuzzy normal random variables. The proposed model innovatively converts fuzzy stochastic problems into deterministic ones using a two-step process: the -cut technique to remove fuzziness and the chance-constrained technique to handle randomness. This approach, involving perfectly normal interval type-2 triangular fuzzy numbers, is illustrated with a numerical example. The critical finding is the effective transformation of complex fuzzy stochastic problems into more manageable deterministic forms, enhancing computational efficiency and solution accuracy. The industrial implications are significant, offering a robust decision-making framework for sectors like manufacturing, logistics, and finance, where uncertainty is a critical factor. This methodology improves accuracy and reliability in operational and strategic planning, making it highly relevant for practical applications.
  Keywords: stochastic programming; normal random variables; optimisation techniques; type-2 fuzzy set; T2FS.
  DOI: 10.1504/IJRIS.2025.10073067
   
  
• Multi-scale semantic awareness fusion transformer for sentiment analysis in electricity marketing  
  by Chunlei Liu, Wei Ge, Yanan Cai, Jinghui Chen 
  Abstract: In the context of electricity market marketing, facial recognition-based emotion analysis systems can help enterprises better understand customers emotional feedback, thereby enhancing service experience and improving the precision of marketing strategies. To address these challenges, this paper proposes a multiscale semantic perception and attention fusion model (MSPAF) aimed at improving the accuracy and robustness of customer emotion recognition in the power industry. During the multimodal feature fusion stage, the model applies a multi-level attention pooling strategy to effectively capture emotional correlations between different modalities while reducing feature dimensionality, thereby improving efficiency and generalization. When using generic image encoding features combined with global semantics and local syntax fusion, the models accuracy drops by 1.64% and 2.34%, respectively.
  Keywords: multiscale semantic awareness; transformer; electricity marketing; sentiment analysis; multihead attention mechanism.
  DOI: 10.1504/IJRIS.2025.10073068
   
  
• An optimising method of Japanese character recognition based on improved support vector machine  
  by Yang Wang, Pan Zhao 
  Abstract: Aiming at the problem of low recognition accuracy and long recognition time due to poor feature extraction, a Japanese character recognition method based on improved support vector machine was studied. First, the mean filtering method is used to denoise Japanese text samples, and then Gabor filter is used to extract features, and Fourier transform is used to optimise the extraction speed. Then, the initial text recognition method is constructed by introducing support vector machine and improving the penalty coefficient algorithm. Finally, Japanese character recognition is realised by using the multi-layer perceptron function as the kernel by processing linear indivisible samples with the relaxation term minimisation. The experimental results show that the proposed method can effectively extract Japanese character features with a recognition accuracy of 97.4% and a recognition time of only 1.3 s, which effectively solves the problems of low recognition accuracy and long recognition time.
  Keywords: support vector machine; Japanese characters; identification method; Gabor filter; Fourier transform; penalty coefficient; Kernel function.
  DOI: 10.1504/IJRIS.2024.10059320
   
  
• A personalised recommendation method for low-carbon tourism routes based on user feature mining  
  by Bing Hou 
  Abstract: In order to overcome the problem of poor user feature mining accuracy and recommendation satisfaction in personalised recommendation methods, a low-carbon tourism route personalised recommendation method based on user feature mining is proposed. Firstly, determine the changes in the cost of low-carbon tourism production behaviour and the demand curve for low-carbon tourism, as well as the characteristics of low-carbon tourism. Secondly, calculate the similarity of low-carbon tourism user features to achieve overall feature mining for low-carbon tourism users. Finally, calculate the user feature entropy and conditional entropy, and analyse the key degree of low-carbon behaviour characteristics of tourism users. Personalise recommendations for low-carbon tourism routes that meet the conditions based on users' preferred low-carbon tourism attractions. The experimental results indicate that the research method can effectively improve the efficiency of fine-grained user feature mining, and the satisfaction with personalised recommendation of low-carbon tourism routes is high.
  Keywords: user feature mining; low carbon tourism; route planning; personalised recommendation; conditional entropy; route planning.
  DOI: 10.1504/IJRIS.2024.10059319
   
  
• Content clustering based propagation feature extraction for short video media platforms  
  by Shiming Zhou 
  Abstract: In order to improve the accuracy of short video media platform propagation feature extraction, this paper proposes a content clustering-based propagation feature extraction method for short video media platforms. Firstly, the data obtained through data crawling is user online comments, which are used to obtain user comments on short video media platforms. Secondly, preprocess the propagation data of short video media platforms through data cleaning and text segmentation. Then, input the short video, calculate the classification loss for each frame separately and sum it up. Finally, the content clustering method is used to cluster the propagation features of short video media platforms, and the final propagation features of short video media platforms are obtained by solving the propagation feature function. The experimental results show that the proposed method can effectively improve the accuracy of propagation feature extraction and enhance the recall rate of feature extraction.
  Keywords: K-means clustering; content clustering; data crawling; cross entropy loss.
  DOI: 10.1504/IJRIS.2023.10059840
   
  
• Enterprise hidden financial information extraction method based on data source dependency  
  by Jingyi Li 
  Abstract: The significance of hidden financial information extraction research lies in the discovery of corporate financial fraud. In order to address the shortcomings of traditional methods such as low recall and precision, and long time overhead, therefore an enterprise hidden financial information extraction method based on data source dependency is proposed. By determining the redundancy of enterprise financial information through data source dependency relationships, the data source dependency relationships are cleaned, and the cleaned data is input into the RDCNN-CRF model to achieve information label classification. Combined with the label classification results, an ordered long-short-term memory-multi-head attention mechanism neural network model is constructed, and the processed data is input into this model. The model output is the result of extracting hidden financial information. The experimental results show that the mean recall rate and precision rate of the proposed method are 97.92% and 98.07%, and the maximum time consumption is 0.82 s.
  Keywords: data source dependency; enterprise; hidden financial information; information extraction; redundancy; RDCNN-CRF model.
  DOI: 10.1504/IJRIS.2024.10059318
   
  
• Adverse COVID-19 vaccination-related events in India: a cross-sectional study using machine learning to predict their severity  
  by Hemangini Mohanty, Santilata Champati, Jyotiranjan Sahoo 
  Abstract: The most effective method of preventing coronavirus illness is vaccination, despite its status as a global outbreak. In this study, India's COVID-19 vaccination's adverse consequences are evaluated, and build a model for predicting the severity of side effects. A cross-sectional study was conducted through an online survey among the Indian population who received at least a single dose of the COVID-19 vaccine. Then, data were statistically analysed, and machine learning tools were used to build a predictive model predicting the severity of side effects. A total of 3,222 participants' records were analysed for those participants receiving three different vaccines, i.e., Covaxin, Covishield, and SputnikV. Only 25% experienced mild-to-moderate side effects. The most common side effects recorded were fever/chills, headache, feeling pain at the injection site, tiredness, and fatigue. The people receiving the first dose (71.93%) had significant side effects compared to the second dose.
  Keywords: SARS-CoV-2 vaccine; COVID-19 vaccine; post-vaccination symptoms; machine learning; predictive model.
  DOI: 10.1504/IJRIS.2024.10059323
   
  
• An online teaching resource recommendation algorithm based on category similarity  
  by Lingyu Chen 
  Abstract: To overcome the problems of poor recall rate, time-consuming resource recommendation list generation, and low ideal loss cumulative gain in traditional online teaching resource recommendation algorithms, a category similarity based online teaching resource recommendation algorithm is proposed. Firstly, segment the user group of online teaching resources and construct a learning user profile of online teaching resources based on dynamic field theory. Secondly, the similarity between user categories and teaching categories is obtained, and the similarity of online teaching resource categories is obtained through sorting. Finally, based on category similarity, an online teaching resource PAF recommender is constructed, taking into account teaching popularity and user loyalty to achieve online teaching resource recommendation. The experimental results show that the online teaching resource recommendation recall rate of this algorithm can reach 99.9%, the ideal cumulative loss gain of teaching resource recommendation is 66.18, and the resource recommendation list generation time is 11 s.
  Keywords: category similarity; teaching resources; recommendation algorithm; dynamics; product adoption forecasting; PAF recommender.
  DOI: 10.1504/IJRIS.2023.10059842
   
  
• Evaluation of the shortest path using a ripple-spreading algorithm in an uncertain environment  
  by Prasanta Kumar Raut, Siva Prasad Behera 
  Abstract: The efficient determination of the SP in a connected network is a crucial problem in various domains, such as transportation, communication, and logistics. Traditional approaches often assume crisp values for the parameters, but in real-world scenarios, uncertainties and imprecision are prevalent. This study proposes a novel approach that utilises the ripple-spreading algorithm with triangular fuzzy numbers as parameters to determine the SP in a given network. The ripple-spreading algorithm, known for its ability to propagate information or updates through a network, is adapted to handle uncertainties by incorporating triangular fuzzy numbers. Each connection in the network is described by a triangular fuzzy number, which utilises the triangular fuzzy numbers consisting of lower bounds, modal values, and upper bounds, forming a triangular-shaped membership function. The propagation is performed considering the triangular fuzzy numbers as parameters, accounting for uncertainties and imprecisions.
  Keywords: ripple-spreading algorithm; shortest path; network; triangular fuzzy number; uncertainty.
  DOI: 10.1504/IJRIS.2024.10059321