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Regulatory data protection for global economy of biopharmaceuticals: comparative legal analysis with focus on innovative biopharma in India
This provides a new global economy of biopharmaceuticals with an exclusive right over clinical data, meaning that no other person or persons may use them for a specified period. This study, therefore, offers a critical analysis of complementary protection granted to biopharmaceuticals by patents and regulatory data protection (RDP) globally with respect to innovation, competition, and access to medicines. This study probes the effectiveness and the challenge RDP is making using statistical analysis, financial modelling, and comparative analysis of the regulatory framework in Central Drugs Standard Control Organization (CDSCO), Food and Drug Administration (FDA), and Emergency Market Authorization (EMA). The justification for this combination is that RDP fosters innovation due to the protection of clinical trial investments, which provides a drive for the introduction of innovative biologics but does not inhibit the launch of biosimilars. With RDP, though they are very different in what they do, patents have created an enabling environment to make sustainable innovation in biopharmaceuticals accessible. International regulatory hurdles have to emerge so that a balance that advances both innovation and affordability becomes the norm within biopharma. Copyright 2025 Inderscience Enterprises Ltd. -
From Prediction to Action: Counterfactual Explanations and Ensemble Learning for Explainable Maternal Health Risk Modelling
Maternal health is critical to women's well-being, particularly during pregnancy, delivery, and postpartum. Early prediction and prevention of health risks are essential for reducing complications and improving outcomes. This research introduces a stacking ensemble model for maternal health risk prediction, combining the strengths of Random Forest, XGBoost, and Gradient Boosting with XGBoost as the meta-model. The ensemble approach enhances accuracy and reliability, achieving a classification accuracy of 91.13%, with precision, recall, and f1-scores exceeding 85% across all risk categories.Beyond accurate prediction, this study emphasizes model interpretability through Diverse Counterfactual Explanations (DiCE), an Explainable AI (XAI) method that provides actionable insights for risk reduction. Counterfactual analysis identifies the minimal changes needed in the patient features to shift a high- or medium-risk classification to low-risk, offering clinically relevant recommendations. These counterfactuals are generated to ensure feasibility, preserving physiological plausibility and practical applicability for healthcare professionals. This work bridges the gap between black-box machine learning models and actionable decision-making by integrating predictive power with explainability, supporting more transparent and patient-centric maternal health interventions. 2025 IEEE. -
Color image segmentation based on improved sine cosine optimization algorithm
Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signalnoise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Transforming Education With Data Science in the AI Era
In this AI era, data science emerges as a transformative tool in education. By using data sets, educators and administrators can make informed decisions that personalize learning and improve resource allocation. As AI technologies become more integrated into educational systems, data science serves as a critical bridge between raw information and actionable strategies, enabling a more adaptive, equitable, and evidence-based approach to teaching and learning. Transforming Education With Data Science in the AI Era explores the intersection of AI and data science in reshaping education. This book offers solutions to key challenges, such as ethical dilemmas, data privacy concerns, and digital inequity, to create a sustainable AI-driven education model. Covering topics such as AI, data science, and education, this book is an excellent resource for academicians, educators, educational leaders, and technology developers. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The use of self-protective measures to prevent COVID-19 spread: an application of the health belief model
This study uses a health belief model to examine the preventive behavioral orientation or self-protective measures adopted by people in the face of the current COVID-19 pandemic. A total of 603 participants were selected from the city of Bangalore, India. The data was collected through an online survey with participants age varying between 17 and 54 and mean as 23 years (SD = 4.32). The findings revealed that perceived barrier has significant negative impact, while perceived threat, perceived consequences, perceived benefits, community and individual self-efficacy, and general health cues have a positive influence on an individuals intention to follow self-protective measures against COVID-19. Based on the constructs of the health belief model, this study proposes multiple health-related interventions to reduce the spread of COVID-19. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Investigating the Impact of Emotional Contagion on Customer Attitude, Trust and Brand Engagement: A Social Commerce Perspective
Social Commerce networks are a powerful platform for spreading positive and negative emotional contagion, which is affecting users from different perspectives, i.e., psychology, attitude, buying decision. Emotional contagion is the phenomenon of having a person's emotions and behaviours directly trigger similar emotions or behaviour in other people. This research proposes a model to analyze the factors influencing emotional contagion that, in turn, impact consumer's attitudes, trust, and brand engagement. This study used a survey approach using a structured questionnaire. Primary data was collected from 174 social media users who shop online. The proposed model was tested using multiple regression analysis. The results demonstrated that effective content, visual or text, triggers customers' emotional contagion, influencing customer attitude and trust leading to brand engagement. The research study's findings can be used for deciding on content strategies of advertisements pertaining to social commerce. 2022 Academy of Taiwan Information Systems Research. All rights reserved. -
An Intelligent Model for Detecting Cervical Cancer Using U-Net Segmentation and YOLO Classification Augmented with Lion Optimization
One of the leading causes of death for women is cervical cancer, and survival rates are significantly increased by early identification using Pap smear analysis. However, manually reviewing Pap smear images takes time and is prone to mistakes. Using an enhanced Convolutional Neural Network (CNN) with Transformer-like classification and U-Net-based nucleus segmen-tation, this study suggests an automated cervical cancer detection system. To increase feature extraction, the image is preprocessed using techniques such as edge detection, contrast enhancement with CLAHE, and greyscale conversion. The PR-processed image is segmented using U Net segmentation. A YoLo-based CNN optimised with the Lion optimiser (Evolved Sign Momentum) is used to classify the segmented nuclei to improve convergence and accuracy. Results from experiments show that our model outperforms con-ventional methods in terms of classification accuracy. By lowering reliance on manual screening and increasing early diagnosis rates, this automated approach can help medical practitioners detect cervical cancer more quickly and accurately. 2025 IEEE. -
Comparing Influence of Depression and Negative Affect on Decision Making
The current study aimed to explore differential value-based decision-making patterns across three groupsindividuals diagnosed with mild-to-moderate depression, a healthy matched control group, and a negative mood induction group. In the current study, drug- and therapy-nae individuals diagnosed with first episode of mild-to-moderate depression (n = 40), healthy individuals matched on age, gender, and education (n = 40), and healthy individuals with no current, past, or family history of any psychiatric conditions in a negative mood-induced state (n = 40) were administered the IOWA Gambling Task (IGT) and the Balloon Analog Risk Task (BART). Results indicated that individuals with depression showed heightened punishment sensitivity on both the IGT and the BART (p < 0.05 on the BART and p < 0.05 on the IGT), andperformed poorly on the IGT indicating poor and slow learning (p < 0.01). A similar, less severe, pattern was observed in the negative mood induction group. Individuals with mild-to-moderate depression performed poorly on tasks of value-based decision making. The significance of process factors in decision making, such as reward and punishment sensitivity, valuation of outcomes and learning, was highlighted in this study. The study also demonstrated how a negative affective state, without the other clusters of depressive symptomatology, can also lead to a less severe, but impaired decision making. 2023, The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India. -
Lung cancer prediction with advanced graph neural networks
This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Lung cancer prediction with advanced graph neural networks
This research aims to enhance lung cancer prediction using advanced machine learning techniques. The major finding is that integrating graph convolutional networks (GCNs) with graph attention networks (GATs) significantly improves predictive accuracy. The problem addressed is the need for early and accurate detection of lung cancer, leveraging a dataset from Kaggle's "Lung Cancer Prediction Dataset," which includes 309 instances and 16 attributes. The proposed A-GCN with GAT model is meticulously engineered with multiple layers and hidden units, optimized through hyperparameter adjustments, early stopping mechanisms, and Adam optimization techniques. Experimental results demonstrate the model's superior performance, achieving an accuracy of 0.9454, precision of 0.9213, recall of 0.9743, and an F1 score of 0.9482. These findings highlight the model's efficacy in capturing intricate patterns within patient data, facilitating early interventions and personalized treatment plans. This research underscores the potential of graph-based methodologies in medical research, particularly for lung cancer prediction, ultimately aiming to improve patient outcomes and survival rates through proactive healthcare interventions. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
Metabolomics Pathway Prediction Using Enhanced-Graph Convolutional Networks with Graph Attention Networks
Metabolomics, the comprehensive study of small molecules in biological systems, has a central role to play in the diagnosis of diseases, biomarker detection, and the design of new drugs. Although there have been major breakthroughs in analytical toolsets such as mass spectrometry (MS) coupled with chromatography, it is hard to predict metabolomics pathways because biochemical interactions are inherently complex. To meet this end, the current research suggests a deep learning-based approach using graph neural networks (GNN), which have shown high efficiency for graph-structured biological data. We specifically propose an enhanced graph convolutional network integrated with graph attention networks (EGCNGAT) to enhance pathway prediction performance. The hybrid framework employs graph convolutional networks (GCN) to represent molecular structural data and graph attention networks (GAT) to provide context-sensitive feature importance, thus improving the models capacity for learning complex pathway patterns. Comparative experiments against current deep learning approaches show that the introduced EGCN-GAT model obtains an accuracy of 98.90 percent, which is a 0.26 percent increase compared to the baseline MLGL-MP model. In addition, it demonstrates a 0.94 percent gain in precision as well as a slight gain in recall. The findings validate the performance of the proposed method and highlight its utility for developing pathway-level predictions in metabolomics studies. 2025 by the authors of this article. Published under CC-BY. -
Enhancing Metabolomics Pathway Prediction with Sequential Graph Convolutional Network
Metabolomics is a powerful tool for the understanding of biological systems by analysis of metabolites and their related pathways. Prediction of metabolic pathways is still one of the most challenging tasks because of the complexity of molecular structures and graph-structured metabolomics data. This article presents a robust framework using Graph Convolutional Networks (GCNs) to address the challenges. The methodology proposed includes first preprocessing through metabolite identification by mass spectrometry, and then it utilizes feature extraction through the RDKit library. The objective of the research is aim to metabolic pathway prediction using machine learning algorithm. Complex patterns and relationships are captured from the SMILES representation through the molecular graphs constructed and passed on for the GCN model to learn structured data. ReLU activation functions have been employed within a three-layer sequential GCN architecture that enables it to deliver highly accurate results while ensuring that they are understandable as well. The proposed sequential GCN Model was evaluated on the KEGG dataset with an accuracy of 98.00%, precision of 92.10%, and recall of 93.02%. The performance of these metrics is well beyond traditional approaches such as KNN, ensemble logistic regression, and other GCN based approaches. Thus, this work brings GCN based approaches closer to revolutionizing metabolic pathway prediction and the advancement of the metabolomics field. 2025 IEEE. -
Improved Indian currency recognition: neighbourhood-centred image processing and CNNs with region of pixel selection techniques
The paper proposes an improved approach for Indian currency recognition using neighbourhood-centred image processing and convolutional neural networks (CNNs) with region of pixel selection techniques. The method includes image pre-processing steps such as noise reduction, contrast enhancement, and resizing. A neighbourhood-centred image processing technique is applied to capture contextual information from local neighbourhoods around each pixel. A CNN-based model is then trained on the pre-processed images to learn discriminative features for currency recognition. To enhance accuracy and efficiency, a region of pixel selection technique is introduced to select only relevant regions of interest for CNN training and inference, reducing computational overhead. Experimental results demonstrate the effectiveness of the proposed approach, achieving high accuracy in currency recognition and improved efficiency in terms of computational time and memory requirements. The proposed method has potential applications in automated cash-handling machines, vending machines, and mobile payment systems where reliable currency recognition is essential. Copyright 2025 Inderscience Enterprises Ltd. -
A Travellers Melancholy
On possibly the most humid day in the month of June, I lay on a bed in a backpacking hostel, journalling my melancholy. 2025, The Assosiation FormAkademisk. All rights reserved. -
WELL-BEING AND PROSPERITY: Multidirectional Disciplinary Interactions with Religion
Despite significant advancements in science and technology, religion continues to influence human lives. The twentieth-century perspectives from social sciences, influenced by the secular hypothesis, mainly highlight the negative influence of religion on human progress and practically ignore its influential and positive impact on various fields of knowledge/disciplines. In this paper, we have examined literature from politics, economics, and psychology to understand religions impact on these disciplines and vice versa. We find that religions contribution to human society in the 20th and 21st centuries has been mostly positive, especially in education, healthcare, social justice, economic growth, ethics, and initiatives for eradicating inequality and injustice. For instance, religion provides effective coping measures and strategies when humans face uncertainties and catastrophes and facilitate comfort, confidence, and emotional wellness. Further, we realised that (i) the contemporary research literature in social sciences generally highlights the interaction between religion and various fields of knowledge in a unidirectional way i.e., religion influencing disciplines and not how disciplines influence religion, and (ii) that it fails to reveal a more complex multidirectional and circular relationship between religion and social sciences. This paper proposes ways to bring together social scientists and religious scholars to facilitate the much-needed discussion on the multidirectional relationship between religion and social sciences, thereby paving the way toward the well-being of individuals and social transformation. 2022 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore),. -
An exposition on complete androgen insensitivity syndrome and a case report; [??????? ?????? ?????????????????? ? ??????????: ??????????? ??????]
Complete androgen insensitivity syndrome (CAIS) is a rare X-linked sexual development condition typified by 46,XY karyotype, presence of external female genitalia along with intra-abdominal testes in labia majora or inguinal ring region. This syndrome results from alterations in the androgen receptor (AR) gene leading to primary amenorrhea and uterine agenesis (Mlerian agenesis) in adolescent teens or two-sided labial/inguinal hernia with testes in children around prepubertal age. Our paper reports a case of CAIS in a 16-year-old woman with no menarche and 46,XY karyotyping. Gonadectomy results showed hyperplasia of Leydig cells. The current research encompasses the case report and the available knowledge to date on the understanding, diagnosis, treatment, and management of CAIS. 2025 IRBIS LLC. All rights reserved. -
A review on anti-cancer plants of India
India has a high level of endemism and a diverse range of floral species. Cancer is one of the most significant challenges facing global health today. The indigenous peoples and residents who live in India have, for a very long time, made use of specific medicinal plants to fight cancer. This practice is still prevalent today. Several different drugs may be utilized in the treatment of cancer. Because of the potential drawbacks associated with such treatments and the development of drug resistance, the quest for new therapies that are both safer and much more effective is still the most challenging field of study. Several cancer medicines used today come from natural sources. We're returning to our old ways because medicinal plants are a good, natural way to make medicines that prevent cancer without causing major side effects. Within the scope of this study, a few herbs traditionally used to treat cancer are looked at to see what they might be good for. The cytotoxicity of these plants, the processes that lead to them, and the different compounds they make were looked into. This study has tried to focus on how these plants fight cancer. The Author(s). -
Investigations on Affective Computing to Improve Classroom Engagement Analysis in Higher Education by Deep Learning
The learning and teaching experience can be improved by using approaches that are not obtrusive to perform a comprehensive student engagement analysis throughout the classroom. In these modern times, when courses are conducted online, it is vital to accurately measure the levels of participation that each individual student has. It is crucial and essential to provide assistance to educators so that they may annotate and comprehend the signifcant learning rate of the students. A system that can perceive data and transpire it into information automates the learning and teaching experience in a classroom. In this study, videos are collected from online and ofand#64258;ine classes that have one single student per frame or many students per frame and are analysed for emotions and behavioural engagement through a multimodal system. newlineLarge amounts of video data processing call for an increase in the hardware resources newlineas well as the time required for processing images. This is particularly true in a newlineclassroom setting, where there are a large number of frames to analyse each and every minute in order to handle classroom involvement detection. Hierarchical Video newlineSummarization is used as a preliminary step on the videos to detect important frames newlinethat have the sum of all the information in the local neighborhood. These key frames newlineserve as important information units that provide details of facial emotions and behavioural aspects. The local maxima estimation based on the frst derivative provides summative information about the local neighborhood. The key frames serve newlineas an input for face detection and emotional analysis. In this research, the method newlinecan perform video summarization on a varied category of videos and with different newlineresolutions. Face detection in a temporal environment have not been trivial. Though there are methods that can identify multiple faces with varied sizes in a frame, it is still a current research topic to address false localization of faces in a frame. -
A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization
Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance. 2019 IEEE. -
A classified study on semantic analysis of video summarization
In today's world data represented in the form of a video are prolific and has increased the requisite of storage devices unconditionally. These video sets takes up a huge space for amassing data and takes a long time to ascertain the content that requires a higher cognitive process for content search and retrieval. The efficient method for storing video data is to remove high-degree redundancies and for creating an index of important events, objects and a preview video based on vital key-frames. These requirements imbibes the need to build algorithms that can concise the necessity of space and time for video and adequate approaches are to be developed to solve the needs of summarization. The three effective attributes for a semantic summarized video system are Un-supervision, efficient and dynamically scalable system that can help in reducing time and space complexities. Dimensionality reduction based on sub space analysis helps in plummeting the multidimensional data into a low-dimensional data to enable faster feature extraction and summarization. In this paper we have made a study and description related to several summarization methodologies for video's that are available. 2017 IEEE.