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98 articles for “Data Heterogeneity”
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An Adaptive and Privacy-Aware Federated Learning Framework for Efficient and Secure Model Training Across Heterogeneous Datasets
Abstract: The problem of efficiency and privacy regarding heterogeneous data in modern distributed machine learning systems is a vital point that should be taken into account. The absence of IID data distribution, client heterogeneity, and privacy invasion during the aggregation model are the bane of conventional federated learning (FL) approaches to learning like FedAvg and FedProx. The paper proposes that the adaptive and privacy-aware FL framework (AFL-P) can be used to …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 16–25 Read article
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AI Approaches in Gait and Posture Analysis: A Review
Abstract: This review synthesizes current research on the application of artificial intelligence (AI) in gait and posture analysis, focusing on methodologies, algorithms, and clinical applications. It examines the use of machine learning (ML) and deep learning (DL) techniques to extract relevant features from sensorderived data, offering objective, and automated assessments that surpass traditional methods. A systematic literature review was conducted, analyzing studies that utilized AI for gait and posture analysis with …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 1–3 Read article
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Data Integration and Visualization in Bioinformatics: Techniques and Challenges
Abstract: Data integration and visualization play essential roles in bioinformatics, facilitating the thorough analysis, and interpretation of intricate biological datasets. In the field of bioinformatics, vast amounts of data are generated from various experimental platforms, such as genomic sequencing, proteomics, transcriptomics, and metabolomics. However, the heterogeneity of these datasets, coupled with their large scale and complexity, presents significant challenges in terms of integration, analysis, and visualization. Data integration techniques aim to …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 1–8 Read article
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Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems
Abstract: In the era of intelligent technologies and data-driven systems, multi-objective decision-making (MODM) has become an essential aspect of managing complex environments such as smart cities, autonomous systems, and cyber-physical networks. As decision-making scenarios become increasingly dynamic and uncertain, there is a growing need for advanced methodologies that can handle diverse objectives, conflicting constraints, and incomplete information. This review highlights the emerging role of entropy, symmetry, and data fusion as foundational …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 44–49 Read article
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Association Rule Mining for Predicting Heart Disease: Challenges and Opportunities
Abstract: The exponential growth of digital healthcare data has spurred innovative applications of data mining techniques in medical research and practice. Among these, association rule mining stands out for its ability to uncover meaningful correlations within diverse datasets, such as electronic health records, imaging data, and genetic information. This paper reviews the application of association rule mining in predicting heart diseases, emphasizing its potential to enhance early detection, risk stratification, and …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 29–34 Read article
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Next-Generation Biorepositories: Accelerating Infectious Disease Research and Vaccine Innovation
Abstract: Next-generation biorepositories have emerged as critical infrastructure for advancing infectious disease research and accelerating vaccine development. By integrating cellular, genomic, and clinical data within harmonized frameworks, these repositories overcome traditional limitations of fragmented datasets and limited interoperability. The evolution from conventional biobanks to digitally enabled, multi-omics platforms has enabled comprehensive analysis of pathogen–host interactions, facilitating the identification of novel vaccine targets and correlates of protection. The COVID-19 pandemic underscored the …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article
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Big Data in Chemistry: Problems and Answers
Abstract: The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 9–14 Read article
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Toxicology 4.0: Integrating Artificial Intelligence, Big Data, Health Informatics, and Precision Analytics for Predictive Toxicity Assessment, Real-Time Toxicovigilance, and Personalized Patient Safety
Abstract: Background: Toxicology is undergoing a major transformation, increasingly described as Toxicology 4.0, driven by the integration of artificial intelligence (AI), big data analytics, health informatics, and precision analytics. Conventional toxicity testing is limited by high costs, lengthy timelines, and challenges in translating animal and low-throughput in vitro findings to humans. Aim and Objectives: To comprehensively evaluate the emerging role of Toxicology 4.0 in predictive toxicity assessment, real-time toxicovigilance, and personalized …
Published in Research and Reviews: A Journal of Toxicology · Vol. 16, Issue 2, 2026 Read article
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Topology and Geometry in Data Science: Persistent Homology and Beyond
Abstract: In recent years, the interplay between topology, geometry, and data science has gained substantial momentum, offering powerful frameworks to analyze and interpret complex datasets. Traditional statistical and machine learning methods often rely on linear or metric- based assumptions, which may fail to capture the intrinsic structure of high-dimensional or nonlinear data. In contrast, topological and geometric methods provide shape-oriented, scale- invariant tools that focus on the continuity, connectivity, and global …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 21–27 Read article
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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article
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Federated Learning for Energy Management in Next Generation Smart Cities
Abstract: Federated learning has emerged as a promising approach for addressing the challenges of energy management in next-generation smart cities. This decentralized approach to machine learning allows collaborative model training among distributed data sources, while safeguarding data privacy and security. In this study, we explore the application of federated learning techniques to optimize energy consumption, enhance grid stability, and promote sustainability in smart city environments. By aggregating data from diverse sources …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 1, 2024 · pp. 19–27 Read article
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Revolutionizing Vaccine Development:The Transformative Role of Bioinformatics in Designing Next-Generation Immunotherapies
Abstract: Vaccines have long been central to the prevention and control of infectious diseases, dramatically reducing morbidity and mortality worldwide. In the modern era, the integration of bioinformatics has revolutionized vaccine development by enabling rapid, precise, and cost-effective identification of potential vaccine targets. This seminar explores the multifaceted applications of bioinformatics in vaccinology, including antigen discovery, epitope prediction, structural modeling, molecular docking, and immunoinformatics-driven vaccine design. Special emphasis is placed on …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 19–33 Read article
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Optimizing Data Processing Efficiency in Big Data: Advanced MapReduce Algorithm Innovations
Abstract: The exponential growth of big data in recent years has created an urgent need for innovative and efficient processing frameworks capable of managing and analyzing massive and complex datasets. Among these, MapReduce has gained prominence as a powerful tool for distributed data processing due to its simplicity and scalability. However, traditional MapReduce frameworks often encounter significant limitations in terms of efficiency, scalability, and resource optimization, particularly when handling large-scale and …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 1–7 Read article
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Computational Approaches to Understanding Cellular Signaling Pathways
Abstract: Cellular signaling pathways are fundamental in regulating vital processes, such as cell growth, differentiation, and apoptosis. The intricate and interconnected nature of these signaling networks requires sophisticated methods for their analysis. Computational approaches, including mathematical modeling, network analysis, and machine learning, have revolutionized the way researchers analyze and simulate cellular signaling. This article provides a comprehensive overview of computational strategies employed to model signaling pathways, with a focus on integrating …
Published in International Journal of Cell Biology and Cellular Functions · Vol. 2, Issue 2, 2024 · pp. 8–13 Read article
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Assessing the Effectiveness of Machine Learning Algorithms in Simulating Malware Detection Processes
Abstract: On the information system comprehends, computer virus attacks play a very important role and, by undermining regionally and globally, are considered one of the most critical threats. The traditional malware detection methods, more varied, being based on signature, are incapable of providing sufficient coverage over malicious programs in a short time. This is due to the speed of evolution and growing complexity of malware variants. This situation, in turn, requires …
Published in Journal of Electronic Design Technology · Vol. 14, Issue 3, 2023 · pp. 11–20 Read article
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Advances in Biological Systems Modeling for Predicting Drug Effects in Chronic Disease
Abstract: Biological systems modeling has emerged as a promising tool for understanding and predicting the effects of drugs in the treatment of chronic diseases. Chronic diseases, such as diabetes, cardiovascular diseases, and neurodegenerative disorders pose significant challenges to traditional drug development due to their complex, multifactorial nature. Systems biology approaches, which integrate computational modeling with experimental data, provide a holistic view of disease mechanisms and treatment responses. This review explores recent …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 17–22 Read article
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IntelliGaurd WebScan: Uncovering Dark Patterns on E-Commerce Websites
Abstract: Dark patterns are deceptive design elements that influence user behavior online, frequently with unexpected results that go beyond personal experiences. These deceptive methods unintentionally encourage excessive consumption, which can seriously impede sustainability initiatives. This paper presents IntelliGuard WebScan, a system created specially to identify and combat these dishonest strategies. Employing painstaking examinations and assessment of heterogeneous datasets and rigorous experimentation with multiple algorithms, among them a support vector classifier (SVC), …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 3, 2024 · pp. 24–32 Read article
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Revolutionizing oncology - role of artificial intelligence in early cancer detection and diagnostic advances-A comprehensive review
Abstract: Oncology has experienced a remarkable transformation with the adoption of artificial intelligence (AI), which has greatly enhanced cancer detection and diagnosis. As one of the leading causes of death worldwide, cancer highlights the importance of early detection in improving patient outcomes and survival rates. AI’s ability to analyze vast and complex datasets has enabled groundbreaking innovations in imaging, pathology, biomarker discovery, and predictive analytics. This review highlights key AI-driven advancements …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 1, 2025 · pp. 18–22 Read article
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Pathophysiology Reimagined: Integrating Systems Biology and AI for Disease Understanding
Abstract: Pathophysiology, the study of disease mechanisms at molecular, cellular, and systemic levels, has traditionally relied on reductionist approaches that often fail to capture the complex, dynamic, and interconnected nature of biological systems. Diseases such as cancer, neurodegenerative disorders, and infectious diseases arise from intricate interactions among genetic, epigenetic, metabolic, and environmental factors, necessitating integrative, data-driven methodologies for a deeper understanding. Systems biology has emerged as a powerful approach by leveraging …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 16, Issue 2, 2025 · pp. 63–71 Read article
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Melanoma Skin Cancer Detection Using Deep Learning
Abstract: Melanoma, a fatal type of skin cancer, is a major global health concern. For better patient outcomes, early and precise detection is essential. A branch of artificial intelligence called deep learning has demonstrated encouraging outcomes in medical image analysis, particularly the identification of skin cancer, in recent years. We present a new method for detecting melanoma skin cancer in this paper by utilizing the ResNet-50 architecture, a deep convolutional neural …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 · pp. 1–9 Read article