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158 articles for “Privacy mode”
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Artificial Intelligence in Microbiological Research: Methods, Applications and Implications
Abstract: Artificial Intelligence (AI) is revolutionising microbiological research by enabling the rapid analysis of complex biological data and improving the accuracy, efficiency, and reliability of scientific investigations. Recent advances in machine learning, deep learning, and bioinformatics have transformed AI into a powerful tool for studying microorganisms, their genetic composition, evolutionary patterns, and interactions with hosts and the environment. AI-driven computational models can process large and complex datasets far more efficiently than …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 16, Issue 2, 2026 Read article
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Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques
Abstract: The integration of hybrid ensemble learning and automated machine learning (AutoML) is revolutionizing disease prediction by addressing the complexity, imbalance, and high dimensionality inherent in medical datasets. This paper proposes an advanced pipeline that combines diverse ensemble learning models with AutoML-based optimization to predict chronic diseases such as kidney diseas-e, Parkinson’s disease, and lung cancer. Publicly available datasets from UCI and PhysioNet repositories were preprocessed using outlier removal, normalization, and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 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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Machine Learning-Based Approach for Heart Disease Prediction
Abstract: Heart disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 64–73 Read article
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A Mathematical Perspective on Recent Cloud-Computing Trends for Scalable and Secure Social-Media Platforms
Abstract: Cloud computing underpins modern social-media platforms by providing elastic compute, storage, and data-processing pipelines capable of absorbing highly bursty workloads. This paper surveys recent cloud-native trends—serverless and event-driven design, container orchestration, edge/CDN offload, streaming analytics, and privacy-enhancing security controls—and formalizes their impact through a compact mathematical model. We express workload volatility using arrival-rate functions, use queueing-based capacity sizing to derive auto-scaling rules, and formulate an optimization objective that balances cost …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 35–40 Read article
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Statistical Models for Predicting Genetic Variability and Disease Susceptibility
Abstract: Differences in genetics are key to understanding why some individuals are more prone to certain diseases than others. Recent advancements in genomic research, combined with statistical modeling techniques, have made significant strides in predicting disease risk based on genetic factors. This review explores the application of statistical models for predicting genetic variability and their role in disease susceptibility. We discuss traditional methods like linear regression and genome-wide association studies (GWAS), …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 1, 2025 · pp. 30–34 Read article
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Statistical Methods in Law: Analysing Trends and Patterns in Judicial Outcomes
Abstract: The research paper explores the integration of statistical techniques within the domain of legal science, emphasizing their role in assessing and interpreting ongoing trends. By employing methods such as descriptive statistics, inferential statistics, and multivariate analysis, the study highlights how empirical data can effectively uncover disparities in areas like sentencing practices, risk assessments, and the evaluation of policy outcomes. These statistical tools enable researchers and legal professionals to identify patterns, …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 3, 2024 · pp. 32–36 Read article
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Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography
Abstract: In modern technological landscapes, private cloud security is of paramount concern due to the ever-increasing volume and complexity of cyber threats. This research work explores the integration of machine learning and cryptography to enhance security within private cloud environments. This study aims to mitigate vulnerabilities that may compromise data integrity, confidentiality, and availability in private cloud infrastructures by using machine learning algorithms and strong cryptography. By detecting anomalous cloud patterns …
Published in International Journal of Advanced Control and System Engineering · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery
Abstract: Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
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Implementing Blockchain to Enhance Security in the Pharmaceutical Industry and Combat Drug Counterfeiting
Abstract: Drug counterfeiting has emerged as a critical threat to public health, as it has enabled inferior and counterfeit drugs to flood many markets around the world thereby eroding trust in healthcare systems and patient safety. This paper seeks to address the glaring need for adequate security safeguards to curb the circulation of counterfeit drugs by proposing a blockchain model designed specifically for the pharmaceutical industries. In general, the idea of …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 1, 2025 · pp. 33–46 Read article
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Blockchain Enabled Security Framework: Smart Healthcare
Abstract: To explores the transformative potential of blockchain technology in managing, securing, and ensuring the integrity of Electronic Health Records (EHRs). EHRs, which store vital patient information such as medical histories, diagnoses, prescriptions, and imaging results, are essential for enhancing healthcare delivery. Conventional centralized Electronic Health Record (EHR) systems encounter issues such as susceptibility to single points of failure, security risks, and constraints in maintaining data integrity. By leveraging blockchain’s inherent …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 11–17 Read article
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Enhancing Security and Privacy in Smart Factory: A Blockchain Based Solution
Abstract: The manufacturing industry is currently experiencing a transformative shift due to the rise of smart factories, propelled by interconnected devices and automated systems. However, this integration of diverse technologies within smart factories introduces significant concerns regarding security and privacy. This research paper thoroughly investigates the vulnerabilities and threats prevalent in smart factory ecosystems, shedding light on the potential risks associated with centralized data storage and communication channels. The study emphasizes …
Published in International Journal of Wireless Security and Networks · Vol. 2, Issue 1, 2024 · pp. 13–20 Read article
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Multi-Layered AI-Driven Security in Wireless Ecosystems
Abstract: The proliferation of next-generation wireless technologies, from 5G/6G networks to the pervasive Internet of Things (IoT), has birthed a hyperconnected digital ecosystem of unprecedented scale and dynamism. This interconnectedness, however, introduces a vast and volatile attack surface, rendering conventional, signature-based security paradigms fundamentally obsolete. This paper posits that the only viable defense is an offensive, self-adaptive one, predicated on the integration of artificial intelligence (AI) directly into the wireless security …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 21–28 Read article
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Security of Cloud Computing from a Blockchain Perspective
Abstract: Originally, the foundation of the Internet was trust. There are increased threats and problems after many information disclosures. We have employed even more modern Internet-based devices in recent years. Among the primary issues raised in literature, privacy, data protection, and trust require particular consideration. In this case, a new paradigm for information security has arisen, one that is built on transparency rather than the closed, cryptic methods used in present …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 1, 2024 · pp. 7–11 Read article
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An Analysis of Multimodal Fusion in Deepfake Detection for Video Samples
Abstract: In today’s rapidly evolving digital landscape, deepfake technology stands as both a marvel and a threat to privacy and security. Deepfakes, hyper-realistic synthetic media created using artificial intelligence (AI), can deceive and manipulate on an unprecedented scale, from political propaganda to compromising videos of public figures. This research navigates deepfake detection, focusing on two advanced methodologies: the vision transformers (ViT) image classifier and the Meso4 method. The ViT model utilizes …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 3, 2024 · pp. 19–27 Read article
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Assessing Knowledge and Awareness of Artificial Intelligence in Academic Research Among Tertiary Students in Ghana
Abstract: Background: While Artificial Intelligence (AI) technologies are evolving rapidly in education, a major deficit in tertiary students' awareness and use of AI-based academic research is observed in Ghana. Prior research notes general awareness but limited use in practice and mentions issues with trust, ethics, and accessibility. Methodology: A cross-sectional survey was undertaken with a sample size of 450 undergraduates and postgraduates from diverse disciplines through stratified random sampling. The study …
Published in Current Trends in Information Technology · Vol. 15, Issue 3, 2025 · pp. 19–25 Read article
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Cybersecurity Early Detection Algorithms for Threats
Abstract: Cybersecurity plays a vital role in protecting digital systems, networks, and data from unauthorized access, misuse, and cyberattacks in an increasingly interconnected world. As reliance on internet-based technologies continues to grow, the frequency and sophistication of cyber threats have also increased, making effective cybersecurity strategies essential. Cybersecurity encompasses a comprehensive framework that integrates technological solutions, organizational processes, and human awareness to ensure the confidentiality, integrity, and availability of information. Key …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 · pp. 31–37 Read article
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Academia to Industry: The Impact of AI on Information Retrieval Technologies
Abstract: Artificial intelligence (AI) has significantly reshaped the field of information retrieval (IR), bridging theoretical advancements from academia with practical applications across various industries. This article explores the transformative impact of AI on IR technologies, highlighting key contributions from academic research and how they have been adapted for industry-scale implementations. Academic innovations, such as neural ranking models and semantic search techniques, have improved the accuracy and relevance of search results by …
Published in Journal of Advancements in Library Sciences · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
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A Conceptual Framework for AI-Integrated Metabolomics in Predictive Health Systems for Resource-Constrained Environments
Abstract: The increasing prevalence of non-communicable diseases (NCDs) continues to place a significant strain on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure is limited. Conventional healthcare approaches remain largely reactive, often detecting diseases at advanced stages when treatment effectiveness is reduced. This challenge underscores the need for predictive, cost-effective, and data-driven healthcare solutions. This study presents a conceptual framework that integrates metabolomics with artificial …
Published in Emerging Trends in Metabolites · Vol. 3, Issue 2, 2026 Read article
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Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and GPU-based architectures without centralizing sensitive data. This work proposes a parallel Adaptive Federated Learning (AFL) framework that integrates Differential Privacy and Secure Aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, and aggregation weights …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article