Search
57 articles for “deep learning analytics”
-
Deep Learning-Enhanced Polymer-Based Wearable Biosensors for Continuous Health Tracking via IoT
Abstract: The rapid proliferation of wearable biosensor technologies has transformed approaches to real-time health monitoring, yet challenges persist in achieving both mechanical robustness and reliable, continuous data analytics in dynamic environments. Conventional polymer-based sensing systems often fall short due to limited signal fidelity, inadequate adaptive analytics, or insufficient integration with secure, low-latency IoT frameworks. Addressing these deficiencies, this work introduces a flexible, deep learning-enhanced wearable biosensor platform that combines a nanostructured …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 18–31 Read article
-
AI-Powered Defense: Advancing Cybersecurity Through Artificial Intelligence Innovations
Abstract: In the current landscape of escalating cyber threats and increasingly sophisticated attack vectors, integrating artificial intelligence (AI) within cybersecurity strategies has become a critical step forward. This study explores the role of AI in strengthening cybersecurity by utilizing its strengths in data analysis, pattern recognition, and predictive modeling. Through AI, organizations can greatly enhance their ability to detect and respond to threats. Machine learning algorithms enable ongoing adaptation to new …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 1, 2026 · pp. 35–41 Read article
-
An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids
Abstract: The increasing adoption of next-generation smart grids has introduced significant cybersecurity challenges due to their reliance on interconnected digital infrastructures and IoT-based control mechanisms. This study aims to analyze cybersecurity threats in smart grids and explore AI-driven mitigation strategies to enhance grid security and resilience. The research examines common cyber threats such as malware attacks, denial-of-service (DoS), data breaches, and insider threats while evaluating the effectiveness of AI-based solutions, including …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 16–25 Read article
-
Predicting Multiple Diseases Using Machine Learning: A Data-Driven Approach
Abstract: The increasing prevalence of chronic and life-threatening diseases highlights the need for innovative healthcare solutions that enable early detection and proactive management. The Multiple Disease Prediction Platform is a web-based system utilizing machine learning (ML) and deep learning (DL) algorithms to analyze user-inputted health data, generating real-time predictions of potential health risks. By leveraging Python’s Streamlit library, the platform provides an interactive and accessible diagnostic experience, eliminating the need for …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 16–35 Read article
-
AI and ML-Driven Immersive Technologies: A New Era in Education
Abstract: The very fast adoption of Artificial Intelligence (AI) and Machine Learning (ML) in education has transformed contemporary teaching and learning ecosystems driven by advances in immersive technologies and the growing engagement of global technology leaders with virtual environments. AI-powered educational platforms enable adaptive and personalized learning pathways by dynamically adjusting content, pace and instructional strategies to learners’ preferences, abilities and learning styles by improving engagement, retention and academic outcomes. Deep …
Published in International Journal of Education Sciences · Vol. 3, Issue 1, 2026 · pp. 116–123 Read article
-
Predictive Maintenance Strategies for Safety-critical Mechanical Systems
Abstract: Ensuring the reliability and safety of industrial systems is essential, especially in high-risk sectors such as aerospace, manufacturing, and energy. Predictive maintenance (PdM) has become a crucial approach for minimizing operational failures and improving maintenance efficiency. This research introduces an advanced PdM framework that enhances industrial safety by integrating Internet of Things (IoT) technology, machine learning (ML), and big data analytics. By enabling real-time monitoring and predictive fault detection, this …
Published in Journal of Industrial Safety Engineering · Vol. 12, Issue 1, 2025 · pp. 12–17 Read article
-
Leveraging Deep Learning and Cloud Computing for Water Usage Optimization in Agriculture: A Study
Abstract: Water scarcity and inefficient irrigation practices are significant challenges in modern agriculture. This research investigates how deep learning and cloud computing can be combined to enhance water efficiency in agricultural practices. Leveraging advancements in deep learning and cloud computing, researchers have developed innovative solutions for optimizing water usage. This review examines the state-of-the-art methodologies, technologies, and applications in smart irrigation systems. It explores how deep learning models and cloud platforms …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 83–91 Read article
-
A Study on AI-Enhanced Environmental Toxicology: Sensor-Driven Predictive Framework
Abstract: Traditional environmental toxicology relies heavily on labor-intensive, often retrospective, sampling and analysis, limiting our understanding of dynamic pollutant behaviors and their real-time impact on ecosystems and human health. This study presents a novel, integrated framework leveraging advanced sensor networks and artificial intelligence (AI) to revolutionize the monitoring, assessment, and predictive modeling of environmental contaminants. We deployed a sophisticated array of multi-parameter sensors (e.g., electrochemical, optical, biosensors for heavy metals, organic …
Published in Research and Reviews: A Journal of Toxicology · Vol. 15, Issue 3, 2025 Read article
-
Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing
Abstract: The exponential growth of internet of things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very …
Published in International Journal of Satellite Remote Sensing · Vol. 2, Issue 2, 2024 · pp. 46–62 Read article
-
Artificial Intelligence in Early Diagnosis and Personalized Treatment of Alzheimer’s Disease
Abstract: Artificial intelligence (AI) has become a disruptive technology in the medical care industry, with potential solutions to early diagnosis and customized treatment of Alzheimer’s disease (AD), a progressive neurodegenerative disease and the most prevalent cause of dementia globally. Conventional diagnostic techniques, such as cognitive, neuroimaging and biomarker techniques, are usually limited in the ability to detect disease at its most susceptible stage when treatment interventions are most effective. The recent …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 · pp. 15–27 Read article
-
Intrinsic Evaluation of Graph Embeddings: Assessing Clustering and Community Detection Performance
Abstract: This paper presents an intrinsic evaluation of some graph embedding techniques on clustering and community detection tasks. We analyze a diverse set of embedding methods, ranging from traditional techniques such as Laplacian eigenmaps to more recent approaches like graph autoencoders, high-order proximity preserved embedding (HOPE), and graph attention network (GAT), using two widely studied datasets, Cora and CiteSeer. Our evaluation relies on two main metrics: Silhouette score with respect to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 40–48 Read article
-
AI-Driven Pharmacogenomics and Precision Medicine: Future of Personalized Therapy
Abstract: Pharmacogenomics and artificial intelligence (AI) are emerging as important drivers of precision medicine, enabling healthcare systems to adopt individualized therapeutic approaches. Pharmacogenomics examines how genetic variations influence drug response, efficacy, metabolism, and toxicity, while AI provides advanced computational tools for analyzing complex genomic and clinical data. This review highlights the integration of AI-driven pharmacogenomics in personalized therapy and its potential to improve treatment outcomes. Machine learning, deep learning, natural language …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 1–12 Read article
-
Antimicrobial Resistance and AI-Based Strategies for Rapid Pathogen Detection
Abstract: Antimicrobial resistance (AMR) has become a major global health threat, significantly reducing the effectiveness of antimicrobial therapies and increasing the burden of infectious diseases worldwide. The rapid emergence of multidrug-resistant pathogens has created an urgent need for faster, more accurate, and scalable diagnostic approaches to support timely treatment and effective infection control. Artificial intelligence (AI) has emerged as a promising technology capable of transforming pathogen detection and AMR surveillance through …
Published in Recent Trends in Infectious Diseases · Vol. 3, Issue 2, 2026 · pp. 26–36 Read article
-
Natural Language Processing in Education: A Review of Applications, Challenges, and Future Directions
Abstract: Natural Language Processing (NLP) has increasingly become a transformative force within the field of education, offering innovative solutions and reshaping traditional methods of teaching, learning, assessment, and educational research. This review explores the evolving landscape of NLP applications in education, shedding light on significant advancements, ongoing challenges, and emerging opportunities. The integration of NLP into intelligent tutoring systems has enabled more personalized learning experiences, while automated assessment tools have enhanced …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 11–18 Read article
-
GenChrome-ML: A Machine Learning Framework for Early Detection of Chromosomal Disorders Using Genomic Data
Abstract: The increasing burden of chronic disease and cancer demands innovative, more rapid and effective diagnostic tools in the field of healthcare. The majority of current diagnostic tools are dependent upon clinical symptomology and manual evaluation, leading to delays in early detection and treatment. The development of artificial intelligence (AI) and machine learning (ML), in recent years, has offered opportunities for the enhancement of disease prediction, diagnosis and personalization of treatment …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
-
Real-Time Cab Fare and ETA Prediction Using API Integration
Abstract: The exponential proliferation of ride-hailing platforms has necessitated the formulation of sophisticated and highly responsive predictive models for cab fare estimation and estimated time of arrival (ETA) computation. This work elucidates a robust framework leveraging real-time application programming interface (API) integration from Uber and Ola within a Flutter-based ecosystem to enhance predictive analytics. By assimilating real-time geospatial data, dynamic pricing algorithms, and latency-optimized API responses, this study investigates the empirical …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 08–15 Read article
-
Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 1–10 Read article
-
Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites
Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article
-
Survey Paper on Multilingual Live Call Translation Using Deep Learning
Abstract: This research work surveys cutting-edge language translation technologies, including multi-lingual, real-time translation, voice recognition, speech-to-text conversion, and transcription in the hearing process. The study explores the complex mechanisms behind voice call language translation, focusing on sophisticated machine learning models integrated with cloud-based or local applications to facilitate seamless communication across language barriers. Furthermore, conducting research in live communication analyzes the complexity of text and voice techniques to deliver translated content …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 2, 2024 · pp. 13–21 Read article
-
A Hybrid Multi-Physics Ensemble Deep Learning Framework for Simultaneous Prediction of Thermal and Electrical Conductivity in Functional Polymer-Based Nanocomposites
Abstract: Polymer-based nanocomposites have become promising materials for applications in energy storage, flexible electronics, biomedical devices, aerospace components, and advanced engineering because of their tunable thermal and electrical properties. Reliable prediction of these properties is essential for accelerating material design; however, existing analytical models and conventional machine learning techniques often fail to represent the complex interactions among filler characteristics, polymer matrices, processing conditions, and interfacial transport phenomena. This work presents HMEP-Net, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1062–1082 Read article