Machine Learning
573 articles · search the full text for this term
-
AI-Driven Optimisation of Renewable Energy Systems: A Review of Smart Grid Integration, Lightweight Models, and IoT-Enabled Energy Management
Abstract: The energy sector is undergoing a transformation with the help of artificial intelligence (AI), which enhances the efficiency, reliability, and sustainability of renewable energy systems. The world is increasingly moving towards a low-carbon economy and away from fossil fuels, and AI-powered tools are becoming increasingly popular. The paper highlights recent strides in the field of AI for sustainable energy, emphasizing lightweight AI models, systems with Internet of Things (IoT) integration, …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 3, 2026 Read article
-
EcoSense AI: An Intelligent IoT-Based Environmental Monitoring and Forecasting System
Abstract: Environmental degradation caused by air, water, and soil pollution poses a severe global threat to human health and ecosystem stability. Existing IoT- based monitoring systems classify only current pollution levels but lack predictive capability, geospatial context, and tamper-proof data integrity. This paper presents EcoSense AI—a unified low-cost intelligent environmental monitoring system built on an ESP32 microcontroller with MQ135, DHT11, turbidity, and GPS sensors. The system integrates a Random Forest classifier …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 3, 2026 Read article
-
AI-Powered Cloud Storage Security: Intelligent Threat Detection, Adaptive Access Control and Resilient Data Protection
Abstract: Cloud storage has become a basic service for enterprises, educational institutions, digital platforms and electrical/electronic systems that continuously generate data. Its convenience, scalability and remote accessibility also increase exposure to credential theft, ransomware, accidental sharing, malicious insiders, misconfiguration and large-scale data leakage. Conventional security controls such as passwords, static access rules and signature-based detection remain necessary, but they often react after a known pattern has already appeared. This review presents …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 13, Issue 3, 2026 Read article
-
Framework for Edge AI and intelligent IoT with emphasis EEE integration, lightweight ML, communication, security, and deployment monitoring
Abstract: Edge Artificial Intelligence (Edge AI) brings machine learning inference and, in selected cases, learning close to the sensors and devices that generate data. When combined with the Internet of Things (IoT), it creates intelligent connected systems that can sense, interpret, decide, and act without depending continuously on a remote cloud. This review presents a practical framework for Edge AI and intelligent IoT with emphasis on electrical and electronics integration, lightweight …
Published in Recent Trends in Electronics Communication Systems · Vol. 13, Issue 3, 2026 Read article
-
Digital Twin and Machine Learning Framework for Predictive Maintenance of Nuclear Power Systems
Abstract: Nuclear power systems contain safety-critical components whose degradation can affect availability, reliability, and maintenance planning. Conventional maintenance strategies are largely based on scheduled inspection, threshold monitoring, and condition-based assessment, which may not fully exploit the large volume of continuously generated plant data. Digital Twin (DT) technology provides a mechanism for maintaining a synchronized virtual representation of a physical nuclear system, while machine learning (ML) can extract degradation patterns from multivariate …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
-
AI-Assisted Design of a Compact UWB Antenna with Tunable Band-Notch Characteristics
Abstract: Ultra-wideband antennas require wide impedance bandwidth while maintaining stable radiation characteristics and suppressing interference from coexisting narrowband wireless services. Conventional optimization of UWB antenna geometries generally requires repeated full-wave electromagnetic simulations, making parameter exploration computationally expensive. This paper proposes an artificial-intelligence-assisted methodology for designing a compact UWB monopole antenna with a tunable band-notch characteristic. The proposed antenna uses a microstrip-fed planar monopole printed on an FR-4 substrate, while a tunable …
Published in Journal of Microwave Engineering and Technologies · Vol. 13, Issue 2, 2026 Read article
-
AI-Assisted Design of a Compact Bandpass Microwave Filter for 5G/6G Applications
Abstract: The rapid development of fifth generation (5G) and emerging sixth generation (6G) wireless communication systems require compact, low-loss, highly selective and frequency-stable radio- frequency front-end components. Microwave bandpass filters play an important role in selecting desired communication channels while suppressing out-of-band interference. However, conventional filter design generally requires repeated electromagnetic simulation and manual optimization of geometrical parameters, resulting in considerable computational time. This paper presents an artificial intelligence (AI)-assisted methodology …
Published in Journal of Microwave Engineering and Technologies · Vol. 13, Issue 2, 2026 Read article
-
A Machine Learning-Based Approach for Assessing and Monitoring Women’s Safety Across Social Media Platforms
Abstract: This research introduces a machine learning-based approach for examining and assessing women’s safety concerns across social media platforms. The proposed framework aims to identify potentially harmful online interactions, recognize patterns associated with harassment, and support systematic safety assessment. Rather than replacing human judgment with fully automated moderation, the framework serves as an analytical support tool to assist in identifying risks and improving awareness of online threats. The approach focuses on …
Published in Current Trends in Signal Processing · Vol. 16, Issue 3, 2026 Read article
-
A Novel WPFM-Transformer Framework for DNA Sequence Classification using FASTA Data
Abstract: The identification of genetic variants, tracing of disease-associated mutations, and the reconstruction of evolutionary relationships from raw sequence data are all achieved by the analysis of DNA sequence. The FASTA format is plain text and is an easy-to-read format, which is why it has endured four decades of the field's growth into bioinformatics. Classical alignment programs like BLAST and Smith-Waterman work well with small data sets, but are limited by …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
-
Emote_X: An AI-Driven Framework for Real-Time Twitter Sentiment Classification Using Machine Learning
Abstract: Social media has become one of the ways people share their thoughts, feelings and responses to what is happening in the world. Twitter, which is now called X is an example. It creates a constant flow of short messages. These messages contain clues about how people feel in areas like business, health care, politics, entertainment and when there is a disaster. Tweets are very messy. They have abbreviations, hashtags, emojis, …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 3, 2026 Read article
-
Impact of Thermal Image Preprocessing Methods on a ResNet50 Model Baseline Performance for Ball Bearing Fault Diagnosis
Abstract: Ball bearings are an important part for the operational rotating machinery. To prevent it from failing, traditional engineering uses many methods. One of these is infrared thermography (IRT). IRT is a useful tool that helps monitor the situation without physical contact. But in the current world, various fields use artificial intelligence to do it more accurately & for fast detection. Deep learning models play an important role in fault detection …
Published in Trends in Mechanical Engineering & Technology · Vol. 16, Issue 3, 2026 Read article
-
Artificial Intelligence in Healthcare for Implants and Tissue Regeneration: Advances, Challenges, and Future Directions
Abstract: Artificial intelligence (AI) has been a revolutionary influence in contemporary healthcare, especially in implant design, biomaterials research, and tissue regeneration. In regenerative medicine, AI facilitates predictive modeling, optimization, and decision-making via the analysis of intricate biological, material, and clinical information. This study analyzes current research on AI applications in implant technologies and tissue regeneration, specifically addressing scaffold engineering, biomaterial characterisation, stem cell and gene treatments, smart biomaterials, and implant planning. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Artificial Intelligence for Sustainable Agriculture, Forestry and Rural Development: Recent Advances, Applications and Research Opportunities
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize agriculture, forestry, and rural development by enabling data-driven decision-making, resource optimization, and sustainable management practices. Rapid developments in computer vision (CV), machine learning (ML), deep learning (DL), natural language processing (NLP), and predictive analytics have increased the use of AI in a variety of rural industries. In agriculture, AI-driven technologies support precision farming, crop yield prediction, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Machine Learning-Based Task Scheduling and Resource Optimization in Edge-IoT Systems
Abstract: The rapid expansion of Internet of Things (IoT) applications has introduced significant challenges in managing computational workloads across distributed edge environments. Edge- IoT systems are characterized by limited computational capacity, dynamic task arrivals, and strict latency constraints. Traditional heuristic-based scheduling techniques often fail to adapt to fluctuating workloads and heterogeneous resource availability. This study proposes a machine learning-based task scheduling and resource optimization framework for Edge-IoT systems. The proposed model …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Artificial Intelligence–Driven Decision Support Systems for Smart and Efficient Data Analysis
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology reshaping the way organizations process information, analyze data, and support decision-making. With the rapid growth of digital data across sectors, traditional analytical methods often face limitations in handling large, complex, and dynamic datasets. AI-based techniques, particularly machine learning and intelligent decision support systems, provide advanced capabilities for identifying hidden patterns, predicting outcomes, and assisting in data-driven decisions. This paper examines the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Artificial Intelligence in Trigonometry: Innovations, Applications, and Future Prospects
Abstract: Artificial Intelligence (AI) has transformed numerous scientific fields, yet its integration with classical mathematics such as trigonometry is still emerging. This paper explores how AI enhances trigonometric problem solving, learning, and real-world applications. We analyse AI-driven tools for teaching trigonometry, AI in geometric and spatial reasoning, usage in robotics and computer vision, and future directions for research. Key challenges, methodologies, and case studies are discussed to provide a comprehensive overview …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Intelligent Paradigms in Subsea Connectivity: A Comprehensive Review of Artificial Intelligence in Underwater Communications
Abstract: Underwater wireless communication (UWC) plays a critical role in ocean exploration, environmental monitoring, offshore energy operations, disaster management, and naval defense. However, the underwater environment presents significant communication challenges, including severe signal attenuation, multipath propagation, Doppler effects, limited bandwidth, high latency, and energy constraints. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising solutions to address these limitations and enhance the efficiency, reliability, and adaptability …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Confident but Wrong: A Lifecycle Analysis of Hallucination in Large Language Models Understanding AI's Confidence Problem
Abstract: A Large Language Model (LLM) is likely to produce sentences that are fluent and confident in many instances, but completely wrong. In many cases a Large Language Model (LLM) will produce a fluent and confident sentence that is completely incorrect. This review tries to analyze this phenomenon using the most recent literature regarding natural language generation, computational learning theory and benchmark experiments. The argument is that it is not a …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Machine Learning Based Optical Fiber Fault Detection and Localization Using OTDR Signals
Abstract: Optical fiber networks provide high bandwidth, low transmission loss, and immunity to electromagnetic interference, making them fundamental to modern communication infrastructure. However, fiber faults caused by cable cuts, excessive bending, connector degradation, splicing defects, and physical disturbances can significantly affect network reliability. Optical time-domain reflectometry (OTDR) is widely used for identifying and locating such faults, but conventional OTDR interpretation often depends on threshold-based signal processing and manual analysis. This study …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 · pp. 41–52 Read article
-
Machine Learning Based Optimization of Optical Fiber Parameters for Minimizing Dispersion and Attenuation
Abstract: Optical fibers provide high-bandwidth and low-loss transmission for modern communication networks; however, attenuation and chromatic dispersion remain two important physical limitations affecting transmission distance, pulse integrity, and system capacity. Conventional optical-fiber design generally relies on analytical calculations and repeated numerical simulations to determine suitable geometric and material parameters. Such approaches become computationally demanding when several fiber parameters are simultaneously optimized. This paper presents a machine-learning-based methodology for predicting and optimizing …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 · pp. 18–28 Read article