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307 articles for “Neural Network Model”
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Alzheimer’s Disease Detection Using ML Algorithm
Abstract: A degenerative neurological state of affairs, Alzheimer's disease (AD) gradually impairs cognitive and functional capacities, especially in people over 65. Early AD detection is crucial for efficient management and treatment prep. This study delves into novel approaches for the early detection of AD using non-invasive methods. We've implemented a blend of neuroimaging data analysis and machine learning algorithms to pinpoint markers indicative of the disease during its initial phases. Our …
Published in Journal of Experimental & Applied Mechanics · Vol. 15, Issue 3, 2024 · pp. 53–57 Read article
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The Rise of Fractional Calculus: Novel Applications in Engineering and Biological Systems
Abstract: Fractional calculus (FC) is an advanced mathematical framework that generalizes the classical concepts of differentiation and integration to non-integer, or fractional, orders. This extension of traditional calculus allows for the modeling of complex dynamic systems that exhibit behavior not easily captured by integer-order differential equations. Over the last few decades, fractional calculus has seen a rapid rise in popularity, particularly in applied mathematics, engineering, and biological sciences, due to its …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 7–11 Read article
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AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
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A Review of AI-Based Intrusion Detection Systems for Mobile Ad Hoc Networks (MANETs)
Abstract: Mobile Ad Hoc Networks (MANETs) comprise wireless networks that lack any conventional infrastructure . Their chief features include highly changing network topologies, lack of centralized administration, and open nature of communication, which collectively result in making MANETs of the wireless kind very susceptible to a diverse range of cyber-attacks like blackhole, greyhole, wormhole, flooding, Sybil and denial-of-service (DoS) among others. Conventionally, Intrusion Detection Systems (IDS) relying on static rule-based methods …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Visual Recognition with Convolutional Neural Networks for Object Detection
Abstract: Various research and development have taken place over the years on computer vision which is a branch of AI. AI disciplines like a vision system is applied in various fields like self-driving cars, face detection by social media apps and law enforcement software’s google lens and so on. The proposed system deals with design and implementation of an efficient way of training a GPU using python libraries to process and …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 2, 2024 · pp. 07–13 Read article
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AI-Driven Optimization of Biopolymer Composite Formulations Using IoT Data Streams
Abstract: Biodegradable polymer composites have emerged as a sustainable alternative to petroleum-based materials in packaging, biomedical, and structural applications. However, traditional formulation techniques for reinforced polymer composites often lack precision and fail to adapt to real-time variations during processing, resulting in suboptimal material performance. This research proposes a real-time AI-IoT-enabled framework to optimize biopolymer composite formulations. The goal is to intelligently tune composite properties such as mechanical strength, moisture resistance, and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 85–100 Read article
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Advancements in Machine Learning: A Comprehensive Review of Algorithms, Applications, and Future Directions
Abstract: Gaining knowledge of Machine learning (ML)-guided format algorithms leverage predictive models to generate novel devices with optimized properties across several domains, which include drug discovery, fabric synthesis, and biomolecular engineering. Selecting an effective format set of policies consists of identifying appropriate hyperparameters, predictive models, and generative mechanisms to maximize format fulfilment. This study introduces an established method for set of policies requirements, ensuring that generated designs meet predefined fulfilment criteria, …
Published in Recent Trends in Programming languages · Vol. 12, Issue 2, 2025 · pp. 17–33 Read article
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Use of Artificial Intelligence to Access and Ensure Safe Drinking Water Supply: A Review
Abstract: Ensuring access to safe drinking water is a critical public health challenge. Traditional water quality assessment methods are often labor-intensive and time-consuming. Artificial intelligence offers a promising alternative, providing rapid, accurate, and scalable solutions for monitoring and predicting water quality. This systematic review examines the application of AI. The review highlights various AI models, including artificial neural networks, support vector machines, decision trees, and ensemble methods, in predicting water quality …
Published in Journal of Water Resource Engineering and Management · Vol. 11, Issue 2, 2024 · pp. 21–28 Read article
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Renewable Solar Energy Prediction in India : 2025-2030
Abstract: As India endeavors to realize its objective of achieving 500 GW of renewable energy capacity by the year 2030, the precise forecasting of solar power generation is rendered increasingly essential. This scholarly article conducts a comprehensive review of the utilization of machine learning (ML) methodologies in the prediction of solar energy across diverse Indian states, underscoring their potential to address the complexities associated with the variability of solar irradiance. Conventional …
Published in Journal of Power Electronics and Power Systems · Vol. 14, Issue 3, 2024 · pp. 41–46 Read article
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Harnessing NLP for Automation and Intelligence Across Sectors
Abstract: Natural Language Processing or NLP is a vital subset of Artificial Intelligence or AI which enables machines to interpret, understand, and communicate using human language in a remarkable way. From the traditional rule-based approaches to the modern advanced deep learning techniques such as transformers, neural networks, and hybrid models, NLP has been evolving year by year. This study reflects on various applications of NLP, including sentiment analysis, machine translation, analysis …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 23–32 Read article
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Intelligent Power Quality Enhancement Strategies for PV-Integrated Smart Distribution Networks: A State-of-the-Art Review
Abstract: The rapid integration of photovoltaic (PV) systems into modern power distribution networks has introduced significant challenges related to power quality. Issues such as voltage fluctuations, harmonic distortion, flicker, and reactive power imbalance arise due to the intermittent and nonlinear nature of solar energy generation. This paper presents a concise literature review of various power quality enhancement techniques employed in PV-integrated networks. Key approaches include the use of active power filters …
Published in International Journal of Electrical Power and Machine Systems · Vol. 4, Issue 1, 2026 · pp. 30–53 Read article
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AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review
Abstract: The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1083–1097 Read article
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Automated Plant Disease Detection and Treatment Advisor Using Artificial Intelligence
Abstract: Automated plant disease detection and treatment advisors using artificial intelligence represent a significant advancement in modern agriculture. The identification of plant leaf diseases is essential to maintaining food security and agricultural output. Machine learning models, particularly deep learning algorithms like convolutional neural networks (CNNs), are trained on labeled datasets containing images of healthy and diseased plants. These models learn to classify images into different disease categories with high accuracy. Convolutional …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 1–7 Read article
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Innovative Eyewear for the Visually Impaired
Abstract: Object detection systems are essential tools for identifying and locating objects within images or videos. When integrated into spectacles or wearable devices, these systems provide users with real-time information about objects present in their surroundings. This functionality serves diverse purposes, such as assisting visually impaired individuals in navigating their environment or offering augmented reality data to workers during tasks. Region-based Convolutional Neural Networks (RCNN) represent a prominent machine learning model …
Published in International Journal of Optical Innovations & Research · Vol. 2, Issue 1, 2024 · pp. 27–34 Read article
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Mechanical Strength Prediction of Nano-Silica Concrete Composites Using Machine Learning Techniques
Abstract: Nano-silica, or nanosilica, refers to silicon dioxide nanoparticles, which are a kind of silica (SiO₂) with diameters that often fall below 100 nanometers. This nanomaterial has attracted considerable attention because of its distinctive characteristics and diverse array of uses, notably in augmenting the performance of materials such as concrete. The integration of nanoparticles with cementitious matrix in nano-silica concrete offers a viable approach to improving the mechanical characteristics and longevity …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 963–973 Read article
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Artificial Intelligence-Assisted Multi-Objective Optimization of Agricultural Biomass-Reinforced Polymer Composites
Abstract: Agricultural biomass can reduce the environmental burden of polymer composites, yet its heterogeneous structure creates competing effects on strength, moisture resistance, density, and process ability. This study developed an artificial intelligence-assisted framework for balanced composite formulation. Experimental data of agricultural biomass reinforced polymer composites were gathered, harmonized and validated using leakage controlled validation. The mechanical and physical properties were predicted by artificial neural networks and conventional regression models. Explainable analysis …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Using Machine Learning for Key phrase Extraction in Digital Libraries
Abstract: Machine learning has revolutionized various aspects of information retrieval, including key phrase extraction in digital libraries. Key phrase extraction is crucial for summarizing and categorizing vast amounts of textual data, enabling efficient search and retrieval processes. This study explores the application of machine learning techniques for automatic key phrase extraction in digital libraries. We review various supervised and unsupervised learning algorithms, including deep learning models, that are employed to identify …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 8–13 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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Enhancing Power Conversion Efficiency in Tandem Solar Cells with Temporal Dynamic Graph Neural Network
Abstract: In modern homes, people want good comfort and also less electricity bill, so managing heating load and cooling load become very important. Heating Load (HL) and Cooling Load (CL) depend on many things like wall material, window size, sunlight, ventilation, and weather. Because of this many factors, calculation and optimization of HL and CL is little difficult and many time normal formulas give wrong or not perfect results. So in …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 2, 2026 Read article
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CNN-Based Wound Segmentation: A Review of Models and Performance Evaluation
Abstract: Deep learning, particularly convolutional neural networks (CNNs), has altered medical image processing by automating and precisely segmenting complex medical pictures. Wound segmentation, a critical application in automated wound assessment, is essential for wound size estimation, classification, and healing progress monitoring. This study presents a comprehensive review of CNN-based wound segmentation models, focusing on their architectures, methodologies, and performance on diverse datasets. Four deep learning models, including two U-Net variants (5-layer …
Published in Current Trends in Signal Processing · Vol. 15, Issue 1, 2025 · pp. 33–46 Read article