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906 articles for “Learning Effect”
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Heart Disease AI-based Prediction: A Comparative Analysis
Abstract: The present investigation looks at how well various machine learning algorithms predict cardiac disease. Since heart disease is one of the major causes of death worldwide, early detection and precise diagnosis are essential for managing and treating the condition. Our goal is to enhance diagnostic processes and improve patient outcomes by leveraging machine learning techniques. Six widely-used machine learning algorithms are evaluated in this research paper. These algorithms were selected …
Published in Trends in Mechanical Engineering & Technology · Vol. 14, Issue 2, 2024 · pp. 21–29 Read article
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Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India
Abstract: Commodity price forecasting is instrumental in financial markets, providing framework for investment choices and risk management practices. Traditional models, including statistical and machine learning approaches, have limitations in capturing the nonlinear and volatile nature of commodity prices. Deep learning (DL) techniques have emerged as promising alternatives, leveraging advanced neural networks to enhance predictive accuracy. This study presents a thorough and comprehensive examination of deep learning applications in commodity price prediction, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 08–12 Read article
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Early Alzheimer’s Disease Prediction Using Vision Transformers and Attention-Guided MRI Analysis
Abstract: Alzheimer’s Disease (AD) continues to be a major global health concern, with early detection being crucial for effective intervention. While conventional machine learning and convolutional neural network (CNN) approaches have made notable progress in automated AD diagnosis using MRI data, they often struggle with capturing long-range dependencies and maintaining spatial contextual awareness. In this research, we propose a novel framework using Vision Transformers (ViTs) for early Alzheimer’s prediction from 3D …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 30–40 Read article
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Academic Performance and Its Associated Factors Among Pawe Health Science College Students in Benishangul-Gumuz Regional State, North-West Ethiopia
Abstract: Background: Academic performance can be defined as the degree to which a teacher, an institution, or a student meets short-term or long-term learning objectives. Continuous assessments and the student’s overall cumulative grade point average (CGPA) are typically used to measure academic achievement. Objectives: To assess academic performance and its associated factors among students, in the case of Pawi Health Science College, Benishangul-Gumuz Regional State, North-West Ethiopia, 2025. Methods: An institution-based …
Published in Journal of Nursing Science & Practice · Vol. 16, Issue 1, 2026 · pp. 24–35 Read article
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Review on Machine Learning Techniques for Heart Failure Analysis in Health Industries
Abstract: There are few bodily components as crucial as the heart. It aids in the filtration and distribution of blood to every area of a body. The world's biggest cause of death is heart disease. It has been reported that symptoms include breathing difficulties, fast heartbeat, and chest discomfort. They analyze this data on a regular basis. This review begins with a brief introduction of cardiac disease and the present methods …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 1, 2024 · pp. 29–43 Read article
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Transformer Health Monitoring System
Abstract: Rising demands for reliable and efficient power distribution in modern electric control grid increasingly call up for robust monitoring systems for critical substructure. Being a vital part of the power conduction system, transformer are subjected to mechanical, electrical, and environmental stresses, which, if not properly controlled, can cause failures. In this project, we propose a Transformer Health Monitoring System (THMS) using machine learning (ML) models and real-time monitoring method to …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 3, 2025 · pp. 1–9 Read article
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Early Detection of Alzheimer’s Disease Using Machine Learning Techniques
Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative condition impacting a large global population. Detecting AD early is critical for timely intervention and effective management. Conventional diagnostic approaches involve cognitive assessments and neuroimaging, which are often lengthy, costly, and prone to human error. In this paper, we propose a novel approach for early detection of AD using machine learning techniques applied to multimodal data, including neuroimaging, cognitive assessments, and biomarkers. Our …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 2, 2024 · pp. 32–43 Read article
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A Dual-Model Deep Learning Framework for Early Alzheimer’s Detection Using Clinical Data and Neuroimaging with Architectural Performance Analysis
Abstract: Alzheimer’s disease (AD) poses a significant global health challenge due to its increasing prevalence and the absence of definitive cures. Early diagnosis is crucial for effective intervention and management. This study presents a dual-model deep learning framework for the early detection and classification of AD using both structured clinical data and neuroimaging datasets. Model 1 utilizes a greedy layer-wise autoencoder approach applied to structured data, achieving optimal binary classification accuracy …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 1, 2026 · pp. 1–12 Read article
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Detection and Classification of Brain Tumor from MRI And CT Images using Harmony Search Optimization and Deep Learning
Abstract: Primary brain tumor detection and classification are critical factors in ensuring effective treatment and, ultimately, improving patient well-being. This paper describes a novel method for detecting and classifying brain tumors with the help of magnetic resonance imaging (MRI) and computed tomography (CT) images. The suggested method combines harmony search optimization (HSO) and Convolution Neural Networks (CNN) based on deep learning techniques, yielding an impressive accuracy rate of 99.13% for both …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 31–49 Read article
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**Innovation and Quality Assurance in School Management: A Research Study on Indian Schools in the Context of NEP 2020**
Abstract: The quality of school education is strongly influenced by the efficiency and vision of school management practices. In today’s rapidly changing educational landscape, schools are expected to perform responsibilities that extend beyond routine administration. They must ensure academic excellence, learner well-being, value- based education, inclusion, transparency, and accountability to parents and society. However, many schools still function through traditional management patterns that focus mainly on record-keeping, examination routines, and compliance …
Published in OmniScience: A Multi-disciplinary Journal · Vol. 16, Issue 1, 2026 · pp. 29–36 Read article
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Exploring Adaptive Acoustics for Educational Spaces
Abstract: Acoustics play a major role in educational buildings as these have high density, occupying classrooms, lecture halls, conference rooms, auditoriums, etc., engaged by students for several activities. Due to their multi-functional nature, architectural studios in particular require larger spaces and these often accommodate furnishing such as drafting board/table, screens and podium. The considerable volume of these studios can lead to compromised acoustical comfort, which may result in issues like echoes …
Published in International Journal of Architectural Design and Planning · Vol. 2, Issue 2, 2024 · pp. 18–37 Read article
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Dynamic Price Model for E-Commerce
Abstract: Retailers may now respond to fluctuating demand, inventory levels, competitor activities, consumer traits, and market circumstances using dynamic pricing, a data-driven technique that has become more significant in e-commerce. Modern pricing systems are much more complex and responsive than they were in the past due to the fast advancements in artificial intelligence (AI), machine learning (ML), big data analytics, and reinforcement learning. This review critically examines recent developments in dynamic …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 2, 2026 · pp. 45–55 Read article
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Recent Advances in Content-based Image Retrieval: Techniques and Applications
Abstract: Content-based image retrieval (CBIR) plays a vital role in computer vision, driven by the increasing need for fast and accurate image retrieval across fields like healthcare, e-commerce, and digital libraries. This study offers a detailed review of CBIR methodologies, charting their progression from traditional feature extraction techniques, such as Local Binary Patterns (LBP), to contemporary deep learning-driven methods. The transformative impact of convolution neural networks (CNNs) is highlighted, emphasizing their …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 67–71 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 1–8 Read article
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Design And Implementation Of A Multi-Modal Mobile Application Safety Analytics Utilizing Nlp
Abstract: This study suggests a multi-modal mobile app safety analytics platform that uses natural language processing (NLP) to handle voice, text, and SOS messages. For effective intent recognition and decision-making, the platform processes all messages in a standard text or SOS flag format. Tokenization and normalization are used to process text communications, whereas noise reduction and text conversion are used to handle voice messages. For a quicker response, the SOS messages …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 2, 2026 Read article
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Improving The Accuracy of Medical Diagonosis Detection Using Machine Learning
Abstract: While accurate and timely medical diagnosis is a fundamental aspect of effective health care delivery, traditional methods have not been able to overcome major hurdles such as inefficiencies in data analysis with Gi Human Error as well as limitations in scalability. The “Improved Accuracy of Medical Diagnosis Detection Using Machine Learning” project seamlessly integrates advanced machine learning (M L) technologies with efficient preprocessing and feature selection techniques to outperform all …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 3, 2025 · pp. 1–8 Read article
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Data Mining and Machine Learning in Security
Abstract: ABSTRACTGrowth of the internet and its complexity in had an effect in the demand for security assurance. The catastrophic attack of 9/11 had even increased the concerns security in many fields. The most faced challenge with regards to security is to effectively and reliably analyze the increasing volumes of crime data. This paper aims to give an overview about various security aspects and the rule of both machine learning and …
Published in Journal of Computer Technology & Applications · Vol. 1, Issue 1-3, 2011 · pp. 46–55 Read article
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Mario Ai Model Using Gaming Reinforcement Learning
Abstract: It is essential for research on computational and/or artificial intelligence (CI/AI) applied to games to have relevant games to apply AI algorithms to. This is pertinent. It doesn't matter if one is studying how to use CI/AI techniques to test and improve AI (e.g., games provide challenging yet scalable problems which engage many central aspects of human cognitive capacity) or how to use CI/AI techniques to improve games (e.g., player …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 · pp. 1–6 Read article
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Random Forrest Based Man-in-the-Middle Attack Detection in Advanced Metering Infrastructure
Abstract: Advanced metering infrastructure (AMI) plays a central role in the operation of modern smart grid (SG) systems by enabling continuous, two-way communication between utility providers and consumers. Through this communication, AMI supports real-time monitoring, dynamic pricing, and efficient energy management. However, the same connectivity that makes AMI effective also increases its exposure to cyber threats. One of the most critical threats is the man-in-the-middle (MITM) attack, in which an attacker …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 · pp. 1–8 Read article