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66 articles for “multi-task learning”
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A Study on Science Teachers’ Perception of Newly Developed Science Workbooks and Their Effectiveness in Fostering Scientific Attitude in Learners
Abstract: This study examines science teachers’ perceptions of newly developed science workbooks and evaluates their effectiveness in fostering scientific attitude among learners. Using a structured opinionnaire, the research gathered comprehensive feedback from teachers across multiple grade levels regarding the clarity, relevance, and pedagogical value of the workbooks. Analysis of opinionnaire responses revealed strong teacher agreement regarding the workbook’s clarity, activity-based structure, and alignment with curriculum goals. Teachers reported noticeable improvements in …
Published in International Journal of Education Sciences · Vol. 2, Issue 2, 2025 · pp. 82–90 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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Virtual Method to Predict Dental Disease
Abstract: The integration of technology and medicine in the healthcare domain has led to the emergence of inventive strategies to improve patient care and diagnostics. One such groundbreaking methodology is the utilization of Convolutional Neural Networks (CNNs) within the domain of deep learning, particularly for image recognition and processing tasks. In this paper, we propose a novel approach to image recognition that employs state-of-the-art deep learning algorithms to create a user-friendly …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 8–15 Read article
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Greener 3D Printing: The Role of Artificial Intelligence in Sustainable Polymer and Composite Manufacturing
Abstract: The integration of sustainable materials with additive manufacturing (AM) technologies marks a significant step towards environmentally responsible production. Biodegradable polymers, recycled thermoplastics, and bio-based composites, when used in 3D printing, offer the potential to reduce the ecological footprint of manufacturing. However optimizing the interplay between material properties process parameters, and product performance remains a complex challenge. This review examines how artificial intelligence (AI) is being applied to address these challenges …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 288–300 Read article
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An Analysis of Pronunciation Errors in The Production of Silent Letters of English Language by Secondary School Students in Borno State
Abstract: Perfect pronunciation as one of the effective language skills in communication greatly affects teaching, learning and usage of English as a second language because it enhances transfer of information from source to target. This paper adopted Corder explanation of errors and mistakes which sees them as gaps/deficiencies in learners’ knowledge of a language as theoretical framework and analysed pronunciation errors of the English silent letters by Senior Secondary School students …
Published in Emerging Trends in Languages · Vol. 2, Issue 1, 2025 · pp. 33–39 Read article
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Multi-functional UAV for Disaster Response and Management
Abstract: Unmanned Aerial Vehicles (UAVs), commonly known as drones, have become integral across diverse fields such as agriculture, surveillance, and defense, with expanding roles in critical operations like search and rescue and post-disaster management. Despite their versatility, current UAVs encounter challenges in disaster response due to limitations in flight time, costs, and accuracy, particularly in dynamic weather conditions. The UAV is equipped with features essential for disaster site surveillance, human detection, …
Published in Journal of Aerospace Engineering & Technology · Vol. 14, Issue 1, 2024 · pp. 1–6 Read article
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Innovative CNN Strategies for Superior Handwritten Digit Recognition
Abstract: Handwritten digit recognition is a fundamental problem in the field of computer vision and machine learning with numerous applications, such as postal code recognition, bank check processing, and digitizing historical documents. Convolutional Neural Networks have demonstrated remarkable success in various image recognition tasks, making them a popular choice for digit recognition. In this study, we present an enhanced approach to handwritten digit recognition using CNNs. Handwritten digit recognition plays a …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 2, Issue 1, 2024 · pp. 27–34 Read article
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Data Integration and Visualization in Bioinformatics: Techniques and Challenges
Abstract: Data integration and visualization play essential roles in bioinformatics, facilitating the thorough analysis, and interpretation of intricate biological datasets. In the field of bioinformatics, vast amounts of data are generated from various experimental platforms, such as genomic sequencing, proteomics, transcriptomics, and metabolomics. However, the heterogeneity of these datasets, coupled with their large scale and complexity, presents significant challenges in terms of integration, analysis, and visualization. Data integration techniques aim to …
Published in Research and Reviews : Journal of Computational Biology · Vol. 13, Issue 3, 2024 · pp. 1–8 Read article
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The Digital Lifeline: How Computers Shape Everyday Life
Abstract: Computers have become an integral part of everyday life, significantly influencing the way the common man interacts with the world. From revolutionizing communication to transforming industries, education, healthcare, and entertainment, computers have reshaped the fabric of modern society. This study explores the multifaceted impact of computers on daily life, examining how they have made tasks easier, faster, and more efficient. While enhancing productivity and connectivity, computers have also introduced new …
Published in Current Trends in Information Technology · Vol. 15, Issue 3, 2025 · pp. 33–47 Read article
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Agentic AI: Architectures, Types, Capabilities, Mathematical Equations and Governance in the Era of Autonomous Intelligence
Abstract: Agentic Artificial Intelligence (Agentic AI) represents a major advancement in the evolution of intelligent systems by enabling autonomous planning, decision-making, and action execution. Unlike traditional AI models, which are primarily reactive and designed to respond to predefined inputs, Agentic AI systems possess capabilities such as memory, reasoning, goal-oriented planning, tool integration, and dynamic adaptation to changing environments. These characteristics allow them to perform complex, multi-step tasks with minimal human intervention, …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 Read article
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Monitoring of Ship Deployment Through Emerging Technologies
Abstract: The mission for naval vessels encompasses defining combat tasks, deployment statuses, and timing requirements to optimize combat patrol effectiveness and daily ship management. This involves inheriting, developing, and optimizing ship deployment strategies while establishing new deployment categories with distinct names, connotations, personnel, and equipment needs to ensure organic integration and synergy. Emphasis is placed on maintaining continuity, stability, and forward-thinking to meet the demands of warship combat operations, facilitate management …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 59–66 Read article
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Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis
Abstract: Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 67–85 Read article
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Enhancement in Biomedical Polymer Nanocomposites: Biocompatibility and Mechanical Property Predictions using Machine Learning
Abstract: A machine learning (ML)-based framework is developed and validated through experimental analysis and comparative modeling to enhance system dependability and improve prediction performance. The proposed framework includes key stages such as data preprocessing, feature evaluation, model training, and performance benchmarking to determine the most effective prediction technique. Several machine learning models were evaluated, including Ensemble models, Artificial Neural Networks (ANN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 275–296 Read article
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AI-Driven Robotics for Sustainable Solutions in Disaster Management
Abstract: Disasters, whether natural or man-made, present significant challenges to societies worldwide. Efficient response, recovery, and mitigation strategies are crucial to minimizing human suffering, loss of life, and economic damage. Traditional disaster management strategies, while effective to some degree, often face limitations related to human resources, response time, accessibility, and safety. The integration of artificial intelligence (AI) and robotics into disaster management offers transformative potential for overcoming these challenges. This paper …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 3, Issue 1, 2025 · pp. 24–30 Read article
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Dynamic Performance Enhancement of Polymer Composites through Metaheuristic machinining optimization
Abstract: This work aims to provide an optimization of meta-heuristic algorithms in order to improve the dynamic behavior of composite materials utilized in various practical engineering tasks. Based on the Comprehensive literature review it has been observed that composite sandwich panels with PVC foam cores accomplished mechanical characteristics superior than those ones that were produced on PU foam core mainly in flexural, compression, and impact tests Thus the study establishes the …
Published in Journal of Polymer & Composites · Vol. 12, Issue 2, 2024 · pp. 114–129 Read article
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Secure Forge: Deepfake Image Detection Using Vision Transformers
Abstract: Deepfake technologies have become a major risk to the credibility and trustworthiness of digital visual information. Using powerful generative models like GANs and autoencoders, deepfakes can generate highly realistic fake videos and images, resulting in misinformation, identity theft, and public loss of trust in digital media. Classic Convolutional Neural Networks (CNNs) while being highly effective in initial-stage, deepfake detection tend to be limited by their local receptive fields and dependency …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 32–45 Read article
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Stock Market Analysis Using Data Science
Abstract: Stock market prediction using data science has become a popular area of research and application in recent years. This is because the stock market is a complex system with many variables and factors that affect its behavior, making it difficult to predict with certainty. The stock market has always been the aggression of buyers and sellers of stocks, therefore in the global finance market, stock trading is one of the …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 1, 2024 · pp. 1–4 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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Innovative Applications of AI and Machine Learning in Voice-Driven Systems: Friday AI 2.0
Abstract: The development of artificial intelligence (AI) has enabled the development of increasingly sophisticated models that can understand and react to human questions. This study introduces Friday AI-2.0, a cutting-edge AI model that improves user engagement by using natural language processing (NLP) and voice synthesis technologies to produce text-based and audio-based responses. By increasing engagement through an intuitive and engaging interface, Friday AI-2.0 seeks to improve the user experience. By describing …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 3, 2025 · pp. 01–09 Read article
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Prediction of Customer Churn Using Machine Learning Classification Models
Abstract: Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 86–92 Read article