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260 articles for “pre-trained models”
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article
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Deep Learning Based Detection and Classification of Brain Tumors Using MRI Images
Abstract: Brain tumor detection using magnetic resonance imaging (MRI) is a critical task in the early detection and treatment of brain tumors. Manual analysis of brain tumor detection using MRI is a tedious task that requires expertise in the field. Therefore, this study proposes a deep learning-based approach for brain tumor detection and classification using Convolutional Neural Networks (CNN). The proposed approach preprocesses the MRI image using normalization, resizing, and noise …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 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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Continuous Learning in Language Models: A Survey of Streaming Data Processing Techniques
Abstract: The integration of continual learning with Large Language Models (LLMs) and Natural Language Processing (NLP) represents a transformative step toward creating adaptive, intelligent systems capable of functioning effectively in ever-changing environments. Traditional LLMs are typically trained on large, pre-collected datasets, which limits their ability to evolve as new information emerges. Continual learning, in contrast, enables models to acquire new knowledge incrementally without the need for complete retraining, thereby supporting long-term …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 23–34 Read article
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Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials
Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 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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Retinal Disease Detection Using Deep CNN
Abstract: Age-related macular degeneration, glaucoma, and diabetic retinopathy are the three main causes of blindness in the globe. To avoid visual loss, early identification and treatment of these disorders are essential. The goal of this research is to create an automated method for detecting retinal diseases by analyzing retinal fundus pictures with machine learning techniques. Python and the Tkinter package for the graphical user interface are used in the construction of …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 2, 2024 · pp. 46–50 Read article
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AI-Driven Exam Evaluation Systems: Challenges, Innovations, and Future Directions
Abstract: A proposed AI system is used to grade exams automatically. It addresses inefficiencies in human assessment. A GPT model trained on graded replies is used for evaluation, and TrOCR is used for precise handwritten text recognition. Efficiency and less bias are provided by this method, although there are still issues. More work is needed to assess open-ended questions and make sure they are understandable. To automate many aspects of exam …
Published in International Journal of Electronics Automation · Vol. 2, Issue 2, 2024 · pp. 7–13 Read article
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Design and Validation of an Artificial Intelligence-Driven Digital Twin for Real-Time Monitoring and Control in Polymer Composite Manufacturing
Abstract: Polymer Matrix Composites (PMCs) have become indispensable in high-performance sectors such as aerospace and automotive engineering, offering exceptional strength-to-weight ratios that outperform traditional metals in many demanding applications. However, the reliability of manufacturing PMCs via Vacuum-Assisted Resin Transfer Molding (VARTM) is frequently undermined by stochastic process variabilities. Unpredictable fluctuations in thermal history, preform permeability, resin rheology, and ambient conditions often lead to some defects; namely voids, dry spots, and incomplete …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 224–233 Read article
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AI-Powered Emotion Recognition in Dog
Abstract: Understanding animal emotions is important for improving veterinary care, human animal interaction, and overall pet well-being. Inspired by previous research that utilized a modified EfficientNetB5 model for emotion classification in cats and dogs, our study builds upon this foundation with a focus on real-time emotion recognition in dogs. While earlier approaches achieved high accuracy using Dense Residual and Squeeze-and-Excitation blocks, they often lacked real-time applicability and were not optimized for …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 20–32 Read article
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Artificial Intelligence in Robotics: Current Trends, Applications, and Future Challenges
Abstract: The incorporation of artificial intelligence (AI) into robotics has transformed the industry by greatly improving robots' capacity to carry out complex and autonomous functions in a wide range of sectors. This paper explores the evolution, applications, and challenges associated with AI-driven robotics. It examines key AI methodologies employed in robotics, including machine learning, natural language processing (NLP), computer vision, and planning/control algorithms, which enable robots to perceive, learn, and interact …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 2, 2025 · pp. 31–43 Read article
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Identification of Papaya Fruit Ripening Process Using AI
Abstract: Identifying the ripening process of papaya fruit using artificial intelligence involves employing machine learning algorithms to analyze various features such as color changes, texture alterations and chemical compositions. This model is capable of analyzing visual cues to determine the stage of ripeness. The dataset compares images of papaya at various ripening stages, and our AI model demonstrated high accuracy in classifying these stages. Employing machine learning algorithms and image processing …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 13, Issue 2, 2024 · pp. 23–30 Read article
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article
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Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 11–23 Read article
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Machine-Learning-Assisted Development of Polymer-Biochar Composite Adsorbents for the Removal of Heavy Metals from Gomti River Water
Abstract: Rapid urbanization, industrial discharge, and agricultural runoff pose a significant threat to freshwater sustainability and public health. Within these ecosystems, polymer pollutants—such as microplastics, nanoplastics, synthetic fibres, and additive residues—have emerged as persistent vectors capable of adsorbing and transporting toxic heavy metals. Because these polymeric contaminants dynamically interact with conventional aquatic parameters to alter pollutant mobility and ecological risk profiles, there is an urgent need to transition from passive environmental …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 72–95 Read article
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Integrative Machine Learning Approaches for Predicting the Rheological Behaviour of Soft Magnetorheological Elastomers
Abstract: Magnetorheological Elastomers (MREs) are advanced composite materials known for their ability to alter mechanical properties under external magnetic fields, making them highly valuable in adaptive damping systems, vibration control, and smart devices. The accurate prediction of rheological behavior in soft MREs remains a significant challenge due to the complex interplay between material composition and magnetic fields. To address this challenge, this study employs a multi-pronged approach that integrates traditional material …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 1083–1096 Read article
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Unveiling Fairness: A Quest for Ethical Artificial Intelligence and Bias Mitigation
Abstract: Artificial intelligence (AI) systems have become ubiquitous across areas like finance, healthcare, employment, and criminal justice. However, they suffer from issues of unfair bias, lack of transparency, and broad ethical implications impacting vulnerable societal groups disproportionately. This paper reviews key challenges around AI ethics and bias while proposing data-driven guidelines mitigating such algorithmic harms through rigorous statistical testing, predictive modeling ensembles adjusting distortion vectors and AI audits by domain experts …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 2, 2023 · pp. 28–31 Read article
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Deep Learning-Based Alzheimer’s Disease Detection: A CNN Approach
Abstract: Alzheimer’s disease (AD) is a neurological condition that worsens with time and impairs a patient’s quality of life by causing cognitive loss. For prompt intervention and management of AD, early identification is essential. In this work, we propose a deep learning-based method for automatically classifying Alzheimer’s disease from medical imaging data using convolutional neural networks (CNNs). Our algorithm is intended to evaluate brain MRI images and detect anatomical variations suggestive …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 Read article
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Complications with Malware Identification in IoT and an Overview of Artificial Immune Approaches
Abstract: An immunity is facilitated by lymphocyte T&B-cells; that possess a wide range of T&B-cell; receptors, respectively. These cells can identify and react to pathogens and diseased cells by presenting peptide antigens by means of significant histocompatibility complexes (MHCs). The amount of data on the repertoire of adaptive immune receptors has increased dramatically in recent years because to advancements in deep sequencing. Furthermore, the presentation of peptides with MHC has been …
Published in Research and Reviews : A Journal of Immunology · Vol. 14, Issue 1, 2024 · pp. 54–62 Read article
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Leveraging Full Stack Data Science for Healthcare Transformation: An Exploration of the Microsoft Intelligent Data Platform
Abstract: The rapid progress of the Fourth Industrial Revolution has been largely driven by the evolution of artificial intelligence (AI), with notable contributions from technologies such as Generative Pre-trained Transformers (GPT). This revolution has seen the convergence of physical, digital, and biological technologies, leading to transformative impacts across various sectors. Data science, serving as a crucial enabler, has enabled the development of intelligent value chains. However, the application of data science …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 1–8 Read article