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504 articles for “model accuracy”
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Additive Manufacturing of Polymer-Based Advanced Composites: Mechanical Properties and Performance Evaluation
Abstract: Fabrication of large-scale and geometrically complex polymer-based advanced composites via fused deposition modelling (FDM) has been shown to be a promising technology for the production of such materials, however there are challenges in using short carbon fibre-reinforced polylactic acid (CF-PLA) which include obtaining high mechanical performance and maintaining dimensional accuracy with dynamic robot motion and complex interactions occurring between process parameters. The conventional approaches are mostly static feed rates or …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1326–1335 Read article
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Augmented Reality Based Wrist Scan Watch App
Abstract: Augmented reality (AR) has emerged as a transformative technology with profound implications for various industries, including fashion retail. In this context, the development of the AR wrist scan watch application represents a significant advancement, offering consumers an engaging and immersive way to virtually interact with timepieces before making purchase decisions. This research paper delves into the intricate process of creating such applications using Unity, a versatile game engine, and Vuforia, …
Published in Recent Trends in Sensor Research & Technology · Vol. 11, Issue 3, 2024 · pp. 1–11 Read article
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Phisherman: A Phishing Email Detection Browser Extension
Abstract: Phishing attacks continue to pose significant security risks, exploiting email as a primary vector to deceive users and compromise sensitive information. To counter these threats, Phisherman presents a sophisticated, real-time phishing detection system that integrates both rule-based methods and deep learning for heightened accuracy. Built as a cross-browser extension, compatible with Chrome, Firefox, and Edge through the WebExtension API, Phisherman combines traditional verification checks, such as DNS blacklisting, SPF, DKIM, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 99–105 Read article
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Enhancing Road Safety with the Latest Breakthrough: Real-time Vehicle Classification, Counting, and Speed Estimation Using YOLOv8n and Deep SORT Algorithm
Abstract: The real-time vehicle classification, counting, and speed estimation system based on YOLOv8n is an important tool for monitoring traffic flow on highways. However, because they are distinct objects from their surroundings, it is still difficult to detect them, which has an impact on how accurate vehicle counts are. To tackle this concern, this paper suggests the implementation of a vision-centric system for real-time vehicle monitoring and identification. The approach involves …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 1, 2024 · pp. 10–18 Read article
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Analysis of White Matter, Gray Matter, and Cerebrospinal Fluid Alterations in Neurological Disorders: A Deep Learning Approach
Abstract: This paper investigates the role of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) alterations in the pathophysiology of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, schizophrenia, and epilepsy. By leveraging advanced deep learning methodologies, we aim to automate the segmentation and analysis of brain structures from MRI scans, enabling a more detailed and precise evaluation of their roles in disease progression. These techniques allow for the identification …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 14, Issue 3, 2024 · pp. 21–27 Read article
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Cardiovascular Illness Detection and Categorization with Innovative Neural Networks
Abstract: Health-related problems are increasingly prevalent in modern-day societies and are significantly shaped by a multitude of factors encountered in everyday life. Among these, cardiovascular diseases have emerged as one of the primary causes of death on a global scale, posing serious challenges to public health systems. In response to this growing concern, the present study proposes a machine learning-based framework that is not only highly effective but also reliable and …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 21–30 Read article
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A Comparative Study of Transfer Learning-Based Deep Learning Models for Breast Cancer Detection
Abstract: Breast cancer is a major concern in the world today, and early and accurate diagnosis is most crucial in the case of breast cancer, as it is among the disorders where the total cost of loss of life is high. Traditional screening processes are subjective and vulnerable to inter-observer reliability issues and diagnostic errors, being primarily based on manual interpretation of medical images. To address these limitations, Deep Learning (DL) …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 · pp. 24–34 Read article
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Design of an Integrated Health Monitoring System on an IoT-Based Framework
Abstract: With the advent of industrialization, health ailments have become a major cause of concern owing to an inactive and fast lifestyle, polluted environments, and detrimental eating habits. However, IoT has become a boon for the health sector and humanity on account of its countless benefits, rendering improved quality of service and patient-centric care. An Integrated Health Monitoring System (IHMS) is presented in this paper. This new IoT-based framework integrates wearable …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 1–7 Read article
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A Review of Recent Advancements in Machine Learning and Deep Learning Approaches for Pet Diseases Prediction
Abstract: This systematic study assesses recent developments in Machine Learning (ML) and Deep Learning (DL) approaches to predict pet diseases. With the increasing role of Artificial Intelligence (AI) in pet healthcare, this study identifies recent research trends, limitations, and future directions. A comprehensive search was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines in selecting 20 relevant studies from over 300 articles published between 2020 and …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 14, Issue 3, 2025 · pp. 1–6 Read article
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Textual Clues to Stress: A Machine Learning Approach
Abstract: Nowadays, numerous individuals utilize social media platforms to share tweets about their daily lives, which often reflect their mental well-being. Recognizing and managing stress is essential before it becomes a serious issue. Each day, a significant volume of informal messages is posted on discussion forums, blogs, and social networking sites. This study introduces a method for detecting stress using information gathered from social media, with a focus on Twitter. The …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 72–76 Read article
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Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System
Abstract: Convolutional Neural Networks exhibit remarkable capabilities in flow pattern recognition, pressure drop prediction, leak detection, and system optimization through their ability to process complex spatial and temporal data patterns. The study examines CNN architectures specifically adapted for fluid dynamics applications, including data preprocessing techniques, feature extraction methods, and performance optimization strategies. Key applications include real-time flow monitoring, predictive maintenance, design parameter optimization, and anomaly detection in pipe networks. Comparative analysis …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 3, 2025 · pp. 1–9 Read article
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Non-Invasive Glucose Monitoring Device Using Max30102 Sensor
Abstract: Diabetes mellitus is a chronic metabolic disorder affecting millions globally, requiring continuous blood glucose monitoring to prevent complications such as cardiovascular disease, kidney failure, neuropathy, and retinopathy. Conventional invasive finger- prick techniques result in pain, skin irritation, and an increased risk of infection, which lowers patient compliance, particularly in young patients and the elderly. This paper presents a non-invasive glucose monitoring prototype using the MAX30102 optical biosensor interfaced with the …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 1, 2026 · pp. 36–44 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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An Automation Detection for Sign Language Using AI
Abstract: Sign language recognition has attracted considerable interest because of its ability to facilitate communication between the deaf community and the public, thereby bridging communication divides. Traditional approaches to sign language recognition often face challenges in accurately interpreting the complex and nuanced gestures inherent in sign languages. However, recent advancements in deep learning techniques have shown promising results in improving the accuracy and robustness of sign language recognition systems. This study …
Published in Recent Trends in Programming languages · Vol. 11, Issue 1, 2024 · pp. 1–14 Read article
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Optimizing PLA Filament Production for Enhanced 3D Printing Performance
Abstract: This research explores the optimization of polylactic acid (PLA) filament production to improve its performance in 3D printing applications. PLA is a widely used biodegradable polymer known for its eco-friendliness and ease of processing in additive manufacturing. The study investigates various parameters affecting PLA filament production, including material purity, extrusion temperature, filament diameter consistency, and cooling methods. Furthermore, the research evaluates how these fabrication parameters influence the printability of PLA …
Published in International Journal of Energy and Thermal Applications · Vol. 2, Issue 1, 2024 · pp. 21–29 Read article
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Numerical Investigation on Concrete Filled Steel Tube Composite Circular Columns Under General Loading
Abstract: Concrete-filled steel tube (CFST) composite columns are extensively utilized in civil engineering constructions because of their numerous structural advantages, such as superior seismic performance, high load- bearing capacity, fire resistance, remarkable ductility, and ability to absorb energy effectively, especially in areas prone to seismic activity. CFST columns are employed in tall buildings and bridges to enhance rigidity and increase bearing capacity, but their behavior can be affected by buckling. There …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 164–199 Read article
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Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge
Abstract: Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 1–5 Read article
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ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 Read article
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AI Approaches in Gait and Posture Analysis: A Review
Abstract: This review synthesizes current research on the application of artificial intelligence (AI) in gait and posture analysis, focusing on methodologies, algorithms, and clinical applications. It examines the use of machine learning (ML) and deep learning (DL) techniques to extract relevant features from sensorderived data, offering objective, and automated assessments that surpass traditional methods. A systematic literature review was conducted, analyzing studies that utilized AI for gait and posture analysis with …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 1–3 Read article
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Explainable Sentiment Mining Model in Mental Health Forums for Emotion Classification and Justification
Abstract: Understanding and interpreting emotions expressed in online mental health discussions plays a crucial role in enabling early detection of psychological distress and facilitating timely interventions. As individuals increasingly turn to digital platforms to share personal experiences and seek support, automated systems capable of accurately identifying emotional states can significantly assist clinicians, moderators, and support communities. This paper presents a deep learning–based sentiment mining and emotion classification framework specifically designed to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 22–32 Read article