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56 articles for “Robust K-means”
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Integrating Sensor Technologies for Modern Soldier Sentinel Systems
Abstract: The Soldier Sentinel Sensor System (S4) is a cutting-edge technology developed for enhancing situational awareness and safety for military personnel in operational environments. This abstract provides an overview of the key features and benefits of the S4 system. S4 integrates advanced sensor technologies, including biometric sensors, environmental sensors, and location tracking modules, into a wearable and robust platform. The system continuously monitors vital signs, such as heart rate, body temperature, …
Published in Journal of Remote Sensing & GIS · Vol. 15, Issue 2, 2024 · pp. 6–11 Read article
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VHDL Programming for Secure Bootloaders in IoT Security
Abstract: The proliferation of Internet of Things (IoT) devices has brought unparalleled connectivity and convenience. However, this increased connectivity comes with a heightened risk of security vulnerabilities. One of the most critical areas for securing IoT devices is the bootloader, the software responsible for initializing the device and loading the operating system. A compromised bootloader can lead to a complete takeover of the device, making it a prime target for malicious …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 3, Issue 1, 2025 · pp. 19–28 Read article
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Integrated Analysis of Stress Patterns in Transparent Polycarbonate Specimens: A Comparative Study between Photoelasticity and FEA Simulation for Compact Circular Testing
Abstract: Photoelasticity stands as a robust experimental technique within the realms of mechanics and materials science, offering a means to visually assess and analyze stress distribution within materials possessing transparency or translucency. This method, a non-destructive testing approach, involves the visualization of stress on a model subjected to a load, leveraging the unique property of materials known as birefringence or double refraction. The procedure entails the careful selection of a suitable …
Published in Journal of Polymer & Composites · Vol. 12, Issue 4, 2024 · pp. 79–87 Read article
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Machine Learning Assisted Optimization of Nanoscale MOSFET Parameters Using TCAD Simulation
Abstract: This paper presents a machine learning (ML) assisted framework for the multi-objective optimization of nanoscale bulk n-channel metal-oxide-semiconductor field-effect transistors (nMOSFETs) with a 10 nm physical gate length, high-k HfO₂ gate dielectric, and TiN metal gate. Technology computer-aided design (TCAD) simulations employing drift-diffusion transport, Shockley-Read-Hall recombination, Lombardi mobility degradation, and density- gradient quantum correction models are used to generate a parametric dataset of 2,400 device configurations spanning gate length (L), …
Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 10–19 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 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 · pp. 24–31 Read article
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A Comprehensive Review of Statistical Analysis Techniques in Economic Data Studies
Abstract: Statistical analysis plays a key role in the field of economics, enabling scientists to extract meaningful information from large and complex data sets. It is essential for forming policies, comprehending market movements, and forecasting future economic scenarios. Choosing the right statistical analysis tools is crucial for gaining useful insights from economic data in this era of expanding data availability. Robust statistical analysis methods further increase the credibility and reliability of …
Published in Research & Reviews : Journal of Statistics · Vol. 12, Issue 2, 2023 · pp. 42–48 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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Aspect-Based Sentiment Analysis Using a Hybrid Approach with Dependency Parsing
Abstract: The rapid expansion of digital communication has resulted in an unprecedented volume of consumer-generated textual data across online reviews, social media platforms, forums, and e-commerce websites. Extracting meaningful insights from this data is increasingly important for organizations seeking to understand customer opinions, preferences, and behavioral trends. Despite significant advances in sentiment analysis, many existing approaches primarily focus on surface-level features and often overlook deeper syntactic and semantic relationships within text. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 01–09 Read article
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Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
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Robust Design of Subsonic Airfoil by Multi-Objective Six Sigma Approach
Abstract: It is well known that aerodynamic performance of a UAV is very sensitive to the wing shape and flight conditions, and inevitable uncertainties such as wing manufacturing errors and wind variations may lead to drastic deterioration in its aerodynamic performance. In the UAV wing design, therefore, it is required not to use the conventional design optimization approach considering only optimality of performance at the design point, but to use the …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 3, 2025 · pp. 14–23 Read article
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The Role of Adaptive Filters in Enhancing Acoustic Echo Cancellation Efficiency in Noisy Environments
Abstract: The novel approach that this work discusses is a DCD-based iterative learning filter approach improved with deep learning methodologies, designed to improve the efficiency of acoustic echo cancellation. The proposed system can really manage both linear and nonlinear echo scenarios, dynamically adapting to fluctuating acoustic environments. The above comparative evaluations with standard filter, the standard RLS filter, indicate that the mean square error, and the standard deviation of the correlation …
Published in International Journal of Radio Frequency Innovations · Vol. 2, Issue 2, 2024 · pp. 9–24 Read article
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A Comprehensive Survey on Detection of Video Transitions
Abstract: Video shot boundary detection (SBD) is a fundamental task in the field of video processing and analysis. It plays a critical role in various video applications such as content-based video retrieval, video indexing, editing, summarization, and browsing. Identifying shot boundaries helps segment a continuous video stream into distinct shots, each representing a meaningful visual unit. This segmentation is essential for organizing and interpreting video data efficiently. This study provides an …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 12, Issue 3, 2025 · pp. 17–26 Read article
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Robust Classification of Traffic Signs Using Relief Feature Reduction Technique
Abstract: Ensuring driver safety amidst the rapid growth of global population and vehicular density continues to be a paramount challenge for transportation authorities and governments worldwide. With the rise of smart mobility solutions and autonomous driving technologies, the ability to detect, classify, and respond to traffic signs accurately has become critically important, especially under diverse and adverse environmental conditions such as rain, fog, or poor lighting. Reliable traffic sign recognition not …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 30–37 Read article
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BUILDING A SMART CHATTING PLATFORM FOR SOCIAL NETWORKING:”GUFTGU”
Abstract: This research paper details the development of a location-based social networking application with a focus on offline first functionality. Guftgu is a communication application developed using React Native for the frontend and Node.js for the backend. It facilitates seamless, real-time conversations between strangers while prioritizing user privacy through complete anonymity. The platform’s intuitive interface, cross-platform compatibility, and efficient backend architecture enable smooth performance and user engagement. Designed with privacy at …
Published in Journal of Microelectronics and Solid State Devices · Vol. 12, Issue 3, 2025 · pp. 32–37 Read article
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Interpretable Skin Cancer Detection via Optimized CNN Models for Smart Healthcare Solutions
Abstract: Skin cancer is a common and potentially life-threatening condition, highlighting the importance of reliable and efficient diagnostic techniques. Recently, convolutional neural networks (CNNs) have demonstrated significant potential in automating the classification of skin cancer using thermoscopic images. Despite these advancements, the lack of interpretability in these models poses a barrier to their widespread use in clinical settings. In this study, we propose an interpretable CNN architecture optimized for skin cancer …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 41–45 Read article
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Analytical Method Development and Validation for the Simultaneous Estimation of Elvitegravir and Cobicistat by RP-HPLC Method
Abstract: A new method was established for simultaneous estimation of Elvetigravir and Cobicistat by RP-HPLC method. The chromatographic conditions were successfully developed for the separation of Elvetigravir and Cobicistat by using Xterra C18 5 µm (4.6×150 mm) column, flow rate was 1 ml/min, mobile phase ratio was phosphate buffer (0.05 M) pH 7: MEOH (30:70%v/v) (pH was adjusted with orthophosphoric acid), and the detection wave length was 270 nm. The instrument …
Published in Research & Reviews: A Journal of Drug Formulation, Development and Production · Vol. 5, Issue 2, 2018 · pp. 1–6 Read article
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Physics-Informed Machine Learning and Multiscale Modeling for Structure–Property Quantification of Polymer Composites
Abstract: The growing need for light-weight, high strength, and sustainable polymer composites has led to the development of smart methods that enable accurate structural-property quantification and material design. However, conventional methods have been predominantly data-based, thus ignoring physical constraints as well as multi-scale interactions involving fiber, matrix, interface, and process parameters, leading to lower accuracy and poor robustness and interpretability of the models. In this study, a Cat Swarm Optimization-Tuned Physics-Informed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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IoT and Smart Sensors for Structural Health Monitoring: Trends, Challenges, and Future Directions
Abstract: Structural Health Monitoring (SHM) plays a critical role in ensuring the safety, resilience, and sustainability of civil infrastructure systems. In recent years, the convergence of Internet of Things (IoT) technologies and smart sensor systems has revolutionized the field of SHM. This integration enables continuous, real- time monitoring, facilitates predictive maintenance, and reduces the costs associated with structural inspections. IoT-based SHM frameworks leverage wireless sensor networks, cloud computing platforms, and intelligent …
Published in Recent Trends in Sensor Research & Technology · Vol. 12, Issue 3, 2025 · pp. 1–6 Read article
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Enzyme Stability Prediction using BERT and CNN-A Deep Learning Approach for Enhanced Biocatalysis
Abstract: An important factor in determining the efficacy of industrial enzymes used in various biotechnological applications is their stability. The goal of this study is to develop a predictive model for industrial enzyme stability, which is essential to the efficiency of these enzymes in biotechnological applications. The research takes a comprehensive strategy to comprehend the parameters affecting enzyme stability by combining statistical analysis, deep learning algorithms (BERT and CNN), and molecular …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 14, Issue 2, 2024 · pp. 19–35 Read article