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57 articles for “Embedded Machine Learning”
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ML-Enhanced Smart Sensing Framework for IoT- Based Structural Health Monitoring Using Conductive Polymer Composites
Abstract: The growing demand for intelligent structural health monitoring (SHM) in dynamic infrastructures necessitates flexible sensing systems that are not only mechanically robust but also capable of real-time interpretation. Conventional SHM frameworks often rely on brittle sensor configurations and cloud-dependent processing pipelines, which suffer from latency, limited durability, and poor adaptability under variable loading conditions. Despite recent advances in composite materials and machine learning, current approaches lack a unified framework that …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 348–369 Read article
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TinyML-Based Real-Time Industrial Fault Detection Using Vibration Signals
Abstract: Industrial rotating machinery is commonly monitored through vibration measurements because changes in bearing condition can alter signal amplitude, impulsiveness, and spectral distribution. Cloud-centred analytics may introduce communication dependence, bandwidth demand, and response delay. Tiny Machine Learning (TinyML) offers a complementary approach by executing compact inference locally on resource-constrained edge hardware. This paper presents a reproducible, deployment-oriented workflow for vibration-based machine-state classification using statistical and spectral features with small classifiers suitable …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 3, 2026 Read article
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Smart Polymer Composite Scaffolds for Tissue Engineering with Integrated Machine Learning Feedback
Abstract: Another potential solution to improving the results of tissue engineering is smart polymer composite scaffolds, which are capable of dynamic adaptation to changing biological factors, but typical scaffolds cannot change dynamically. This paper suggests a comprehensive system to integrate biodegradable polymer composite scaffolds with sensing and machine learning-based feedback to allow the real-time monitoring and active regulation of tissue regeneration events. The system uses biocompatible materials of PLA/PCL composite of …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Intelligent Biocomposites for Real-Time Health Monitoring Applications
Abstract: Intelligible biocomposites are emerging as an enhanced material in the sense that they provide the capability to monitor health in real time because they have the inbuilt sensing and adjusting features. In this paper, the concepts of the intelligent biocomposites that have the ability to capture both mechanical and biochemical cues are to be presented as an informatics of designing, fabricating, and modeling. Multiphysics is used to couple mechanical deformation …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Framework for Edge AI and intelligent IoT with emphasis EEE integration, lightweight ML, communication, security, and deployment monitoring
Abstract: Edge Artificial Intelligence (Edge AI) brings machine learning inference and, in selected cases, learning close to the sensors and devices that generate data. When combined with the Internet of Things (IoT), it creates intelligent connected systems that can sense, interpret, decide, and act without depending continuously on a remote cloud. This review presents a practical framework for Edge AI and intelligent IoT with emphasis on electrical and electronics integration, lightweight …
Published in Recent Trends in Electronics Communication Systems · Vol. 13, Issue 3, 2026 Read article
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Machine Learning–Guided Cognitive RF System with Dynamic FFT Resolution and Multiplier Reconfiguration for Adaptive Anti-Jamming Communication
Abstract: This paper presents a hierarchical adaptive RF communication system that integrates signal quality-based pre- processing with machine learning-driven signal classification to achieve robust and resource-efficient operation in dynamic, interference-prone environments. Unlike prior art that addresses adaptive RF, ML classification, or anti-jamming individually, this work uniquely combines real-time SNR/RSSI-based signal strength estimation with dynamic FFT size selection (64-, 256- , or 512-point) and arithmetic-level multiplier reconfiguration (CORDIC, Distributed Arithmetic, and hybrid …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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Intrinsic Evaluation of Graph Embeddings: Assessing Clustering and Community Detection Performance
Abstract: This paper presents an intrinsic evaluation of some graph embedding techniques on clustering and community detection tasks. We analyze a diverse set of embedding methods, ranging from traditional techniques such as Laplacian eigenmaps to more recent approaches like graph autoencoders, high-order proximity preserved embedding (HOPE), and graph attention network (GAT), using two widely studied datasets, Cora and CiteSeer. Our evaluation relies on two main metrics: Silhouette score with respect to …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 3, Issue 1, 2025 · pp. 40–48 Read article
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Misinformation Detection and Analysis of Social Media Platforms Using Machine Learning and Deep Learning
Abstract: Social media platforms have become deeply embedded in everyday life, reshaping how people produce, distribute, and engage with information. Although these platforms facilitate quick communication, they have also developed into important avenues for the spread of false information and fake news. The increasing volume of online content makes manual verification impractical, creating a need for reliable automated detection systems. The effectiveness of deep learning and machine learning models in spotting …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 2, 2026 · pp. 22–34 Read article
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Power Estimation Approach for Artix 7 FPGA using Machine Learning Technique
Abstract: Abstract This paper presents the power estimation approach using a suitable machine learning technique. Artix7 FPGA has been chosen as the target FPGA (Field Programmable Gate Arrays) platform for understanding the methodology of power estimation. There are various approaches of power estimation for FPGAs that have been given in the literature viz. probabilistic, statistical, and LUT-based, etc. In the past few years, the demand for hand-handled devices like smartphones, tabs, …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 9, Issue 1, 2021 · pp. 1–5 Read article
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Enhanced Task Automation through IoT and ChatGPT Integration in Personal AI Companions
Abstract: A new breed of clever, likable, and natural-sounding personal AI companions could be produced by integrating ChatGPT with IoT gadgets. The Internet of Things (IoT) and artificial intelligence (AI) are developing at a rapid pace, which has created exciting new opportunities for the creation of intelligent and personalized companions. The goal of this suggested system is to improve user experiences by offering a flexible AI-driven assistant. It does this by …
Published in Recent Trends in Sensor Research & Technology · Vol. 11, Issue 2, 2024 · pp. 19–24 Read article
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Machine Learning Based Optical Fiber Fault Detection and Localization Using OTDR Signals
Abstract: Optical fiber networks provide high bandwidth, low transmission loss, and immunity to electromagnetic interference, making them fundamental to modern communication infrastructure. However, fiber faults caused by cable cuts, excessive bending, connector degradation, splicing defects, and physical disturbances can significantly affect network reliability. Optical time-domain reflectometry (OTDR) is widely used for identifying and locating such faults, but conventional OTDR interpretation often depends on threshold-based signal processing and manual analysis. This study …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 · pp. 41–52 Read article
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Finding Risk Factors in Thyroid and Cardiovascular System Using Naive Bayesian (NB) Machine Learning Technique
Abstract: Thyroid malady could be a common condition. A TSH test is carried out to check for the capacities of your thyroid organ. Illnesses of thyroid can be caused by conditions which cause thyroid organ over or under function. The test can moreover assist you recognize thyroid conditions some time recently indications happen. It is either as well (hyperthyroidism) or under-active (hypothyroidism), you might know. In case untreated, thyroid clutter may …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 8, Issue 1, 2020 · pp. 1–5 Read article
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An Ultra-Low Latency Embedded sEMG-Based Intelligent Control System for Wireless Assistive Wheelchair Navigation Using ESP- NOW Protocol
Abstract: This research provides an ultra-low-latency intelligent control framework for wireless assistive wheelchair movement through single-channel surface electromyography (sEMG) and the ESP-NOW communication technology. The framework presented incorporates sEMG acquisition, simple real-time signal processing, determinable decision logic, and wireless communication for controlling the devices using inexpensive ESP32/ESP8266 microcontrollers. It also allows a system to function as an effective interface between a person and a machine suffering from severe motor disability. The …
Published in Recent Trends in Sensor Research & Technology · Vol. 13, Issue 3, 2026 Read article
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Facial Recognition System Utilizing Real-time Deep Learning Techniques
Abstract: This research introduces an openly accessible deep learning-based framework designed for facial recognition. The system encompasses five key stages: face segmentation, detection of facial features, face alignment, embedding, and classification. Deep learning methods are employed for the extraction of fiducial points and embedding within the system. For the classification task, a Support Vector Machine (SVM) is utilized due to its efficiency in both training and inference phases. Notably, the system …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 11, Issue 1, 2024 · pp. 14–20 Read article
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ML Model Comparison for Sentiment Analysis Across Diverse Datasets
Abstract: Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 26–33 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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IOT and algorithmic intelligent motor health monitoring as well as maintenance prediction
Abstract: Manufacturing, transportation, and energy systems rely largely on industrial electric motors, and their untimely failure can result in expensive downtime, safety hazards, and decreased operational efficiency. The majority of traditional motor maintenance procedures rely on reactive methods or routine inspections, which frequently miss early-stage problems and lead to needless maintenance or unexpected breakdowns. This project offers an Intelligent Motor Health Monitoring and Predictive Maintenance System that combines Internet of Things …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 28–37 Read article
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AI Powered Fault Detection in DC Motor using STM32
Abstract: This work presents the design and implementation of an embedded artificial intelligence system for real-time fault detection in a direct current (DC) motor using the STM32 Nucleo- F411RE microcontroller. The objective of the study is to develop a low-cost and efficient predictive maintenance solution capable of identifying abnormal motor behavior at an early stage. Vibration and temperature signals are acquired using an MPU6050 sensor and processed directly on the microcontroller …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 39–49 Read article
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Android-Based Real-Time Driver Drowsiness Detection and Alert System
Abstract: Traffic accidents cause millions of people to lose their lives every year. Statistics assert that most fatal accidents are due to driver drowsiness. So the rate of fatality due to accidents caused by drivers falling asleep is high. There have been various researches and developments on driver drowsiness and fatigue detection systems by industries and academic researchers over the years. However, the solutions produced are usually found on luxurious vehicles …
Published in Journal of Operating Systems Development & Trends · Vol. 9, Issue 2, 2022 · pp. 1–6 Read article
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Combining Unstructured and Structured Clinical Data in a Hybrid Transformer Model to Enhance Cardiovascular Analytics and Clinical Decision- Making
Abstract: Since cardiovascular disease (CVD) continues to be a major global cause of morbidity and mortality, early and accurate risk prediction is essential for prompt intervention and individualized treatment. This study introduces a new hybrid transformer-based model that combines unstructured clinical narratives, structured data, and customized lifestyle characteristics. A comprehensive understanding of disease progression is made possible by the model's ability to capture contextual, temporal, and patient- specific insights through the …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 1, 2026 · pp. 30–37 Read article