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238 articles for “Machine learning”
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Review and Analysis of Fault Detection in Self-Healing Hardware System
Abstract: Self-healing hardware systems represent a transformative advancement in fault tolerance, enabling devices to autonomously detect, diagnose, and recover from malfunctions to ensure continuous and reliable operation. This paper explores the critical role of fault detection techniques in self-healing systems, focusing on methods such as built-in self-test (BIST), redundancy, and machine learning-based approaches. These techniques facilitate proactive and real-time fault resolution, addressing the limitations of traditional reactive strategies. The study highlights …
Published in International Journal of Analog Integrated Circuits · Vol. 11, Issue 1, 2025 · pp. 11–15 Read article
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Exploration of Explainable Artificial Intelligence (XAI) Techniques to Enhance Model Interpretability
Abstract: Explainable Artificial Intelligence (XAI) has become a major research field because of the rising application of complex machine learning and deep learning models in actual decision-making systems. Although such models are accurate, they are in many cases black boxes and therefore the users do not have much understanding of the way the decisions are generated. This absence of transparency brings up some serious concerns connected with trust, accountability, fairness, and …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 2, 2026 Read article
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SYNTHETIC MEDICAL DATA GENERATIONUSING GAN MODEL
Abstract: The healthcare industry has witnessed growing interest in synthetic data generation, driven by increasing demands for large and diverse datasets. Real medical data is often inaccessible due to stringent privacy laws, data scarcity, and ethical considerations. Sophisticated models, such as Generative Adversarial Networks (GANs), have emerged as powerful tools to generate synthetic data that closely resembles real-world data. This approach is particularly effective for addressing the challenges of researching rare …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 2, 2025 Read article
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Utilizing AI in Civil Engineering for the Construction of Green Buildings
Abstract: Artificial Intelligence (AI) is a discipline in computer science dedicated to creating and applying intelligent machines. This includes exploring recent innovations and methodologies for implementing AI in civil engineering and providing an overview of advancements in the field. The significant progress in big data, deep learning, and machine learning technologies has been successfully applied across various civil engineering sectors. Key areas of AI research in civil engineering encompass structural management, …
Published in International Journal of Construction Engineering and Planning · Vol. 10, Issue 1, 2024 · pp. 31–38 Read article
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Region-based Object Detection and Classification using Faster R-CNN
Abstract: With the advent of Deep Learning the machine learning systems are able to recognize and classify objects of interest in an image. Various advancement has been done in the field of object recognition and classification. Our research work focusses on improving the R-CNN, Fast R-CNN, YOLO architecture. The work focused on using Region Proposals Network (RPN) to extract region of interest in an image. RPN outputs an image based on …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 6, Issue 2, 2020 · pp. 22–30 Read article
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Artificial Intelligence Applications in Mechanical Engineering: A Pathway to Industry 4.0
Abstract: Abstract— The emergence of Industry 4.0 signifies a groundbreaking change in the industrial landscape, propelled by the integration of cyber-physical systems, the Internet of Things (IoT), and artificial intelligence (AI). This period represents the convergence of digital technologies with conventional manufacturing practices, resulting in the development of intelligent, interconnected, and autonomous production settings. For mechanical engineering—traditionally focused on physical systems, manual design, and empirical approaches—this shift has brought about an …
Published in International Journal of Mechanics and Design · Vol. 11, Issue 2, 2025 · pp. 1–6 Read article
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Real-Time Language Translation Application Using Tkinter
Abstract: This application, "Real Time Language Translation Application Using Tkinter," was created to develop a friendly tool by which users can instantaneously translate text from one language to another. Built using Python and the Tkinter library, it uses the integration of the Google Translate API to deliver an accurate translation with context while relying on Natural Language Processing technology through the system for Google's Neural Machine Translation. The GUI developed with …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 26–32 Read article
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Health Care System Analysis Using Different Datasets
Abstract: In this research work, we have discussed data science and various machine learning algorithms like KNN, Support Vector Machine, Artificial neural network, Random forest etc. and the results obtained from these algorithms which can be used for the prediction of heart disease, cancer and in many other fields of medical science. Requirement of data cleaning along with various methods of data cleaning are mentioned here, along with these various issues …
Published in International Journal of Distributed Computing and Technology · Vol. 8, Issue 2, 2022 · pp. 29–37 Read article
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Transportable Water and Air Quality Monitoring Equipment Karishma
Abstract: The purpose of this article is to provide a broad summary of recent discoveries, identify important constraints, and suggest future lines of inquiry. In recent years, pollution has surpassed the environment's bearing capacity. There hasn't been much progress despite the installation of multiple pollution-control initiatives. Natural resources are occasionally exploited by unavoidable waste discharges, despite our government's best attempts to safeguard them. For instance, the water-intensive and polluting sectors of …
Published in International Journal of Environmental Chemistry · Vol. 12, Issue 1, 2026 Read article
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Advanced Diabetic Retinopathy Detection Using Deep Learning Classifiers
Abstract: Diabetic Retinopathy (DR) is an eye condition that arises from diabetes-related complications, making early detection essential for effective treatment. As diabetes advances, an individual’s vision may deteriorate, potentially resulting in diabetic retinopathy. This condition is divided into two primary types: Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). Early treatment to maintain a strategic distance from a change less visual impedance is basic as it may save diabetic retinopathy …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 11, Issue 1, 2025 · pp. 26–33 Read article
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An Approach for prediction of Heart Diseases using Data Mining Techniques
Abstract: ABSTRACT The healthcare enterprise collects big quantities of Healthcare statistics; however, sadly nolonger all of the facts are mined that is required for discovering hidden styles and effectivedecision making. In this paper, we advise efficient genetic algorithm with the againpropagation approach technique for heart disorder prediction. This paper has analyzedprediction systems for coronary heart diseases the use of records mining strategies. Thegadget makes use of scientific phrases inclusive of gender, …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 6, Issue 1, 2020 · pp. 8–21 Read article
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Semantic Segmentation for Object Detection in Videos using Mask RCNN
Abstract: The segmentation of object instances is an essential step in real-time video detection. Object detection is the problem of detecting the information of all types of items in an image by designating their location with a rectangular box. Using its remarkable feature-learning capabilities, deep learning promotes object detection research. Numerous researchers have utilised various machine learning algorithms for object detection and have focused on enhancing the accuracy of feature extraction. …
Published in International Journal of Digital Communication and Analog Signals · Vol. 8, Issue 2, 2022 · pp. 7–15 Read article
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Crop Disease Classification and Detection Using Deep Learning Approach
Abstract: Early diagnosis of plant diseases is essential because they have an effect on the evolution of their unique species. For the identification and classification of plant diseases, a variety of Machine Learning (ML) models have been employed, but with the advent of Deep Learning (DL), a subset of ML, this area of research now seems to offer substantial potential for increased accuracy. The 4th most common staple food consumed worldwide …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 9, Issue 1, 2023 · pp. 22–27 Read article
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Smart Agriculture Using IoT: A Paradigm Shift in Modern Farming
Abstract: The rapid advancements on the Internet of Things (IoT) have revolutionized agriculture by integrating sensors, automation, and data analytics to enhance productivity, resource efficiency, and sustainability. With growing challenges such as climate change, population growth, and the need for efficient resource management, IoT-driven smart agriculture presents a transformative approach to modern farming. By leveraging IoT-enabled devices, farmers can gain real-time insights into soil conditions, weather patterns, crop health, and resource …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 33–37 Read article
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Power and Body-Bias Optimization for FD-SOI Systems-Via Simulations and Algorithms
Abstract: The field of Integrated Circuits is very vast from digital to analog circuits dealing withvarious types of digital circuits our paper deals with the CMOS digital circuits poweroptimization. The paper deals auto-characterizing different FD-SOI circuits with certainmachine learning techniques verifying their outputs along with managing power losses andbody bias voltages in different temperature conditions the machine learning techniques helpoptimize power losses by varying body bias voltages according to the functional …
Published in International Journal of Electrical Power System and Technology · Vol. 6, Issue 1, 2020 · pp. 45–55 Read article
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Classification of Fetal ECG Arrhythmia by Using Advance Hybrid Deep Learning Technique CNN-LSTM
Abstract: Fetal heart abnormalities, particularly fetal heart arrhythmias, are becoming an increasing concern in maternal healthcare. Early and accurate detection remains challenging due to limitations in conventional diagnostic techniques and inadequate signal processing methods. Existing studies have applied various machine learning and deep learning approaches; however, capturing the complex characteristics of fetal electrocardiogram (ECG) signals remains a significant gap. This study proposes an advanced deep learning-based approach for detecting fetal heart …
Published in International Journal of Cardiovascular Nursing · Vol. 12, Issue 1, 2026 · pp. 27–36 Read article
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Employing ML and DL to Optimize an ElectrochemicalBiosensor
Abstract: A major field of study in science, that have made the understanding of the world and made homo sapiensthe dominant species on the planet is our understanding of medicine. Under this vast umbrella is thedetection and classification of microorganisms. Electrochemical biosensors have emerged aspromising tools in this regard due to their high sensitivity, selectivity, and potential for miniaturization.By integrating a Machine Learning & Deep Learning model for the detection of …
Published in International Journal of Chemical Separation Technology · Vol. 10, Issue 1, 2024 · pp. 33–38 Read article
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Classification of power quality disturbances using advanced deep learning algorithms for Accurate and efficient electrical signal analysis
Abstract: Power systems today have to deal with more and more power quality issues (PQDs) like voltage drops, rises, harmonics, and transients because more people are using renewable energy sources and electronic devices. These problems can hurt equipment, stop services, and lower the overall reliability of power. In real-time settings, traditional detection methods often don't work as quickly or accurately as they should. This paper looks at how deep learning models, …
Published in International Journal of Power Electronics Controllers and Converters · Vol. 12, Issue 2, 2026 Read article
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A Sensor Integrated Tool for Cutting Force Monitoring in Gantry Milling to Improve Flatness and Surface Finish: Review Study
Abstract: AbstractPrecision machining of sector plates plays a vital role in the efficient functioning of Air Pre-Heaters(APH), which are integral components of thermal power plants. These plates are subjected to harshthermal cycles and mechanical loads, making dimensional accuracy critical. For optimal heatexchange performance and mechanical reliability, it is essential to maintain tight manufacturingtolerances – flatness within ±400 microns and thickness up to 13 mm. Deviation from theseparameters can lead to air …
Published in International Journal of Manufacturing and Materials Processing · Vol. 11, Issue 1, 2025 · pp. 25–45 Read article
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Real Time Face Mask Detection with Deep Learning
Abstract: The pandemic because of COVID-19 needs no introduction. An all-too-important and successful weapon against it has been the utilization of face masks. A special technique is presented in this paper for detecting automatically whether someone is wearing a mask or not, contingent on the machine learning technique. The findings can be utilized by authorities to warn or fine people for not wearing mask, especially at public places. This work relies …
Published in International Journal of Analog Integrated Circuits · Vol. 7, Issue 1, 2021 · pp. 8–14 Read article