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492 articles for “Machining”
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Hybrid Quantum-Classical Signal Processing for High-Dimensional Feature Extraction in Smart Instrumentation
Abstract: Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning …
Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 2, 2026 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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Assessment of M30 Concrete Compressive Strength Using Digital Rebound Hammer and Destructive Testing
Abstract: Abstract The assessment of in-situ concrete strength using non-destructive testing (NDT) techniques hasbecome increasingly important for quality assurance, structural evaluation, and conditionassessment of concrete infrastructure. Among the available NDT methods, the digital reboundhammer offers a rapid, economical, and non-destructive means of estimating concretecompressive strength. This study experimentally investigates the correlation between reboundnumbers obtained from a digital rebound hammer and the compressive strength of M30 gradeconcrete determined using a Compression Testing …
Published in International Journal of Structural Engineering and Analysis · Vol. 12, Issue 2, 2026 Read article
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Deep Learning Techniques for Optimizing Resource Allocation and Sustainability in the Next Generation of 6G Wireless Networks
Abstract: The upcoming sixth generation (6G) of wireless communication networks aims to meet challenging requirements such as ultra-reliable low-latency services, massive connectivity for billions of smart devices, and intelligent automation across highly diverse environments. Along with these goals, the issues of energy efficiency and environmental sustainability are becoming equally important, as the rising number of users and traffic volumes will directly increase power demand and carbon emissions. To lower total energy …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 Read article
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Hybrid Quantum-Classical Signal Processing for High-Dimensional Feature Extraction in Smart Instrumentation
Abstract: Hybrid quantum–classical signal processing has emerged as a promising paradigm for overcoming the computational limitations of conventional algorithms in analyzing high-dimensional data generated by modern smart instrumentation systems. This paper presents a novel hybrid quantum–classical signal processing framework designed for efficient feature extraction from complex, multidimensional sensor signals encountered in industrial monitoring, biomedical diagnostics, environmental sensing, and intelligent manufacturing. The proposed architecture integrates quantum variational circuits with classical deep learning …
Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 2, 2026 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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Speech Emotion Recognition using Multi- Kernel CNN for Features extraction and Random Forest Classifier
Abstract: Identifying human emotional states from speech signals is the main goal of Speech Emotion Recognition (SER), which has grown to be a significant area of study in affective computing. However, class imbalance and efficient feature extraction from speech data pose problems for many of the current methods. This study suggests a hybrid framework that combines a Random Forest (RF) classifier with a multi-kernel Convolutional Neural Network (CNN) to address these …
Published in International Journal of Digital Communication and Analog Signals · Vol. 12, Issue 2, 2026 Read article
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Experimental Investigation of the Influence of Coarse Aggregate Size and their Proportioning on the M30 Concrete for CC Pavement
Abstract: Abstract The nominal maximum size (NMS) and proportion of coarse aggregates significantly influence the mechanical properties and performance of cement concrete pavements. While extensive research has focused on concrete mix design, the combined effect of coarse aggregate size and aggregate proportioning on the compressive strength of pavement-grade concrete remains inadequately understood. This study experimentally investigates the influence of coarse aggregate characteristics on the compressive strength of M30 grade concrete. Two …
Published in International Journal of Concrete Technology · Vol. 12, Issue 2, 2026 · pp. 13–23 Read article
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Smart Voting System using Biometrics
Abstract: Election integrity and openness are essential components of democratic governance. Voter impersonation, duplicate voting, and the absence of real-time verification are common problems with traditional voting systems, including paper-based and electronic voting machines. This paper proposes a secure and efficient Biometric Voting System that leverages biometric authentication techniques, such as fingerprint or iris recognition, to ensure the uniqueness and authenticity of each voter. The system integrates a centralized database containing …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 12, Issue 2, 2026 Read article
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Entropy-Based Damage Accumulation in Tribological Systems: A Comprehensive Review
Abstract: This paper reviews classical wear models and entropy-based degradation approaches used for predicting failure in tribological systems. Various wear models, thermodynamic frameworks, and applications of the Degradation Entropy Generation (DEG) theorem are discussed. Friction, which is the primary source of heat generation in tribological contacts, is responsible for material degradation through a variety of irreversible processes. While classical wear approaches have been widely used for wear prediction and life estimation, …
Published in International Journal of Mechanics and Design · Vol. 12, Issue 1, 2026 · pp. 1–12 Read article
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AI-Based Image Detection of Rice Crop Diseases Using Deep Learning and Grad-CAM
Abstract: The Indian economy relies heavily on agriculture, and rice is one of the most significant staple crops grown in the country. Nevertheless, rice cultivation faces numerous challenges due to several leaf diseases, such as bacterial leaf blight, brown spot, and leaf smut. These diseases can lead to substantial yield losses and financial burdens for farmers. Therefore, the early detection and classification of these diseases are essential to minimize crop damage …
Published in International Journal of Plant Biotechnology · Vol. 12, Issue 2, 2026 Read article
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Automated Vehicle classification and counting for Smart City Traffic management using AI/ML and DL
Abstract: Traffic jams and safety on the road have become a serious problem in modern cities. They need smart and versatile solutions. This project is a proposal to use automated vehicles to count and classify real- time vehicles. The system will enhance the traffic monitoring and management in smart cities. It can identify and differentiate the various kinds of vehicles: cars, trucks, buses, and motorcycles by utilizing machine learning on real-time …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 1, 2026 Read article
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Predicting Traffic Congestion in Mumbai Using Artificial Intelligence
Abstract: Abstract — Mumbai's ongoing traffic problems are brought on by the city's fast urbanization, population expansion, and growing reliance on private automobiles. The majority of traffic management technologies in use today are reactive, frequently reacting only after congestion has already arisen. Using information from a variety of sources, such as commuter surveys, weather reports, and traffic incident logs, this study explores the application of artificial intelligence (AI) and machine learning …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 12, Issue 2, 2026 Read article
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Remote Sensing Technologies for Weather Forecasting: Techniques, Uses, andAssessment
Abstract: Remote sensing has become an important tool for modern weather forecasting since it lets us see the Earth's atmosphere and surface all the time and on a huge scale. Remote sensing uses satellites and sensors to collect data in real time on important weather variables like temperature, humidity, cloud cover, precipitation patterns, and atmospheric motion. These numbers are used in numerical weather prediction models to make forecasts more accurate and …
Published in International Journal of Microwave Engineering and Technology · Vol. 12, Issue 1, 2026 · pp. 25–34 Read article
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AI-Based Deepfake Voice Detection Model for Hindi Language
Abstract: Most current research on deepfake voice detection focuses heavily on English or Mandarin, creating a significant security gap for Hindi speakers. To address this, we developed a detection framework specifically tailored for the Hindi language using a lightweight approach: Mel-Frequency Cepstral Coefficient (MFCC) feature extraction paired with an XGBoost classifier. We trained the system on a large dataset comprising over 99,000 real voice samples and roughly 3,800 synthetic samples, each …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 2, 2026 Read article
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A Comprehensive Review of Convolutional Neural Network Architectures and Evolution
Abstract: Convolutional Neural Networks (CNNs) have become a foundational deep learning framework in computer vision because they can automatically extract layered, increasingly complex features directly from raw image data. CNNs use convolutional, pooling, and activation layers to extract spatial patterns from basic edges to intricate object components, drawing inspiration from the human visual cortex. An overview of CNNs’ basic architecture is given in this study, along with an explanation of important …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 32–40 Read article
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Enhancing Facial Expression Analysis Using CNNs: Insights from ML and Deep Learning Comparisons
Abstract: Emotion recognition from facial manifestations increases the interaction between humans and computers by enabling the interaction between humans and computers to effectively explain human feelings. This study presents a comparative analysis of three approaches to the emotional recognition of faces: a custom Convolutional Neural Network (CNN) model, Traditional Machine Learning (ML) methods, and other deep learning architectures such as VGG, ResNet, and Hybrid CNN–RNN models. The proposed CNN-based system, developed …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 23–31 Read article
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Recent Advances in the Development and Performance Optimization of Advanced Metallic Alloys
Abstract: The continuous demand for high-performance materials in aerospace, automotive, energy, and biomedical industries has accelerated research and development in advanced metallic alloys. Metallurgy and alloy design play a crucial role in achieving superior mechanical, thermal, and chemical properties while maintaining cost efficiency and sustainability. This study presents a comprehensive overview of recent advances in metallic alloy development, focusing on novel alloying strategies, microstructural control, and performance optimization techniques. Emphasis is …
Published in International Journal of Metallurgy and Alloys · Vol. 12, Issue 1, 2026 · pp. 1–6 Read article
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Phase-Change Material Integrated Semiconductor Circuits for In-Memory Analog Computing Applications
Abstract: The continuous demand for high-speed, energy-efficient computing has intensified the exploration of in- memory analog computing (IMAC) architectures, which aim to minimize the latency and power overhead associated with conventional von Neumann architectures. This research investigates the integration of phase- change materials (PCMs) with semiconductor circuits to realize programmable, non-volatile in-memory analog computing platforms. PCMs exhibit reversible resistance switching between amorphous and crystalline states, enabling precise analog weight storage directly …
Published in International Journal of Analog Integrated Circuits · Vol. 12, Issue 1, 2026 · pp. 32–42 Read article
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High-Tech Battery Management Systems with Integrated Battery Electronics
Abstract: “Sophisticated BMSs with onboard battery electronics are a paradigm shift in battery technology, sensitive enough to meet the increasing needs of contemporary applications like electric vehicles, renewable energy storage, and portable devices. This paper discusses the architecture and functional aspects of the integrated systems in optimizing performance, safety, and lifespan through real-time monitoring and control of individual cells. Integrating critical electronics directly onto the battery structure, such systems minimize complexity …
Published in International Journal of Power Electronics Controllers and Converters · Vol. 12, Issue 1, 2026 · pp. 21–28 Read article