Search
1938 articles for “S-MAC” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
-
Using Machine Learning to Guess Photochemical Reaction Pathways
Abstract: Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
-
A Comprehensive Analysis of Machine Learning Models for Credit Card Fraud Detection
Abstract: This paper presents an indepth comparison of various machine learning models—Logistic Regression, Support Vector Classification (SVC), and Neural Networks (NN)—in the context of credit card fraud detection. The analysis spans multiple performance metrics, including accuracy, F1 score, precision, recall, and computational efficiency. Logistic Regression demonstrates competitive performance in terms of accuracy, but its poor precision renders it unsuitable for fraud detection tasks. Conversely, the Neural Network exhibits balanced precision and …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
-
Energy-Efficient Design Strategies for Sustainable Machine Tool Development
Abstract: The design of energy-efficient machine tools is essential for promoting sustainable manufacturing, as it addresses the significant energy consumption and carbon emissions generated by these tools in industrial settings. This study investigates innovative design approaches that enhance energy efficiency, with a focus on lightweight materials, improved drive systems, smart control frameworks, and methods for recovering energy. Implementing lighter structural materials allows machine tools to operate with less force, which in …
Published in Trends in Machine design · Vol. 12, Issue 3, 2025 · pp. 30–34 Read article
-
Eco-Design Approaches in Modern Machine Tool Construction
Abstract: In the contemporary landscape of manufacturing, sustainability is no longer optional; it is imperative. Within this context, machine tool construction presents a unique set of challenges and opportunities: these heavy, energy-intensive pieces of capital equipment operate across long lifecycles, consume substantial embodied energy and materials, and eventually require disposal or remanufacture. This study explores eco-design approaches tailored specifically to modern machine tools, covering the full lifecycle from material sourcing and …
Published in Trends in Machine design · Vol. 12, Issue 3, 2025 · pp. 23–29 Read article
-
Machine Learning for Finding Materials for Membranes
Abstract: Traditionally, finding and improving membrane materials has depended on trial-and-error experiments, which can take a long time, cost a lot of money, and only cover a small area. Recent improvements in machine learning (ML) have the potential to change the way membrane materials are designed by making it possible to make predictions about performance, selectivity, and stability based on data. ML algorithms can find hidden links between the structure, composition, …
Published in International Journal of Membranes · Vol. 3, Issue 1, 2026 · pp. 1–7 Read article
-
Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 55–66 Read article
-
PREDICTIVE MAINTENANCE IN SEMICONDUCTOR SYSTEMS: INSIGHTS FROM MACHINE INTELLIGENCE AND DATA-DRIVEN METHODS
Abstract: With the fast-paced development of semiconductor technology comes the need to focus on device reliability, or how long devices will function and the likelihood of devices having operational issues. Predicting failures and avoiding downtime with the implementation of timely, actionable, and data-driven maintenance strategies are essential to insure devices function sustainably within predetermined performance levels. The implementation of predictive maintenance within artificial intelligence and machine learning technologies will provide the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 · pp. 1–9 Read article
-
Design & Implementation of 16-BIT MAC Unit Using Vedic Mathematics
Abstract: Multiply and Accumulate (MAC) units are essential parts of digital systems, particularly in embedded systems, digital signal processing (DSP), and image processing. The performance of these systems is significantly impacted by how well the multiplication process works. The design and construction of a fast 16-bit MAC unit utilizing Vedic mathematics are presented in this study.The proposed design utilizes the Urdhva Tiryagbhyam sutra to perform multiplication in a parallel manner, reducing …
Published in Journal of VLSI Design Tools and Technology · Vol. 16, Issue 1, 2026 · pp. 52–58 Read article
-
Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning
Abstract: The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 22–31 Read article
-
Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article
-
Mechanical Characterization and Machinability Optimization of Stir-Cast Al6061–SiC Metal Matrix Composites
Abstract: This study presents the fabrication, mechanical characterization, metallurgical analysis, and machinability optimization of Aluminum 6061 reinforced with Silicon Carbide (SiC) metal matrix composites (MMCs) at three weight fractions: 5%, 7.5%, and 10%. Composites were manufactured using the stir casting technique, followed by comprehensive mechanical testing (tensile, hardness, and impact), optical microscopy, and scanning electron microscopy (SEM). Machinability was assessed through turning experiments on a lathe using an L9 Taguchi orthogonal …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 833–848 Read article
-
Smart Wearable Health Pendant Using IoT and Machine Learning for Heart Rate Monitoring and Detecting Falls
Abstract: Cardiovascular illnesses and accidental falls frequently occur among individuals and at-risk populations. This is the reason we genuinely require a device that can monitor their well-being continuously. We are referring to a health pendant designed for seniors and groups at risk can wear. This health pendant leverages Internet of Things technology and machine learning to monitor heart rate and identify falls. Our health pendant system incorporates an ESP32 microcontroller, a …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 2, 2026 Read article
-
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
-
Analyzing and Predicting Academic Behavior from Peer Pressure Indicators Using Machine Learning
Abstract: The academic achievement of a student is determined by their capability, but also by the companions with whom they associate. Friends can have a positive impact on students' motivation for school, and at times friends are distractions leading to a lack of attention on their school assignments. This particular study focuses on the number and quality of companions students associate with and to what extent that could be used as …
Published in International Journal of Education Sciences · Vol. 3, Issue 2, 2026 · pp. 1–7 Read article
-
Graphene-Based Electronic Skin for Wearable Health Monitoring and Human–Machine Interaction, Materials, Structures, and AI Integration
Abstract: Graphene-based electronic skin (e-skin) has emerged as a transformative technology for next-generation wearable health monitoring and advanced human–machine interaction (HMI). Owing to its outstanding electrical conductivity, mechanical flexibility, atomic-scale thickness, and biocompatibility, graphene enables the fabrication of ultrathin, conformal, and multifunctional sensors capable of mimicking the sensory functions of natural human skin. Over the past decade, research in this domain has progressed rapidly across four interconnected fronts: material synthesis and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 175–180 Read article
-
Implementation of Renewable Energy Resource in 5-In-1 Agro-Machine
Abstract: Since agriculture is the backbone of Indian economy and above 70% of Indian people are depending on agriculture, the development of agriculture process takes a vital role. From few decades, even though improved agricultural machineries have been invented, they have not been reached. Agri-process is the most important endeavor in the world as it imparts about 8.4% to the total gross domestic product and provides employment to over 60 of …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 10, Issue 2, 2019 · pp. 35–39 Read article
-
CONDITION BASED MONITORING AND FAULT DIAGONISTICS OF A MACHINE
Abstract: This piece of study shows the existing condition based monitoring and fault diagnostics techniques in a machine. Vibrations are produced in every machine and their amplitude depends upon the type of faults present in the machine, for example, gear fault, journal bearing, rolling contact bearing, flexible coupling and electrical machine faults. The main challenge for the condition monitoring of the machines is the cost and complexity of condition machine monitoring …
Published in Journal of Experimental & Applied Mechanics · Vol. 9, Issue 2, 2018 · pp. 84–89 Read article
-
Optimization of Machining Parameters for Minimum Surface Roughness Indicators during the Turning of AISI 316 L Stainless Steel
Abstract: Turning is the most common process associated with the production of cylindrical shapes because of its simplicity, rapidity and economy. However, it is one of the most complex cutting processes. Turning is considered to be a complementary process of other more important processes, and yet 70% of the generated chip comes from this cutting technique. Quality plays important role in manufacturing industry. In on-line quality control, controller is provided with …
Published in Journal of Mechatronics and Automation · Vol. 7, Issue 1, 2020 · pp. 24–30 Read article
-
Optimization of Machining Process Parameters in Hard Turning of AISI D3 Tool Steel
Abstract: Machining operation is still irrefutably vital process in manufacturing, that involves complex relation of input parameters (like cutting speed, feed rate, depth of cut, nature of cooling, etc.) to output parameters (like surface finish, tool life, tool wear, machining time, machining cost, etc.). Numerous efforts were noticed in literature, to relate these parameters with each other so as to implement cost effective machining with least efforts. This paper presents one …
Published in Journal of Mechatronics and Automation · Vol. 4, Issue 1, 2017 · pp. 23–30 Read article
-
Compensation of Volumetric Error in CNC Machine Tools: A Review
Abstract: In today’s high competitive market, the customer requirement for quality products has increased rapidly. Abundant researches are carried out to make machine tools more versatile and accurate in the fulfilment of such requirements. Now days the CNC machine tools of various configurations with more motion flexibility are used to prepare high quality in machined components. But still, the accuracy is not achieved up to desirable level for some of crucial …
Published in Journal of Mechatronics and Automation · Vol. 4, Issue 2, 2017 · pp. 12–20 Read article