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185 articles for “machining time”
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Intelligent Nanobots: Real-Time Framework for Molecular Pollution Detection and Health Risk Mitigation
Abstract: Nanobots are machines made at a very small scale. They can do jobs. These machines are very versatile and accurate. They are helping to start an era in cleaning up the environment. Nanobots can focus on pollutants in the environment. Their tiny structures can. Break down bad stuff at the molecular level. They can reach places that regular cleaning methods cannot. Water pollution is a problem. Water pollution is mostly …
Published in International journal of Nanobiotechnology · Vol. 12, Issue 2, 2026 Read article
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Brain Tumor Detection using Machine Learning Methods-KNN, SVM, DT, RF
Abstract: Brain tumor is a major contributor to illness and death worldwide, and timely diagnosis is essential for successful treatment and improved outcomes Recent research has looked into the possibility of machine learning algorithms for detecting and classifying brain tumours in medical pictures. In this study, four commonly used machine learning algorithms (K-Nearest Neighbours, Decision Trees, Support Vector Machines, and Random Forests) were evaluated for their effectiveness in brain tumor detection. …
Published in International Journal of Chemical Engineering and Processing · Vol. 9, Issue 2, 2023 · pp. 48–59 Read article
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Development of Smart Fire Extinguishing
Abstract: The invention of smart fire extinguishing agents, which satisfy the growing need for efficient, eco-friendly, and adaptable fire suppression systems, is a significant advancement in fire safety. Traditional fire extinguisher chemicals, such as water, foam, and chemical powders, are effective, but they often have disadvantages, including toxicity, negative environmental effects, and inefficiency in difficult situations. Conversely, smart extinguishing agents maximize fire suppression through intelligent, tailored responses by utilizing advancements in …
Published in International Journal of Environmental Chemistry · Vol. 11, Issue 2, 2025 · pp. 1–19 Read article
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Experimental Investigation And Optimization Of Process Parameters For Thrust Force, Power Consumption And Energy Consumption During Drilling Of Mild Steel On Lathe Machine
Abstract: Drilling is one of the major operations to be carried out in almost every manufacturing industry. During this operation drilling tool drills the metal continuously which increases thrust force. Due to this force, power consumed during drilling operation. So in this research work an attempt to experimentally investigate and then optimization of the effect of different drill parameter like feed and spindle speed on the thrust force, power consumption and …
Published in International Journal of Mechanics and Design · Vol. 2, Issue 1, 2016 · pp. 08–19 Read article
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A Review on Crypto coin price prediction
Abstract: Due to its advantages over the established banking system, cryptocurrencies have experienced a sharp increase in price inrecent years, which has had a positive impact on the economy. As cryptocurrencies are organized by blockchain technology, itinherits its properties such as Decentralization, Transparency, Immutability. In recent years, investors and stakeholdershave become increasingly interested in machine learning and AI-assisted trading. The effectiveness of machine learningtechniques in predicting the stock market leads one …
Published in International Journal of Distributed Computing and Technology · Vol. 8, Issue 1, 2022 · pp. 37–42 Read article
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The Implementation of Machine Learning in the Search to Fight Plant Disease
Abstract: The agriculture business loses money and time due to plant diseases. Diagnosing disease accurately takes skill and devotion. Infected plants may have spots or streaks of a different color on their leaves. Several fungal, bacterial, and viral species can also infect plants. Individual plant disease signs and indications are assessed. Neural network applications are growing. Recent research studies have determined how well machine learning reviews traditional plant disease diagnosis methods. …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 1, 2024 · pp. 25–34 Read article
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A study AI-Driven IoT in Self-Healing Grid Power Systems
Abstract: Modern electricity infrastructure is undergoing a transformation thanks to the combination of artificial intelligence (AI) and the Internet of Things (IoT), especially in the development of self- healing grid systems. In the context of smart grid automation and resilience, this article provides a thorough review of AI-driven IoT infrastructures. By leveraging distributed sensor networks, real-time data acquisition, and advanced KSK Approach, and machine learning algorithms—includes ANN, DT and K-NN—the proposed …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 15–24 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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Leveraging Artificial Intelligence for Traffic Flow Optimization: A Case Study Approach
Abstract: AbstractTraffic congestion poses a significant challenge in urban environments, contributing to economiclosses, increased fuel consumption, environmental pollution, and diminished quality of life. Traditionaltraffic management approaches, such as pre-timed traffic signals and manual interventions, often failto adapt dynamically to fluctuating traffic conditions. This study explores the integration of artificialintelligence (AI) in traffic management to optimize traffic flow and reduce congestion. By leveragingAI-driven predictive models, deep reinforcement learning, and real-time traffic data, …
Published in International Journal of Transportation Engineering and Traffic System · Vol. 11, Issue 1, 2025 · pp. 37–42 Read article
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Parameter Optimization of Electro Discharge Machining of AISI P20 Steel Material
Abstract: In this paper, the cutting of P20 Steel material using electro discharge machining (EDM) with a copper electrode by applying Taguchi methodology has been reported. The Taguchi method is used to formulate the experimental layout, to analyze the effect of each parameter on the machining characteristics, and to predict the optimal choice for each EDM parameter for example peak current, gap voltage, duty cycle and pulse on time. It is …
Published in International Journal of Mechanics and Design · Vol. 3, Issue 2, 2017 · pp. 7–16 Read article
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Bank Loan Approval Automation
Abstract: Because of the pandemic everyone need loan to grow their business so lot of people apply for the bank loan but bank has its limited assets which it has to grant to limited people only, so finding out the person who return the loan in a given time is a difficult job. So Here our machine learning model come in to picture the main motive of our model is try …
Published in International Journal of Software Computing and Testing · Vol. 7, Issue 1, 2021 · pp. 16–20 Read article
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SMART SURVEILLANCE: AI-DRIVEN DRIVER DROWSINESS DETECTION SYSTEM
Abstract: In this study, a real-time application for driver drowsiness detection based on machine learning and deep learning is developed. Accidents, including traffic accidents, are now a frequent cause of fatalities and injuries among people. One of the major contributing factors to traffic accidents and a significant threat to road safety is drowsy driving. As a result, there are more people dying and getting hurt every year. Serious traffic accidents have …
Published in International Journal of Computer Aided Manufacturing · Vol. 9, Issue 1, 2023 · pp. 29–35 Read article
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Deep Learning for Smarter Farming: Innovations in Plant Disease Identification
Abstract: Agriculture is the backbone of global food security, yet plant diseases significantly impact crop yields and farmers' livelihoods. Traditional disease detection methods are often time-consuming and require expert intervention, making them less feasible for large-scale farming. Recent advancements in deep learning have revolutionized plant disease identification by offering automated, accurate, and efficient solutions. This article explores innovative deep learning techniques, including convolutional neural networks (CNNs) and transformer-based models, that enhance …
Published in International Journal of Microwave Engineering and Technology · Vol. 11, Issue 1, 2025 · pp. 27–32 Read article
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IMPLEMENTATION OF VOTING MACHINE TO AVOID RIGGING USING VERILOG HDL
Abstract: With almost 970 million eligible voters, India holds the title of the largest democratic nation. The traditional voting method that used paper was a lengthy process and highly susceptible to mistakes. Electronic voting machines (EVMs) make voting easy, safe, and quick. It decreases the human effort, errors in the time of vote counting. But in the existing EVM there is lack of authentication, i,e one’s vote can be stolen by …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 12, Issue 1, 2026 · pp. 27–32 Read article
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A Comprehensive Review of IoT-Enabled RC Boats for Real-Time Water Pollution Monitoring
Abstract: For all people today, drinking water is the most significant and precious resource. Real-time operations present new difficulties for drinking water utilities. Due to factors including dwindling water supplies, population growth, deteriorating infrastructure, etc., this problem developed. Better methods of checking the quality of the water are therefore required. The traditional approach to measuring water quality entails manually collecting water samples at various sites, followed by laboratory analytical procedures to …
Published in International Journal of Microwave Engineering and Technology · Vol. 8, Issue 2, 2022 · pp. 20–27 Read article
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Support Vector Machine-based Flower Image Classification for Commercial Applications
Abstract: Flowers are crucial raw materials in many industries, including pharmaceuticals and cosmetics. Classifying flowers manually is time-consuming, inconsistent, and requires professional judgment from a botanist. Computer-aided flower classification is a promising approach to this problem. Image classification often employs support vector machines (SVMs) as widely favored algorithms. This study evaluated the performance of SVMs for flower image classification. The results showed that SVMs can achieve good accuracy on this task, …
Published in International Journal of Software Computing and Testing · Vol. 9, Issue 2, 2023 · pp. 44–53 Read article
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The Impact of CAM on Digital Twin Technology inManufacturing Systems
Abstract: The integration of Computer-Aided Manufacturing (CAM) and Digital Twin technology is revolutionizing the manufacturing landscape by bringing together the strengths of process automation, real-time monitoring, and predictive analytics. CAM, which has long been used to optimize machining processes, automate toolpath generation, and control production workflows, plays a vital role in improving manufacturing efficiency and precision. On the other hand, Digital Twin technology, which creates a virtual replica of physical systems, …
Published in International Journal of Computer Aided Manufacturing · Vol. 10, Issue 2, 2024 · pp. 6–10 Read article
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Maculopathy Detection and Recommendation using Machine Learning
Abstract: Maculopathies, a diverse group of eye diseases affecting the central portion of the retina, the macula, are a leading cause of vision loss globally. Early detection and accurate diagnosis are critical for timely intervention and preventing irreversible vision loss. However, traditional methods such as manually detecting the disease are time-consuming, tiring, and often lack sensitivity and specificity, hindering optimal patient care. Here we delve into the applications of machine learning …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 10, Issue 1, 2024 · pp. 10–16 Read article
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Health Monitoring System for Fault Identification on Centrifugal Pump Using ML
Abstract: Centrifugal pumps are crucial equipment widely used in various industries, and their reliable operation is essential for maintaining production and preventing costly downtimes. This paper proposes a comprehensive health monitoring system for centrifugal pumps that combines model-driven and data- driven approaches, leveraging artificial intelligence (AI) and machine learning (ML) techniques. The system incorporates demand prediction using artificial neural networks and fault diagnosis employing different ML classifiers, such as decision trees …
Published in International Journal of Mechanics and Design · Vol. 10, Issue 2, 2024 · pp. 1–12 Read article
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article