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513 articles for “Machining Techniques”
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A Review of Machine and Deep Learning Techniques for Cyber Security
Abstract: Nowadays in the digital landscape, cyber threats and attacks are increasing in an exponential manner, posing server risks to organizations and critical infrastructures. Data breaches often result from sophisticated threat models that exploit vulnerabilities in networks, systems and user behaviors. Cyber solutions are increasingly incorporating machine learning and deep learning to prevent and mitigate these security issues. These technologies have the potential to detect anomalies, classify threats and predict potential …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 01–07 Read article
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Leafguard: Smart Plant Health Detection
Abstract: Machine learning techniques, including traditional (shallow) ML, deep learning (DL), and augmented learning (AL), are being increasingly utilized for leaf disease classification. These methods involve feature extraction, data augmentation, and transfer learning to enhance model effectiveness and reduce the need for labeled data. The success of machine learning approaches in this domain hinges on the quality and quantity of data available. LeafGuard is a cutting-edge device with intelligent sensing systems …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 32–39 Read article
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Transcript Summarizer of YouTube Videos Using Deep Learning
Abstract: Transcriber Sum is a deep learning-based YouTube transcript summarization tool. It employs advanced machine learning techniques to automatically generate concise summaries of YouTube video transcripts, enabling users to quickly grasp the key content and insights of videos without the need to watch or read the entire transcript. This Transcriber Sum addresses the challenge of providing users with concise and informative summaries of video content by harnessing the power of deep …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 119–126 Read article
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A Comprehensive Review of Machine Learning and Explainable AI Techniques for Disease Prediction Systems
Abstract: Large amounts of diverse medical data have been produced because of the quick development of digital healthcare systems, offering substantial chances to use machine learning methods for clinical decision support and illness prediction. By identifying intricate patterns in clinical data, machine learning-based models have shown great promise in early disease detection, risk assessment, and personalised healthcare. However, issues with transparency, interpretability, and reliability have been brought up by the growing …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 4, Issue 1, 2026 · pp. 20–28 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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Optimization of Lightweight Polymer Composites Using Finite Element Analysis Machine Learning and Topology Optimization Techniques for Aerospace Applications
Abstract: The advancement of aerospace engineering depends on lightweight polymer matrix composites (PMCs) because they help decrease weight while improving fuel efficiency and payload capacity together with increased structural integrity. Research developed a computer program comprising FEA with ANN and TO optimize high-performance PMCs through integrated design approaches. The combination of Python-controlled LS-DYNA simulations measured hybrid composite laminate resistance to impact while an ANN model obtained data from simulations to forecast …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 693–709 Read article
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AI and Machine Learning Approaches for Estimating Depression Severity: Techniques, Trends, and Applications
Abstract: Depression is a very common mental health disorder that results in a disorder of a person’s behavior, emotions, and cognitive abilities. Depression can be caused by environmental factors or hereditary factors. The person suffering from depression might have symptoms of suicidal thoughts, altering food patterns as well as sleeping issues. Depression is a global issue that has impacted millions of people globally having more effect on women worldwide. The complexity …
Published in Journal of Electronic Design Technology · Vol. 15, Issue 3, 2024 · pp. 29–38 Read article
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A Comprehensive Ahp-Topsis Approach For Non-Traditional Machining Process Selection In The Manufacturing Sector
Abstract: Non-traditional machining processes (NTMPs) are being used more widely in modern industry as a result of the increased use of advanced materials in that sector. Selecting the best Non-Traditional Machining Process (NTMP) is essential to a manufacturing company's success and competitiveness. One way to think of the process of choosing the best NTMP for a given machining processes application is as a multi-criteria decision-making (MCDM) problem with a lot of …
Published in International Journal of Manufacturing and Production Engineering · Vol. 2, Issue 1, 2024 · pp. 1–8 Read article
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Experimental Investigations of EDM Parameters on Machining Square Blind Holes in Maraging Steels
Abstract: Square blind holes have certain qualities that make them useful in fields where accuracy and efficiency are crucial, like aerospace, automotive, molding, and general manufacturing industries where structural integration is required in precise assembly. It is challenging to machine square blind holes with traditional machining due to geometrical complexity as precision is required for sharp corners which is difficult to get at the corners due to tool wear. Electrical discharge …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 390–397 Read article
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Integration of Taguchi and MCDM Techniques for the Optimization of Experimental Parameters in Electrical Discharge Machining: A Research
Abstract: Electric discharge machining (EDM) represents a non-conventional approach to machining, particularly beneficial for processing hard-to-machine materials or components with high length-to-diameter ratios or intricate shapes. Widely employed across various industries such as automotive, chemical, aerospace, biomedical, and tool and die, EDM offers a unique method for achieving precise shapes and dimensions. Unlike traditional machining methods where form is attained through the interaction of the tool and workpiece, EDM operates without …
Published in Trends in Mechanical Engineering & Technology · Vol. 14, Issue 1, 2024 · pp. 6–14 Read article
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Optimum Machining Parameters for Al 7075 Hybrid Metal Matrix Composites Using Multi-objective Optimization Technique and the Modified Taguchi Approach
Abstract: Lightweight composite materials with improved mechanical properties are widely used in industries. There is a need to obtain optimum machining parameters of such hybrid composites. This paper uses reliable multi-objective optimization technique and modified Taguchi approach to determine optimal machining parameters such as speed (NS) varying from 1000 rpm to 1500 rpm, feed rate (FR) from 0.10 mm/rev to 0.20 mm/rev, depth-of-cut (DC) varied from 0.5 mm to 1.5 mm …
Published in Journal of Polymer & Composites · Vol. 11, Issue 8, 2023 · pp. 269–278 Read article
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Hybrid Machining Processes in Advanced Manufacturing: A Review of Mechanisms and Industrial Applications
Abstract: Hybrid machining processes (HMPs) have gained considerable attention in recent years as an effective approach to address the growing complexity and performance demands of modern manufacturing systems. These processes combine two or more machining techniques—such as mechanical, thermal, chemical, or electrical methods—into a single setup, enabling enhanced productivity, precision, and adaptability, particularly for hard-to-machine materials like ceramics, composites, and superalloys. The integration of distinct energy sources results in synergistic effects …
Published in Journal of Production Research & Management · Vol. 15, Issue 2, 2025 · pp. 19–24 Read article
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Fraud Detection in Government Procurement Using Machine Learning
Abstract: Fraud represents a significant challenge in the realm of procurement, with estimates indicating that between 12 and 30% of global procurement budgets are lost to fraudulent activities (OECD, 2023). The pervasive nature of procurement fraud, which may encompass a range of deceptive practices such as bid rigging, invoice fraud, and procurement kickbacks, not only undermines the integrity of financial operations but also results in substantial losses for organizations. These losses …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 19–34 Read article
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Content-based Image Retrieval: Recent Trends and Techniques
Abstract: Due to the affordability of digital devices and the accessibility of internet technologies, a vast number of multimedia databases have been established for various applications. These image databases increase the need for efficient picture retrieval search strategies that meet user requirements. When compared to other systems, the content-based image retrieval (CBIR) system is one of the most widely used systems for retrieving images from enormous databases. Much work has been …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 1, 2025 · pp. 01–32 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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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
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Using Machine Learning for Key phrase Extraction in Digital Libraries
Abstract: Machine learning has revolutionized various aspects of information retrieval, including key phrase extraction in digital libraries. Key phrase extraction is crucial for summarizing and categorizing vast amounts of textual data, enabling efficient search and retrieval processes. This study explores the application of machine learning techniques for automatic key phrase extraction in digital libraries. We review various supervised and unsupervised learning algorithms, including deep learning models, that are employed to identify …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 8–13 Read article
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Investigating the Influence of Process Parameters on Photochemical Machining of Phosphor Bronze Alloy Microchannels
Abstract: Microchannels are widely employed in microfluidic devices, biomedical systems, and compact heat exchangers, where their functional efficiency depends strongly on surface finish, dimensional control, and edge quality. Traditional machining techniques often face limitations in producing such features with the required precision, prompting the use of advanced micromachining methods. In the present work, photochemical machining (PCM) has been applied to fabricate serpentine-shaped microchannels in phosphor bronze. The study systematically investigates the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 837–846 Read article
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Sentiment Analysis of E-Commerce Reviews using Machine Learning
Abstract: In e-commerce, sentiment pertains to the emotional responses, opinions, or perceptions that customers have about their online shopping experiences, including factors like product quality, service, and various processes such as ordering, shipping, and customer support. Sentiment analysis, which involves machine learning techniques, plays a crucial role in deciphering these sentiments. By using sentiment analysis, companies can obtain valuable insights from customer feedback from diverse online sources, including social media, surveys, …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 3, 2024 · pp. 25–37 Read article