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153 articles for “Machine learning”
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Predictive Analytics in E-Learning: Enhancing Student Performance Through Machine Learning Models
Abstract: With the explosion of electronic devices and internet technologies in today’s world, the amount of data we store has grown massively. Data is simply information that has been organized in a way that makes it easy to share or work with. A potent technology that enables computers to learn from data without direct programming is machine learning, which we employ in our research. The focus of this study is on …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 10, Issue 2, 2024 · pp. 1–5 Read article
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Unlocking Insights, Optimizing Processes: A Review Of Machine Learning In Action
Abstract: Machine learning (ML) has become a disruptive technology with applications in many different fields and sectors. Machine learning techniques are transforming the way data is examined, patterns are found, and decisions are made in a variety of industries, including healthcare, finance, manufacturing, and transportation. ML is improving drug discovery, treatment strategies, and diagnostics in the healthcare industry. In the financial sector, this means maximizing risk mitigation, credit scoring, and fraud …
Published in International Journal of Power Electronics Controllers and Converters · Vol. 10, Issue 1, 2024 · pp. 14–22 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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Advances in Machine Learning and Data Analytics: A Multidisciplinary Synthesis
Abstract: Machine learning has emerged as a key driver of innovation across a wide range of fields. Its transformative potential is evident in natural language processing, computer vision, and real-time automation, where it addresses intricate challenges with remarkable efficiency. This paper delves into recent developments in machine learning, spotlighting techniques, such as semi-supervised learning frameworks, stable diffusion models for generative tasks, and advanced lightweight architecture for real-time defection detection. By integrating …
Published in International Journal of Distributed Computing and Technology · Vol. 11, Issue 1, 2025 · pp. 12–17 Read article
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A Role of Machine Learning for Students' Academic Success Prediction
Abstract: This research paper aims to identify early predictions of students’ academic success with the help of different indicators. One of the crucial parts of the study is identifying additional indicators that play an essential role in defining students' academic success. Early prediction of success will be helpful for various stakeholders such as students, parents, educators/teachers, institutions, universities, and the government. Machine Learning is a part of Artificial Intelligence (AI) and …
Published in International Journal of Computer Aided Manufacturing · Vol. 10, Issue 1, 2024 · pp. 1–6 Read article
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Machine Learning Approaches for Customer Churn Prediction in Telecommunications
Abstract: Customer churn prediction is a critical challenge for companies in the telecommunications industry. The competition is intense, and acquiring new customers can be costly. Predicting when existing customers might leave (churn) helps companies take proactive measures to retain them, reducing churn rates and costs. By analyzing historical customer data, including demographics, service usage, billing, and customer service interactions, this study aims to identify customers at risk of leaving their current …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 10, Issue 2, 2024 · pp. 1–7 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
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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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Predictive Models for Drinking Water Quality: Leveraging Machine Learning for Improved Safety and Efficiency
Abstract: Quality of drinking water is one of the critical determinants of public health, leading to severe environmental and health-related issues when poor. The paper examines the integration of machine learning techniques that could improve accuracy and efficiency in assessing drinking water quality. The traditional techniques of monitoring drinking water are resource-intensive yet lack timely insight into contamination events. In this scenario, an evaluation of the performance of ML models such …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 1, 2025 Read article
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Securing Smart Electrical Systems through Comparative Analysis of Machine Learning Models for Classification
Abstract: In the realm of cybersecurity, the intersection of technology and security measures has spurred significant advancements, particularly in safeguarding electrical systems against cyber threats. This research delves into the evolving landscape of cybersecurity within electrical systems, leveraging machine learning techniques to mitigate challenges. By incorporating cybersecurity protocols with machine learning algorithms, this study demonstrates enhanced security measures tailored for electrical infrastructures, pivotal in ensuring efficiency and resilience. Through meticulous examination, …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 2, 2026 Read article
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Network Intrusion Detection Using Wireshark and Machine Learning
Abstract: The growth of networked devices has highlighted the desire for advanced intrusion detection (IDS) tools to protect digital systems from evolving cyber threats. Traditional IDS systems are often difficult to adapt to the threat environment because they rely on predefined signature lists. This study presents a new approach that combines Wireshark, a widely used network packet analysis tool, with advanced machine learning for intrusion detection. Our system leverages Wireshark's data …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 10, Issue 1, 2024 · pp. 23–31 Read article
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Climate Change Modeling Using Machine Learning and Big Data
Abstract: Climate change is now one of the biggest issues that affect the world today and studying its impacts for it is very significant in enabling the formulation of social policies. This paper also discusses how climate sciences, machine learning, and big data analytics can greatly help in the improvement of the current capacities in climate modeling. Explaining the methods of climate modeling, we look at how the big data offers …
Published in International Journal of Software Computing and Testing · Vol. 11, Issue 1, 2025 · pp. 6–11 Read article
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A Hybrid Approach to Secure Cloud Storage: Machine Learning and Blockchain Integration
Abstract: With the rapid adoption of cloud computing across industries, securing cloud storage has become a paramount concern for organizations that rely on cloud-based systems to manage sensitive data. Traditional security approaches often struggle with the complex and evolving nature of cyber threats, leading to vulnerabilities that malicious actors can exploit. In response to these challenges, this paper proposes a hybrid approach that integrates machine learning and blockchain technologies to create …
Published in International Journal of Distributed Computing and Technology · Vol. 10, Issue 2, 2024 · pp. 35–47 Read article
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Predicting User Engagement on Social Media: A Comparative Study of Machine Learning-based Modeling Approaches
Abstract: Abstract Even while mobile social apps are becoming more and more important in people's daily lives, little is known about the elements that motivate users to interact with these apps. The goal of this work is to analyze and define the transition patterns of individual users' in-app actions as a temporally changing action graph. According to our investigation, action graphs are a useful tool for characterizing user behaviour patterns and …
Published in International Journal of Computer Aided Manufacturing · Vol. 11, Issue 2, 2025 · pp. 41–62 Read article
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An Overview on Metal Processing using Machine Learning
Abstract: Metalworking, which is an important part of modern industry, includes a wide range of steps, such as shaping and casting the metal, machining it, and riveting it. The industry has always relied on trial and error and other empirical methods, but machine learning (ML) is now causing a big change. It is expected that this change will lead to higher production, better product quality, and better use of resources. The …
Published in International Journal of Manufacturing and Materials Processing · Vol. 12, Issue 1, 2026 · pp. 43–53 Read article
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Integrating Machine Learning with Edge Computing for Real Time Data Analytics
Abstract: Smart devices such as mobile phones, sensors, cameras, etc., are producing a huge amount of data every second. This data is normally sent to cloud servers for processing. This approach is normally slow and expensive, and it also raises a lot of privacy issues as data is sent over the internet, which takes a lot of time to return with results. This study aims to address the issue through the …
Published in International Journal of Distributed Computing and Technology · Vol. 12, Issue 2, 2026 Read article
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A Study on Machine-Learning-Driven Insight into theThermal Behavior of Polymers and Composite Materials
Abstract: The thermal performance of polymeric and composite systems underpins their suitability for aerospace, electronics, and energy‑storage applications, yet reliable prediction of temperature‑dependent properties remains a formidable challenge. In this work we present a data‑driven framework that harnesses modern machine‑learning (ML) techniques to forecast key thermal metrics – glass‑transition temperature (Tg), melting point (Tm), thermal conductivity (k), and decomposition onset (Td) – from a chemically informed descriptor space. A curated database …
Published in International Journal of Polymer Science and Engineering · Vol. 12, Issue 1, 2026 · pp. 1–11 Read article
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Personalized Movie Recommendations: Leveraging Machine Learning to Enhance Viewer Experience
Abstract: The film industry has experienced a tremendous surge in content creation over the past few decades, leading to an overwhelming array of movies across various genres and languages. As a result, movie enthusiasts often find it challenging to discover films that align with their unique preferences and tastes. To address this issue, the application of machine learning techniques for movie recommendation has gained prominence. An introduction to the idea and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 10, Issue 2, 2024 · pp. 1–6 Read article
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Evaluation of Machine Learning and Deep Learning Models for Classification Tasks on Crime Data: A Comparative Study
Abstract: The classification of crime data has grown more and more important for public safety and law enforcement due to the increasing demand for precise and effective predictive models. To classify crime data, this study assesses and contrasts the effectiveness of several machine learning (ML) and deep learning (DL) models, such as decision trees, support vector machines, neural networks, and convolutional neural networks. The study evaluates the models' accuracy, efficiency, and …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 1, 2025 · pp. 1–15 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