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104 articles for “machine learning algorithms”
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Machine Learning Approach for Analysis of Ionosphere Parameters for Earthquake Precursors
Abstract: This research explores the relationship between seismic events and predictive indicators, focusing on machine learning-based strategies for early earthquake prediction. It dissects existing prediction approaches, highlighting ionospheric anomalies' correlation with seismic occurrences, particularly preceding earthquakes of magnitudes higher than 5.5. By identifying a lack of comprehensive long-term analyses in the field, the study emphasizes future trends in machine learning-driven EQ-PD techniques using GPS-TEC data for real-time anomaly detection. The methodology …
Published in International Journal of Geological and Geotechnical Engineering · Vol. 9, Issue 2, 2023 · pp. 37–46 Read article
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Review of Feature Selection Methods and Semi Supervised Feature Selection Algorithms for Classification
Abstract: Current evolution in technology has resulted in tremendous growth of data with respect to dimensionality and sample size. Knowledge discovery from these high dimensional data are becoming cumbersome. Though powerful machine learning algorithms are prevalent, noisy data, imperfect sensing and collection of data leads to poor and defective knowledge discovery. Noise and redundant features cannot be circumvented due to which data collection process is biased. Dimensionality reduction techniques namely feature …
Published in International Journal of Software Computing and Testing · Vol. 6, Issue 1, 2020 · pp. 39–51 Read article
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A Sensor Integrated Tool for Cutting Force Monitoring in Gantry Milling to Improve Flatness and Surface Finish: Review Study
Abstract: AbstractPrecision machining of sector plates plays a vital role in the efficient functioning of Air Pre-Heaters(APH), which are integral components of thermal power plants. These plates are subjected to harshthermal cycles and mechanical loads, making dimensional accuracy critical. For optimal heatexchange performance and mechanical reliability, it is essential to maintain tight manufacturingtolerances – flatness within ±400 microns and thickness up to 13 mm. Deviation from theseparameters can lead to air …
Published in International Journal of Manufacturing and Materials Processing · Vol. 11, Issue 1, 2025 · pp. 25–45 Read article
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Advancing Early Detection of Menkes Disease Through Image Analysis and Biosensor Integration
Abstract: Menkes disease, a rare X-linked recessive disorder of copper metabolism, poses significant challenges in early diagnosis due to its nonspecific initial symptoms. This paper presents a comprehensive approach to enhance the early detection of Menkes disease by leveraging image processing techniques, pattern recognition algorithms, and innovative biosensor technologies. A novel framework is proposed that combines analysis of hair microscopy images, facial feature recognition, and copper-sensitive biosensors to create a multi-modal …
Published in International Journal of Image Processing and Pattern Recognition · Vol. 10, Issue 2, 2024 · pp. 24–34 Read article
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The Role of Artificial Intelligence in Mental Health: New Approaches for Diagnosis and Treatment
Abstract: Artificial intelligence (AI) is increasingly reshaping mental health care by supporting early identification of mental health conditions, improving diagnostic accuracy, and enabling personalized intervention strategies for disorders such as depression, anxiety, and post-traumatic stress disorder. Advanced AI technologies, including machine learning algorithms and natural language processing techniques, allow analysis of large-scale behavioral, textual, and speech-based data to identify patterns related to emotional and cognitive health. These tools help clinicians make …
Published in International Journal of Software Computing and Testing · Vol. 12, Issue 1, 2026 · pp. 22–26 Read article
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Recommendation System: An Algebraic Perspective of Machine Learning with Knowledge Endorsement
Abstract: ABSTRACTLearning is continual process and machine learning is innovative approach towards information management as well as knowledge management. The endorsement of knowledge in association of big data is crucial task. The machine learning algorithm and technologies gives an insight towards better expansions and consolidation of knowledge. For optimization of knowledge recommendation system plays an important role and gives ground to produce solution. The role of recommendation system is basically one …
Published in International Journal of Distributed Computing and Technology · Vol. 6, Issue 2, 2020 · pp. 30–34 Read article
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Bot Defender: A Collaborative Defense Framework for Botnet Intrusion Mitigation
Abstract: Botnet attacks represent a significant threat to cybersecurity, compromising vast numbers of devices and causing extensive harm. BOT DEFENDER is a novel collaborative defense framework designed to mitigate botnet intrusions effectively. This framework leverages the power of collective intelligence, integrating data from multiple sources to detect and respond to botnet threats in real-time. By utilizing advanced machine learning algorithms, BOT DEFENDER can identify anomalous network behavior indicative of botnet activities, …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 · 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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COVID-19 Detection Using Decision Tree Support Vector Machine Random Forest.
Abstract: The rapidly spreading novel coronavirus (SARS-CoV-2) presents a substantial global health risk, necessitating swift detection through efficient diagnostic methods. Reports highlight lung-related issues in many COVID-19 patients, and both chest CT scans and X-rays are effective in identification. Chest X-ray (CXR) imaging, due to its convenience, faster imaging, simplicity, and lower cost, stands out. In our study, we applied Decision Tree, Random Forest, and Support Vector Machine to identify COVID-19 …
Published in International Journal of Embedded Systems and Emerging Technologies · Vol. 9, Issue 2, 2023 · pp. 7–21 Read article
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Unlocking the Potential: Machine Learning Driven Explainable AI Applications Across Diverse Domains
Abstract: Explainable artificial intelligence (XAI) aims to make discoveries and output generated by machine learning algorithms accessible to human users in a trustworthy and easy-to-understand manner. It describes the impact of an AI model and any potential biases, helping in characterizing models with accuracy, fairness, transparency, and outcomes in decision-making powered by AI. XAI focuses on understanding the data used to train the model, its predictions, the specific elements involved in …
Published in International Journal of Software Computing and Testing · Vol. 9, Issue 1, 2023 · pp. 24–32 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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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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AI-Assisted Conversion and Optimization of Analog-to-Digital Signal Transmission forLow-Power IoT and Edge Devices
Abstract: The booming implementation of Internet of Things (IoT) and edge computing devices is requiring theimplementation of efficient signal transmission mechanisms to balance between performance and energyefficiency. The current paper introduces an AI-assisted conversion and optimization system of the analog-to-digital (A/D) signal transmission, aimed at low-power IoT and edge devices. The given strategy combines both machine learning algorithms and adaptive sampling and quantization methods to optimize the A/D conversion process in …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 11, Issue 2, 2025 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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A Machine Learning Framework for Fake News Detection Using Embeddings and Dimensionality Reduction
Abstract: The broad sharing of information throughout human history was greatly accelerated by the emergence of the World Wide Web and the swift rise of social networking platforms such as Facebook and Twitter. Due to the extensive use of social media, users now generate and distribute vast amounts of content at an unprecedented rate, and a portion of this information is inaccurate or detached from factual reality. Automatically classifying a written …
Published in International Journal of Software Computing and Testing · Vol. 12, Issue 1, 2026 · pp. 8–14 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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The Role of Artificial Intelligence in Mental Health: New Approaches for Diagnosis and Treatment
Abstract: Artificial intelligence (AI) is increasingly reshaping mental health care by supporting early identification of mental health conditions, improving diagnostic accuracy, and enabling personalized intervention strategies for disorders such as depression, anxiety, and post-traumatic stress disorder. Advanced AI technologies, including machine learning algorithms and natural language processing techniques, allow analysis of large-scale behavioral, textual, and speech-based data to identify patterns related to emotional and cognitive health. These tools help clinicians make …
Published in International Journal of Software Computing and Testing · Vol. 12, Issue 1, 2026 Read article
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Hybrid Signal Processing Framework for Seamless Integration of Analog and Digital Communication Systems in 6G Networks
Abstract: The next 6G phase of evolution of the communication system is expected to deliver data speedexceeding terabits per second, very low latency, and connectivity everywhere among heterogeneous devices. These performance goals can only be met through signal processing architecture paradigm shift. The paper will describe a Hybrid Signal Processing Framework (HSPF) which combines the analog and digital communication methods to leverage the complementary benefits of the two spheres. The framework …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 2, 2025 · pp. 28–42 Read article
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Integration of CAD – CAM SYSTEM FOR NEXT – Generation smart manufacturing
Abstract: Abstract The emergence of Industry 4.0 has transformed traditional manufacturing by promoting automation, intelligence, and connectivity across production systems. Integrating Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) has become essential for achieving seamless data exchange, efficient product design, and precise manufacturing operations. This integration enables a unified digital workflow that connects design intent directly to machining processes, thereby reducing errors, improving productivity, and shortening product development cycles. Advanced technologies such …
Published in International Journal of Computer Aided Manufacturing · Vol. 11, Issue 2, 2025 · pp. 1–8 Read article
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Hybrid Signal Processing Framework for Seamless Integration of Analog and Digital Communication Systems in 6G Networks
Abstract: The next 6G phase of evolution of the communication system is expected to deliver data speed exceeding terabits per second, very low latency, and connectivity everywhere among heterogeneous devices. These performance goals can only be met through signal processing architecture paradigm shift. The paper will describe a Hybrid Signal Processing Framework (HSPF) which combines the analog and digital communication methods to leverage the complementary benefits of the two spheres. The …
Published in International Journal of Digital Communication and Analog Signals · Vol. 11, Issue 2, 2025 Read article