Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning by Vijay Govindarajan, Junaid Hussain Muzamal

“…Abstract This paper presents a modular and scalable intrusion detection framework that combines graph-based feature extraction, Transformer-based autoencoding, and contrastive learning to improve detection accuracy in cloud environments. …”
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Composite Learning-Based Inverse Optimal Fault-Tolerant Control for Hierarchy-Structured Unmanned Helicopters by Qingyi Liu, Ke Zhang, Bin Jiang, Yushun Tan

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Predictive Analytics for Sucker Rod Pump Failures in Kazakhstani Oil Wells Using Machine Learning by Laura Utemissova, Timur Merembayev, Bakbergen Bekbau, Sagyn Omirbekov

“…In order to increase the smooth operation of downhole pumping equipment in oil and gas wells, companies use various methods and techniques. This article presents a novel methodology for predicting downhole pumping equipment failures. …”
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Fruit Detection Methods Based on Deep Learning in Agricultural Planting: A Systematic Literature Review by Xinyu Gong, Qiufeng Wu

“…As an important task in agricultural computer vision, fruit target detection in real-world planting environments presents numerous technical challenges. This paper provides a systematic review of recent breakthroughs and representative studies in this field. …”
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Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks by Skowron Maciej

“…The application of the idea of transfer learning (TL) allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. …”
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Strategies for Conducting Blended Learning in VET: A Comparison of Award-Winning Courses and Daily Courses by Yiran Cui, Meng Li, Yangyang Luo

“…Secondly, a questionnaire survey among 215 VET teachers revealed positive perceptions of the strategies in terms of usability, ease of use, perceived behavioral control, and intention to use them. The present research provides valuable guidance for VET teachers to effectively implement blended learning strategies in diverse course types, contributing to the understanding of effective blended learning strategies in VET and addressing the gap in research for this unique teaching stage.…”
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Threat analysis model to control IoT network routing attacks through deep learning approach by K. Janani, S. Ramamoorthy

“…The IoT routing dataset is then augmented into larger volumes using ADASYN, which is also used to solve the class imbalance problems. A deep learning hybrid model based on a Long-Short-Term Memory (LSTM) network and adaptive Mayfly Optimization Algorithm (LAMOA) was presented for the classification of IoT attacks. …”
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Advanced deep learning models based on neutrosophic logic for the analysis of brain tumor medical images by Dina Atef, Doaa El Shaha

“…This work presents a hybrid methodology that combines Neutrosophic Set (NS) theory with deep learning models to improve magnetic resonance imaging (MRI) picture classification in uncertain settings. …”
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A Bibliometric Systematic Literature Review on the Relationship Between Problem-Based Learning Methodology and Entrepreneurship by Ricardo Jorge Gomes Raimundo, Albérico Travassos Rosário

“…In recent years, the literature on entrepreneurship has progressively focused on the problem-based learning (PBL) methodology, particularly in response to evolving challenges within the learning environment. …”
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Predictive Analysis of Cardiovascular Disease Risk Factors in Romania using Machine Learning and Medical Statistics by Radu-Anton MOLDOVAN, Sebastian-Aurelian ŞTEFĂNIGĂ

“… Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality in Romania, being a severe public health problem. The aim of the present study was to identify and assess the significant risk factors of CVD and develop evidence-based prevention strategies. …”
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Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction by Mahdy Khari, Ali Dehghanbanadaki, Danial Jahed Armaghani, Manoj Khandelwal

“…The challenge of predicting relative dry density (Dr) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. A novel approach to optimise ensemble learning algorithms is presented, with a focus placed on the mathematical foundations of these methods. …”
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Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 by Liping Bai, Chenglei Xia, Fei Liu, Xing Yang, Tai Zhang

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A New Approach to Model Machine Learning by Using Complex Bipolar Intuitionistic Fuzzy Information by Naeem Jan, Rabia Maqsood, Abdul Nasir, Mohsin S. Alhilal, Amerah Alabrah, Naziha Al-Aidroos

“…Many industries are developing robust models, capable of analyzing huge and complex data by using machine learning (ML) while delivering faster and more accurate results on vast scales. …”
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“Simply Math”—A Hybrid MOOC Supporting Advanced Mathematics Learning in Israeli Secondary Schools by Halima Sharkia, Zehavit Kohen

“…The present study explores the integration of a hybrid MOOC (H-MOOC) called <i>Simply Math</i> in the setting of advanced mathematics lessons in school. …”
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Learning Capabilities of Extension Professionals in Using Social media Platforms for Service Delivery in Southeast, Nigeria by Charles Ekene Udoye, Michael Chukwuneke Madukwe

“… The study ascertained extension professionals learning capabilities in using social media platform for service delivery in southeast, Nigeria. …”
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CPrecNet: Enhanced Nowcast of High‐Resolution Short‐Term Precipitation Using Deep Learning by Jun Park, Changhoon Lee

“…Traditional numerical weather prediction faces challenges in delivering high‐resolution nowcasts due to computational limitations. We presents CPrecNet, a deep learning model utilizing a Swin Transformer‐based architecture and high‐resolution radar data (500 m and 5 min) to improve nowcasting accuracy. …”
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Improving the performance of damage repair in thin-walled structures with analytical data and machine learning algorithms by Abdul Aabid, Md Abdul Raheman, Meftah Hrairi, Muneer Baig

“…On the other hand, machine learning (ML) has made it possible to employ a variety of approaches for mechanical and aerospace problems and such significant approach is the repair mechanism and hence ML algorithms used to enhance in the present work. …”
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Intelligent deep learning-based dual-task approach for robust power quality event classification by Lipsa Ray, Pampa Sinha, Siddhanta Pani, Anshuman Nayak, Kaushik Paul, Chitralekha Jena, Md. Minarul Islam, Taha Selim Ustun

“…A novel dual-task deep learning model is developed, incorporating a dynamic nonstationary redundancy factor, r ( t , f ), to enhance the localization of signal components across time and frequency domains. …”
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A Deep Learning Strategy for the Retrieval of Sea Wave Spectra from Marine Radar Data by Giovanni Ludeno, Giuseppe Esposito, Claudio Lugni, Francesco Soldovieri, Gianluca Gennarelli

“…To reach this goal, this paper proposes a fully data-driven, deep learning approach based on a convolutional neural network. …”
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Deep Learning-Based Speech Recognition and LabVIEW Integration for Intelligent Mobile Robot Control by Kai-Chao Yao, Wei-Tzer Huang, Hsi-Huang Hsieh, Teng-Yu Chen, Wei-Sho Ho, Jiunn-Shiou Fang, Wei-Lun Huang

“…This study addresses the limitations inherent in conventional voice control methods, demonstrates the potential of integrating deep learning technology with industrial control platforms, and presents a novel approach for robotic voice control.…”
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