A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…To process small sample categories, data enhancement techniques (e.g., random flip and rotation) and K-fold cross-validation are applied to optimize the model parameters. The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. …”
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Employing sentinel-2 time-series and noisy data quality control enhance crop classification in arid environments: A comparison of machine learning and deep learning methods by Zahra Mohammadi Mobarakeh, Saeid Pourmanafi, Mohsen Ahmadi

“…In this study, we employed a novel hybrid approach, integrating time-series analysis, noisy data quality control, and different machine learning and deep learning models to classify croplands of complex multi-crop systems in central Iran. …”
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Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach by José E. Teixeira, Pedro Afonso, André Schneider, Luís Branquinho, Eduardo Maio, Ricardo Ferraz, Rafael Nascimento, Ryland Morgans, Tiago M. Barbosa, António M. Monteiro, Pedro Forte

“…This study applied machine learning (ML) models to classify mental fatigue in U15, U17, and U19 male players using wearable signals, tracking data, and psychophysiological features. …”
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A Dynamic Scheduling Method Combining Iterative Optimization and Deep Reinforcement Learning to Solve Sudden Disturbance Events in a Flexible Manufacturing Process by Jun Yan, Tianzuo Zhao, Tao Zhang, Hongyan Chu, Congbin Yang, Yueze Zhang

“…A real-time DRL production environment model is established for the flexible job scheduling problem. …”
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A comparative study of machine learning algorithms for fall detection in technology-based healthcare system: Analyzing SVM, KNN, decision tree, random forest, LSTM, and CNN by Afuan Lasmedi, Isnanto R. Rizal

“…Additionally, this research opens avenues for optimizing deep learning models and leveraging edge computing technologies to reduce response times in wearable device applications.…”
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Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models by Samira Mohammadi, Vahid Rahmanian, Sasan Sattarpanah Karganroudi, Mehdi Adda

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Mathematical Modeling and Generating Method of Hourglass Worm Gear Hob’s Rake Face Based on a Rotating Paraboloid Surface by Yupeng Li, Jie Yang, Run Gu, Zhijiang Chen, Chunming Xia

“…First, the conjugate condition equations for the rake face generated by the rotating paraboloid surface are derived. A mathematical model for the helical rake face of planar double-enveloping hourglass worm gear hob is established. …”
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A multi-task genetic programming approach for online multi-objective container placement in heterogeneous cluster by Ruochen Liu, Haoyuan Lv, Ping Yang, Rongfang Wang

“…., resource request types and physical machine types), but traditional single-task optimization modeling cannot exploit potential common knowledge, resulting in repeated optimization during resource allocation. …”
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Strength Characteristics Prediction of the Metal Obtained by Wire Arc Additive Manufacturing by Evgeny Tongov, Vladimir Petkov, Vanya Dyakova, Tatiana Simeonova, Manahil Tongov

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A New Zero Waste Design for a Manufacturing Approach for Direct-Drive Wind Turbine Electrical Generator Structural Components by Daniel Gonzalez-Delgado, Pablo Jaen-Sola, Erkan Oterkus

“…However, the greatest challenge associated with these types of machines is the structural optimization of the electrical generator due to the demanding operating conditions. …”
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Physical Property Prediction and Simulation Analysis of Hydrogen‐Doped Natural Gas Pipeline by Lianghui Guo, He Zhang, Ran Liu, Keke Zhi, Xinzhe Li

“…The REFPROP software is utilized to construct databases for predicting the physical properties of hydrogen‐doped natural gas. Among various machine learning models, the Wide Neural Network emerges as optimal, exhibiting an exceptional R2 value exceeding 0.9999 and the ability to predict over 264,000 data points per second for density and viscosity. …”
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An AIoT-Based Automated Farming Irrigation System for Farmers in Limpopo Province by Relebogile Langa, Michael Nthabiseng Moeti, Thabiso Maubane

“…However, artificial irrigation is wasteful as it is regulated and monitored through human intervention, leading to water scarcity which is one of the obstacles that threatens the agricultural sector in the province of Limpopo. A machine learning precipitation prediction algorithm optimizes water usage. …”
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The Interaction of Fitness and Fatigue on Physical and Tactical Performance in Football by Mauro Mandorino, Tim J. Gabbett, Antonio Tessitore, Cedric Leduc, Valerio Persichetti, Mathieu Lacome

“…Elite football players face increasing physical and tactical demands due to rising match schedules emphasizing the need for effective load monitoring strategies to optimize performance and reduce injury risk. This study integrates fitness and fatigue indices derived from a machine learning approach to develop a performance score based on Banister’s fitness–fatigue model. …”
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Performance Analysis of Carbon Fiber Reinforced Epoxy Composites in Particle‐Assisted EDM by Abhijit Bhowmik, Raman Kumar, Ramdevsinh Jhala, Nagaraj Patil, Abinash Mahapatro, Manoj Kumar Ojha, Parveen Kumar, Deepak Gupta, A. Johnson Santhosh

“…The highest discrepancy in the MT between the experimental value and the fitted value according to the regression relation was determined to be merely 3.2258%, indicating the adequacy of the generated regression model. The examination of the surface characteristics of the micro‐channels demonstrated that non‐uniform machining leads to the development of a rough and irregular surface on the material. …”
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Amplitude-frequency characteristics of the seat vibration protection system with a three-segment static force characteristic with a section of quasi-zero stiffness by Korytov M.S., Sherbakov V.S., Kashapova I.E.

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An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images by Kiran Jabeen, Muhammad Attique Khan, Ameer Hamza, Hussain Mobarak Albarakati, Shrooq Alsenan, Usman Tariq, Isaac Ofori

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Unleashing the power of AI in predicting the technological and phenolic maturity of pomegranates cultivated in Lebanon by Rim Ghannoum, Nourhan Taha, David D. Gaviria, Hiba N. Rajha, Nada El Darra, Shadi Albarqouni

“…This study is the first of its kind in Lebanon to address this limitation by applying advanced machine learning techniques to predict key food quality indicators, which can aid in forecasting or determining the optimal harvesting date. …”
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Ensemble Techniques Based Risk Classification for Maternal Health During Pregnancy by Nurul Fathanah Mustamin, Ariyani Buang, Firman Aziz, Nur Hamdani Nur

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Prediction and optimization of surface quality and material removal rate in wire-EDM cutting of tungsten–copper alloy (W70Cu30) by Abdullah Eaysin, Muhammad Ali Zinnah, Md. Nayem, Hosney Ara Begum, Md.Injamamul Haque Protyai, Salahuddin Ashrafi, Adib Bin Rashid

“…The Taguchi L16 orthogonal array was employed to design experiments with 16 square samples, each representing a unique combination of process parameters to identify optimal machining settings. Predictive modeling was conducted using Support Vector Regression (SVR) and gradient-boosted regression Trees (GBRT) to assess the accuracy of MRR and Ra predictions. …”
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Modeling of regasification and recirculation processes during the transportation of liquefied natural gas by A. Yu. Baranov, E. S. Seredenko, T. A. Malysheva, Yu. A. Kravchenko

“…The study is based on the methods of thermodynamic analysis, full-scale and computational modeling of processes and objects of refrigeration and cryogenic equipment in order to find optimal solutions in terms of efficiency, reliability and service life of low-temperature installations, machines and apparatus.Result. …”
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