Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting by Abdul Wadood, Hani Albalawi, Aadel Mohammed Alatwi, Hafeez Anwar, Tariq Ali

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A Hybrid Machine Learning Framework for Early Fault Detection in Power Transformers Using PSO and DMO Algorithms by Mohammed Alenezi, Fatih Anayi, Michael Packianather, Mokhtar Shouran

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Detection of kidney bean leaf spot disease based on a hybrid deep learning model by Yiwei Wang, Qianyu Wang, Yue Su, Binghan Jing, Meichen Feng

“…By leveraging the Optuna tool for hyperparameter optimization, 16 combined models were evaluated. …”
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SBNNR: Small-Size Bat-Optimized KNN Regression by Rasool Seyghaly, Jordi Garcia, Xavi Masip-Bruin, Jovana Kuljanin

“…On the other hand, researchers are interested in using machine learning methods to analyze this scale of data. …”
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Slope stability prediction under seismic loading based on the EO-LightGBM algorithm by Ning Ma, Ning Ma, Yuqi Zhang, Zaizhen Yao

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Intelligent Robot in Unknown Environments: Walk Path Using Q-Learning and Deep Q-Learning by Mouna El Wafi, My Abdelkader Youssefi, Rachid Dakir, Mohamed Bakir

“…A distinctive aspect of this work is the adaptive tuning of hyperparameters, where alpha and gamma values are dynamically adjusted throughout training. …”
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Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in nor... by Jian Wu, Soumitra S Bhuyan, Xiaoli Fu, Quanman Li, Clifford Silver Tarimo, Michael Johnson J Mahande

“…Extensive research into the performance of other classifier algorithms is warranted.…”
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Deep Learning for Traffic Scene Understanding: A Review by Parya Dolatyabi, Jacob Regan, Mahdi Khodayar

“…By critically analyzing current technologies, the paper identifies limitations in existing research and proposes areas for future exploration. …”
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Optimization of Offshore Saline Aquifer CO₂ Storage in Smeaheia Using Surrogate Reservoir Models by Behzad Amiri, Ashkan Jahanbani Ghahfarokhi, Vera Rocca, Cuthbert Shang Wui Ng

“…Machine learning-based Surrogate Reservoir Models (SRMs) can replace/augment multi-physics numerical simulations by replicating the reservoir simulation results with reduced computational effort while maintaining accuracy compared with numerical simulations. This research will demonstrate SRMs’ potential in long-term simulations and optimization of geological carbon storage in a real-world geological setting and address challenges in big data curation and model training. …”
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Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering by Lizhou Zhang, Siqiao Ye, Deping He, Linfeng Wang, Weiping Li, Bijing Jin, Taorui Zeng

“…Current research lacks an in-depth exploration of ensemble learning and factor engineering applications in regard to landslide susceptibility modeling. …”
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Bayesian Prototypical Pruning for Transformers in Human–Robot Collaboration by Bohua Peng, Bin Chen

“…As such, it has become an emerging research direction for robots to understand human intentions with video Transformers. …”
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Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study by Mahmoud B Almadhoun, MA Burhanuddin

“…ConclusionsIt is demonstrated in this research that optimized ML models, especially random forest and XGBoost, are effective tools for assessing early prediabetes risk. …”
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LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…Effective preprocessing methods and hyperparameter fine-tuning are selected to improve the accuracy of the model. …”
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Enhancing Tire Condition Monitoring through Weightless Neural Networks Using MEMS-Based Vibration Signals by Siddhant Arora, Sridharan Naveen Venkatesh, Vaithiyanathan Sugumaran, Anoop Prabhakaranpillai Sreelatha, Vetri Selvi Mahamuni

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An efficient bearing fault detection strategy based on a hybrid machine learning technique by Khalid Alqunun, Mohammed Bachir Bechiri, Mohamed Naoui, Abderrahmane Khechekhouche, Ismail Marouani, Tawfik Guesmi, Badr M. Alshammari, Amer AlGhadhban, Abderrahim Allal

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Machine Learning-Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete by Muhammad Izhar Shah, Shazim Ali Memon, Muhammad Sohaib Khan Niazi, Muhammad Nasir Amin, Fahid Aslam, Muhammad Faisal Javed

“…In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. …”
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Optimizing Bearing Fault Diagnosis in Rotating Electrical Machines Using Deep Learning and Frequency Domain Features by Eduardo Quiles-Cucarella, Alejandro García-Bádenas, Ignacio Agustí-Mercader, Guillermo Escrivá-Escrivá

“…Results indicate that precise hyperparameter tuning enhances diagnostic accuracy, achieving a classification accuracy of 99.37% using the amor wavelet. …”
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An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization by Zhaofei Wang, Hao Li, Qiuping Wang

“…In addition, the speed mean has the highest feature importance of 14%, which needs to be focused on when preventing truck driving risks. The research results can provide policy support for transportation management departments to formulate risk control measures for trucks.…”
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Physics-data hybrid dynamic model of a multi-axis manipulator for sensorless dexterous manipulation and high-performance motion planning by Wu-Te Yang, Jyun-Ming Liao, Pei-Chun Lin

“…Meanwhile, the physics-based and data-driven based dynamic models are studied in this research to select the best model for planning. The physics-based model is constructed using the Lagrangian method, and the loss terms include inertia loss, viscous loss, and friction loss. …”
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Methodological Validation of Machine Learning Models for Non-Technical Loss Detection in Electric Power Systems: A Case Study in an Ecuadorian Electricity Distributor by Carlos Arias-Marín, Antonio Barragán-Escandón, Marco Toledo-Orozco, Xavier Serrano-Guerrero

“…Although CGB achieved the best performance in terms of accuracy (Acc = 0.897) and F1 (0.894), it was slower than LGB, so it is considered the ideal classifier for the data provided by the electrical distribution company. This research study highlights the importance of the techniques used for fraud detection in electricity metering systems, although the results may vary depending on the characteristics of the training, the number of variables, and the available hardware resources.…”
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