Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach by David Carrascal, Paula Bartolomé, Elisa Rojas, Diego Lopez-Pajares, Nicolas Manso, Javier Diaz-Fuentes

“…A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. …”
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Machine learning-based assessment of flood susceptibility in the Eastern Mediterranean: a case study of Baniyas River basin by Hazem Ghassan Abdo, Sahar Mohammed Richi, Taorui Zeng, Saeed Alqadhi, Pankaj Prasad, Thong Nguyen-Huy, Maged Muteb Alharbi, Javed Mallick

“…In this analysis, the performance of four ensemble machine learning algorithms (ML), that is support vector machine (SVM), random forest (RF), artificial neural network (ANN) and extreme gradient boost (XGBoost), was compared and tested in mapping flood susceptibility in the Eastern Mediterranean region. …”
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Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation by Sara Asadi, Patricia Jimeno-Sáez, Adrián López-Ballesteros, Javier Senent-Aparicio

“…This study conducts a comprehensive evaluation of the Soil and Water Assessment Tool (SWAT+), support vector regression (SVR), feed forward neural network (FFNN), and long short-term memory (LSTM) models in simulating the rainfall-runoff process at four gauging stations within the THRB. …”
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Measurement-Based Neural Network Technique for Modeling the Low-Frequency Electric Field Radiated Behavior of Satellite Units by Anna N. Lampou, Anargyros T. Baklezos, Konstantinos K. Spyridakis, Dimitrios A. Rigas-Papakonstantinou, Ioannis O. Vardiambasis, Christos D. Nikolopoulos

“…The network uses the electric field vector, measured at 14 discrete locations, as input and reports the position and moment of the electric dipole that best matches the measured fields. …”
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Discretization-independent surrogate modeling of physical fields around variable geometries using coordinate-based networks by James Duvall, Karthik Duraisamy

“…DV-MLP achieves generalization through the use of a design-variable embedding vector, while DVH conditions the main network weights on the design variables using a hypernetwork. …”
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Efficient feature selection based on Gower distance for breast cancer diagnosis by Salwa Shakir Baawi, Mustafa Noaman Kadhim, Dhiah Al-Shammary

“…This normalization prevents features with larger scales from overshadowing those with smaller scales. This process facilitates the identification of features that exhibit high harmony and are the most relevant for classification. …”
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Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH by Yanfeng Peng, Junhang Chen, Yanfei Liu, Junsheng Cheng, Yu Yang, He Kuanfang, Guangbin Wang, Yi Liu

“…ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. …”
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Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs by Yue Zhang, Sheng Quan Xie, Chaoyang Shi, Jun Li, Zhi-Qiang Zhang

“…Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. …”
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Advanced long-term actual evapotranspiration estimation in humid climates for 1958–2021 based on machine learning models enhanced by the RReliefF algorithm by Ahmed Elbeltagi, Salim Heddam, Okan Mert Katipoğlu, Abdullah A. Alsumaiei, Mustafa Al-Mukhtar

“…AET was estimated using support vector machine (SVM), ensemble bagged and boosted trees, robust linear regression (RLR), and Matern 5/2 Gaussian process regression (M-GPR) models. …”
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Intervention of machine learning in bladder cancer research using multi-omics datasets: systematic review on biomarker identification by Blessy Kiruba, P. S. Athul Narayan, Badhari Raj, S. Rohieth Raj, Sam George Mathew, Sudhakaran Sajitha Lulu, Vino Sundararajan

“…Hence, machine learning (ML), with its ability to process high-dimensional data and identify complex patterns, offers a promising patient outcome. …”
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Trends and Peculiarities of Educational Migration in Modern Russia by S. I. Chernykh, A. A. Izgarskaya

“…This means that educational migration as part of the internationalization process is becoming an indicator of the regional educational systems’ competitiveness. …”
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Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions by Zeyad T. Aklah, Hussein T. Hassan, Amean Al-Safi, Khalid Aljabery

“…The automated fall detection process enables the game to take the required actions, such as transferring possession of the visual ball or applying fouls, without the need for manual intervention. …”
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Machine Learning-Based Cerrado Land Cover Classification Using PlanetScope Imagery by Thanan Rodrigues, Frederico Takahashi, Arthur Dias, Taline Lima, Enner Alcântara

“…A post-processing process was applied to avoid misclassification of forest in areas of savanna. …”
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Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations by Badriah Alkalifah, Muhammad Tariq Shaheen, Johrah Alotibi, Tahani Alsubait, Hosam Alhakami

“…To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. …”
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Butterfly magnetoreception based neighbour awareness strategy protocol for autonomous aerial vehicles by Janjhyam Venkata Naga Ramesh, C. Dastagiraiah, Suraya Mubeen, W. Deva Priya, M. Kameswara Rao, B. H. K. Bhagat Kumar

“…However, navigating through high-density obstacle environments poses substantial challenges due to process overheads, dynamic environmental conditions, and scalability issues. …”
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Terahertz Metasurfaces for Polarization Manipulation and Detection: Principles and Emerging Applications by Yongliang Liu, Yifei Xu, Bo Yu, Wenwei Liu, Zhengren Zhang, Hua Cheng, Shuqi Chen

“…Abstract Terahertz frequencies locating between microwave and infrared regions can record and process optical information for next‐generation information technology such as 6G communication. …”
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Axial Capacity Estimation of FRP-strengthened Corroded Concrete Columns by Chandan Gupta, Harish Arora, Aman Kumar, Prashant Kumar, Nishant Raj Kapoor, Madhu Sharma

“…Considering the scarcity of reliable design guidelines for estimating the axial strength of RC columns considering corrosion effects, artificial neural network (ANN), Gaussian process regression (GPR), and support vector machine (SVM) techniques were employed. …”
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A review of machine learning and internet-of-things on the water quality assessment: Methods, applications and future trends by Gangani Dharmarathne, A.M.S.R. Abekoon, Madhusha Bogahawaththa, Janaka Alawatugoda, D.P.P. Meddage

“…Explainable AI (XAI) which can explain the decision making process of ML, is underutilised, appearing in only a few recent studies. …”
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Estimating Total Sediment Load Using Water Quality Parameters in the Sufi Chay River, Iran: A Comparative Analysis of Soft Computing and Dimensionless Approaches by Jafar Chabokpour

“…Comparison of performances using three soft computing methods, namely Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Support Vector Regression, are performed. The highest R2 value of 0.91 and RMSE of 53.2 tons/ day were obtained with ANFIS model. …”
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Modelling of Regional Economy Growth Strategy by I. S. Mezhov, E. V. Kletskova

“…Regional economy as a system of foreign economic activity in the model has a vector representation of the production and technological function of each industry, and development strategies are presented in the form of a special investment matrix. …”
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