Water quality index modelling and its application on artificial intelligence (AI) in conjunction with machine learning (ML) methodologies for mapping surface water potential zones... by Abhijeet Das

“…., World Health Organization guidelines is implanted to decipher the values ranging from natural to anthropogenic contribution.In the Mahanadi River Basin, Odisha, however, this study has highlighted the evaluation of surface water quality (WQ) for drinking reasons by the combined use of Machine Learning (ML) methodologies like Genetic Algorithm Particle Swarm Optimization-based WQI (GAPSO-WQI), with dependability-oriented decision-making approaches such as Firefly Algorithm (FA) and Algorithm of Weeds (AW), that have been used for river water quality monitoring and assessment due to their dependability and feasibility. …”
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Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost by Yingjie Meng, Chengwu Xu, Tingting Li, Tianyong Liu, Lu Tang, Jinyou Zhang

“…In this study, for the shale of the Qingshankou Formation of the Gulong Sag in the Songliao Basin, TOC content prediction models using various machine learning algorithms are established and compared based on measured data, principal component analysis, and the particle swarm optimization algorithm. …”
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Dynamic Portfolio Return Classification Using Price-Aware Logistic Regression by Yakubu Suleiman Baguda, Hani Moaiteq AlJahdali, Altyeb Altaher Taha

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State Identification of Charging Module Based on SN‐EMD‐SSEE and DBO‐HKELM by Bingyu Li, Xianhai Pang, Xuhao Du, Ziwen Cai

“…In data pre‐processing, SN‐EMD‐SSEE is developed to extract state characteristics values for high adaptability to full working condition and high significance for easy identification. In modeling, DBO‐HKELM identification model is constructed by improving ELM (Extreme Learning Machine) and optimizing parameters based on DBO 60 state characteristics values and 24 states including normal state, which are, respectively, used as the input and output of the identification model. …”
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Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content by Xuguang Sun, Baoyuan Zhang, Menglei Dai, Cuijiao Jing, Kai Ma, Boyi Tang, Kejiang Li, Hongkai Dang, Limin Gu, Wenchao Zhen, Xiaohe Gu

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Enhancement and Expansion of the Neural Network-Based Compact Model Using a Binning Method by Jinyoung Choi, Hyunjoon Jeong, Sangmin Woo, Hyungmin Cho, Yohan Kim, Jeong-Taek Kong, Soyoung Kim

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Rate of penetration prediction in drilling operations: a comparative study of AI models and meta-heuristic approaches by Fatemeh Mohammadinia, Ali Ranjbar, Fatemeh Ghazi, Seyyed Taha Hosseini

“…To further enhance model performance, metaheuristic optimization strategies such as the Crow Search Algorithm (CSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) are integrated. …”
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Determination of Pipe Diameters for Pressurized Irrigation Systems Using Linear Programming and Artificial Neural Networks by Ezgi Kurtulmuş, Ferhat Kurtulmuş, Hayrettin Kuşçu, Bilge Arslan, Ali Osman Demir

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Exploring the Role of Artificial Intelligence in Wastewater Treatment: A Dynamic Analysis of Emerging Research Trends by Javier De la Hoz-M, Edwan Anderson Ariza-Echeverri, Diego Vergara

“…Regional contributions highlight a strong focus on advanced oxidation processes, microbial sludge treatment, and energy optimization. The Latent Dirichlet Allocation (LDA) model further identifies emerging topics such as real-time process monitoring and AI-driven effluent prediction as pivotal areas for future research. …”
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Prediction of COVID-19 Effect on Patients during Six Month After Recovery, by Using AI Algorithm by hanan aldabagh, Ghaydaa Al-Talib

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Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification by Anas Bilal, Muhammad Shafiq, Waeal J. Obidallah, Yousef A. Alduraywish, Alishba Tahir, Haixia Long

“…The classification process, QCOA optimizes the Support Vector Machine (SVM) parameters and performs feature selection. …”
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Deep-Learning Approach for an Analysis of Real-Estate Prices and Transactions by Cheng-Hong Yang, Borcy Lee, Yu-Da Lin

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Integrating Multilayer Perceptron and Support Vector Regression for Enhanced State of Health Estimation in Lithium-Ion Batteries by Sadiqa Jafari, Jisoo Kim, Wonil Choi, Yung-Cheol Byun

“…We utilized Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models, which were fine-tuned using hyperparameter optimization. …”
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Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models by Sun Jae Baek, Minhyeok Lee

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Geometric properties of quantum entanglement and machine learning by S. V. Zuev

“…The Python programming language is used as a development tool. Optimization tools for machine learning are taken from the SciPy module. …”
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DeepSeek vs. ChatGPT vs. Claude: A comparative study for scientific computing and scientific machine learning tasks by Qile Jiang, Zhiwei Gao, George Em Karniadakis

“…However, different models exhibit distinct strengths and preferences, resulting in varying levels of performance. …”
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Prediction of instability of formwork concrete pier based on big data machine learning for secondary mining without coal pillar mining by Yanhui Zhu, Ye Tian, Peilin Gong, Kang Yi, Tong Zhao

“…A Gaussian process regression (GPR)-based stress prediction model was developed (optimal kernel: ARD-Rational-Quadratic-Kernel, with MSE = 1.3463, RMSE = 1.1603, MAE = 0.6138, and MAPE = 0.4041), demonstrating significantly higher accuracy than linear regression models (error reduced by 1–2 orders of magnitude) and BP neural networks (MSE = 2.0962). …”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…Tumor progression or regression within three months post-GKRS was the primary outcome, with 71 cases of regression and 6 cases of progression. Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. …”
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Adaptive Multitask Neural Network for High-Fidelity Wake Flow Modeling of Wind Farms by Dichang Zhang, Christian Santoni, Zexia Zhang, Dimitris Samaras, Ali Khosronejad

“…Wind turbine wake modeling is critical for the design and optimization of wind farms. …”
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Survey of Mechatronic Techniques in Modern Machine Design by Devdas Shetty, Lou Manzione, Ahad Ali

“…Another effect is the growing influence of interactions between machine components on achievable machine dynamics and precision and quality of components. …”
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