A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

“…Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. …”
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Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study by Mostafa A. Arafa, Karim H. Farhat, Sherin F. Aly, Farrukh K. Khan, Alaa Mokhtar, Abdulaziz M. Althunayan, Waleed Al-Taweel, Sultan S. Al-Khateeb, Sami Azhari, Danny M. Rabah

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AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP) by Elif Kartal, Fatma Önay Koçoğlu, Zeki Özen, İlkim Ecem Emre, Gürcan Güngör, Pervin Sutaş Bozkurt

“…The performance of RF was considered, while the kNN algorithm has no stable model. According to RF and Classification and Regression Tree (CART) algorithms’ attribute importance ranking, “Incision site”, “Age”, and “Primary diagnosis for operation” are common attributes. …”
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Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine by Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa

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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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Modeling the Relationship between Financial Stability and Banking Risks: Artificial Intelligence Approach by Hakeem Faraj Gumar, Parviz Piri, Mehdi Heydari

“…A wide range of artificial neural network approaches and machine learning algorithms have been used for data analysis. These methods include artificial neural network, deep neural network, convolutional neural network, recurrent neural network, self-organizing neural network, gradient boosting, random forest, decision tree, spatial clustering, k-means algorithm, k-nearest neighbor, support vector regression and support vector machine. …”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. …”
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Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique by Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li

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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

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Unveiling shadows: A data-driven insight on depression among Bangladeshi university students by Sanjib Kumar Sen, Md. Shifatul Ahsan Apurba, Anika Priodorshinee Mrittika, Md. Tawhid Anwar, A.B.M. Alim Al Islam, Jannatun Noor

“…After rigorous analysis, Random Forest emerged as the best-performing algorithm, exhibiting remarkable accuracy (87%), precision (78%), recall (95%), and f1-score (86%). …”
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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

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AI-powered IoT and UAV systems for real-time detection and prevention of illegal logging by Montaser N.A. Ramadan, Mohammed A.H. Ali, Shin Yee Khoo, Mohammad Alkhedher

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Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“… This paper aimed to investigate human emotion recognition through the analysis of facial expressions, using both classical machine learning methods and advanced techniques based on deep neural networks. The research compares the performance of classical machine learning algorithms (such as K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree, and Random Forest) with the modern deep learning methods (such as Convolutional Neural Networks, Deep Neural Networks, and Recursive Neural Networks) using standardized datasets. …”
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An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

“…In addition, the study area's hydro-chemical facies were examined, and machine learning models’ hyperparameters such as Random Forest (RF), Borda Scoring Algorithm (BSA), Decision Tree (DT), Multilayer Perception (MLP), and Naïve Bayes (NB), were executed before, to training and testing the samples of surface water. …”
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Machine Learning for Prediction of Relapses in Multiple Drug Resistant Tuberculosis Patients by A. S. Аlliluev, O. V. Filinyuk, E. E. Shnаyder, S. V. Аksenov

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Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya by Roman Shults, Ashraf Farahat, Muhammad Usman, Md Masudur Rahman

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Old Drugs, New Indications (Review) by I. I. Miroshnichenko, E. A. Valdman, I. I. Kuz'min

“…Machine learning (ML) algorithms: Bayes classifier, logistic regression, support vector machine, decision tree, random forest and others are successfully used in biochemical pharmaceutical, toxicological research. …”
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Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods by Nataliya Boyko, Oleksii Lukash

“…The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). …”
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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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Comparative Analysis of Diabetes Prediction Models Using the Pima Indian Diabetes Database by Zhao Yize

“…The K-means model operates by grouping data points into separate clusters according to their characteristics, achieving an accuracy of 90.04% in diabetes prediction. In comparison, the random forest model, which builds multiple decision trees (DT) to do their predictions, demonstrates superior performance over several widely used algorithms such as K-Nearest Neighbours (KNN), Logistic Regression (LR), DT, Support Vector Machines (SVM), and Gradient Boosting (GB). …”
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