AI-Based Damage Risk Prediction Model Development Using Urban Heat Transport Pipeline Attribute Information by Sungyeol Lee, Jaemo Kang, Jinyoung Kim, Myeongsik Kong

“…The model with optimal performance was selected by comparing evaluation indicators including the F2-score, accuracy, and area under the curve (AUC). …”
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Deep convolutional neural network (DCNN)-based model for pneumonia detection using chest x-ray images by S. I. Ele, U. R. Alo, H. F. Nweke, A. H. Okemiri, E. O. Uche-Nwachi

“…This study focuses on developing and implementing a machine learning model tailored specifically for medical diagnosis, leveraging advancements in computer vision and deep learning algorithms. …”
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Construction and evaluation of a height prediction model for children with growth disorders treated with recombinant human growth hormone by Feng Zhu, Anle Wu, Lingling Chen, Ya Xia, Xiaoju Luo, Jieqian Zhu, Lina Huang, Yu Zhang

“…Predicting treatment outcomes is essential for optimizing individualized treatment strategies. Objective To develop and evaluate a predictive model using clinical data to assess early height growth response in children with growth disorders undergoing rhGH therapy. …”
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Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients by Li Li, Wenjun Ren, Yuying Lei, Lixia Xu, Xiaohui Ning

“…Methods We developed a novel hybrid model integrating convolutional neural network (CNN), Transformer, and Whale Optimization Algorithm (WOA) for arrhythmia prediction in AMI patients. …”
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An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…The Tree-based Pipeline Optimization Tool (TPOTclassifier) has been used to choose suitable supervised learning models. …”
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Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses by Esra Imamoglu

“…This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. …”
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A special machine for solving NP-complete problems by Jin Xu, Le Yu, Huihui Yang, Siyuan Ji, Pu Wu, Yu Zhang, Anqi Yang, Quanyou Li, Haisheng Li, Enqiang Zhu, Xiaolong Shi, Zehui Shao, Huang Leng, Xiaoqing Liu

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A survey on autonomous navigation for mobile robots: From traditional techniques to deep learning and large language models by Abderrahim Waga, Said Benhlima, Ali Bekri, Jawad Abdouni, Fatima Zahrae Saber

“…Furthermore, we explore hybrid models that integrate traditional methods with machine learning, such as reinforcement learning (RL) and neural networks (NN). …”
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An interpretable integrated machine learning framework for genomic selection by Jinbu Wang, Jia Zhang, Wenjie Hao, Wencheng Zong, Mang Liang, Fuping Zhao, Longchao Zhang, Lixian Wang, Huijiang Gao, Ligang Wang

“…In this study, we conducted a comprehensive analysis comparing the performance of various ML models, along with investigations into parameter optimization, dimensionality reduction, feature selection, and the “black box” problem. …”
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Applying Machine Learning on Big Data With Apache Spark by Elias Dritsas, Maria Trigka

“…This paper explores the application of machine learning (ML) models within the Apache Spark ecosystem, focusing on the performance and scalability of these models in big data environments. …”
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Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms by Nian Liu, Yuehan Zhao

“…Methodology In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. …”
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Financial Sentiment Analysis and Classification: A Comparative Study of Fine-Tuned Deep Learning Models by Dimitrios K. Nasiopoulos, Konstantinos I. Roumeliotis, Damianos P. Sakas, Kanellos Toudas, Panagiotis Reklitis

“…Traditional methods for financial sentiment classification, such as Support Vector Machines (SVM), Random Forests, and Logistic Regression, served as our baseline models. …”
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Mathematical Models for Management Information Systems on Digital Platforms: from Resource Management to Demand Forecasting by Viktor Godliuk

“…The use of optimization methods, graph algorithms, forecasting, and machine learning to improve the efficiency of digital systems is investigated. …”
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COVID-19 detection using federated machine learning. by Mustafa Abdul Salam, Sanaa Taha, Mohamed Ramadan

“…During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models' loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.…”
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Incidence and Risk Factors of Lower Limb Deep Vein Thrombosis in Psychiatric Inpatients by Applying Machine Learning to Electronic Health Records: A Retrospective Cohort Study by Xu L, Da M

“…Logistic regression and random forest models exhibited optimal overall performance, while XGBoost excelled in recall. …”
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Unsupervised Machine Learning Approaches for Test Suite Reduction by Anila Sebastian, Hira Naseem, Cagatay Catal

“…Over the past decade, machine learning-based solutions have emerged, demonstrating remarkable effectiveness and efficiency. …”
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Metabolome Profiling and Predictive Modeling of Dark Green Leaf Trait in Bunching Onion Varieties by Tetsuya Nakajima, Mari Kobayashi, Masato Fuji, Kouei Fujii, Mostafa Abdelrahman, Yasumasa Matsuoka, Jun’ichi Mano, Muneo Sato, Masami Yokota Hirai, Naoki Yamauchi, Masayoshi Shigyo

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Using Machine Learning to Predict Linezolid-Associated Thrombocytopenia by Wei R, Li K, Wang H, Cai X, Liu N, An Z, Zhou H

“…The filtered data were then randomly divided into training and validation sets at a 3:1 ratio using stratified sampling. Four machine learning methods-logistic regression, Lasso regression, support vector machine (SVM), and random forest-were employed to develop predictive models on the training set, with optimal hyperparameters determined through grid search. …”
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Classification-Based Parameter Optimization Approach of the Turning Process by Lei Yang, Yibo Jiang, Yawei Yang, Guowen Zeng, Zongzhi Zhu, Jiaxi Chen

“…To address this issue, a classification-based parameter optimization approach of the turning process is proposed in this paper, which aims to provide feasible optimization suggestions of process parameters and consists of a classification model and several optimization strategies. …”
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