A review of the use of technology in sport coaching: current trends and future directions by Fadli Ihsan, Ahmad Nasrulloh, Sigit Nugroho, Rina Yuniana

“…Recent trends include the integration of artificial intelligence and machine learning algorithms, utilizing big data analytics, and the development of mobile applications for remote training and athlete monitoring. …”
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Data augmentation-assisted muck image recognition during shield tunnelling by Tao Yan, Shui-Long Shen, Annan Zhou

“…The deep image recognition algorithms (AlexNet and GoogLeNet) were trained and enhanced by the augmentation images, which were used to establish the muck types identification models and assessed by the evaluation indices. …”
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Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities by Gift Echefu, Rushabh Shah, Zanele Sanchez, John Rickards, Sherry-Ann Brown

“…Multidisciplinary teams have been formed to implement and evaluate the efficacy of these technologies, assessing AI-driven clinical decision support tools. …”
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Comparative Analysis of Random Forest and Logistic Regression Methods in Predicting Leukemia Blood Cancer Using Microscopic Blood Cell Images by Jepri Banjarnahor, Galuh Wira Relungwangi

“…Our evaluation incorporated multiple performance metrics: accuracy, sensitivity, specificity, and AUC. …”
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Detection of Rat Pain-Related Grooming Behaviors Using Multistream Recurrent Convolutional Networks on Day-Long Video Recordings by Chien-Cheng Lee, Ping-Wing Lui, Wei-Wei Gao, Zhongjian Gao

“…In this study, we developed and evaluated a deep learning, multistream algorithm to detect pain-related grooming behaviors in rats. …”
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Predicting graduation grades using Machine Learning: A case study of Can Tho University students by Minh Khiem Nguyen, Van Tu Huynh, Hung Dung Nguyen

“…The efficacy of the employed algorithms was assessed through performance evaluation metrics encompassing accuracy, precision, recall, and F-measure. …”
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Utilizing Machine Learning Approach to Forecast Average Location Determination Errors in Wireless Sensor Networks by Zhihui Zhu, Meifang Zhu

“…This paper introduces collaborative localization algorithms for wireless sensor networks (WSNs) in outdoor environments without GPS. …”
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A comprehensive Malabar Spinach dataset for diseases classificationMendeley Data by Mushfiqur Rahman, Md Al Mamun

“…There is still a lack of research focused on Malabar spinach, although advances in machine vision have considerably increased the detection of largescale crop diseases. By developing and evaluating machine vision algorithms specifically designed for accurate detection of diseases in Malabar spinach, this research aims to fill this gap. …”
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Predicting graduation grades using Machine Learning: A case study of Can Tho University students by Minh Khiem Nguyen, Van Tu Huynh, Hung Dung Nguyen

“…The efficacy of the employed algorithms was assessed through performance evaluation metrics encompassing accuracy, precision, recall, and F-measure. …”
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Prediction of UHPC mechanical properties using optimized hybrid machine learning model with robust sensitivity and uncertainty analysis by ZhiGuang Zhou, Jagaran Chakma, Md Ahatasamul Hoque, Vaskar Chakma, Asif Ahmed

“…Each dataset was standardized and split into training (80%) and testing (20%) subsets. Hyperparameter optimization was conducted using a random search algorithm to improve prediction accuracy. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. …”
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Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…Finally, the synthetic minority over-sampling technique was employed to balance the training set. Subsequent retraining and evaluation confirmed that the imbalanced dataset does not adversely affect SRC model performance. …”
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An Interpretable Machine Learning Model Based on Inflammatory–Nutritional Biomarkers for Predicting Metachronous Liver Metastases After Colorectal Cancer Surgery by Hao Zhu, Danyang Shen, Xiaojie Gan, Ding Sun

“…Seven machine learning (ML) models were constructed using the training set, with the optimal predictive model selected based on comprehensive evaluation metrics. …”
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Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome by Weirong Xue, Bing Xu, Hui Wang, Xiaoxiao Zhu, Jiajia Qin, Guangshuang Zhou, Peilin Yu, Shengli Li, Yingliang Jin

“…Models were constructed by evaluating all combinations of four kernel functions in the training set. …”
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Benchmark Pashto Handwritten Character Dataset and Pashto Object Character Recognition (OCR) Using Deep Neural Network with Rule Activation Function by Imran Uddin, Dzati A. Ramli, Abdullah Khan, Javed Iqbal Bangash, Nosheen Fayyaz, Asfandyar Khan, Mahwish Kundi

“…This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. …”
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Intelligent diagnosis of thyroid nodules with AI ultrasound assistance and cytology classification by Xiaojuan Cai, Ya Zhou, Jie Ren, Jinrong Wei, Shiyu Lu, Hanbing Gu, Weizhe Xu, Xun Zhu

“…This study aims to develop an AI classifier that integrates the AI-assisted ultrasound diagnosis system, FNAC, and demographic data to enhance the differentiation of benign and malignant thyroid nodules, and to compare the diagnostic performance of the models, with a focus on diagnosing ITNs.Materials and methodsIn the present research, 620 thyroid nodules were collected from a single medical center and divided into training and testing cohorts (Testing1). We developed five AI models using distinct classification algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine) that integrate demographic data, cytological findings, and an AI-assisted ultrasound diagnostic system for thyroid nodule assessment. …”
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Development and validation of a nomogram for predicting cancer-related fatigue in patients with glioma: a multicenter study by Qiuxia Wu, Cuiqun Su, Manxia Xing, Liangmei Ouyang

“…The AUC values of nomogram were 0.916 (CI: 0.879-0.953) in the training cohort and 0.885 (CI: 0.829-0.941) in the validation cohort. …”
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From Simulation to Field Validation: A Digital Twin-Driven Sim2real Transfer Approach for Strawberry Fruit Detection and Sizing by Omeed Mirbod, Daeun Choi, John K. Schueller

“…In contrast, this work relies solely on photorealistic simulation outputs for training, eliminating the need for real images or specialized adaptation approaches. …”
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Artificial Intelligence in Forensic Expertology by E. V. Chesnokova, A. I. Usov, G. G. Omel’yanyuk, M. V. Nikulina

“…The authors have developed the criteria for choosing the direction for the introduction of AI in the system of forensic expertology, and specifically in its innovative section – the management system of forensic expert activity.The prospects for the development of AI technologies in forensic examination are associated with the processing of big data based on the expert situation, the reliability of the data used in AI training and further validation (assessment of suitability) of the applied training methodology, analysis of the results of AI technology. …”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…The cohort was randomly divided into a training set (447 patients) and a validation set (150 patients). …”
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