Data Poison Detection Schemes for Distributed Machine Learning by Yijin Chen, Yuming Mao, Haoyang Liang, Shui Yu, Yunkai Wei, Supeng Leng

“…We prove that the proposed cross-learning mechanism would generate training loops, based on which a mathematical model is established to find the optimal number of training loops. …”
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Production Dynamic of Coal-bed Methane After Well Pressure Based on Multi-layer Perceptron Model Inversion Study by LI Jingsong, WANG Tao, WANG Jinwei, WEI Zhipeng, XIAO Cong, TANG Jizhou

“…It is concluded that the combination of machine learning modeling technology and intelligent inversion algorithm is helpful to promote the application and development of intelligent optimization technology of tight gas reservoirs, and provide theoretical guidance and technical support for accelerating the intelligent development process of unconventional oil and gas reservoirs in China.…”
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GINSER: Geographic Information System Based Optimal Route Recommendation via Optimized Faster R-CNN by S. D. Anitha Selvasofia, B. SivaSankari, R. Dinesh, N. Muthukumaran

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Machine learning for discrimination of phase‐change chalcogenide glasses by Qundao Xu, Meng Xu, Siqi Tang, Shaojie Yuan, Ming Xu, Wei Zhang, Xian‐Bin Li, Zhongrui Wang, Xiangshui Miao, Chengliang Wang, Matthias Wuttig

“…Leveraging the established structure–property relations in chalcogenide glasses, we select suitable features to train accurate machine learning models, even with a modestly sized dataset. …”
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ON GAME-THEORY APPROACH TO PROCESS MACHINE ADJUSTMENT PROBLEMS by Lyudmila Victorovna Borisova, Inna Nikolayevna Nurutdinova, Valery Petrovich Dimitrov

“…A model example is used to illustrate the one-step procedure for decision-making under uncertainty with the application of the specified criteria. …”
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Using machine learning algorithms to predict colorectal polyps by Xingjian Xiao, Shiyou Liu, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Xianglong Xu

“…The optimal model was used to identify predictors of colorectal polyps. …”
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Using machine learning algorithms to predict colorectal cancer by Xingjian Xiao, Bo Hong, Kubra Maqsood, Xiaohan Yi, Guoqun Xie, Hailei Zhao, Bo Sun, Jianying Mao, Shiyou Liu, Xianglong Xu

“…The optimal model was used to identify predictors of colorectal cancer. …”
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Machine learning for experimental design of ultrafast electron diffraction by Mohammad Shaaban, Sami El-Borgi, Aravind Krishnamoorthy

“…These examples show the ability of machine learning to design self-correcting diffraction experiments to optimize the use of large-scale user facilities.…”
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CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES by Erol Özçekiç, Ümit Yılmaz

“…Statistical analyses identified key predictors such as age, body mass index (BMI), lifestyle factors, and liver function tests, which were used to train and evaluate the models. The performance of the models was evaluated using metrics such as accuracy, precision, recall and AUC-ROC. …”
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Machine Learning‐Enabled Drug‐Induced Toxicity Prediction by Changsen Bai, Lianlian Wu, Ruijiang Li, Yang Cao, Song He, Xiaochen Bo

“…However, the optimal AI model for different types of toxicity usually varies, making it essential to conduct comparative analyses of AI methods across toxicity domains. …”
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Machine Learning-Based Predictive Maintenance for Photovoltaic Systems by Ali Al-Humairi, Enmar Khalis, Zuhair A. Al-Hemyari, Peter Jung

“…A comparative study of four conventional machine learning models, including logistic regression, k-nearest neighbors, decision tree, and support vector machine, was conducted to determine the most appropriate approach to classifying cleaning needs. …”
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An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models by J. D. Dorathi Jayaseeli, J Briskilal, C. Fancy, V. Vaitheeshwaran, R. S. M. Lakshmi Patibandla, Khasim Syed, Anil Kumar Swain

“…Finally, the gray wolf optimization (GWO) method optimally adjusts the ensemble DL models’ hyperparameter values, resulting in more excellent classification performance. …”
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Machine Learning-Based Prediction of No-Show Telemedicine Encounters by C. Mahony Reategui-Rivera, Wanting Cui, Stefan Escobar-Agreda, Leonardo Rojas-Mezarina, Joseph Finkelstein

“…Aim: This study aimed to evaluate the performance of machine learning (ML) models in predicting patient no-shows for telemedicine appointments within Peruvian health system and identify key predictors of nonattendance. …”
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State of Charge Estimation for Li-Ion Batteries: An Edge-Based Data-Driven Approach by Sesidhar Dvsr, Chandrashekhar Badachi, Chandrashekar Nagawaram, Panduranga Chary Kondoju, C. Dhanamjayulu, Innocent Kamwa

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MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information by Nalini Schaduangrat, Phisit Khemawoot, Apisada Jiso, Phasit Charoenkwan, Watshara Shoombuatong

“…In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. …”
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Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit by Nadea Putri Nur Fauzi, Siti Khomsah, Aditya Dwi Putra Wicaksono

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Deep Learning-Based Detection of Aflatoxin B1 Contamination in Almonds Using Hyperspectral Imaging: A Focus on Optimized 3D Inception–ResNet Model by Md. Ahasan Kabir, Ivan Lee, Sang-Heon Lee

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Agents for Change: Artificial Intelligent Workflows for Quantitative Clinical Pharmacology and Translational Sciences by Mohamed H. Shahin, Srijib Goswami, Sebastian Lobentanzer, Brian W. Corrigan

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Hybrid Naïve Bayes Models for Scam Detection: Comparative Insights From Email and Financial Fraud by Lebede Ngartera, Mahamat Ali Issaka, Saralees Nadarajah

“…Our empirical results reveal that a strategically optimized Naïve Bayes model can deliver competitive accuracy, while maintaining transparency and computational efficiency—key attributes for real-world fraud prevention systems. …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…Abstract Background This study aimed to develop and validate machine learning models for preoperative identification of metastasis to station 4 mediastinal lymph nodes (MLNM) in non-small cell lung cancer (NSCLC) patients at pathological N0-N2 (pN0-pN2) stage, thereby enhancing the precision of clinical decision-making. …”
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