Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants by Charanpreet Singh, Gurjit S. Randhawa, Aitazaz A. Farooque, Yuvraj S. Gill, Lokesh Kumar KM, Mathuresh Singh, Khalil Al-Mughrabi

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Safety Status Prediction Model of Transmission Tower Based on Improved Coati Optimization-Based Support Vector Machine by Xinxi Gong, Yaozhong Zhu, Yanhai Wang, Enyang Li, Yuhao Zhang, Zilong Zhang

“…The predictive outcomes indicate that the proposed ICOA-SVM model exhibits rapid convergence and high prediction accuracy, with a 62.5% reduction in root mean square error, a 59.6% decrease in average relative error, and a 75.0% decline in average absolute error compared to the conventional support vector machine. …”
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Research on the Mechanism of the Impact of Green View Index of Urban Streets on Thermal Environment: A Machine Learning-Driven Empirical Study in Hangzhou, China by Qiguan Wang, Yanjun Hu, Hai Yan

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Engineering a multi model fallback system for edge devices by Gaurav Kadve, Abishi Chowdhury, Vishal Krishna Singh, Amrit Pal

“…Machine learning (ML) is an effective way to extract information from data and perform decision making on it. …”
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Robust Optimal Sizing of a Stand-Alone Hybrid Renewable Energy System Using Machine Learning-Based Uncertainty Sets by Ali Keyvandarian, Ahmed Saif, Ronald Pelot

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Enhancing Business Success Prediction: A Data-Driven Machine Learning Mode by Deo Arpit, Korde Manish, Tiwari Anant, Jain Anant, Choudhary Akash

“…This study presents a machine learning model for predicting company failure, utilizing logistic regression, random forest, and neural networks. …”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…By using a dataset of 121,401 voyage records, we evaluated nine regression models, including conventional, ensemble-based, and deep learning models. …”
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Assessing cyber risks in construction projects: A machine learning-centric approach by Dongchi Yao, Borja García de Soto

“…This study develops a Machine Learning (ML)-centric approach to assess common cyber risks for construction projects. …”
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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

“…Because traditional assessment methods are difficult to monitor and cannot effectively predict the population growth trend of R. opimus, an R. opimus activity prediction model was constructed using the particle swarm optimization algorithm‐extreme learning machine (PSO‐ELM). …”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…Abstract Background This study aims to develop a machine learning (ML)-based predictive model for evaluating the efficacy of percutaneous pulmonary balloon angioplasty (BPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) by integrating clinical and echocardiographic parameters. …”
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Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…Abstract Objective To investigate machine learning‐based regression models to predict the postoperative apnea‐hypopnea index (AHI) for evaluating the outcome of velopharyngeal surgery in adult obstructive sleep apnea (OSA) subjects. …”
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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

“…However, there are currently no effective methods of predicting who is likely to experience suicidal thoughts and behavior. Machine learning (ML) approaches can be leveraged to develop models that evaluate the complex etiology of suicidal behavior, facilitating the timely identification of at-risk individuals and promoting individualized treatment allocation.Materials and methodsThis retrospective case-control study used longitudinal sociodemographic, psychosocial, and clinical data of 1,126 PLWH from Uganda to evaluate the potential of ML in predicting suicidality. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…These characteristics limit both traditional empirical forecasting and machine learning methods. This paper proposes a novel method called dynamically modeled machine learning to predict monthly temperature anomalies over China. …”
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Prediction and Optimization of Civil Aviation Flight Delays Based on Machine Learning Algorithms by Qingwei Zhong, Yingxue Yu, Yiru Huang, Tianhang Zhang

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Occupant-Centric Load Optimization in Smart Green Townhouses Using Machine Learning by Seyed Morteza Moghimi, Thomas Aaron Gulliver, Ilamparithi Thirumarai Chelvan, Hossen Teimoorinia

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Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study by Shayan Nejadshamsi, Vania Karami, Negar Ghourchian, Narges Armanfard, Howard Bergman, Roland Grad, Machelle Wilchesky, Vladimir Khanassov, Isabelle Vedel, Samira Abbasgholizadeh Rahimi

“…For depression classification, we proposed a HOPE (Home-Based Older Adults’ Depression Prediction) machine learning model with feature selection, dimensionality reduction, and classification stages, evaluating various model combinations using accuracy, sensitivity, precision, and F1-score. …”
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Evaluation of Time-Domain Acoustic Signature in TIG Welding of 5083 Aluminum Alloy: A Methodological Comparison of Feature Reduction Approaches by V M Gautham, A Sumesh, E V Jithin, K Rameshkumar, Dinu Thomas Thekkuden

“…In the present study, a machine learning model was developed to identify weld conditions such as good weld, porosity, and burn-through in TIG welding of aluminium alloy. …”
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Predictive Model for Erosion Rate of Concrete Under Wind Gravel Flow Based on K-Fold Cross-Validation Combined with Support Vector Machine by Yanhua Zhao, Kai Zhang, Aojun Guo, Fukang Hao, Jie Ma

“…To address this, the study utilized a machine learning (ML) model for a more precise prediction and evaluation of CER. …”
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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

“…This study is focused on developing a machine learning (ML) model that is clinically acceptable for accurately detecting vitamin D status and eliminates the need for 25-OH-D determination while addressing overfitting. …”
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Predicting Oil Price Trends During Conflict With Hybrid Machine Learning Techniques by Hicham Boussatta, Marouane Chihab, Mohamed Chiny, Younes Chihab

“…Using advanced machine learning techniques, we developed a hybrid system combining Random Forest, ElasticNet, K-Nearest Neighbors, Gradient Boosting, and Support Vector Regressor models. …”
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