Non-parametric Statistical Methods to Predict the Benefits of Switching to E-learning, by Application on Saudi Universities by Afrah Yahya AL Alrezami

“…It  had been seen that the most significance of indicators related to the benefits of switching to e-learning are: (e-learning solves the problem of increasing the number of students, e-learning focuses more on knowledge, it reduces time, it is flexible, e-learning has succeeded in developing  programs, e-learning offers topics are well organized, it increases the sharing of experiences between students,  assessment methods are fair;  and it gives me enough time to think). …”
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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods by Yihao Yu, Yuqi Yang, Qian Li, Jing Yuan, Yan Zha

“…The recursive feature elimination method was applied to select the optimal feature subset. …”
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Threat analysis and defense methods of deep-learning-based data theft in data sandbox mode by Hezhong PAN, Peiyi HAN, Xiayu XIANG, Shaoming DUAN, Rongfei ZHUANG, Chuanyi LIU

“…The threat model of deep-learning-based data theft in data sandbox model was analyzed in detail, and the degree of damage and distinguishing characteristics of this attack were quantitatively evaluated both in the data processing stage and the model training stage.Aiming at the attack in the data processing stage, a data leakage prevention method based on model pruning was proposed to reduce the amount of data leakage while ensuring the availability of the original model.Aiming at the attack in model training stage, an attack detection method based on model parameter analysis was proposed to intercept malicious models and prevent data leakage.These two methods do not need to modify or encrypt data, and do not need to manually analyze the training code of deep learning model, so they can be better applied to data theft defense in data sandbox mode.Experimental evaluation shows that the defense method based on model pruning can reduce 73% of data leakage, and the detection method based on model parameter analysis can effectively identify more than 95% of attacks.…”
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Soot Mass Concentration Prediction at the GPF Inlet of GDI Engine Based on Machine Learning Methods by Zhiyuan Hu, Zeyu Liu, Jiayi Shen, Shimao Wang, Piqiang Tan

“…To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. …”
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Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression by Jehier Afifi, Tahani Ahmad, Alessandro Guida, Michael John Vincer, Samuel Alan Stewart

“…Purpose: Is machine learning (ML) superior to the traditionally used logistic regression (LR) in prediction of neurodevelopmental outcomes in preterm infants? …”
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“It took a village” - Stories from students in the social sciences about learning quantitative methods by Jully Paola Merchán Tamayo, Simon G. Beaudry, Dominique Gagnon, Nada Nasser, Meredith A. Rocchi

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Comparative Analysis of Deep Learning Methods for Classification of Ablated Regions in Hyperspectral Images of Atrial Tissue by Yeva Gabrielyan, Arpi Hunanyan, Sona Bezirganyan, Lusine Davtyan, Ani Avetisyan, Narine Sarvazyan, Aram Butavyan, Varduhi Yeghiazaryan

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Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges by Xiaolong Xiao, Jianfeng Zhang, Yuan Shao, Jialong Liu, Kaibing Shi, Chunlei He, Dexing Kong

“…This paper reviews ultrasound image and video segmentation methods based on deep learning techniques, summarizing the latest developments in this field, such as diffusion and segment anything models as well as classical methods. …”
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Landslide and Collapse Susceptibility Analysis in Wenchuan Earthquake-damaged Area Based on Ensemble Learning Methods by DING Jiawei, WANG Xiekang

“…Future research can involve more comprehensive data collection methods and investigate broader applications of ensemble learning models, improving the reliability and practical implementation of predictions in disaster management.…”
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Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods by Huibao Yang, Bangshuai Li, Xiujing Gao, Bo Xiao, Hongwu Huang

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Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods by Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan

“…Background Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. …”
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Data Augmentation and Deep Learning Methods for Pressure Prediction in Ignition Process of Solid Rocket Motors by Huixin Yang, Pengcheng Yu, Yan Cui, Bixuan Lou, Xiang Li

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Using Deep Learning (CNN, RNN, LSTM, GRU) methods for the prediction of Protein Secondary Structure by Ezgi Çakmak, İhsan Hakan Selvi

“…Therefore, predicting the protein secondary structure becomes important for studying its structure and function. Many emerging methods, including machine learning, as well as deep learning, have been used to predict the secondary structure of proteins and comprise a crucial part of Structural Bioinformatics. …”
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Sydney’s residential relocation landscape: Machine learning and feature selection methods unpack the whys and whens by Maryam Bostanara, Amarin Siripanich, Milad Ghasri, Taha Hossein Rashidi

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Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang, Zhenqing Wang

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Comparison of suansun fermentation methods based on SBSE-GC-MS combined with SVM machine learning by Jianwen Wu, Yizhao Li, Mi Qiu, Jihua Guan

“…The combination of SBSE-GC-MS, electronic nose, 16S rRNA, and SVM machine learning was used for comprehensive discrimination.MethodsThe flavor components and microbial community structure were analyzed using SBSE-GC-MS, electronic nose, and 16S rRNA sequencing. …”
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Reproduction of reservoir pressure in oil field development: prospects and problems of using methods machine learning by I. N. Ponomarevа, D. A. Martyushev

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Evaluation of Ground Displacements Caused by Installing Jet Grouted Columns Using Machine Learning Methods by Zhi-Feng Wang, Xing-Bin Peng, Yong Liu, Wen-Chieh Cheng, Ya-Qiong Wang, Chao-Jun Wu

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Learning-Based 3D Reconstruction Methods for Non-Collaborative Surfaces—A Metrological Evaluation by Ziyang Yan, Nazanin Padkan, Paweł Trybała, Elisa Mariarosaria Farella, Fabio Remondino

“…In the experimental evaluation, geometric comparisons were carried out between the reference data and learning-based reconstructions. The results indicate that no method can outperform all the others across all evaluations.…”
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Spectroscopy-Based Methods and Supervised Machine Learning Applications for Milk Chemical Analysis in Dairy Ruminants by Aikaterini-Artemis Agiomavriti, Maria P. Nikolopoulou, Thomas Bartzanas, Nikos Chorianopoulos, Konstantinos Demestichas, Athanasios I. Gelasakis

“…The objectives of the current review were (i) to describe the most widely applied spectroscopy-based and supervised machine learning methods utilized for the evaluation of milk components, origin, technological properties, adulterants, and drug residues, (ii) to present and compare the performance and adaptability of these methods and their most efficient combinations, providing insights into the strengths, weaknesses, opportunities, and challenges of the most promising ones regarding the capacity to be applied in milk quality monitoring systems both at the point-of-care and beyond, and (iii) to discuss their applicability and future perspectives for the integration of these methods in milk data analysis and decision support systems across the milk value-chain.…”
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