XSShield: A novel dataset and lightweight hybrid deep learning model for XSS attack detection by Gia-Huy Luu, Minh-Khang Duong, Trong-Phuc Pham-Ngo, Thanh-Sang Ngo, Dat-Thinh Nguyen, Xuan-Ha Nguyen, Kim-Hung Le

“…To enhance the efficiency of XSS attack detection, the adoption of machine learning (ML) and deep learning (DL) techniques offers promising solutions, but their effectiveness is limited by the lack of comprehensive and diverse datasets. …”
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Machine learning approaches for predicting feed intake in Australian Merino, Corriedale, and Dohne Merino sheep by Fernando Amarilho-Silveira, Ignacio De Barbieri, Elly A. Navajas, Jaime Araujo Cobuci, Gabriel Ciappesoni

“…Feed intake is a challenging trait to measure due to the high costs associated with labor, feeding, and facilities. Applying machine learning approaches, considering traits as potential predictors, offers a cost-effective alternative to direct feed intake measurement. …”
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Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome by Gabriela Meza-Fuentes, Iris Delgado, Mario Barbé, Ignacio Sánchez-Barraza, Mauricio A. Retamal, René López

“…Conclusion This study presents an approach to improve subphenotype categorization in ARDS. …”
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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…In this study, ensemble learning (EL) models are designed to enhance the accuracy and efficiency in predicting the flexural ultimate capacity of reinforced ultra-high-performance concrete (UHPC) beams with the aim of providing a more reliable and efficient design experience for structural applications. …”
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Machine Learning-Based Intrusion Detection Systems for the Internet of Drones: A Systematic Literature Review by Mostafa Ogab, Sofiane Zaidi, Abdelhabib Bourouis, Carlos T. Calafate

“…Moreover, there is a lack of a comprehensive study that systematically consolidates existing research. In this paper, we present a systematic literature review to examine the current research area of intrusion detection systems for IoD, focusing on the effectiveness of implemented machine learning models, employed datasets, existing challenges and limitations, as well as emerging trends and future research directions. …”
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A Multi-Task Based Deep Learning Framework With Landmark Detection for MRI Couinaud Segmentation by Dong Miao, Ying Zhao, Xue Ren, Meng Dou, Yu Yao, Yiran Xu, Yingchao Cui, Ailian Liu

“…Our model achieved an average Dice Similarity Coefficient (DSC) of 85.29%, surpassing the next best-performing models by 3.12%.Our research presents a pioneering automated approach for segmenting Couinaud segments using CE-MRI. …”
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Performance of unmarked abundance models with data from machine‐learning classification of passive acoustic recordings by Cameron J. Fiss, Samuel Lapp, Jonathan B. Cohen, Halie A. Parker, Jeffery T. Larkin, Jeffery L. Larkin, Justin Kitzes

“…Abstract The ability to conduct cost‐effective wildlife monitoring at scale is rapidly increasing due to the availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human‐based surveys. …”
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MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning by Yu Wang, Zaiyi Liu, Xiaoke Ma

“…Integrative analysis of multi-modal SRT data holds immense potential for understanding biological mechanisms. Here, we present a flexible multi-modal contrastive learning for the integration of SRT data (MuCST), which joins denoising, heterogeneity elimination, and compatible feature learning. …”
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A Survey of Machine Learning Techniques Leveraging Brightness Indicators for Image Analysis in Biomedical Applications by Hajer Ghodhbani, Suvendi Rimer, Khmaies Ouahada, Adel M. Alimi

“…This paper presents a comprehensive survey of machine-learning techniques that leverage brightness indicators for image analysis within biomedical applications. …”
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PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control by Rohit Bokade, Xiaoning Jin

“…Multi-Agent Reinforcement Learning (MARL) presents a promising approach for addressing the complexity of Traffic Signal Control (TSC) in urban environments. …”
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A framework for the formulation of security issues in the field of e-learning using Meta-Synthesis method by Abouzar Arabsorkhi, Afshin Khodabandeh, Laleh Tashakori

“…We have presented a three-dimensional model for security issues and requirements of e-learning, based on the findings of research. …”
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Learning verbs of movement in a Foreign Language: Spanish students of English in a formal context by Gema Alcaraz Mármol

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Enhancing the Teaching-Learning Process through Neutrosophic Statistical Analysis of Professional Competencies and Metacognitive Strategies by Dante Manuel Macazana Fernandez, Wilder Fabio Ramos Palacios, Daniel Amílcar Pinto Pagaza, Mary Liz Mendoza Hidalgo, Tula Margarita Espinoza Moreno, Mayra Susan Albán Taipe

“…In the educational field, teaching effectiveness depends on the interaction between professional competencies and teachers' metacognitive strategies. Therefore, the present study has proposed to evaluate the relationship between professional competencies and metacognitive strategies, by using the management of indeterminacies to improve the teaching-learning process. …”
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Testing for approximate measurement invariance of instructional quality in the Teaching and Learning International Survey (TALIS) 2018 by Guangbao Fang, Timothy Teo, Philip Wing Keung Chan

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Flow Visualization In Closed Loop Pulsating Heat Pipe (CLPHP) Using Deep Learning Techniques by N Santhi Sree, P Bhramara, Medidi Veerabhadra Rao, Anthani Kamala Priya, G Avinash, K Sindhu

“…The present work describes an alternative method for recognizing and tracking the flow in PHP which is based on visualization to address the issue of examining individual flows graphically. …”
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Cardioattentionnet: advancing ECG beat characterization with a high-accuracy and portable deep learning model by Youfu He, Youfu He, Youfu He, Yu Zhou, Yu Zhou, Yu Qian, Jingjie Liu, Jinyan Zhang, Debin Liu, Qiang Wu, Qiang Wu

“…This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.MethodsThe present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet). …”
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Encoding-Based Machine Learning Approach for Health Status Classification and Remote Monitoring of Cardiac Patients by Sohaib R. Awad, Faris S. Alghareb

“…In particular, machine learning (ML) techniques have been extensively utilized to analyze electrocardiogram (ECG) signals in cardiac patients to classify heart health status. …”
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Using the CIPP Model to elicit perceptions of health professions faculty and students about virtual learning by Sakineh Gerayllo, Mohammadali Vakili, Leila Jouybari, Zahra Moghadam, Ali Jafari, Alireza Heidari

“…Results The scale items for each of the CIPP components that elicited the highest levels of agreement by both professors and students were as follows based on a five point scale where higher scores indicated higher levels of respondent agreement: Context: Topics presented in the virtual training are determined according to the course plan (3.63), and virtual education reduces the teacher's control over class (3.56); Input: Designated hours are suitable for virtual learning classes (3.29); Process: Professors have less commitment and responsibility in providing virtual courses (3.48); and Product: Student participation in virtual classes is low (3.78), and virtual learning saves time (3.67).” …”
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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

“…Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine learning on whole-genome sequencing data (WGS) presents significant potential for uncovering the genomic mechanisms underlying drug resistance in MTB. …”
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How accurately can supervised machine learning model predict a targeted psychiatric disorder? by Haitham Jahrami, Amir H. Pakpour, Waqar Husain, Achraf Ammar, Zahra Saif, Ali Husain Alsalman, Adel Aloffi, Khaled Trabelsi, Seithikurippu R. Pandi-Perumal, Michael V. Vitiello

“…This study evaluates the ability of a supervised machine learning (ML) model to match the diagnostic skills of psychiatrists when presented with equivalent information pertinent to symptoms of HD. …”
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