Machine learning prediction model for lateral lymph node metastasis in rectal cancer by Longchun Dong, Shiyong Du, Hongjie Yang, Hongjie Yang, Hongjie Yang, Hongjie Yang, Xipeng Zhang, Xipeng Zhang, Xipeng Zhang, Xipeng Zhang, Zhichun Zhang, Zhichun Zhang, Zhichun Zhang, Zhichun Zhang, Shuan Geng, Shuan Geng, Shuan Geng, Shuan Geng, Yuanda Zhou, Yuanda Zhou, Yuanda Zhou, Yuanda Zhou, Peng Li, Peng Li, Peng Li, Peng Li, Qingsheng Zeng, Qingsheng Zeng, Qingsheng Zeng, Qingsheng Zeng, Yi Sun, Yi Sun, Yi Sun, Yi Sun, Peishi Jiang

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Enhancing the Sustainability of Machine Learning-Based Malware Detection Techniques for Android Applications by Seyeon Park, Hojun Lee, Daeun Kim, Hyeun Jun Moon, Seong-Je Cho, Youngsup Hwang, Hyoil Han, Kyoungwon Suh

“…The rapid increase in smartphone usage has led to a corresponding rise in malicious Android applications, making it important to develop efficient and sustainable malware detection methods that maintain high accuracy. This paper presents a two-stage machine learning approach aimed at improving both detection accuracy and sustainability in Android malware classification. …”
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A Secure and Robust Machine Learning Model for Intrusion Detection in Internet of Vehicles by Pradeep Kumar Tiwari, Shiv Prakash, Animesh Tripathi, Tiansheng Yang, Rajkumar Singh Rathore, Manish Aggarwal, Narendra Kumar Shukla

“…Although these technologies provide unparalleled connectivity and communication capabilities, they also introduce new security challenges, particularly in terms of Intrusion Detection. This paper presents a robust machine learning (ML) technique to enhance the security of IoV networks by developing an efficient intrusion detection system (IDS). …”
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Comparative analysis of automated foul detection in football using deep learning architectures by Abdallah Rabee, Zakaria Anwar, Ahmed AbdelMoety, Ahmed Abdelsallam, Mahmoud Ali

“…Abstract Automated foul detection in football represents a challenging task due to the dynamic nature of the game, the variability in player movements, and the ambiguity in differentiating fouls from regular physical contact. This study presents a comprehensive comparative evaluation of eight state-of-the-art Deep Learning (DL) architectures — EfficientNetV2, ResNet50, VGG16, Xception, InceptionV3, MobileNetV2, InceptionResNetV2, and DenseNet121 — applied to the task of automated foul detection in football. …”
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Blockchain framework with IoT device using federated learning for sustainable healthcare systems by B. Bhasker, P. Muralidhara Rao, P. Saraswathi, S. Gopal Krishna Patro, Javed Khan Bhutto, Saiful Islam, Mohammed Kareemullah, Addisu Frinjo Emma

“…Consolidating medical information into a singular repository to train a robust ML model engenders apprehensions around privacy, ownership, and adherence to regulatory standards due to increased concentration. Federated learning (FL) addresses previous challenges using a centralized aggregate server to distribute global learning models. …”
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Real Time Vehicle Classification Using Deep Learning—Smart Traffic Management by Tejasva Maurya, Saurabh Kumar, Mritunjay Rai, Abhishek Kumar Saxena, Neha Goel, Gunjan Gupta

“…In response to this critical issue, this research presents a novel deep learning‐based approach to vehicle classification aimed at enhancing traffic management systems and road safety. …”
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Risks and Regulations for Application of the LLaMA Model in University Future Learning Centers by QIAO Jinhua, MA Xueyun

“…[Results/Conclusions] The study identifies three principal challenges encountered in the application of the LLaMA within university learning centers. The first challenge arises from reliability risks linked to content generated by the AI, which may be affected by biases present in the training data. …”
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Belief in building a full-fledged distance learning course in athletic training by Andrii Yefremenko, Illia Shutieiev

“… The problem of developing distance learning in physical education and sports is relevant to the growing demand for flexible educational models and modern technologies. …”
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Challenges and Strategies of Remote Foreign Language Learning for Students with Autism Spectrum Disorder by Iryna Haidai, Liudmyla Suvorova, Nataliia Pankovyk, Yuliia Herasymchuk, Olha Zadoienko

“…These are a case-block approach to presenting material using distance technologies, game practices, and programs that help in learning a foreign language. …”
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Machine learning reveals the dynamic importance of accessory sequences for Salmonella outbreak clustering by Chao Chun Liu, William W. L. Hsiao

“…By training machine learning models on a curated set of historical Salmonella outbreaks, we revealed an enriched presence of outbreak distinguishing features in a wide range of mobile genetic elements. …”
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HybridFusionNet: Deep Learning for Multi-Stage Diabetic Retinopathy Detection by Amar Shukla, Shamik Tiwari, Anurag Jain

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Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling by Khalil Moulay Brahim, Ahmed Hadjadj, Aissa Abidi Saad, Elfakeur Abidi Saad, Hichem Horra

“…A detailed machine learning model is presented, emphasizing the capability to handle complex data and improve prediction under diverse operational conditions. …”
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Efficient Hotspot Detection in Solar Panels via Computer Vision and Machine Learning by Nayomi Fernando, Lasantha Seneviratne, Nisal Weerasinghe, Namal Rathnayake, Yukinobu Hoshino

“…Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. …”
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An evaluation methodology for machine learning-based tandem mass spectra similarity prediction by Michael Strobel, Alberto Gil-de-la-Fuente, Mohammad Reza Zare Shahneh, Yasin El Abiead, Roman Bushuiev, Anton Bushuiev, Tomáš Pluskal, Mingxun Wang

“…Result In this work, we present the creation of a new evaluation methodology using a train/test split that allows for the evaluation of machine learning models at varying degrees of structural similarity between training and test sets. …”
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Liver fibrosis stage classification in stacked microvascular images based on deep learning by Daisuke Miura, Hiromi Suenaga, Rino Hiwatashi, Shingo Mabu

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The Geometry of Flow: Advancing Predictions of River Geometry With Multi‐Model Machine Learning by Shuyu Y. Chang, Zahra Ghahremani, Laura Manuel, Seyed Mohammad Hassan Erfani, Chaopeng Shen, Sagy Cohen, Kimberly J. Van Meter, Jennifer L. Pierce, Ehab A. Meselhe, Erfan Goharian

“…Although well‐established, power‐law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of their limitations. In the present study, we have moved beyond these traditional power‐law relationships, testing the ability of machine‐learning models to provide improved predictions of river width and depth. …”
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Supporting complex skill learning with digital tools in tertiary education: educator’s perspective by Bernarda Letnar, André Klostermann, Mirko Schmidt

“…Interviews consisted of practical case of skill acquisition, followed by further discussion on educator’s general teaching approaches and how skill learning is implemented and supported. Educators were presented with a video of a student performing a sports skill in suboptimal manner to prompt a discussion on their approach to optimize student’s movement. …”
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New roles and competencies of teachers in the ICT-mediated learning environment of Russian universities by I. N. Ainoutdinova, T. M. Tregubova, J. Ng, V. A. Kopnov

“…The conceptual apparatus is concretised; conditions for the ICTs inclusion in education are defined; the authors’ classification of the roles, skills and competencies of teachers for ICT-based professional activity, growth and development is given; the authors’ diagnostic scale (toolkit) for rating and grading humanities teachers’ level of ICT proficiency is designed and presented.Scientific novelty. The scientific novelty of the work lies in the definition and description of the systemic integrity of educational content based on knowledge and technology, the appropriate conditions for integration of ICT in the curriculum, the level of teachers’ ICT proficiency sufficient for the learning needs of their students and the expected learning outcomes.Practical significance. …”
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Advanced predictive machine and deep learning models for round-ended CFST column by Feng Shen, Ishan Jha, Haytham F. Isleem, Walaa J.K. Almoghayer, Mohammad Khishe, Mohamed Kamel Elshaarawy

“…Using an extensive dataset of 200 CFST stub column tests, this research evaluates three machine learning (ML) models – LightGBM, XGBoost, and CatBoost – and three deep learning (DL) models – Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). …”
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PEDAGOGICAL DESIGN AS A MEANS OF INCREASING STUDENTS’ MOTIVATION IN THE E-LEARNING PROCESS by Nakhod Svitlana Anatoliivna

“…Thanks to e-learning, the attendance of events increases due to online participants and records reviewing; new participants and those who cannot be present due to lack of time or geographical distance are involved; access to electronic materials and videos after the lecture is provided; accessibility and cost-effectiveness of education for all categories of citizens is ensured, including the socially disadvantaged and the less mobile is guaranteed; the possibility of choosing individual content and pace of learning is activated; the student’s independent cognitive activity is stimulated. …”
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