Evaluating soil erosion zones in the Kangsabati River basin using a stacking framework and SHAP model: a comparative study of machine learning approaches by Javed Mallick, Saeed Alqadhi, Swapan Talukdar, Md Nawaj Sarif, Tania Nasrin, Hazem Ghassan Abdo

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NSA-CHG: An Intelligent Prediction Framework for Real-Time TBM Parameter Optimization in Complex Geological Conditions by Youliang Chen, Wencan Guan, Rafig Azzam, Siyu Chen

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Auto forensic detecting algorithms of malicious code fragment based on TensorFlow by Binglong LI, Jinlong TONG, Yu ZHANG, Yifeng SUN, Qingxian WANG, Chaowen CHANG

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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). …”
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Getting NBA Shots in Context: Analysing Basketball Shots with Graph Embeddings by Schmid Marc, Schöpf Moritz, Kolbinger Otto

“…The messages between spatial and temporal features are separated, and an attention mechanism is implemented, making the graph neural network interpretable. …”
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Super-Resolution of Medical Images Using Real ESRGAN by Priyanka Nandal, Sudesh Pahal, Ashish Khanna, Placido Rogerio Pinheiro

“…Image super-resolution techniques based on deep learning can assist us in extracting spatial features from a low-resolution image captured with current technologies. …”
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Machine Learning-Based Prediction of Fatigue Fracture Locations in 7075-T651 Aluminum Alloy Friction Stir Welded Joints by Guangming Mi, Guoqin Sun, Shuai Yang, Xiaodong Liu, Shujun Chen, Wei Kang

“…In this study, we investigate fatigue fracture location prediction in 7075-T651 aluminum alloy FSW joints by applying four machine learning methods—decision tree, logistic regression, a three-layer back-propagation artificial neural network (BP ANN), and a novel Quadratic Classification Neural Network (QCNN)—using maximum stress, stress amplitude, and stress ratio as input features. …”
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Optimizing Boride Coating Thickness on Steel Surfaces Through Machine Learning: Development, Validation, and Experimental Insights by Selim Demirci, Durmuş Özkan Şahin, Sercan Demirci, Armağan Gümüş, Mehmet Masum Tünçay

“…Additionally, a deep neural network (DNN) architecture demonstrated robust predictive performance, achieving an R² of 0.93. …”
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Computational modeling of visual salience alteration and its application to eye-movement data by Yoshihisa Fujita, Toshiya Murai, Jun Miyata, Jun Miyata

“…It handles diverse image-derived features, as seen in functional approximation models, while implementing center-surround competition—the core process of salience computation—via an artificial neural network, akin to neurobiological models. …”
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A rapid recognition method for radar active jamming based on the Hybrid 3_CNN‐Transformer model by Jingjing Wei, Lei Yu, Yinsheng Wei, Rongqing Xu

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AI-Driven Framework for Enhanced and Automated Behavioral Analysis in Morris Water Maze Studies by István Lakatos, Gergő Bogacsovics, Attila Tiba, Dániel Priksz, Béla Juhász, Rita Erdélyi, Zsuzsa Berényi, Ildikó Bácskay, Dóra Ujvárosy, Balázs Harangi

“…Several machine learning classifiers, including random forest and neural networks, are evaluated, with feature selection techniques applied to improve the classification accuracy. …”
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Optimization of CNN Activation Functions using Xception for South Sulawesi Batik Classification by Aswan Aswan, Eva Yulia Puspaningrum, Billy Eden William Asrul

“…This study improves the performance of convolutional neural networks for South Sulawesi batik classification by optimizing activation functions within the Xception architecture which exploits depthwise separable convolutions for efficient and detailed feature extraction. …”
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Biologically inspired hybrid model for Alzheimer’s disease classification using structural MRI in the ADNI dataset by Houmem Slimi, Imen Cherif, Sabeur Abid, Mounir Sayadi

“…This study proposes a hybrid convolutional neural network-spiking neural network (CNN-SNN) architecture to classify AD stages using structural MRI (sMRI) data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). …”
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Enhancing leaf disease classification using GAT-GCN hybrid model by Shyam Sundhar, Riya Sharma, Priyansh Maheshwari, Suvidha Rupesh Kumar, T. Sunil Kumar

“…The research presented in this paper addresses this need by analyzing a hybrid model built using Graph Attention Network (GAT) and Graph Convolution Network (GCN) models. …”
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Botnet detection in internet of things using stacked ensemble learning model by Mudasir Ali, Muhammad Faheem Mushtaq, Urooj Akram, Daniel Gavilanes Aray, Manuel Masias Vergara, Hanen Karamti, Imran Ashraf

“…The UNSW-NB15 dataset is used to train machine learning models and evaluate their effectiveness in detecting cyber-attacks on IoT networks. …”
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The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ by Xuhui Wang

“…ResNet50 is chosen for its powerful feature extraction capabilities and outstanding performance in image classification tasks. …”
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Intrusion Detection and Real-Time Adaptive Security in Medical IoT Using a Cyber-Physical System Design by Faeiz Alserhani

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Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations by Chen YT, Chen GJ, Lin YS

“…These results demonstrate the potential of the proposed ANN machine learning model for the accurate prediction of first-trimester Down syndrome risk.Keywords: machine learning, first trimester down syndrome screening, deep neural network…”
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ResTreeNet: A Height-Aware LiDAR Tree Classification Model With Explainable AI for Forestry Applications by Asrat Kaleab Taye, Jeong-Mook Park, Hyung-Ju Cho, Jin-Taek Kang, Yeon-Ok Seo

“…Our innovative approach combines residual networks for hierarchical feature extraction, a height-based grouping strategy to enhance structural representation, and a parameterized geometric transformation module to improve translation invariance and model adaptability. …”
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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

“…This study identified key predictors of job satisfaction among Peruvian school principals by applying an ensemble of feature selection methods and evaluating five machine learning algorithms (Random Forest, Decision Trees-CART, Histogram-Based Gradient Boosting, XGBoost, and LightGBM) with data from the 2018 National Survey of Directors. …”
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