An interpretable machine learning model with demographic variables and dietary patterns for ASCVD identification: from U.S. NHANES 1999–2018 by Qun Tang, Yong Wang, Yan Luo

“…Five ML models were developed to predict ASCVD, and the best-performing model was selected for further analysis. …”
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Domain generalization for image classification based on simplified self ensemble learning. by Zhenkai Qin, Xinlu Guo, Jun Li, Yue Chen

“…Finally, a dynamic loss adaptive weighted voting strategy ensures more accurate predictions across diverse domains. Experimental results on three public benchmark datasets (OfficeHome, PACS, and VLCS) demonstrate that our proposed algorithm achieves an improvement of up to 3 . 38% over existing methods in terms of generalization performance, particularly in complex and diverse real-world scenarios, such as autonomous driving and medical image analysis. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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On nonlinear coupled differential equations for corrugated backward facing step (CBFS) with circular obstacle: AI-neural networking by Khalil Ur Rehman, Wasfi Shatanawi, Weam G. Alharbi

“…ANN has 10 neurons in the hidden layer and is trained with the Levenberg-Marquardt algorithm. Mean square error and regression analysis are performed to validate the model. …”
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Generative AI-powered multilingual ASR for seamless language-mixing transcriptions by Puspita Dash, Sruthi Babu, Logeswari Singaravel, Devadarshini Balasubramanian

“…Here, we use a generative pre-trained transformer model, which learns to predict the subsequent word in a language during the pre-training stage in order to get an understanding of language structure and semantics. …”
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Designing transit routes based on vehicle routing behavior determined through location-based services data by Yuhan Tang, Abdullah Alhadlaq, Alben Rome Bagabaldo, Marta C. Gonzalez

“…Abstract The disparity between transit agency travel predictions and the unpredictable nature of real-world travel behavior contributes to inefficiencies within the transit system. …”
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Lightweight construction safety behavior detection model based on improved YOLOv8 by Kan Huang, Mideth B. Abisado

“…YOLO (You Only Look Once) is an object detection algorithm that can achieve real-time and efficient object detection by dividing images into grids and predicting the bounding boxes and categories of objects in each grid. …”
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Learning behavior analysis and personalized recommendation system of online education platform based on machine learning by Feng Ma

“…For example, the accuracy rate reached more than 70 % in predicting learners' preference for specific course types. …”
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Indoor fire and smoke detection based on optimized YOLOv5. by Md Shafak Shahriar Sozol, M Rubaiyat Hossain Mondal, Achmad Husni Thamrin

“…It also used the Grad-CAM technique to provide visual explanations for model predictions, ensuring interpretability and transparency. …”
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The Potentials of Contrast-Free Renal ASL MRI Perfusion in the Diagnosis and Dynamic Follow-Up of Renal Lesions in Patients with Diffuse Liver Diseases by А. А. Telesh, Т. G. Morozova

“…Contrast-free renal ASL-perfusion is an informative method for predicting, diagnosis and dynamic follow-up of renal lesions in patients with various DLD.…”
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Advancing thyroid diagnosis: integrating AI-driven CAD framework with numerical data and ultrasound images by Saleh Ateeq Almutairi

“…The proposed CAD framework employs the sparse search algorithm (SSA) for optimized feature selection from numerical data and the tree-structured Parzen estimator for tuning the hyperparameters. …”
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LSTM-Based State-of-Charge Estimation and User Interface Development for Lithium-Ion Battery Management by Abdellah Benallal, Nawal Cheggaga, Amine Hebib, Adrian Ilinca

“…The Hyperband optimization algorithm accelerates model training and enhances adaptability to varying operating conditions, making it scalable for diverse battery applications. …”
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Probability of Pulse Overlap as a Quantitative Indicator of Signal Environment Complexity by A. S. Podstrigaev, A. V. Smolyakov, I. V. Maslov

“…The principles of disturbances in the WBA receiver and algorithmic errors in the processing of overlapped signals are described. …”
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Beyond accuracy: Multimodal modeling of structured speaking skill indices in young adolescents by Candy Olivia Mawalim, Chee Wee Leong, Guy Sivan, Hung-Hsuan Huang, Shogo Okada

“…The experimental results demonstrate that fusing interpretable features, including prosody, action units, and turn-taking, significantly enhances the accuracy of spoken English skill prediction, achieving an overall accuracy of 83% when a machine learning model based on the light gradient boosting algorithm is used. …”
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Plastic failure and deformation calculation of shaft structure in soil under lateral explosion by Shanzheng Sun, Yuan Liu

“…We compared the discrepancies between the finite element calculation model and the theoretical algorithm and examined the impacts of concrete strength, concrete thickness to radius ratio, steel plate thickness to radius ratio, and charge mass on steel plate displacement. …”
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Research on the pneumatic-electric braking collaborative control strategy for heavy-haul trains based on heuristic dual-end optimization by ZHANG Zhengfang, SHI Ke, LIU Haitao, WANG Yue, FENG Ling

“…At the same time, a neural network was designed to identify parameters for a nonlinear pneumatic braking model, thereby yielding predicted pneumatic braking forces. Subsequently, an A * algorithm was developed for iterative refinement within the finite set sequence, collaborative control sequence that satisfies the end constraints. …”
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Energy intensity of hydrocarbons in liquid and solid states by G. J. Кабо, L. A. Kabo, L. S. Karpushenkava, A. V. Blokhin

“…This analysis is required to substantiate the algorithm for locating energy-intensive CnHm structures.Methods. …”
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Detección de situaciones de emergencias usando el modelo Naive- Bayes de machine learning. by Iván Leonel Vásquez-Rojas, Miguel José Vívas-Cortéz

“…In general, the obtained model has an accuracy of 74.6% in its classification predictions. It is considered that the use of a Naive-Bayes model for a prototype in the classification of emergency messages from the social network X could be very useful based on the results of the evaluation of its classification performance.…”
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A Novel Vision Sensing System for Tomato Quality Detection by Satyam Srivastava, Sachin Boyat, Shashikant Sadistap

“…Zigbee module has been interfaced with developed system for wireless transmission from host system to PC based server for further processing. Algorithm development consists of three major steps, preprocessing steps like noise rejection, segmentation and scaling, classification and recognition, and automatic disease detection and classification. …”
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Calcium and Vitamin D supplements in the treatment of osteoporosis. by Ahmed I Alobaidi, Ahmed Hazim Al Aoodh, Nabeel Yousif Abdullah, HUSHAM JASSIM, Mubder Abdul_Raheem Mohammed saeed, Housamaddeen Alfarhan

“…The fracture risk assessment algorithm is a tool used by medical professionals to predict people at risk of fracture due to osteoporosis and to take steps to prevent that. …”
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