Condition Monitoring of Chain Sprocket Drive System Based on IoT Device and Convolutional Neural Network by Sang Kwon Lee, Jiseon Back, Kanghyun An, Sunwon Kim, Changho Lee, Pungil Kim

“…To update the learning parameters of the CNN, the RMSprop learning algorithm was applied, and the CNN was trained using 500 image samples. …”
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Enhancing intrusion detection systems: Innovative deep learning approaches using CNN, RNN, DBN and autoencoders for robust network security by Yakub HOSSAIN, Zannatul FERDOUS, Tanzillah WAHID, Md. Torikur RAHMAN, Uttam Kumar DEY, Mohammad Amanul ISLAM

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Visual Automatic Localization Method Based on Multi-level Video Transformer by Qiping ZOU, Botao LI, Saian CHEN, Xi GUO, Taohong ZHANG

“…This approach incorporates advanced image processing techniques to analyze video sequences captured by the industrial camera. Sophisticated algorithms enable the system to identify the frame with optimal clarity and sharpness. …”
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Machine learning approaches for predicting the structural number of flexible pavements based on subgrade soil properties by Asadullah Ziar

“…Abstract This study presents a machine learning approach to predict the structural number of flexible pavements using subgrade soil properties and environmental conditions. Four algorithms were evaluated, including random forest, extreme gradient boosting, gradient boosting, and K nearest neighbors. …”
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An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer by Lihuan Dai, Jinxue Yin, Xin Xin, Chun Yao, Yongfang Tang, Xiaohong Xia, Yuanlin Chen, Shuying Lai, Guoliang Lu, Jie Huang, Purong Zhang, Jiansheng Li, Xiangguang Chen, Xi Zhong

“…Results Nine features significantly associated with PD-L1 expression status were ultimately selected to construct the predictive model. …”
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Simulation model for determining the rational dimensions of the irreducible reserve of locomotives at the stations of its turnover by E. A. Sotnikov, M. M. Gonik, S. V. Khomyakov, S. V. Mikhaylov

“…Performing mass calculations for various conditions will allow determining the rational size of the irreducible reserve of locomotives at the stations of its turnover and eliminate the losses associated with the cancellation of the schedule threads due to the lack of provision of finished trains by locomotives. …”
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A hybrid elastic-hyperelastic approach for simulating soft tactile sensors by Berith Atemoztli De la Cruz Sánchez, Jean-Philippe Roberge

“…By leveraging a dataset of 53,400 real-world tactile maps, this methodology enables effective training, validation, and testing of each pipeline. …”
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Automatic text generation system for endangered languages based on conditional generative adversarial networks by Zhong Luo

“…The focus is on overcoming challenges associated with discrete data handling in natural language generation by utilizing an improved CGAN model. …”
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Explainable Machine Learning for Mapping Minerals From CRISM Hyperspectral Data by Sandeepan Dhoundiyal, Moni Shankar Dey, Shashikant Singh, Pattathal V. Arun, Guneshwar Thangjam, Alok Porwal

“…This amalgamation of Random Forest and SHAP addresses limitations associated with existing CRISM classification methods, offering stability during training, reduced manual intervention, and interpretability while achieving a Kappa (κ) of 0.91 over the CRISM Machine Learning Toolkit's mineral data set with ∼470,000 labeled spectra.…”
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Simultaneous, Non-Contact and Motion-Based Monitoring of Respiratory Rate in Sheep Under Experimental Condition Using Visible and Near-Infrared Videos by Beatriz Leandro Bonafini, Lukas Breuer, Lisa Ernst, René Tolba, Lucas Ferrari de Oliveira, Mauren Abreu de Souza, Michael Czaplik, Carina Barbosa Pereira

“…A motion-tracking algorithm was developed to assess abdominal movement associated with the sheep’s respiratory cycle. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Additionally, 14 machine learning algorithms were trained and validated using SMOTE-balanced data and five-fold cross-validation. …”
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AI-Based Linguistic Frameworks for Monitoring Physical Health, Sports Performance, and Educational Contexts by Rakhimov Vladimir, Saidnazarov Ulugbek, Mustafaev Ispandiyar

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Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data by Sai Balakavi, Vineet Vadrevu, Kristofer Lasko

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Classification of Biological Data using Deep Learning Technique by Azha Javed, Muhammad Javed Iqbal

“…Recently machine learning algorithms got huge attention and are widely used. These algorithms are based on deep learning architecture and data-driven models. …”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…In contrast, other algorithmic models exhibited certain deficiencies in either convergence behavior or computational efficiency.…”
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Novel Cuproptosis-Related Gene Signature for Precise Identification of High-Risk Populations in Low-Grade Gliomas by Ping Chen, Hailing Han, Xuejie Wang, Bing Wang, Zhanfeng Wang

“…Cuproptosis is a recently described form of cell death associated with the abnormal aggregation of lipid acylated proteins. …”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…K-means clustering was used for unsupervised learning, whereas six supervised machine learning algorithms were trained and validated for EOA classification. …”
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A Data-Driven Comparative Analysis of Machine-Learning Models for Familial Hypercholesterolemia Detection by Tomasz Kocejko

“…The primary objective is to explore an enhanced method for estimating FH probability, surpassing the currently recommended Dutch Lipid Clinic Network (DLCN) Score. The models were trained using the largest Polish cohort of patients enrolled in an FH clinic, all of whom underwent genetic testing for FH-associated mutations. …”
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soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes by Yi‐Jen Sun, Shih‐Ching Yen, Tzu‐Hao Lin

“…Abstract Soundscapes contain rich acoustic information associated with animal behaviours, environmental characteristics and human activities, providing opportunities for predicting biodiversity changes and associated drivers. …”
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Exploring interconnections among atoms, brain, society, and cosmos with network science and explainable machine learning by Daniele Caligiore, Daniele Caligiore, Anna Monreale, Anna Monreale, Giulio Rossetti, Angela Bongiorno, Giuseppe Fisicaro

“…For instance, if common aspects of criticality in neuroscience and cosmology are identified, an algorithm trained on brain data could be repurposed to detect critical states in cosmic systems, even with limited cosmic data. …”
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