Intelligent gear decision method for vehicle automatic transmission system based on data mining by Yong Wang, Jianfeng Zeng, Pengfei Du, Huachao Xu

“…The research further proposes the utilization of Kalman filter, Hidden Markov Models, and Long Short-Term Memory networks for condition feature recognition and time series classification. …”
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Low‐Frequency Reconstruction for Full Waveform Inversion by Unsupervised Learning by Ningcheng Cui, Tao Lei, Wei Zhang

“…In this study, we employed an unsupervised learning method, namely cycle‐consistent adversarial networks (CycleGAN), to reconstruct large‐scale‐feature related low‐frequency information based on the high‐frequency input data. …”
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Assessment of NUDT5 in Endometrial Carcinoma: Functional Insights, Prognostic and Therapeutic Implications by Hongfei Yu, Lingling Zu, Yuqin Zang, Fei Teng, Tao Wang, Ming Wu, Yingmei Wang, Fengxia Xue

“…Weighted gene co-expression network analysis (WGCNA) and experimental validation were performed to explore the impact of NUDT5 on the PI3K-AKT signaling pathway, while tumor growth assays in xenograft models assessed the therapeutic potential of NUDT5 inhibition in vivo. …”
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Qseek: A data-driven Framework for Automated Earthquake Detection, Localization and Characterization by Marius Isken, Sebastian Heimann, Peter Niemz, Jannes Münchmeyer, Simone Cesca, Hannes Vasyura-Bathke, Torsten Dahm

“…We present a user-friendly and high-performance open-source software framework based on established frameworks, featuring event detection in layered 1D and complex 3D velocity models and event feature extraction capabilities, such as moment and local magnitude calculation from peak ground motions. …”
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Sustainable Polyurethane-Based Polymer Concrete: Mechanical and Non-destructive Properties with Machine Learning Technique by S. I. Haruna, Han Zhu, Yasser E. Ibrahim, Jian Yang, AIB Farouk, Jianwen Shao, Musa Adamu, Omar Shabbir Ahmed

“…The microstructure analysis shows micro-voids with crack propagation between the aggregate and PU binder in the specimen containing 10% PU after testing. All the developed models showed high prediction accuracy. In addition, SVM outperformed other models at the training phase with R 2 values of 0.9614, and ANN demonstrated the highest performance at the testing phase with R 2 values of 0.9502.…”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…Performance metrics were compared across models, including XGBoost and LightGBM, to identify the most accurate algorithms. …”
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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…The study examines the challenges encountered when working with datasets related to cardiovascular diseases, such as class imbalance and feature selection. It demonstrates how addressing these issues enhances the reliability and applicability of predictive models. …”
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FACTORS OF SOCIO-PSYCHOLOGICAL ADAPTATION AND ANXIETY OF REFUGEES DURING THE WAR by Harkusha Inesa, Dubinskyi Serhii

“…Common and distinctive features between anxiety and alarm have been identified. …”
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A High-Precision Defect Detection Approach Based on BiFDRep-YOLOv8n for Small Target Defects in Photovoltaic Modules by Yi Lu, Chunsong Du, Xu Li, Shaowei Liang, Qian Zhang, Zhenghui Zhao

“…Firstly, the visual transformer RepViT is constructed as the backbone network, based on the dual-path mechanism of Token Mixer and Channel Mixer, to achieve local feature extraction and global information modeling, and combined with the structural reparameterization technique, to enhance the sensitivity of detecting small defects. …”
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Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases by William DeGroat, Habiba Abdelhalim, Elizabeth Peker, Neev Sheth, Rishabh Narayanan, Saman Zeeshan, Bruce T. Liang, Zeeshan Ahmed

“…The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. …”
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Implementation of Neural Style Transformation Technique for Artistic Image Processing Using VGG19 by Xin Cheng, Feng Wang, Ali Akbar Siddique, Zain Anwar Ali

“…Content loss is calculated by comparing the feature maps of the derived content with the processed or stylized image generated by the model. …”
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Robust Forward-Looking Sonar-Image Mosaicking Without External Sensors for Autonomous Deep-Sea Mining by Xinran Liu, Jianmin Yang, Changyu Lu, Enhua Zhang, Wenhao Xu

“…The framework explicitly models the noise characteristics of sonar images captured in deep-sea environments and integrates bidirectional cyclic consistency filtering with a soft-weighted feature refinement strategy during the feature-matching stage. …”
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YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

“…The proposed architecture employs a multi-level lightweight design incorporating enhanced feature fusion mechanisms and spatial-channel attention modules, effectively improving detection performance in complex farm environments with occlusions and variable lighting. …”
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Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players by Ui-jae Hwang, Kyu Sung Chung, Sung-min Ha

“…Louvain clustering identified four distinct kinematic profiles with varying proportions of EOA (ranging from 41.2% to 70.7%). Supervised learning models achieved high performance in classifying EOA status, with Random Forest, gradient boosting, and decision tree algorithms achieving 100% classification accuracy (AUC = 1.000) on the test dataset. …”
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Bayesian Deep Learning Meets Self-Attention: A Risk-Aware Approach to Advertisement Optimization by Arti Jha, Ashutosh Bhatia, Kamlesh Tiwari

“…Ablation studies confirm that attention-driven feature selection and calibrated uncertainty quantification significantly enhance both predictive performance and explainability, identifying key drivers of campaign success. …”
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Assessment of The Potential Interference and Conflict of Artificial Intelligence With Human Rights and Citizenship: Challenges and Solutions by Hossein Khazaei, Mojtaba Hemmati

“…Furthermore, this research has proposed models for the legal personality of artificial intelligence, and it has identified the challenges and conflicts that AI faces in relation to human rights and citizenship, such as Automatic performance of military weapons, privacy violations, job displacement, social manipulation, fake news, and more. …”
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Drone hyperspectral imaging and artificial intelligence for monitoring moss and lichen in Antarctica by Juan Sandino, Johan Barthelemy, Ashray Doshi, Krystal Randall, Sharon A. Robinson, Barbara Bollard, Felipe Gonzalez

“…Light models using eight key wavelengths (i.e., 404, 480, 560, 655, 678, 740, 888, and 920 nm) performed well, with CatBoost at 95.5% and UNet at 99.8%, demonstrating suitability for preliminary monitoring of moss health and lichen. …”
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Hybrid GIS-Transformer Approach for Forecasting Sentinel-1 Displacement Time Series by Lama Moualla, Alessio Rucci, Giampiero Naletto, Nantheera Anantrasirichai, Vania Da Deppo

“…A key contribution of this work is the development of a GIS-integrated forecasting toolbox that incorporates LSTM models for regular sequences and TG-LSTM/TFT models for irregular ones. …”
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Intravenous esketamine in pediatric Rett syndrome: An open-label, early phase 1 pilot study by Huiping Li, Shu Liu, Caimei Lin, Yuchao Wu, Xiuping Wu, Yukun Huang, Yajun Wu, Xiubin Tong, Xiu Xu

“…N-Methyl-d-aspartate receptor (NMDAR) antagonism has shown therapeutic potential in preclinical RTT models. We performed a pilot study to explore whether intravenous esketamine, an NMDAR antagonist, alleviates the symptoms of pediatric RTT. …”
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Distinct microRNA signatures define sporadic PSP-RS and PD in patient-derived midbrain organoids by Desirèe Valente, Clara Zannino, Stefania Scalise, Davide Bressan, Valeria Lucchino, Ermes Filomena, Giorgia Lucia Benedetto, Ida Lazzinnaro, Mariagrazia Talarico, Andrea Quattrone, Fulvio Chiacchiera, Graziano Pesole, Aldo Quattrone, Elvira Immacolata Parrotta, Giovanni Cuda

“…Summary: Progressive supranuclear Palsy–Richardson syndrome (PSP-RS) is a rare, rapidly progressive tauopathy often misdiagnosed as Parkinson’s disease (PD) due to overlapping clinical features and the lack of reliable molecular biomarkers. …”
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