Estimating Self-Confidence in Video-Based Learning Using Eye-Tracking and Deep Neural Networks by Ankur Bhatt, Ko Watanabe, Jayasankar Santhosh, Andreas Dengel, Shoya Ishimaru

“…Our results underscore the superior performance of the deep-learning model in estimating self-confidence in video-based learning contexts compared to hand-crafted feature-based methods. …”
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DEVELOPMENT OF A SOFTWARE MODULE FOR OPERATIONAL DISPATCH CONTROL OF PRODUCTION BASED ON CYBER-PHYSICAL CONTROL SYSTEMS by Igor Nevliudov, Vladyslav Yevsieiev, Nataliia Demska, Sergiy Novoselov

“…Conclusions: the proposed models and methods will not only automate the process of managing the development of CPPS from scratch, but will also make it possible to automate the process of managing the development of the cybernetic component for the modernization and improvement of existing CPPS.…”
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Activity cliff-aware reinforcement learning for de novo drug design by Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang

“…This RL framework is designed to focus model optimization on high-impact regions within the SAR landscape, improving the generation of molecules with targeted properties. …”
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PTK6 mediated immune signatures revealed by single cell transcriptomic and multi omics big data analysis in cervical cancer by Fen Zhao, Huanxin Zhong, Lifang You, Yi Du, Changchang Huang

“…Fifteen machine learning algorithms constructed prognostic models using immune infiltration-related genes. …”
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Enhancing SQL Learning: Gamified tutorials and flipped classroom synergy by Sandor Kiraly, Tamas Balla, Dora Kiraly, Geoffrey Vaughan

“…Teaching students to think algorithmically is a common goal for school educators; however, SQL's declarative nature demands a distinct approach to problem-solving. …”
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Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution by Avijit Paul, Srivalleesha Mallidi

“…Signal quality can be improved by traditional noise removal algorithms, but deep learning models outperform non-learning methods. …”
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Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector by Utsav Pandey, Sanjeet Singh

“…The proposed model is empirically tested using healthcare data of 31 provinces of China for a period from 2013 to 2018. …”
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Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method. by Md Sabbir Hossain, Niloy Basak, Md Aslam Mollah, Md Nahiduzzaman, Mominul Ahsan, Julfikar Haider

“…The extracted features were then processed by a set of ML algorithms along with a voting ensemble approach. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated as an explainable AI (XAI) technique for enhancing model transparency by highlighting key influencing regions in the CT scans, which improved interpretability and ensured reliable and trustworthy results for clinical applications. …”
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Anomaly detection using machine learning and adopted digital twin concepts in radio environments by Mohamed Hussien Moharam, Omar Hany, Ahmed Hany, Amenah Mahmoud, Mariam Mohamed, Sohila Saeed

“…This work presents an anomaly detection framework using Digital Twin (DT) technology to simulate and monitor dynamic radio environments. By modeling network conditions and attack scenarios, the DT enables accurate identification of anomalies, particularly security threats. …”
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Prediction of time averaged wall shear stress distribution in coronary arteries’ bifurcation varying in morphological features via deep learning by Mohammad Hossein Sarkhosh, Mohammad Hossein Sarkhosh, Hadis Edrisnia, Mohammad Reza Raveshi, Mahkame Sharbatdar

“…To overcome this challenge, we integrate a deep learning (DL) method to improve efficiency and responsiveness. This study presents a DL approach for predicting Time-Averaged Wall Shear Stress (TAWSS) values in coronary arteries’ bifurcation.ResultsTo prepare the dataset, 1800 idealized models with varying morphological parameters are created. …”
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A novel molecular classification system for head and neck squamous cell carcinoma: predicting treatment response and metastatic potential through multi-omics analysis by XinYu Liu, YuJun Liu, XuTengYue Tian, Yue Xi, MiaoMiao Lu, Xin Zou, WanTao Chen

“…We developed a machine learning-based risk prediction model using 54 algorithms. Results We identified three molecular subtypes with distinct prognostic implications, showing significant survival differences across independent datasets (TCGA-HNSCC, P < 0.0001; GSE65858, P = 0.018). …”
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An autonomous navigation method for field phenotyping robot based on ground-air collaboration by Zikang Zhang, Zhengda Li, Meng Yang, Jiale Cui, Yang Shao, Youchun Ding, Wanneng Yang, Wen Qiao, Peng Song

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Classification of Lung Nodule Using Hybridized Deep Feature Technique by Malin Bruntha, Immanuel Alex Pandian, Siril Sam Abraham

“…The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. …”
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The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia by Ilija Doknić, Mirjana Mitrović, Zoran Bukumirić, Marijana Virijević, Nikola Pantić, Nikica Sabljić, Darko Antić, Živko Bojović

“…Multilayer Perceptron (MLP) was found to be the best fit among the models evaluated, achieving the C statistic of 0.749. …”
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Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning by Zhixiang Wang, Qingqing Li, Yiran Wang, Linxue Qian, Xiangdong Hu, Dong Liu

“…It can assist doctors in developing more effective treatment plans, improving patient outcomes. Future studies should expand sample sizes, include multicenter data, and explore advanced algorithms to further enhance diagnostic performance.…”
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Carbon Sequestration Strategies in Regenerative Agricultural Systems by Leveraging Wireless Sensor Networks for Precision Carbon Management by Al-Jawahry Hassan M., AI_Sadi Hafidh l., Rajasekhar Boddu

“…In this study, a total approach towards optimizing carbon sequestration strategies using advanced technologies like Wireless Sensor Network (WSN), Digital Twin model, and predictive algorithms like Random Forest Regression and gradient boosting are presented. …”
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PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…When compared to YOLOv11s, it increases mAP50 by 1.0% and mAP50–95 by 5.6% while also significantly reducing the model parameters. The performance of PCES-YOLO is also evaluated against mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8n, etc. …”
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Research on the Enhancement and Development of the Resilience Assessment System for Underground Engineering Disaster Risk by Weiqiang Zheng, Zhiqiang Wang, Bo Wu, Shixiang Xu, Jiacheng Pan, Yuxuan Zhu

“…The integration of intelligent static assessment models based on artificial intelligence algorithms can effectively enhance the accuracy of resilience evaluations. …”
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Development and validation of a screening tool for sepsis without laboratory results in the emergency department: a machine learning studyResearch in context by Shan Jiang, Shuai Dai, Yulin Li, Xianlong Zhou, Cheng Jiang, Cong Tian, Yana Yuan, Chengwei Li, Yan Zhao

“…Timely identification and treatment of sepsis can greatly improve patient outcomes, including survival rates. …”
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A Synthetic Data Generation Approach With Dynamic Camera Poses for Long-Range Object Detection in AI Applications by Misbah Bibi, Anam Nawaz Khan, Muhammad Faseeh, Qazi Waqas Khan, Rashid Ahmad, do-Hyeun Kim

“…To address these limitations, we propose a novel approach utilizing BlenderProc procedural generation and photorealistic rendering to create a synthetic dataset that captures diverse and realistic outdoor scenes for the objects detection. We trained YOLO model on this dataset and compared its performance with standard YOLO model. …”
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