Applications of machine learning in decision analysis for dose management for dofetilide. by Andrew E Levy, Minakshi Biswas, Rachel Weber, Khaldoun Tarakji, Mina Chung, Peter A Noseworthy, Christopher Newton-Cheh, Michael A Rosenberg

“…Principal component analysis and cluster analysis identified 8 clusters as a reasonable data reduction method. These 8 clusters were then used to define patient states in a tabular reinforcement learning model trained on 80% of dosing decisions. …”
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Model experimental study of filling property of ring-type geotextile tube by LAI Zhiqiang, XU Shuling, ZHAO Lianjun, Chen Lin, ZHANG Ming

“…The results were relatively accurate, with <italic>M</italic>_SE values of 5×10^-5, 3×10^-4, and2×10^-5, respectively.The counterclockwise filling mechanism operates as followsConclusionsUnder 12 different working conditions, when the tube bag is filled counterclockwise at a speed of 2.77 m/s, its average height after filling reaches its peak at 0.09 m, with a relatively uniform distribution of sediment particles inside the bag. Leveraging machine learning principles, the heights of the filled tube bags under three different filling methods—counterclockwise, radial, and vertical—were simulated and predicted using the random forest algorithm, based on experimentally measured heights. …”
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Predicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms by Ghasem Azadi Ahmadabadi

“…This study aims to analyze the interrelationships among variables influencing scientific outputs and to identify the most effective machine learning algorithms for predicting their scientific, social, and economic impacts.Methodology: The current research is applied in purpose and descriptive in method, utilizing a scientometric approach. …”
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Machine Learning Predictive Models for Survival in Patients with Brain Stroke by Solmaz Norouzi, Samira Ahmadi, Shayeste Alinia, Farshid Farzipoor, Azadeh Shahsavari, Ebrahim Hajizadeh, Mohammad Asghari Jafarabadi

“…The best-performing model was selected based on diagnostic performance metrics: specificity, sensitivity, precision, accuracy, area under the receiver operating characteristic curve (AUC), positive likelihood ratio, negative likelihood ratio, and negative predictive value. …”
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The Establishment and Evaluation Model of the Thematic Deep-Learning Teaching Module by Kai-Chao Yao, Li-Chiou Hsu, Jiunn-Shiou Fang, Yi-Jung Chen, Zhou-Kai Guo

“…Students’ affective attitudes toward the four dimensions of teaching material and equipment, cognitive development, skills performance, and self-exploration were positive. Feedback revealed that students who participated in the teaching experiment responded positively on all levels of the affective scale, indicating increased motivation and willingness to continue learning. …”
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Few-Shot Learning With Prototypical Networks for Improved Memory Forensics by Muhammad Fahad Malik, Ammara Gul, Ayesha Saadia, Faeiz M. Alserhani

“…As the first application of prototypical networks to the Dumpware10 dataset (to the best of authors knowledge), our findings highlight the potential of few-shot learning for memory forensics-based malware detection, opening new avenues for research in this domain. …”
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Transformers for Vision: A Survey on Innovative Methods for Computer Vision by Balamurugan Palanisamy, Vikas Hassija, Arpita Chatterjee, Arpita Mandal, Debanshi Chakraborty, Amit Pandey, G. S. S. Chalapathi, Dhruv Kumar

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The Views of University Students on Gamification-Enhanced Online Learning Experiences by Özcan Erkan Akgün, Murat Topal

“…The aim of this study is to examine the opinions of students who have a gamification-enhanced online learning experiences to find clues about the design of online learning environments based on gamification. …”
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Dental students’ perceptions of integrating online learning in the dental curriculum by Banan Al-Natour, Rima Safadi, Abed Al-Hadi Hamasha, Reema Karasneh, Mohammad A. Alkandari, Sarah A. Hamasha

“…Conclusion Despite the positive attitude of undergraduate dental students towards online learning, many improvements are required. …”
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A Multi-Spatial-Scale Ocean Sound Speed Profile Prediction Model Based on a Spatio-Temporal Attention Mechanism by Shuwen Wang, Ziyin Wu, Shuaidong Jia, Dineng Zhao, Jihong Shang, Mingwei Wang, Jieqiong Zhou, Xiaoming Qin

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Formation and peculiarities of advertising campaign by Yu.O. Golovchuk, L.V. Dibchuk

“…Brands combine powerful messages based on a well-researched idea and effective delivery methods that impact their audience. …”
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Machine learning in predicting firm performance: a systematic review by Yaseen Hezam, Hoa Luong, Lilian Anthonysamy

“…This study investigates the application of machine learning (ML) techniques in predicting firm performance, responding to the challenges posed by the large volumes of data required for accurate predictions. …”
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Theories and cognitive models of postnonclassical learning in natural science education by Suriya Irekovna Gilmanshina, Lyudmila Vladimirovna Moiseeva, Inna Nikolaevna Pushkareva, Sergey Borisov Ignatov, Iskander Rafailevich Gilmanshin

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Children who screen positive for autism at 2.5 years and receive early intervention: a prospective naturalistic 2-year outcome study by Spjut Jansson B, Miniscalco C, Westerlund J, Kantzer A, Fernell E, Gillberg C

“…The following interventions were available: a comprehensive intervention based on Applied Behavior Analysis &amp;ndash; Intensive Learning (IL) &amp;ndash; in two settings, which included home- and preschool-based (IL Regular) and only home-based (IL Modified) and eclectic interventions.Results: There was considerable variability in terms of outcome, but intervention group status was not associated with any of the chosen outcome variables. …”
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Application of radiomics-based prediction model to predict preoperative lymph node metastasis in prostate cancer: a systematic review and meta-analysis by Yanghuang Zheng, Yuelin Du, Biao Zhang, Helin Zhang, Panfeng Shang, Zizhen Hou

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Foreground-Driven Contrastive Learning for Unsupervised Human Keypoint Detection by Shuxian Li, Hui Luo, Zhengwei Miao, Zhixing Wang, Qiliang Bao, Jianlin Zhang

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Micro-Location Temperature Prediction Leveraging Deep Learning Approaches by Amadej Krepek, Iztok Fister, Iztok Fister

“…As a result, these events have been addressed by artificial intelligence methods more and more frequently. In line with this, the paper focuses on searching for predicting the air temperature in a particular Slovenian micro-location by using a weather prediction model Maximus based on a long-short term memory neural network learned by the long-term, lower-resolution dataset CERRA. …”
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Learning a Mid-Level Representation for Multiview Action Recognition by Cuiwei Liu, Zhaokui Li, Xiangbin Shi, Chong Du

“…To this end, we propose a learning framework based on multitask random forest to exploit a discriminative mid-level representation for videos from multiple cameras. …”
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Heterogeneity-aware device selection for efficient federated edge learning by Yiran Shi, Jieyan Nie, Xingwei Li, Hui Li

“…Federated learning (FL) combined with mobile edge computing (FEEL) provides an end-to-edge synergetic learning approach to allow end devices to participate in machine learning model training parallelly while ensuring user privacy is maintained. …”
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Incorporation of XAI and Deep Learning in Biomedical Imaging: A Review by Sushil K. Singh, Bal Virdee, Saurabh Aggarwal, Abhilash Maroju

“…Concerns about liability in autonomous car accidents are comparable to those associated with deep learning applications in medical imaging. Errors such as false positives and false negatives can negatively affect patients' health. …”
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