Enhanced Hybrid Deep Learning Model With Improved Self-Attention Mechanism for Legal Judgment Prediction by G. Sukanya, J. Priyadarshini

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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

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An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects by Shreya Mishra, Neetesh Pandey, Atul Rawat, Divyanshu Srivastava, Arjun Ray, Vibhor Kumar

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Development of a Success Prediction Model for Crowdfunding Based on Machine Learning Reflecting ESG Information by Jiyoung Park, Hyung Jong Na, Hyungjoon Kim

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Prediction of State-of-Health and Remaining-Useful-Life of Battery Based on Hybrid Neural Network Model by Le Thi Minh Lien, Vu Quoc Anh, Nguyen Duc Tuyen, Goro Fujita

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Enhancing Time-Series Prediction with Temporal Context Modeling: A Bayesian and Deep Learning Synergy by Habib Irani, Vangelis Metsis

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A Mathematical Model for Prediction the Embedment Depth of the Contiguous Piles Used in the Interchange of Zakho Entrance by Najdat S. Akrawi, Shimal A. Ahmed

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Correction: Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19

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OBJECT-ORIENTED APPROACH TO IDENTIFYING THE RELEVANCE OF IT-EDUCATION TO THE LABOR MARKET DEMAND by Masuma H. Mamedova, Faik R. Mamedzade

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PLEKHANOV UNIVERSITY IN THE 1920s – EARLY 1940s: EXPERIENCE OF TEACHING AND EDUCATIONAL WORK by Irina M. Kornilova, Antonina S. Fomina

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Prediction of Thyroid Classes Using Feature Selection of AEHOA Based CNN Model for Healthy Lifestyle by Rachappa Jopate, Piyush Kumar Pareek, DivyaJyothi M. G, Ariam Saleh Zuwayid Juma Al Hasani

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Correction: Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19. by Aldo Córdova-Palomera, Csaba Siffel, Chris DeBoever, Emily Wong, Dorothée Diogo, Sandor Szalma

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Conceptual competence in medicine: promoting psychosomatic awareness in clinics, research and education by Dirk von Boetticher

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Perspective: How complex in vitro models are addressing the challenges of predicting drug-induced liver injury by K. Taylor, R. Ram, R. Ram, L. Ewart, C. Goldring, G. Russomanno, G. P. Aithal, T. Kostrzewski, C. Bauch, J. M. Wilkinson, J. M. Wilkinson, S. Modi, J. G. Kenna, J. G. Kenna, J. Bailey, J. Bailey

“…Predicting which drugs might have the potential to cause drug-induced liver injury (DILI) is highly complex and the current methods, 2D cell-based models and animal tests, are not sensitive enough to prevent some costly failures in clinical trials or to avoid all patient safety concerns for DILI post-market. …”
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Class-balanced negative training sets for improving classifier model predictions of enhancer-promoter interactions by Osamu Maruyama, Tsukasa Koga

“…Further advanced methods in generating negative EPIs should further improve prediction accuracy. The source code is available at https://github.com/maruyama-lab-design/CBOEP2 .…”
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Deep Learning Based LSTM Model for Predicting the Number of Passengers for Public Transport Bus Operators by Joko Siswanto, Danny Manongga, Irwan Sembiring, Sutarto Wijono

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ATTITUDES OF EDUCATIONAL PROCESS PARTICIPANTS TOWARDS THE EVALUATION OF TEACHERS' PROFESSIONAL ACTIVITY by Lidiia Vashchenko

Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction by Chenglong Guo, Binyu Gao, Xuexue Han, Tianxing Zhang, Tianqi Tao, Jinggang Xia, Honglei Liu

“…This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data. …”
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Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease by Ying He, Fan Lin, Xin Zheng, Qiaobin Chen, Meng Xiao, Xiaoting Lin, Hongbiao Huang

“…This study presents a region-specific, interpretable ML model for early IVIG resistance prediction in KD. …”
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Improving drug-drug interaction prediction via in-context learning and judging with large language models by He Qi, He Qi, Xiaoqiang Li, Chengcheng Zhang, Tianyi Zhao, Tianyi Zhao

“…To further refine predictions, we employ GPT-4 as a discriminator to assess the relevance of predictions generated by multiple LLMs.ResultsDDI-JUDGE achieves the best performance among all models in both zero-shot and few-shot settings, with an AUC of 0.642/0.788 and AUPR of 0.629/0.801, respectively. …”
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