Formation stages of the target model of integrated freight transport services by V. V. Zubkov, N. F. Sirina

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A Practical Degradation Based Method to Predict Long-Term Moisture Incursion and Color Change in High Power LEDs by Thong Kok Law, Fannon Lim

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Machine Learning-Based Prediction of Impulse Control Disorders in Parkinson’s Disease From Clinical and Genetic Data by Johann Faouzi, Samir Bekadar, Fanny Artaud, Alexis Elbaz, Graziella Mangone, Olivier Colliot, Jean-Christophe Corvol

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Robustness of Machine Learning Predictions for Determining Whether Deep Inspiration Breath-Hold Is Required in Breast Cancer Radiation Therapy by Wlla E. Al-Hammad, Masahiro Kuroda, Ghaida Al Jamal, Mamiko Fujikura, Ryo Kamizaki, Kazuhiro Kuroda, Suzuka Yoshida, Yoshihide Nakamura, Masataka Oita, Yoshinori Tanabe, Kohei Sugimoto, Irfan Sugianto, Majd Barham, Nouha Tekiki, Miki Hisatomi, Junichi Asaumi

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MATHEMATICAL MODELS OF DECISION SUPPORT IN THE PROBLEMS OF LOGISTICS NETWORKS OPTIMIZATION by Володимир Валентинович Безкоровайний, Оксана Михайлівна Драз

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Sparse Boosting for Additive Spatial Autoregressive Model with High Dimensionality by Mu Yue, Jingxin Xi

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A Novel Dynamic Weight Neural Network Ensemble Model by Kewen Li, Wenying Liu, Kang Zhao, Mingwen Shao, Lu Liu

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The ABCs of Subarachnoid Hemorrhage Blood Volume Measurement: A Simplified Quantitative Method Predicts Outcomes and Delayed Cerebral Ischemia by Fabian Föttinger, Rohan Sharma, Saif D. Salman, Alexander D. Weston, Bradley J. Erickson, Thien Huynh, Rabih G. Tawk, William D. Freeman

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Predicting depression and unravelling its heterogeneous influences in middle-aged and older people populations: a machine learning approach by Ling Zhang, Ruigang Wei, Jingwen Zhou, Lin Tan, Xiaolong Che, Minqinag Zhang, Xiaoyue Ning, Zhiliang Zhong

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A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis. by Magdalena Ydreborg, Vera Lisovskaja, Martin Lagging, Peer Brehm Christensen, Nina Langeland, Mads Rauning Buhl, Court Pedersen, Kristine Mørch, Rune Wejstål, Gunnar Norkrans, Magnus Lindh, Martti Färkkilä, Johan Westin

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A Comparative Study of Machine Learning and Conventional Techniques in Predicting Compressive Strength of Concrete with Eggshell and Glass Powder Additives by Yan Gao, Ruihan Ma

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Enhanced HER-2 prediction in breast cancer through synergistic integration of deep learning, ultrasound radiomics, and clinical data by Meijuan Hu, Lianying Zhang, Xiao Wang, Xuehua Xiao

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Advanced computational approaches for predicting sunflower yield: Insights from ANN, ANFIS, and GEP in normal and salinity stress environments. by Sanaz Khalifani, Reza Darvishzadeh, Majid Montaseri, Sarvin Zaman Zad Ghavidel, Hamid Hatami Maleki, Mojtaba Kordrostami

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Enhanced Prediction of Soil Carbon via Encoder-Decoder Neural Networks for a Boreal Study Area in Northern Ontario by Rory Pittman, Baoxin Hu

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Advancing Coastal Flood Risk Prediction Utilizing a GeoAI Approach by Considering Mangroves as an Eco-DRR Strategy by Tri Atmaja, Martiwi Diah Setiawati, Kiyo Kurisu, Kensuke Fukushi

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Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition by Chenhui Wang, Gaocong Lin, Cuiqiong Zhou, Wei Guo, Qingjia Meng

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Developing Frugal Internet of Things with Backpropagation Neural Network for Predicting Impact of Gemini Artificial Intelligence on Student Meditation and Relaxation by Chun-Kai Tseng, Cheng-Hsiang Chan, Liang-Sian Lin, Fu-Jung Wang, Kai-Hsuan Yao, Chao-Wei Hsu

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Development of the Method for Predicting and Calculating the Operation of Sorption Systems for Cleaning the Generator Gas based on Dolomite Use. Part II by Malko M.V., Vasilevich S.V., Mitrofanov A.V., Mizonov V.E.

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Machine learning based clinical decision tool to predict acute kidney injury and survival in therapeutic hypothermia treated neonates by Elif Keles, Syed Yaseen Ali, Pia Wintermark, Pieter Annaert, Floris Groenendaal, Suzan Şahin, Mehmet Yekta Öncel, Didem Armangil, Esin Koc, Malcolm R. Battin, Alistair J. Gunn, Adam Frymoyer, Valerie Chock, Djalila Mekahli, John van den Anker, Anne Smits, Karel Allegaert, Ulas Bagci

“…The XGBoost model achieved an AUC of 95% and an accuracy of 75.08% in predicting AKI and survival, surpassing other ML classifiers that demonstrated accuracy levels ranging from 54% to 65%. …”
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1DCNN-Residual Bidirectional LSTM for Permanent Magnet Synchronous Motor Temperature Prediction Based on Operating Condition Clustering by Jingrong Cheng, Feifan Ji, Chenglong Huang, Tong Wang, Yan Liu, Yanjun Li

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