Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

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Integration of MRI radiomics and germline genetics to predict the IDH mutation status of gliomas by Taishi Nakase, George A. Henderson, Thomas Barba, Rohan Bareja, Geno Guerra, Qingyu Zhao, Stephen S. Francis, Olivier Gevaert, Linda Kachuri

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Near-Infrared Spectroscopy and Machine Learning for Fast Quality Prediction of Bottle Gourd by Xiao Guo, Hongyu Huang, Haiyan Wang, Chang Cai, Ying Wang, Xiaohua Wu, Jian Wang, Baogen Wang, Biao Zhu, Yun Xiang

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Car Price Prediction and Recognition Using Deep Learning and Computer Vision Algorithms by Hira Farman, Saad Ahmed, Muhammad Hussain Mughal, Qurat -ul-ain Mastoi, Govari Shankar Lalwani

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Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology by Alon Vigdorovits, Gheorghe-Emilian Olteanu, Ovidiu Tica, Andrei Pascalau, Monica Boros, Ovidiu Pop

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CT-based radiomics for prediction of response to neoadjuvant immunochemotherapy in patients with esophageal carcinoma by Peng Zhao, Xianhe Qiao, Yikang Geng, Yaoyi Yv, Ruiqing Meng, Xiaowei Wu

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Bioinformatics reveal macrophages marker genes signature in breast cancer to predict prognosis by Ying Li, Xin Zhao, Qiang Liu, Yujie Liu

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Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States. by Mukhtar Ijaiya, Erica Troncoso, Marang Mutloatse, Duruanyanwu Ifeanyi, Benjamin Obasa, Franklin Emerenini, Lucien De Voux, Thobeka Mnguni, Shantelle Parrott, Ejike Okwor, Babafemi Dare, Oluwayemisi Ogundare, Emmanuel Atuma, Molly Strachan, Ruby Fayorsey, Kelly Curran

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Artificial neural networks in the outcome prediction of adjustable gastric banding in obese women. by Paolo Piaggi, Chita Lippi, Paola Fierabracci, Margherita Maffei, Alba Calderone, Mauro Mauri, Marco Anselmino, Giovanni Battista Cassano, Paolo Vitti, Aldo Pinchera, Alberto Landi, Ferruccio Santini

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Bayesian Joint Probability Approach for Post‐Processing Monthly Precipitation Prediction in Northwest Iran by Z. Javanshiri, M. Pakdaman

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Review of Pre-processing Techniques for Meteorological Satellite Data Assimilation in Numerical Prediction by Ma Gang, Huang Jing, Gong Xinya, Xi Shuang, Xue Lei, Li Juan, Zhang Peng, Gong Jiandong

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Forest Loss and Susceptible Area Prediction at Sefwi Wiawso District (SWD), Ghana by William Osei-Wusu, Jonathan Quaye-Ballard, Terah Antwi, Naa Lamkai Quaye-Ballard, Alfred Awotwi

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A systematic review of computational simulation methods for predicting the toxicity of chemical compounds by Akram Tabrizi, Fatemeh Paridokht, Yaser Khorshidi Behzadi, Rezvan Zendehdel

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Promises in Air‐Sea Fully Coupled Data Assimilation for Future Hurricane Prediction by Fuqing Zhang, Kerry Emanuel

“…Abstract The recent article by Li and Toumi (2018, https://doi.org/10.1029/2018GL079677) published in Geophysical Research Letters explored the potential for improving tropical cyclone intensity forecasts by assimilating synthetic coastal surface currents from high‐frequency radar observations. …”
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Machine learning for asphaltene polarizability: Evaluating molecular descriptors by Arun K. Sharma, Owen McMillan, Selsela Arsala, Supreet Gandhok, Rylend Young

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Application of HRCT-based radiomics to predict interstitial lung disease for juvenile dermatomyositis by Lingke Liu, Minfei Hu, Yun Zhou, Fei Zheng, Xiaohui Ma, Li Yang, Yiping Xu, Liping Teng, Bin Hu, Meiping Lu, Xuefeng Xu

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Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data by Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang, Ting Liu

“…Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, with at least a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>160</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in the fault recall rate.…”
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Supporting Sustainable Workforce Management for Worker Illness Absence Through Predictive Analytics by Ida Lumintu, Achmad Maududie

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Application of Machine Learning Methods for Employee Turnover Prediction Based on Open Data by A. N. Kazinets

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Assessing subseasonal forecast skill for use in predicting US coastal inundation risk by J. R. Albers, M. Newman, M. A. Balmaseda, W. Sweet, W. Sweet, Y. Wang, Y. Wang, T. Xu, T. Xu

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