Machine Learning-Based Scoring System to Predict the Risk and Severity of Ataxic Speech Using Different Speech Tasks by Bipasha Kashyap, Pubudu N. Pathirana, Malcolm Horne, Laura Power, David J. Szmulewicz

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RETRACTED: Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data by Shengtai Ji, Shuxin Han, Jiaxin Hu, Yuguang Li, Jing-Cheng Han

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Real-Time DTM Generation with Sequential Estimation and OptD Method by Wioleta Błaszczak-Bąk, Waldemar Kamiński, Michał Bednarczyk, Czesław Suchocki, Andrea Masiero

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Changes in Nature of Prediction in Speech Acts: An Experimental Study by V. A. Pishchalnikova, N. I. Stepykin

“…Indicators of this process include a quantitative increase in topic-comment structures, syntactic primitives, a reduction in the proportion of complete cycle syntactic speech acts, deactivation of surface syntactic processing, a decline in diversity indices within the core of complete cycle syntactic speech acts, and the stereotypization of core associates.…”
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Application of the RESALT Technology in the FGD Wastewater Concentration Processing of Coal-Fired Power Plant by Yinjia JIN, Zeyu LIU, Liangzhou YOU, Shouqing XIA, Guorui TANG

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Prediction of Residual Stresses During the Hot Forging Process of Spherical Shells Based on Microstructural Evolution by Yupeng Wu, Jiasheng Li, Zhaocheng Wei, Yuxin Fang, Hongxia Li, Ming Huang

“…Meanwhile, the thermoplastic constitutive equation considering the influence of phase transformation was written in the UMAT subroutine and utilized for the implicit FE analysis of the cooling process of thin-walled spherical shells. Through comparison with the measured stress results of the spherical shells after actual forging, it was shown that the proposed constitutive model can effectively predict the microstructural evolution and the final residual stress distribution pattern of medium-carbon steel during the hot forging process.…”
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Observing Array Designed for Improving the Short‐Term Prediction of Kuroshio Extension State Transition Processes by Yu Geng, Qiang Wang, Hong‐Li Ren, Bo Dan, Stefano Pierini, Hui Zhang

“…Furthermore, to demonstrate the superiority of the observing networks in predicting KE transition processes, two existing observation schemes and six random observation schemes are compared with the CNOP‐determined observing array. …”
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A Comprehensive Review of AI Algorithms for Performance Prediction, Optimization, and Process Control in Desalination Systems by Mahmoud Ibnouf, Hadi Jaber, Hadil Abukhalifeh, Mohammed Ghazal, Mohamad Ramadan, Mohammad Alkhedher

“…This is especially true for process modelling where the impact is expected to expand. …”
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Machine Learning Prediction of Foaming in Anaerobic Co-Digestion from Six Key Process Parameters by Sarah E. Daly, Ji-Qin Ni

“…This study demonstrates that the interactions of multiple chemical and physical process parameters are an important consideration for predicting anaerobic digester foaming.…”
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Retracted: Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm by International Journal of Analytical Chemistry

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Prediction of Cutting Tool Stability Based on Temperature Modelling in Power Skiving Gear Cutting Process by Hrytsay Ihor, Slipchuk Andrii, Bosansky Miroslav

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Deep Gaussian process with uncertainty estimation for microsatellite instability and immunotherapy response prediction from histology by Sunho Park, Morgan F. Pettigrew, Yoon Jin Cha, In-Ho Kim, Minji Kim, Imon Banerjee, Isabel Barnfather, Jean R. Clemenceau, Inyeop Jang, Hyunki Kim, Younghoon Kim, Rish K. Pai, Jeong Hwan Park, N. Jewel Samadder, Kyo Young Song, Ji-Youn Sung, Jae-Ho Cheong, Jeonghyun Kang, Sung Hak Lee, Sam C. Wang, Tae Hyun Hwang

“…We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. …”
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Determination and validation of design space for mesenchymal stem cell cultivation processes using prediction intervals by Keita Hirono, Yusuke Hayashi, Isuru A. Udugama, Mohamed Rami Gaddem, Kenjiro Tanaka, Yuto Takemoto, Ryuji Kato, Masahiro Kino-oka, Hirokazu Sugiyama

“…Finally, the established DS was validated with the remaining data; it allowed better predictions of the cell numbers and confluency under specific cultivation conditions and improved the overall robustness of MSC cultivation processes.…”
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Weld Bead Shape and Formation Prediction for Robotic MIG Welding Process Using Numerical Calculation by Ping Yao, Yunyi Huang, Riheng He, Junxi Huang, Meiyi Chen, Wenxiao Yu, Kang Zhou

“…Hence, this model could realize temperature field simulation and weld bead formation prediction. This work can be used to accurately and effectively predict the robotic MIG welding process in the academic research and supply references for actual production.…”
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Data Augmentation and Deep Learning Methods for Pressure Prediction in Ignition Process of Solid Rocket Motors by Huixin Yang, Pengcheng Yu, Yan Cui, Bixuan Lou, Xiang Li

“…During the ignition process of a solid rocket motor, the pressure changes dramatically and the ignition process is very complex as it includes multiple reactions. …”
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A pediatric emergency prediction model using natural language process in the pediatric emergency department by Arum Choi, Chohee Kim, Jisu Ryoo, Jangyeong Jeon, Sangyeon Cho, Dongjoon Lee, Junyeong Kim, Changhee Lee, Woori Bae

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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…The primary goal is to improve the understanding and prediction of mass transfer behavior in adsorption processes through a data-driven approach. …”
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Sea Surface Temperature Prediction Enhanced by Exploring Spatiotemporal Correlation Based on LSTM and Gaussian Process by Zhenglin Li, Qingxiong Zhu, Dan Zhang, Hao Wu, Yan Peng

“…To improve the performance of SST predictions, we propose a hybrid framework that effectively models the spatial and temporal dependencies of SST data with a Gaussian process-enhanced Long Short-Term Memory network. …”
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A Sequential-Interval Optimal Sampling Strategy Based on Reliability Prediction Under Wiener Process by Mengying Ren, Yubin Tian, Xingyu Liu, Furi Guo

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An LJDRNN-based efficient energy intensity prediction in carbon fiber composite material manufacturing process by Rangaswamy Nikhil, Karthikeyan A G, Prabhu Loganathan, Tabrej Khan, Tamer A Sebaey

“…Hence, energy intensity prediction is required. Energy intensity is predicted by the prevailing research techniques; however, it is not predicted accurately. …”
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