PREDICTION OF STRAWBERRY CHEMICAL COMPOSITION BY IMAGING ANALYSIS PROCESSES by Tarek FOUDA, Adel ELMETWALLI, Shimaa SALAH

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Lexical influences on predictive mouse cursor movements by Anuenue Kukona

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EchoPT: A Pretrained Transformer Architecture That Predicts 2D In-Air Sonar Images for Mobile Robotics by Jan Steckel, Wouter Jansen, Nico Huebel

Subjects: “…predictive processing…”
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Speech Intelligibility Prediction Using Binaural Processing for Hearing Loss by Xiajie Zhou, Candy Olivia Mawalim, Masashi Unoki

Subjects: “…speech intelligibility prediction…”
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Stock Price Prediction Based on Natural Language Processing1 by Xiaobin Tang, Nuo Lei, Manru Dong, Dan Ma

“…The keywords used in traditional stock price prediction are mainly based on literature and experience. …”
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Processing and Shelf-Life Prediction Models for Ready-to-Eat Crayfish by Qian Li, Jieyu Lei, Keying Su, Xiaoying Chen, Laihoong Cheng, Chunmin Yang, Shiyi Ou

“…A shelf-life prediction model was developed using the Arrhenius model. …”
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A Predicting Method of the Strong Cooling Process during Winter with Numerical Model Prediction and its Operational Application by Bomin CHEN, Kun ZHOU, Fei XIN, jun MA, Limei JIN

“…At first this paper quantitatively evaluated the low-frequency wave performance of NCEP-CF Sv2 model over the eight key areas on 700 hPa from January to March and from October to December of 2017， and then made operationally the fifteen extended-range operational predictions of strong cooling process for January to April of 2018 and for November to January 2019 with the 1~30 days prediction given by CFSv2 model as well as the low-frequency wave conceptual predicting model.The results show that the phase and evolution trend of the low-frequency wave in the key area predicted by CFSv2 model are highly consistent with the reality， with the correlation coefficients of 0.839 of the predicted low-frequency waves with the observed for the extended-range （11~30 days）， the accuracy of low frequency wave trend by the model over 3~6 pentad up to percent of 83.3 on average， and the percentage of 100-percent accuracy of the trend even up to 45.8.The average accuracy， Cs and Zs scores of 15 strong cooling process operational predicting are 61.2%， 0.149 and 0.158 respectively， and at the same time the occurrence of the two strongest cooling processes at the beginning and the end of 2018 were accurately given with the lead-time of 18 and 16 days in turn， which are significantly higher than those of the operation predicting for the same period of 2015 to 2017 without CFSv2 results.…”
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The predictive embodied mind: a case-based encounter with film aesthetics by Maarten Coëgnarts, Maarten Coëgnarts, Maarten Coëgnarts

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Analytical Fuzzy Predictive Control Applied to Wastewater Treatment Biological Processes by Pedro M. Vallejo LLamas, Pastora Vega

“…A novel control fuzzy predictive control law is proposed and successfully applied to a wastewater treatment process in this paper. …”
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A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes by Qi-Zhi Zhang, Woon-Seng Gan, Ya-li Zhou

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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…However, there is a lack of systematic research on how the development stage of litchi flowers is affected by the meteorological factors. Accurately predicting the development of the inflorescence and the process of flowering duration, as well as correctly understanding the quantitative relationship between the flowering phenology and the meteorological factors, is very important for the high-yield and quality production of litchi. …”
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Study on the Switching Model Predictive Control Algorithm in Batch Polymerization Process by Jong Nam Kim, Chun Bae Ma, Hyok Jo, Un Chol Han, Hyon-Tae Pak, Son Il Hong, Ri Myong Kim

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Model based multi-loop predictive control scheme for multivariable processes by Arun Ramaveerapathiran, Muniraj Rathinam, Karuppiah Natarajan, Muthiah Athi, Patil Mounica

“…A model based multi-loop predictive control method is presented. The proposed method directly uses the process model without approximation and reduction. …”
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Deep Learning Approach for Predicting Psychodiagnosis by Zouaoui Samia, Khamari Chahinez

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PREDICTION ENDOMETRIAL CANCER IN WOMEN WITH ENDOMETRIAL HYPERPLASTIC PROCESSES IN PREMENOPAUSAL by A. L. Unanyan, I. S. Sidorova, E. A. Kogan, D. V. Baburin

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Improving soil moisture prediction using Gaussian process regression by Xiaomo Zhang, Xin Sun, Zhulu Lin

“…In this study, machine learning models including multilinear regression (MLR), support vector machine (SVM), and Gaussian process regression (GPR), were developed and compared for soil moisture predictions at different depths at 29 weather stations in the Red River Valley using features such as time, locations, meteorological data, soil physical properties, and remote sensing data. …”
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Predicting Excavation-Induced Tunnel Response by Process-Based Modelling by Linlong Mu, Jianhong Lin, Zhenhao Shi, Xingyu Kang

“…., soils, excavation support structures, and tunnel structures) make the prediction of the response of tunnel induced by adjacent excavations a rather difficult and complex task. …”
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An Analytical Model for the Prediction of Emptying Processes in Single Water Pipelines by Carlos R. Payares Guevara, Alberto Patiño-Vanegas, Enrique Pereira-Batista, Oscar E. Coronado-Hernández, Vicente S. Fuertes-Miquel

“…Additionally, a practical application demonstrates the effectiveness of the developed tool in predicting the extreme pressure values in the air pocket during the water drainage process in a pipe, within a controlled environment.…”
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Prediction of Water Loss During Potato Vacuum Frying Process by Muhammad Dani Supardan, Satriana Satriana

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Hyperbox Mixture Regression for process performance prediction in antibody production by Ali Nik-Khorasani, Thanh Tung Khuat, Bogdan Gabrys

“…This paper addresses the challenges of predicting bioprocess performance, particularly in monoclonal antibody (mAb) production, where conventional statistical methods often fall short due to time-series data’s complexity and high dimensionality. …”
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