School-level prediction and management of myopia in children and adolescents by Shengsong Xu, Linling Li, Yingting Zhu, Zhenbang Ruan, Yiwen Qu, Zhuandi Zhou, Yin Hu, Zhidong Li, Fei Hou, Xiaohua Zhuo, Yunxia Leng, Xuelin Huang, Yamei Lu, Zhirong Wang, Shuifeng Deng, Yehong Zhuo, Guoming Zhang, Min Fu, Xiao Yang

“…The interactions between age and other variables were revealed by the algorithms, and hence the final prediction models were established based on age segmentation. …”
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Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

“…This study develops a comprehensive, data-driven framework for predicting satellite imagery prices using four state-of-the-art ensemble learning algorithms: XGBoost, LightGBM, AdaBoost, and CatBoost. …”
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Neoantigen vaccines: advancing personalized cancer immunotherapy by Alaa A. A. Aljabali, Yassmen Hamzat, Alaa Alqudah, Lorca Alzoubi

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Exploration of Predictive Models for Lean Services in Government Lean Banking Industry by Fazel Saydi, Morteza Musa Khani, Sedigheh Tootian, Shahrazad Tayaran

“…The present study aims to identify and validate a predictive model of lean services in the context of lean government in the banking industry. …”
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Role of artificial intelligence in enhancing mechanical ventilation – A peek into the future by Neeraj Pal Singh, Masood Ali Mujawar, Akash Golani

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An Improved KNN-Based Slope Stability Prediction Model by Shuai Huang, Mingming Huang, Yuejun Lyu

“…In our study, the k-nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k-nearest neighbor (KNN) algorithm. …”
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Fast full conformal prediction for multiple test points by Ilsang Ohn, Jisu Park

“…This paper attempts to fill this gap by developing a scalable conformal prediction algorithm for multiple test points. We find that when we use kernel ridge regression for the underlying prediction method, it is possible to reuse some computation in constructing prediction intervals across multiple test points, which enables us to avoid repeating the heavy computation of a matrix inverse for each test point. …”
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Research on ionospheric parameters prediction based on deep learning by Yuntian FENG, Xia WU, Xiong XU, Rongqing ZHANG

“…For ionospheric parameter prediction, the short-term and daily mean value prediction of ionospheric parameters was established by long short-term memory (LSTM) predictive neural network modeling.Two methods of point-by-point prediction and sequence prediction were utilized.Furthermore, in order to predict the hourly and daily changes of ionospheric parameters, the proposed scheme was optimized by multidimensional prediction and empirical mode decomposition (EMD) algorithm.Finally, the feasibility of the proposed optimization algorithm in improving the prediction accuracy of ionospheric parameters is verified.…”
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Fall prediction based on biomechanics equilibrium using Kinect by Xu Tao, Zhou Yun

“…We evaluate the model and algorithm on an open database. The performance indicates that the fall prediction algorithm by studying human biomechanics can predict a fall (91.7%) and provide a certain amount of time (333 ms) before the elder injuring (hitting the floor). …”
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Prediction of protein interactions with function in protein (de-)phosphorylation. by Aimilia-Christina Vagiona, Sofia Notopoulou, Zbyněk Zdráhal, Mariane Gonçalves-Kulik, Spyros Petrakis, Miguel A Andrade-Navarro

“…To evaluate the efficacy of our algorithm, we predicted PTM-related PPIs of ataxin-1, a protein which is responsible for Spinocerebellar Ataxia type 1 (SCA1). …”
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Interactive trajectory prediction for autonomous driving based on Transformer by R. Xu, J. Li, S. Zhang, L. Li, H. Li, G. Ren, X. Tang

“…In this paper, we propose a novel trajectory prediction algorithm based on Transformer networks, a data-driven method that ingeniously harnesses dual-input channels. …”
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Prediction of Rheological Parameters of Polymers by Machine Learning Methods by T. N. Kondratieva, A. S. Chepurnenko

“…Machine learning methods open up great opportunities in predicting the rheological parameters of polymers. Previously, studies were conducted on the construction of predictive models using artificial neural networks and the CatBoost algorithm. …”
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Dynamic Prediction of Financial Distress Based on Kalman Filtering by Qian Zhuang, Lianghua Chen

“…The operation of the dynamic prediction is achieved by Kalman filtering algorithm. …”
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Driving behavior recognition and prediction based on Bayesian model by Xinsheng WANG, Zhen BIAN

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Smart City Traffic Flow and Signal Optimization Using STGCN-LSTM and PPO Algorithms by Tuxiang Lin, Rongliang Lin

“…This study presents a novel framework that integrates the Spatiotemporal Graph Convolutional Network-Long Short-Term Memory (STGCN-LSTM) model for traffic flow prediction with the Proximal Policy Optimization (PPO) algorithm for dynamic traffic signal control. …”
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Movie Box Office Prediction Based on IFOA-GRNN by Wei Lu, Xiaoqiao Zhang, Xinchen Zhan

“…This study improves the fruit fly algorithm to optimize the generalized regression neural network (IFOA-GRNN) model to predict whether a movie can become a high-grossing movie. …”
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Synchrophasor-Based Out-of-Step Prediction in Large Grids by Zainab Alnassar, S. T. Nagarajan

“…In this paper, synchrophasor-based bus voltage angle measurement has been used for early prediction of OOS condition in power systems. A new algorithm has been formulated for both generator and tie lines based on the trajectory of first and second derivatives of the bus voltage phase angle for early detection of OOS condition. …”
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Predicting the risk of malignisation of endometrial hyperplasia in reproductive age by I. M. Ordiyants, A. A. Kuular, A. A. Yamurzina, D. S. Novginov

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Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides by Sukhvir Kaur Bhangu, Nicholas Welch, Morgan Lewis, Fanyi Li, Brint Gardner, Helmut Thissen, Wioleta Kowalczyk

“…Herein, to accelerate the discovery process of new AMPs, a predictive and generative algorithm is build, which constructs new peptide sequences, scores their antimicrobial activity using a machine learning (ML) model, identifies amino acid motifs, and assembles high‐ranking motifs into new peptide sequences. …”
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A MACHINE LEARNING DISTRACTED DRIVING PREDICTION MODEL by Samira AHANGARI, Mansoureh JEIHANI, Abdollah DEHZANGI

“…Here we investigated different optimization models to build the best BN in which a Genetic Search Algorithm obtained the best performance. As a result, we achieved a 67.8% prediction accuracy using our model to predict driver distraction. …”
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