Prediction of Urban Scale Expansion Based on Genetic Algorithm Optimized Neural Network Model by Hewu Kuang

“…A genetic algorithm BP neural network (GA-BP) optimized by the genetic algorithm is used to shorten the running time of the algorithm and improve the prediction accuracy, but it is easy to fall into local solution. …”
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An improved algorithm for prediction of vehicle trajectories using short-term goal-driven network by Abdalla Tawfik, Zaki Nossair, Roaa Mubarak

“…Conclusion This article presents an improved algorithm for predicting vehicle trajectories using short-term goals. …”
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New Energy Regional Power Prediction Algorithm Based on Statistical Upscaling in Ningbo Region by Wei WANG, Bo WANG, Jun ZHANG, Chunliang LU, Xu HE

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A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm by Zhixin Xia, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, Yongshan Liu

“…However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. …”
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Prediction of drug target interaction based on under sampling strategy and random forest algorithm. by Feng Chen, Zhigang Zhao, Zheng Ren, Kun Lu, Yang Yu, Wenyan Wang

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Retracted: Highway Traffic Flow Prediction Algorithm Based on Multiscale Transformation and Convolutional Networks by Journal of Control Science and Engineering

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Nonlinear Volatility Risk Prediction Algorithm of Financial Data Based on Improved Deep Learning by Wangsong Xie

“…With the gradual integration of global economy and finance, the financial market presents many complex financial phenomena. To increase the prediction accuracy of financial data, a new nonlinear volatility risk prediction algorithm is proposed based on the improved deep learning algorithm. …”
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Dynamics of link formation in networks structured on the basis of predictive terms by S. O. Kramarov, O. R. Popov, I. E. Dzhariev, E. A. Petrov

“…In order to model and analyze the information conductivity of complex networks having an irregular structure, it is possible to use percolation theory methods known in solid-state physics to quantify how close the given network is to a percolation transition, and thus to form a prediction model. Thus, the object of the study comprises international information networks structured on the basis of dictionaries of model predictive terms thematically related to cutting-edge information technologies.Methods. …”
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Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adil Noor

Subjects: “…Energy consumption prediction…”
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Q-learning based handoff algorithm for satellite system with ancillary terrestrial component by Dan-ni XIONG, Yi LI

“…,mobile satellite system with ancillary terres-trial component (MSS-ATC),the long transmission delay of satellite link was a huge challenge which may lead to high handoff dropping probability.In order to address this problem,a novel handoff decision strategy was proposed based on the predictive RSS and Q-learning algorithm.Extensive simulation results demonstrate that the proposed scheme can de-crease the handoff dropping probability,reduce the unnecessary handoff times and maximize the network reward.In ad-dition,the proposed scheme can also adapt to the situation of high-speed movement very well.…”
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Robust Adaptive Path Tracking Control Scheme for Safe Autonomous Driving via Predicted Interval Algorithm by Cong Phat Vo, Jungeun Lee, Jeong Hwan Jeon

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Dye-cleaning prediction with a variant of nature-inspired algorithms coupled with extreme gradient boosting by Tiyasha Tiyasha, Chijioke Elijah Onu, Mohamed A. Ismail, Rama Rao Karri, Abdelfattah Amari, Vinay Kumar, Suraj Kumar Bhagat

“…The use of advanced models, such as XGBoost, improved model performance and accurately predicted dye removal efficiency. The models performed well across different input scenarios, demonstrating their reliability and effectiveness. …”
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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. …”
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Comparative analysis of machine learning algorithms for predicting tibial intramedullary nail length from patient characteristics by Yujian Hui, Hengda Hu, Jinghua Xiang, Xingye Du

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Machine learning and microfluidic integration for oocyte quality prediction by Hassan Saffari, Davood Fathi, Peyman Palay, Hamid Gourabi, Rouhollah Fathi

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Analytical Device and Prediction Method for Urine Component Concentrations by Zhe Wang, Jianbang Huang, Qimeng Chen, Yuanhua Yu, Xuan Yu, Yue Zhao, Yan Wang, Chunxiang Shi, Zizhao Zhao, Dachun Tang

“…To tackle the low-accuracy problem with analyzing urine component concentrations in real time, a fully automated dipstick analysis device of urine dry chemistry was designed, and a prediction method combining an image acquisition system with a whale optimization algorithm (WOA) for BP neural network optimization was proposed. …”
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Nematophagous Fungi Occurrence: Prediction Using Bioclimatic Variables by Ana Martha Cruz-Avalos, Montserrat Chagoya-Sánchez, César Andres Ángel-Sahagún, Ana Isabel Mireles-Arriaga, Griselda Maki-Díaz, René Loredo-Portales, Jesús Hernández-Ruíz

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Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy by Eduardo Alonso, Andoni Beristain, Jorge Burgos, Ibai Gurrutxaga

“…The results indicate that machine learning techniques can effectively predict Down syndrome risk in first-trimester screening programs.…”
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Enhanced Fall Detection and Prediction Using Heterogeneous Hidden Markov Models in Indoor Environment by Oumaima Guendoul, Hamd Ait Abdelali, Youness Tabii, Rachid Oulad Haj Thami, Omar Bourja

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Artificial intelligence in triage of COVID-19 patients by Yuri Oliveira, Iêda Rios, Paula Araújo, Alinne Macambira, Marcos Guimarães, Lúcia Sales, Marcos Rosa Júnior, André Nicola, Mauro Nakayama, Hermeto Paschoalick, Francisco Nascimento, Carlos Castillo-Salgado, Vania Moraes Ferreira, Hervaldo Carvalho

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