Application of the SARIMA-LSTM model to evaluate the effectiveness of interventions for Visceral Leishmaniasis by Mengchen Han, Chongqi Hao, Zhiyang Zhao, Peijun Zhang, Bin Wu, Lixia Qiu

“…A paired samples t-test revealed a statistically significant difference between predicted and actual case counts (t = -4.058, p < 0.001), indicating that the actual number of cases was lower than predicted. …”
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The Adaptive-Clustering and Error-Correction Method for Forecasting Cyanobacteria Blooms in Lakes and Reservoirs by Xiao-zhe Bai, Hui-yan Zhang, Xiao-yi Wang, Li Wang, Ji-ping Xu, Jia-bin Yu

“…In this study, an adaptive-clustering algorithm is introduced to obtain some typical operating intervals. …”
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Design of a Manned-Unmanned Teaming System and Forward-Formation Control for Crevasse Detection in the Path of a Human-Driven Vehicle by Ji-Wook Kwon, Hyoujun Lee, Taeyoung Uhm, Jongdeuk Lee, Na-Hyun Lee, Young-Ho Choi

“…The stability and performance of the proposed MUM-T system, forward-formation, and motion control algorithm are validated through simulations. Especially, the forward-formation demonstrated more than an 86% improvement in predicting and mimicking the leader&#x2019;s trajectory compared to conventional formation strategies. …”
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Machine Learning and Deep Learning-Based Atmospheric Duct Interference Detection and Mitigation in TD-LTE Networks by Rasendram Muralitharan, Upul Jayasinghe, Roshan G. Ragel, Gyu Myoung Lee

“…Our results show that the Random Forest algorithm achieves the highest prediction accuracy, while a convolutional neural network demonstrates the best mitigation performance with accuracy. …”
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Detection of false data injection in electric energy metering platforms using gradient lifting decision trees and MLP neural networks by Yakui Zhu, Yangrui Zhang, Chao Zhang, Bingyu Zhang, Hongying Wang, Shaokang Feng

“…The predictor was based on a gradient boosting decision tree to predict potential data injection anomalies. The discriminator used a multilayer perceptron (MLP) neural network, combined with difference analysis between the predicted and actual values, to determine false data injection. …”
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Charting γ-secretase substrates by explainable AI by Stephan Breimann, Frits Kamp, Gabriele Basset, Claudia Abou-Ajram, Gökhan Güner, Kanta Yanagida, Masayasu Okochi, Stephan A. Müller, Stefan F. Lichtenthaler, Dieter Langosch, Dmitrij Frishman, Harald Steiner

“…Using machine learning, we predict the entire human γ-secretase substrate scope, revealing numerous previously unknown substrates. …”
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Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery by Hsing-Hao Ho, Huai-Che Yang, Wen-Xiang Yang, Cheng-Chia Lee, Hsiu-Mei Wu, I-Chun Lai, Ching-Jen Chen, Syu-Jyun Peng

“…The algorithm could be used to predict the course of RICs and facilitate clinical management. …”
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Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review by Palpasa Shrestha, Bibek Shrestha, Jati Shrestha, Jun Chen

“…The machine learning algorithm first computes the image characteristics deemed significant for making predictions or diagnoses about unseen images. …”
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Short-Term Load Forecasting Based on Feature Selection and Combination Model by Yusong XU, Shanhua ZOU, Xianling LU

“…Spearman rank-order correlation coefficient and max-relevance & min-redundancy algorithm are individually employed for selection. …”
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Staring Imaging Real-Time Optimal Control Based on Neural Network by Peiyun Li, Yunfeng Dong, Hongjue Li

“…In this paper, a real-time optimal attitude controller is designed for staring imaging, and the output command is based on future prediction. First, the mathematical model of staring imaging is established. …”
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Are Suggested Hiking Times Accurate? A Validation of Hiking Time Estimations for Preventive Measures in Mountains by Marco Vecchiato, Nicola Borasio, Emiliano Scettri, Vanessa Franzoi, Federica Duregon, Sandro Savino, Andrea Ermolao, Daniel Neunhaeuserer

“…MOVE demonstrated superior accuracy, offering personalized hiking time predictions based on user-specific data and trail characteristics. …”
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Modelling of Gust Factor Based Dynamic Loading of Buildings Using Python Programming by Pankaj Dumka, Rishika Chauhan, Amit Srivastava

“…Notably, it has also been observed that the wind loads decrease with the height due to reductions in the tributary area and the top mean floor height.…”
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Physics-informed modeling and process optimization of friction stir welding of AA7075-T6 with a zinc interlayer by Dejene Alemayehu Ifa, Dame Alemayehu Efa, Naol Dessalegn Dejene, Sololo Kebede Nemomsa

“…The models that are included in the hybrid framework of ANN, SVR, and RFR have all demonstrated noteworthy prediction strengths. The optimum FSW parameters were shown to be: a tool speed of 600 rpm, a pin radius of 5.71 mm, a shoulder radius of 20 mm, and a plunge force of 6369.48 N, with a predicted peak temperature value of 675.71 K. …”
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The Relationship Between Horizontal Jumping and Sprinting Ability Across Sexes in Young Active Adults by Adam Kleeberger, Dana Agar-Newman, Ming-Chang Tsai, Marc Klimstra

“…Further, athlete mass is a significant factor in the prediction of acceleration ability but not maximum speed, and horizontal jumps were significant factors in the prediction of max speed but not acceleration.…”
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Multiple Regression-Based Dynamic Amplification Factor Investigation of Monorail Tourism Transit Systems by Hong Zhang, Changxing Wu, Wenlong Liu, Shiqi Wei, Yonggang Wang

“…On this basis, the key regression parameters were selected by using the Pearson correlation coefficient method and the random forest algorithm, and the DAFs prediction model was constructed based on the least absolute shrinkage and selection operator (LASSO) regression theory. …”
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Application of Artificial Intelligence in Tinnitus Diagnosis and Treatment: A Pilot Study by Yu Wang, Kaixiang Pan, Richard Tyler, Zhaoyi Lu, Shan Xiong, Yufei Xie, Tao Pan

“…We describe the curation of the input data, the algorithm (CTGan) used to extend the dataset, and the ML model (Random Forest) to predict each of the suggested treatments. …”
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Grey Wolf Resampling-Based Rao-Blackwellized Particle Filter for Mobile Robot Simultaneous Localization and Mapping by Yong Dai, Ming Zhao

“…In addition, we propose an adaptive local data association (Range-SLAM) scheme to improve the computational efficiency for the algorithm of the nearest neighbor (NN) data association in the iteration of the RBPF prediction. …”
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In Situ Stress Inversion and Distribution Characteristics of Tunnel Based on Numerical Simulation and Neural Network Technology by Pei Zhang

“…The neural network optimized by genetic algorithm is used to establish the nonlinear relationship between the measured value and the simulated value of the variable boundary condition, and the optimal boundary condition function is obtained. …”
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Channel modeling of molecular communication via free diffusion with multiple receiver by Zhuo SUN, Xu BAO, Jie LIN, Wence ZHANG

“…A coexistence scenario with a point source, a pair of absorbing and transparent receiver was considered, an interference factor was introduced in the proposed channel model based on the receiving molecular probability in the transparent receiver considering the influences of the absorbing receiver on the transparent one.Furthermore, the channel model of point source and transparent receiver had been proposed by using Levenberg-Marquardt algorithm combined with artificial neural network to study and predict channel model parameters.The simulation results not only verify the effectiveness of the proposed channel model, but also show that the peak time of any point in the environment is directly proportional to the square of the distance from the point source to the receiver, and inversely proportional to the molecular diffusion coefficient, and the peak time is not affected by the absorbing receiver in the environment.…”
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Joint optimization of edge computing and caching in NDN by Yu ZHANG, Min CHENG

“…Named data networking (NDN) is architecturally easier to integrate with edge computing as its routing is based on content names and its nodes have caching capabilities.Firstly, an integrated framework was proposed for implementing dynamic coordination of networking, computing and caching in NDN.Then, considering the variability of content popularity in different regions, a matrix factorization-based algorithm was proposed to predict local content popularity, and deep reinforcement learning was used to solve the the problem of joint optimization for computing and caching resource allocation and cache placement policy with the goal of maximizing system operating profit.Finally, the simulation environment was built in ndnSIM.The simulation results show that the proposed scheme has significant advantages in improving cache hit rate, reducing the average delay and the load on the remote servers.…”
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