Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information by Ruibin Zhang, Yingshi Guo, Yunze Long, Yang Zhou, Chunyan Jiang

“…A vehicle motion state prediction algorithm integrating point cloud timing multiview features and multitarget interaction information is proposed in this work to effectively predict the motion states of traffic participants around intelligent vehicles in complex scenes. …”
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New physics at the Muon (Synchrotron) Ion Collider: MuSIC for several scales by Hooman Davoudiasl, Hongkai Liu, Roman Marcarelli, Yotam Soreq, Sokratis Trifinopoulos

“…Besides its rich nuclear physics and Standard Model particle physics programs, in this work we show that the MuSIC with a TeV-scale muon beam offers also a unique opportunity to probe New Physics. …”
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Data Mining Techniques for Early Detection and Classification of Plant Diseases: An Optimization-Based Approach by Wagh Swapnil, Sharma Ruchi

“…Besides, this research aims to investigate the feasibility that employs data mining framework together with optimization algorithms as approaches towards plant diseases identification and categorization systems. The model proposed uses the state-of-art algorithms including the decision trees, support vector machines and the deep learning techniques in the feature extraction and pattern recognition as well as binary classification. …”
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Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

“…In comparison, random forest (R2 = 0.9795), linear regression (R2 = 0.9697), and support vector machines (R2 = − 0.4812) show lower predictive accuracy, underscoring the ANN model's superior performance. …”
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CO2 Gas Layer Recognition Method Based on Long Short-Term Memory Network by HE Lina, WU Wensheng, WANG Xiannan, ZHANG Wei, ZHANG Chuanju, SONG Xiaoyu

“…This model is used to identify the CO2 gas layer of well L2 in Enping sag, the Pearl River Mouth basin, and is compared with the recognition results of support vector machine and K nearest neighbor algorithm. …”
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Multi-Class Skin Cancer Classification Using a Hybrid Dynamic Salp Swarm Algorithm and Weighted Extreme Learning Machines with Transfer Learning by Ramya Panneerselvam, Sathiyabhama Balasubramaniam

“…Hence, classification is a more challenging task with imbalances in the dataset. The proposed work is implemented as a hybrid model with a dynamic salp swarm algorithm (DSSA) with a weighted extreme learning machine (DSSA-WELM) that addresses the imbalances in the dataset and performs the classification with higher accuracy. …”
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DiffusionBAS: Estimating Camera External Orientation Through Diffusion Process by V. V. Kniaz, V. V. Kniaz, T. N. Skrypitsyna, V. A. Knyaz, V. A. Knyaz, S. Yu. Zheltov

“…We present an end-to-end framework that receives a single image of an object and predicts its vector of the exterior orientation parameters. The main objective of a present work is reconstruction of 3D model of a partially destroyed 3D object and it 3D in-painting. …”
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EfficientNet B0 Feature Extraction with L2-SVM Classification for Robust Facial Expression Recognition by Ahmad Taufiq Akbar, Shoffan Saifullah, Hari Prapcoyo, Heru Rustamadji, Nur Heri Cahyana

“…Future work will extend the framework to in-the-wild datasets and incorporate model explainability techniques to improve interpretability in practical deployment Keywords: Facial Expression Recognition, EfficientNet, SVM, Deep Features, Emotion Classification…”
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Enhanced prediction of ionic liquid toxicity using a meta-ensemble learning framework with data augmentation by Safa Sadaghiyanfam, Hiqmet Kamberaj, Yalcin Isler

“…The proposed model incorporates the Random Forest, Support Vector Regression, Categorical Boosting, Chemical Convolutional Neural Network as a base classifier and an Extreme Gradient Boosting meta-classifier. …”
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A hybrid molecular dynamics–machine learning framework for boiling point estimation in aromatic fluids by Amirali Shateri, Zhiyin Yang, Nasser Sherkat, Jianfei Xie

“…MD data were used to train and evaluate three regression models—Nearest Neighbours Regression (NNR), Neural Network (NN), and Support Vector Regression (SVR). …”
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Stability analysis of wooden arches with account for nonlinear creep by S. В. Yazyev, V. I. Andreev, А. S. Chepurnenko

“…The creep measure is taken in accordance with the work of I.E. Prokopovich and V.A. Zedgenidze [2] as a sum of exponential functions. …”
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Application of Isokinetic Dynamometry Data in Predicting Gait Deviation Index Using Machine Learning in Stroke Patients: A Cross-Sectional Study by Xiaolei Lu, Chenye Qiao, Hujun Wang, Yingqi Li, Jingxuan Wang, Congxiao Wang, Yingpeng Wang, Shuyan Qie

“…This study employed four machine learning models—Lasso Regression, Random Forest (RF), Support Vector regression (SVR), and BP Neural Network—to model and validate the sensor data. …”
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RIS-Aided Integrated Sensing and Communication Systems: STAR-RIS Versus Passive RIS? by Prajwalita Saikia, Anand Jee, Keshav Singh, Cunhua Pan, Wan-Jen Huang, Theodoros A. Tsiftsis

“…We formulate an optimization problem that jointly optimizes the beamforming vector at the multi-antenna ISAC transmitter and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit the ISAC transmitter and ensuring the performance of sensing model to detect targets and limitations of phase and amplitude of S-RIS elements. …”
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Accounting of the magnetic field of the power supply system of a power-capacitive technical object by A. T. Tarlanov, Z. M. Kurbanismailov

“…Technical objects are understood as a mobile, energy-intensive vehicle, such as an aircraft, a surface or submarine vessel, or a railway locomotive with strong magnetic fields. The aim of the work is to create a simple and intuitive tool for mathematical modeling of the magnetic field vector at an arbitrarily specified observation point. …”
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Universal anomaly detection at the LHC: transforming optimal classifiers and the DDD method by Sascha Caron, José Enrique García Navarro, María Moreno Llácer, Polina Moskvitina, Mats Rovers, Adrián Rubio Jímenez, Roberto Ruiz de Austri, Zhongyi Zhang

“…Abstract In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. …”
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Multiobjective optimization of CO2 injection under geomechanical risk in high water cut oil reservoirs using artificial intelligence approaches by Fankun Meng, Jia Liu, Gang Tong, Hui Zhao, Chengyue Wen, Yuhui Zhou, Vamegh Rasouli, Minou Rabiei

“…Thus, Gaussian-SVR and LSTM are trained as the proxy model to substitute the numerical simulator. Thus, the MOPSO and NSGA-II are utilized to determine the Pareto Front of the optimum result and work schedules. …”
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Workplace Preference Analytics Among Graduates by Sin-Yin Ong, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

“…Various analytics methods such as Decision Tree Analysis, Random Forest Model selection, Naive Bayes Classification Method, Support Vector Machines and K-Nearest Neighbor Algorithms were employed for comparative evaluations within the workplace analytics scope. …”
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Methodology for Estimating the Cost of Construction Equipment Based on the Analysis of Important Characteristics Using Machine Learning Methods by Nataliya Boyko, Oleksii Lukash

“…The study built and analyzed models using machine learning methods (linear and polynomial regression, decision trees, random forest, support vector machine, and neural network). …”
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Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools by Alfredo Cuzzocrea, Abderraouf Hafsaoui, Carmine Gallo

“…Our approach is built to work well with untested data, and our model is able to provide a vector representation for every node that captures its structural information. …”
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HEARING CORRECTION METHOD BASED ON PSYCHOACOUSTICALLY MOTIVATED FREQUENCY TRANSPOSITION IN A SPEECH SIGNAL by M. I. Porhun, M. I. Vashkevich

“…The experiment involved 10 people who were given to listen to the recordings passed through the hearing loss model, as well as recordings passed through the hearing loss model with subsequent correction (using proposed method). …”
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