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support vector machine (SVM) algorithm optimization and innovative talent matching model for employment needs

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Lv, Ying Zhengzhou Coll Finance & Econ Sch Management 36 Tianhe Rd Zhengzhou 450000 Peoples R China

Traditional talent matching methods generally rely on manual rules and static feature analysis, which makes it difficult for the model to adapt to the rapidly changing employment market and the personalized needs of job seekers, resulting in insufficient matching precision and poor adaptability. This paper constructs an innovative talent matching model based on the optimized support vector machine (SVM) algorithm to address this problem. Firstly, dynamic employment market data and multi-dimensional job seeker features are used to build a more intelligent and personalized matching framework. This study proposes an innovative intelligent talent matching model that enhances the understanding of the relationship between jobs and job seekers through data cleaning, standardization, and feature extraction using TF-IDF technology. By optimizing the SVM kernel function and fine-tuning hyperparameters, the model's classification performance in complex matching tasks is improved. Additionally, the integration of real-time dynamic data updates and incremental learning methods enables the model to automatically adapt to market changes, improving the timeliness and accuracy of matching results. In the design of the multi-dimensional matching model, this paper further integrates job seeker potential analysis and job development potential to optimize the recommendation strategy. Compared to traditional keyword matching and logistic regression models, the proposed model significantly outperforms others in talent matching, achieving a maximum matching accuracy of 0.91, a maximum F1-score of 0.93, an average response time of 2.02 minutes, and an average update frequency of 14.03 times per hour. The results demonstrate that this innovative talent matching model provides a more efficient, personalized, and intelligent solution for the dynamic employment market, advancing the development of talent matching technology.

关键词： talent matching model personalized recommendation dynamic adaptability support vector machine incremental learning

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support vector machine for Stratification of Cognitive Impairment Using 3D T1WI in Patients with Type 2 Diabetes Mellitus

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DIABETES METABOLIC SYNDROME AND OBESITY 2025年 18卷 435-451页

作者： Xu, Zhigao Zhao, Lili Yin, Lei Cao, Milan Liu, Yan Gu, Feng Liu, Xiaohui Zhang, Guojiang Third Peoples Hosp Datong Dept Radiol Datong 037046 Peoples R China Changzhi Med Sch Grad Sch Changzhi 046013 Peoples R China Third Peoples Hosp Datong Dept Sci & Educ Datong 037046 Peoples R China Third Peoples Hosp Datong Dept Endocrinol Datong 037046 Peoples R China Third Peoples Hosp Datong Dept Cardiovasol Datong 037046 Shanxi Peoples R China

Purpose: To explore the potential of MRI-based radiomics in predicting cognitive dysfunction in patients with diagnosed type 2 diabetes mellitus (T2DM). Patients and Methods: In this study, data on 158 patients with T2DM were retrospectively collected between September 2019 and December 2020. The participants were categorized into a normal cognitive function (N) group (n=30), a mild cognitive impairment (MCI) group (n=90), and a dementia (DM) group (n=38) according to the Chinese version of the Montr & eacute;al Cognitive Assessment Scale-B (MoCA-B). Radiomics features were extracted from the brain tissue except ventricles and sulci in the 3D T1WI images, support vector machine (SVM) model was then established to identify the CI and N groups, and the MCI and DM groups, respectively. The models were evaluated based on their area under the receiver operating characteristic curve (AUC), Precision (P), Recall rate (Recall, R), F1-score, and support. Finally, ROC curves were plotted for each model. Results: The study consisted of 68 cases in the N and CI group, with 54 cases in the training set and 14 in the verification set, and 128 cases were included in the MCI and DM groups, with 90 training sets and 38 verification sets. The consistency for inter-group and intra-group of radiomics features in two physicians were 0.86 and 0.90, respectively. After features selection, there were 11 optimal features to distinguish N and CI and 12 optimal features to MCI and DM. In the test set, the AUC for the SVM classifier was 0.857 and the accuracy was 0.830 in distinguishing CI and N, while AUC was 0.821 and the accuracy was 0.830 in distinguishing MCI and DM. Conclusion: The SVM model based on MRI radiomics exhibits high efficacy in the diagnosis of cognitive dysfunction and evaluation of its severity among patients with T2DM.

关键词： cognitive dysfunction radiomics magnetic resonance imaging support vector machine diabetes mellitus type 2

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support vector machine-based prediction model for the compressive strength for concrete reinforced with waste plastic and fly ash

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Asian Journal of Civil Engineering 2025年第4期26卷 1429-1447页

作者： Kumar, Anish Sen, Sameer Sinha, Sanjeev Rajkiya Engineering College Azamgarh India Department of Civil Engineering National Institute of Technology Patna India

In the current study, the effect of the inclusion of waste plastic in different quantities (0–10%) on the compressive strength of fly ash reinforced concrete is explored. Compressive strength decreases with increasing plastic waste due to its inert, hydrophobic nature and poor bonding within the concrete matrix, while 10% fly ash improves strength slightly through pozzolanic reactions that densify the matrix and reduce voids. support vector machine (SVM) was explored as a potential machine learning technique for accurately predicting and modeling the compressive strength of concrete. Predictive models were developed using SVM-radial basis function (RBF), SVM-linear, SVM-power and linear regression. The models were analyzed using performance metrics such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), mean squared logarithmic error (MSLE), root mean squared logarithmic error (RMSLE), coefficient of determination (R²), mean absolute percentage error (MAPE), Willmott's index of agreement, Mielke and Berry index, and Legates and McCabe's index. Taylors diagram was also used to analyze the models. SVM-RBF model outperformed all other models with an R² value of 0.969 and 0.771 in training and testing respectively. Sensitivity analysis revealed that % plastic waste is the most influential parameter in predicting the compressive strength of concrete with a score of 59.04%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Compressive strength Kernel Performance metrics support vector machine Waste plastic

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Artificial Flora Algorithm-Based Feature Selection With support vector machine for Cardiovascular Disease Classification

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IEEE ACCESS 2025年 13卷 7293-7309页

作者： Asha, M. M. Ramya, G. Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamil Nadu India

Accurately categorizing medical information is crucial for determining effective cardiac treatment options, especially as the volume of data grows and feature selection becomes increasingly challenging. This work proposes a model to identify the presence of Cardiovascular Disease based on various patient features, aiming to enhance prediction accuracy through a powerful feature selection method. This approach utilizes the Cleveland dataset by combining the Artificial Flora Optimization algorithm with the support vector machine. The proposed algorithm functions as a meticulous gardener, selectively identifying the most significant features for heart disease prediction through an objective function. The model demonstrates impressive performance, achieving an accuracy of 96.63%, specificity of 95.73%, sensitivity of 97.74%, precision of 94.89%, and an F1-score of 96.29%. The model promises high-accuracy heart disease predictions by optimizing feature selection, potentially transforming clinical practice, and advancing research. The novel combination of the proposed technique holds significant potential for improving medical categorization and patient outcomes.

关键词： Heart Feature extraction Classification algorithms Prediction algorithms Genetic algorithms Accuracy Artificial intelligence Particle swarm optimization Artificial flora optimization fitness function feature selection ROC analysis support vector machine cardiovascular disease support vector machine cardiovascular disease

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Evaluation of regional green innovation performance in China using a support vector machine-based model optimized by the chaotic grey wolf algorithm

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2025年第1期27卷 293-307页

作者： Zhao, Pengyi Cai, Yuanying Chen, Liwen Li, Qing Dai, Fuqiang Chongqing Technol & Business Univ Sch Econ Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Sch Publ Adm Chongqing 400067 Peoples R China

The green innovation performance (GIP) evaluation helps to identify strengths and weaknesses in regional innovation systems and has been crucial for policymakers in developing appropriate regional policies. Recent methodology has focused on establishing an indicator framework and calculating composite scores. It is noteworthy that the non-linear relationship between evaluation scores and indicators is rarely considered. In view of this, an evaluation model was proposed in the study which combines support vector machine (SVM) and chaotic grey wolf algorithm (CGWO). Sixteen indicators from the indicator system of European Innovation Scoreboard were retained for the GIP evaluation with an initial screening of indicators using the information entropy method. Then, four different types of optimization algorithms were used to optimize the SVM to generate non-linear predictions and GIP scores. The applicability of the model was verified for the GIP evaluation of China's provinces. According to the training and test results, the SVM-CGWO model achieved significantly better performance than the other three algorithms, which has important benefits in improving the uniformity of the wolf distribution and the traversal of the wolf pack, together with enhancing operation speed and accuracy. It helps users to rank and benchmark regional GIP at the provincial level, taking into account performance improvement and accuracy of dimensions, as well as reliability issues. [GRAPHICS] .

关键词： Green innovation performance Evaluation model support vector machine Chaotic sequence Grey wolf algorithm

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Fault Classification and Detection in Transmission Lines by Hybrid Algorithm Associated support vector machine

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2025年第3期36卷

作者： Kumar, V. Rajesh Jeyanthy, P. Aruna Sir M Visvesvaraya Inst Technol Dept Elect & Elect Engn Bengaluru Karnataka India Kalasalingam Acad Res & Educ Dept Elect & Elect Engn Srivilliputhur Tamil Nadu India

This work proposes a unique machine-learning method based on optimization for the categorization and identification of defects in transmission lines. The novel hybrid optimization algorithm termed as the Chimpanzee inherited Squirrel search strategy (CI-SSS) optimization technique is used in the proposed approach. The proposed CI-SSS algorithm inherits the concept of chimps and squirrels in attaining their food with remarkable intelligence. The proposed approach involves optimizing the SVM's parameters to improve the proposed model's accuracy in identifying and classifying transmission line faults. The accuracy and error metrics of the suggested method is studied. The accuracy CI-SSS is 98.82%, which is 11.35%, 5.41%, 0.84%, and 9.55% higher than methods, like GWO, DA, SSA, and CH, correspondingly. Similarly, the measure of MAE using the proposed CI-SSS-based SVM model is 0.0104, which is 84.5%, 87.7%, 85.73%, and 62.85% finer than the traditional methods, namely GWO, DA, SSA, and CH, respectively. Hence, the suggested strategy offers improved performance in classifying and detecting transmission line faults.

关键词： fault classification fault location optimization support vector machine transmission line

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High-Impedance Fault Detection in Distribution Networks Based on support vector machine and Wavelet Transform Approach (Case Study: Markazi Province of Iran)

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ENERGY SCIENCE & ENGINEERING 2025年第3期13卷 1171-1183页

作者： Attar, Mohammad Sadegh Miveh, Mohammad Reza Tafresh Univ Dept Elect Engn Tafresh Iran

High impedance faults (HIFs) can lead to crucial damage to the utility grid, such as the risk of fire in material assets, electricity supply interruptions, and long service restoration times. Due to their low current magnitude, conventional protective equipment, such as overcurrent relays, cannot detect these faults. Alternatively, the waveform and variation range of current in HIFs are similar to other phenomena, such as linear and nonlinear load changes and capacitor banks. This paper employs a support vector machine (SVM) classification algorithm that demonstrates reliable accuracy and discrete wavelet transform (DWT) in HIF detection. First, the data set containing measured current signals of HIFs is collected to implement this approach. Then, DWT decomposes it to extract the features of each sample in the data set. The extracted features from this part are used as input to the SVM classification algorithm. The proposed idea is initially implemented on the IEEE 34-bus distribution test network. The proposed method achieves high capability and accuracy in detecting high-impedance faults. The proposed method is also applied to a real power distribution network in Markazi Province of Iran, yielding satisfactory results. EMTP-RV simulation software is used to simulate and evaluate the proposed method for power network modeling. Moreover, MATLAB software is used for feature extraction, and Python programming language in Google Colab and Spyder environment is applied to implement the SVM algorithm. The simulation results confirm the high accuracy of the suggested method. The main criteria obtained by the proposed method include accuracy, sensitivity, specificity, precision, F-score, and Dice, which are 99.581%, 98.684%, 100%, 100%, 99.338%, and 99.338%, respectively, for the test network, and 97.94%, 93.45%, 100%, 100%, 96.614%, and 96.618%, respectively, for the real power distribution network.

关键词： discrete wavelet transform distribution networks high impedance fault machine learning support vector machine

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Defect detection in hot stamping process printed matter by beluga optimized support vector machine with opposition-based learning

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-13页

作者： Ru, Xin Yao, Yingdong Li, Jianqiang Peng, Laihu Sun, Zhuocheng Zhejiang Sci Tech Univ Zhejiang Key Lab Modern Text Equipment Technol 9282 St Hangzhou 310018 Zhejiang Peoples R China Zhejiang Sci Tech Univ Longgang Res Inst 3FBldg 2Printing Ind Innovat Serv ComplexCentur Wenzhou 325000 Zhejiang Peoples R China

The high surface reflectivity and concave-convex of hot stamping process printed matters lead to poor defect detection accuracy. To solve this problem, this paper proposes a defect detection model (OBL-BWO-SVM) for hot stamping process printed matters based on oppositional learning (OBL) beluga optimized (BWO) support vector machine (SVM). First, in terms of image acquisition, a new light source illumination method is proposed for the own characteristics of hot stamping, the gray level covariance matrix (GLCM) is applied to extract the image information to build a dataset, and principal component analysis (PCA) is applied to reduce the dimensionality of the dataset. Then, SVM is used as the base model and the category weights of SVM are adjusted for the sample data category imbalance problem. Then, the C and gamma parameters of SVM are optimized using OBL-BWO. Finally, 14400 images are used as the dataset, in which the proportions of defect-free samples and defective samples are 48.9% and 51.1%, respectively, and are divided into the training set and the test set according to the ratio of 7:3. The models in this paper and the commonly used models are tested on this dataset. The experimental results show that the accuracy of the model proposed in this paper is 96.11%, precision is 94.61%, recall is 95.75%, and F1 score is 95.18%. Compared with the commonly used methods, the method proposed in this paper has higher classification accuracy and can improve the accuracy of printed matter defect detection during hot stamping printing.

关键词： Hot stamping process Defect detection Printed matter support vector machine

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Vibration Based Damage Assessment of a Steel Frame Structure Using support vector machine Algorithm

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2025年第2期25卷 329-337页

作者： Mohapatra, Deepti Ranjan Mukharjee, Bibhuti Bhusan Mondal, Subhajit Natl Inst Technol Rourkela Dept Civil Engn Rourkela 769008 India BPUT Dept Civil Engn CAPGS Rourkela 769015 India

The structural damage detection system comes under the vast field of structural health monitoring. This paper deals with the two-stage damage assessment approach, including identification and severity estimation of any damage present in the structure. Free vibrational analysis of the healthy and damaged state of the structure yields two important modal parameters: frequency and mode shape. Eigenvectors, which constitute the mode shape of the structure, are considered for evaluating a damage index by comparing the damaged state with the healthy state. A Normalized Damage Index (NDI)is estimated for the structure subjected to various damage case scenarios. The novel method of estimating NDI provides a unique pattern for each element in the structure. The variation of natural frequency with increasing damage percentage helps estimate damage severity. support vector machine(SVM), with a statistical pattern recognition paradigm, is an efficient supervised machine Learning (ML) algorithm capable of performing classification and regression analysis. The Kernel-based SVM algorithm effectively identifies damaged elements and estimates each element's severity. A four-storey, three-bay steel frame structure developed in the OpenSees framework is subjected to modal analysis. The results are validated with SAP and finite element-based ABAQUS software. The ability of the proposed model is also verified for a complex 3D structure. The viability of this model is also explored experimentally with a four-storeyed and single-bay steel frame structure. This approach provides an effective way of damage assessment.

关键词： Finite element model Vibrational analysis Normalised damage index Inverse problem support vector machine Natural frequency Mode shape

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Remote alpha/beta measurement system with support vector machine algorithm

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2025年 1076卷

作者： Morishita, Yuki Miyamura, Hiroko Nakamura Sato, Yuki Matsubara, Jun Sumali, Brian Mitsukura, Yasue Japan Atom Energy Agcy Collaborat Labs Adv Decommissioning Sci CLADS 45-169 Kaihama SukakebaHaramachi Ku Minamisoma Fukushima 9750036 Japan Keio Univ 4-1-1 HiyoshiKohoku Ku Yokohama Kanagawa 2238521 Japan

Decommissioning nuclear reactor sites presents challenges due to the presence of various radionuclides, including alpha emitters (e.g., Pu, Am, Cm) and beta emitters (e.g., 137Cs, 90Sr-90Y), which pose significant internal exposure risks to workers. Traditional measurement methods require multiple instruments and are timeconsuming, particularly in high gamma-ray environments. To address these issues, we developed a remote alpha and beta discrimination measurement system that integrates a stilbene scintillator detector with a silicon photomultiplier, enabling simultaneous detection of both alpha and beta particles. This study further incorporates machine learning techniques, specifically support vector machines (SVM), for automatic discrimination, eliminating the need for user-defined thresholds and ensuring consistent operational conditions. The system was tested with known radiation sources, demonstrating over 96 % classification accuracy for alpha and beta particles. Measurements conducted in motion effectively identified contamination sources, confirming the system's capability for real-time analysis. This innovative approach enhances radiation safety and efficiency in nuclear decommissioning operations, making it particularly beneficial in environments where human access is limited.

关键词： Stilbene Alpha and beta particles Robot support vector machine

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