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Management and entrepreneurship management mechanism of college students based on support vector machine algorithm

COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 842-854页

作者： Wang, Chao Dong, Yazhi Xia, Yuejun Li, Guoxu Sanjuan Martinez, Oscar Gonzalez Crespo, Ruben Jilin Agr Univ Coll Informat Technol Changchun Peoples R China Jilin Agr Univ Off Students Affairs Changchun Peoples R China Jilin Agr Univ Coll Life Sci Changchun Peoples R China Jilin Agr Univ Dept Org Changchun 130118 Peoples R China Univ Int La Rioja Comp Sci Dept Logrono Spain Univ Int La Rioja Comp Sci Dept Sch Engn & Technol Logrono Spain

For the employment and entrepreneurship management of college students, the application of big data technology can effectively improve their work efficiency, that is, the support vector machine algorithm is applied to the employment and entrepreneurship management of college students. Based on deep learning technology, the deep neural network is constructed based on SVR and restrictive Boltzmann machine, namely, SVR-DBN, including theoretical derivation of model architecture, design and selection of model training algorithms, and the modeling steps and flow charts are given, and finally applied to the influence factor analysis. The multiangle comparison proves that the proposed depth model has excellent feature extraction ability and regression prediction. The results show that the algorithm has higher accuracy and has a 26% improvement over traditional algorithms. The research is of great significance to the improvement of the efficiency of employment and entrepreneurship management and the application of support vector machine algorithms.

关键词： college students employment and entrepreneurship management mechanism support vector machine algorithm

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Real-time Deadlift Form Analysis Using support vector machine algorithm and Kinect V2 Sensor 5

Real-time Deadlift Form Analysis Using Support Vector Machin...

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5th IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2023

作者： Sejera, Marianne M. Arzobal, Eisen Angelo B. Soliongco, James Gabriel R. Mapua University School of Electronics Electrical and Computer Engineering Manila Philippines

ISBN: (纸本)9798350314694

We developed a real-time program utilizing the support vector machine (SVM) algorithm in conjunction with the Microsoft Kinect V2 sensor to analyze the proper execution of the conventional deadlift exercise. A user-defined system was developed to detect movements using the support vector machine algorithm in programming languages and assess the form of the deadlift. We calculated the posture angles based on Chaffin's model to estimate the errors from the user's form in real-time and display numerical values regarding the errors. The results of the system were evaluated based on a professional trainer's analysis. The system was validated by comparing it with five experienced trainers, and the outcomes demonstrated an impressive accuracy rate of 90.54% in correctly identifying the proper form of the conventional deadlift exercise. The proposed system effectively discerned correct and incorrect forms of the conventional deadlift in real-time, leveraging the SVM Image Processing algorithm and Kinect V2 Sensor. Future research is necessary to overcome the study's limitations and enhance its accuracy. © 2023 IEEE.

关键词： conventional deadlift Microsoft Kinect V2 sensor support vector machine algorithm

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A secure home environment for intelligent decision making and block chain technology to ensure authentication using Random Forest algorithm in comparison with support vector machine algorithm 14

A secure home environment for intelligent decision making an...

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14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Naik, R. Aravind Ramkuma, G. Anjaneyulu, K. Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India Saveetha Sch Engn Dept Biomed Engn Chennai Tamil Nadu India Saveetha Univ Dept Biomed Engn Chennai India

ISBN: (纸本)9781665460712

The aim of this study is to perform machine learning based classification using the Novel Random Forest algorithm to predict the home environment for intelligent decision making comparison with support vector machine to measure accuracy, precision and recall. Materials and methods: A total no of 1599 samples are collected from the dataset available in UCI Repository. These samples are divided into 70% for the training dataset (n = 1119) and 30% for testing dataset (n = 480). Accuracy, Recall and Precision values are determined to evaluate the performance of the Novel random forest algorithm. Result: Novel Random forest classifier achieved accuracy of 96% whereas support vector machine achieved 78.50%. Precision obtained for the Novel Random forest classifier is 94.04% and 73.05% for support vector machine. Recall obtained for the Novel Random forest classifier is 94.13% and 78.52% for support vector machine. The significant value achieved is 0.025 (p (sic) 0.05). Conclusion: Novel Random Forest classifier predicts significantly better accuracy for a secure home environment for intelligent decision making and block chain technology when compared to support vector machine classifier.

关键词： support vector machine algorithm Novel Random Forest algorithm machine Learning Smart home Accuracy Block chain

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Predictive models of hypertensive disorders in pregnancy based on support vector machine algorithm

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TECHNOLOGY AND HEALTH CARE 2020年第S1期28卷 S181-S186页

作者： Yang, Lin Sun, Ge Wang, Anran Jiang, Hongqing Zhang, Song Yang, Yimin Li, Xuwen Hao, Dongmei Xu, Mingzhou Shao, Jing Beijing Univ Technol Coll Life Sci & Bioengn Intelligent Physiol Measurement & Clin Translat Beijing Int Base Sci & Technol Cooperat Beijing 100024 Beijing Peoples R China Chinese Acad Med Sci Inst Med Informat Beijing 100020 Peoples R China Haidian Maternal & Children Hlth Hosp Beijing 100080 Peoples R China Beijing Aerosp ChangFeng Co Ltd Beijing 100071 Peoples R China Beijing Yes Med Devices Co Ltd Beijing 100152 Peoples R China

BACKGROUND: The risk factors of hypertensive disorders in pregnancy (HDP) could be summarized into three categories: clinical epidemiological factors, hemodynamic factors and biochemical factors. OBJECTIVE: To establish models for early prediction and intervention of HDP. METHODS: This study used the three types of risk factors and support vector machine (SVM) to establish prediction models of HDP at different gestational weeks. RESULTS: The average accuracy of the model was gradually increased when the pregnancy progressed, especially in the late pregnancy 28-34 weeks and >= 35 weeks, it reached more than 92%. CONCLUSION: Multi-risk factors combined with dynamic gestational weeks' prediction of HDP based on machine learning was superior to static and single-class conventional prediction methods. Multiple continuous tests could be performed from early pregnancy to late pregnancy.

关键词： support vector machine algorithm machine learning model research

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Gait Classification Using A support vector machine algorithm 9

Gait Classification Using A Support Vector Machine Algorithm

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9th Mediterranean Conference on Embedded Computing (MECO)

作者： Savic, Suzana Petrovic Ristic, Branko Prodanovic, Nikola Devedzic, Goran Univ Kragujevac Fac Engn Kragujevac Serbia Univ Kragujevac Fac Med Sci Clin Ctr Kragujevac Kragujevac Serbia

ISBN: (纸本)9781728169491

The gait pattern, as well as the walking process itself, can be an indicator of the overall health of patients. For this reason, it is very important to accurately, clearly and quickly determine the affiliation of the gait pattern (healthy or pathological) and take appropriate measures if necessary. As anterior cruciate ligament injuries are common and may be undetectable, this study presents a classification of gait using a support vector machine (SVM) algorithm. The test data were taken from a Gait LAB laboratory;anterior posterior translation and internal external rotation were used as significant parameters. The classifier performance was evaluated using a confusion matrix. These results showed that the SVM algorithm can be successfully used in tasks of this type of classification.

关键词： classification support vector machine algorithm gait

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The Effect of Pre-Processing on the Classification of Twitter's Flood Disaster Messages Using support vector machine algorithm 3

The Effect of Pre-Processing on the Classification of Twitte...

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3rd International Conference on Applied Engineering (ICAE)

作者： Delimayanti, Mera Kartika Sari, Risna Laya, Mauldy Faisal, M. Reza Pahrul Naryanto, Rizqi Fitri Politekn Negeri Jakarta Dept Comp & Informat Engn Depok Indonesia Lambung Mangkurat Univ Dept Comp Sci Banjarbaru Indonesia Kanazawa Univ Div Mech Sci & Engn Kanazawa Ishikawa Japan

ISBN: (纸本)9781728199177

The eyewitness message on twitter as a social network sensor aims to determine the classification process's performance. In the classification of flood disaster messages, preprocessing data is required before the classification process is carried out. Preprocessing affects the resulting level of accuracy in the classification process. Stopword removal is part of preprocessing so that the effect of stopword removal on classification performance will be examined. support vector machine (SVM) is used to classify by weighting words using Term Frequency-Inverse Document Frequency (TF-IDF). The data taken from Twitter is 3000 data with 1000 data each for each label. The effect of the stopword on accuracy performance can be seen in several experiments that have been carried out. We had already conducted three different experiments, and the highest level of accuracy was 76.6%, 76.87%, and 77.87%. Based on the experiments that have been carried out, stopword is very influential on the accuracy generated by the classification of flood disaster messages on Twitter.

关键词： flood disaster messages twitter classification stopword support vector machine algorithm

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Using Audiometric Data to Weigh and Prioritize Factors that Affect Workers’ Hearing Loss through support vector machine (SVM) algorithm

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Sound & Vibration 2020年第2期54卷 99-112页

作者： Hossein ElahiShirvan MohammadReza Ghotbi-Ravandi Sajad Zare Mostafa Ghazizadeh Ahsaee Student Research Committee Kerman University of Medical SciencesKermanIran Associate Professor Department of Occupational HealthSchool of Public HealthKerman University of Medical SciencesKermanIran Assistant Professor Department of Occupational HealthSchool of Public HealthKerman University of Medical SciencesKermanIran Assistant Professor Department of Computer EngineeringShahid Bahonar UniversityKermanIran

Workers’exposure to excessive noise is a big universal work-related *** of the major consequences of exposure to noise is permanent or transient hearing *** current study sought to utilize audiometric data to weigh and prioritize the factors affecting workers’hearing loss based using the support vector machine(SVM)*** cross sectional-descriptive study was conducted in 2017 in a mining industry in southeast *** participating workers(n=150)were divided into three groups of 50 based on the sound pressure level to which they were exposed(two experimental groups and one control group).Audiometric tests were carried out for all members of each *** study generally entailed the following steps:(1)selecting predicting variables to weigh and prioritize factors affecting hearing loss;(2)conducting audiometric tests and assessing permanent hearing loss in each ear and then evaluating total hearing loss;(3)categorizing different types of hearing loss;(4)weighing and prioritizing factors that affect hearing loss based on the SVM algorithm;and(5)assessing the error rate and accuracy of the *** collected data were fed into SPSS 18,followed by conducting linear regression and paired samples *** was revealed that,in the first model(SPL<70 dBA),the frequency of 8 KHz had the greatest impact(with a weight of 33%),while noise had the smallest influence(with a weight of 5%).The accuracy of this model was 100%.In the second model(7085 dBA),the frequency of 4 KHz had the highest impact(with a weight of 22%),while the frequency of 250 Hz had the smallest influence(with a weight of 3%).The accuracy of this model was 100%*** the fourth model,the frequency of 4 KHz had the greatest effect(with a weight of 24%),while the

关键词： Noise modeling hearing loss data mining support vector machine algorithm

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Development of Data-Driven In-Situ Monitoring and Diagnosis System of Fused Deposition Modeling (FDM) Process Based on support vector machine algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2018年第4期5卷 479-486页

作者： Kim, Jung Sub Lee, Chang Su Kim, Sung-Min Lee, Sang Won Sungkyunkwan Univ Grad Sch Dept Mech Engn 2066 Seobu Ro Suwon 16419 Gyeonggi Do South Korea Sungkyunkwan Univ Sch Mech Engn 2066 Seobu Ro Suwon 16419 Gyeonggi Do South Korea

Fused deposition modeling (FDM), one of representative additive manufacturing (AM) technologies, has been widely used for fabricating functional parts with geometrical complexity. However, it has suffered from degraded part quality and low process reliability and controllability. Therefore, it is of much significance to develop a monitoring and diagnosis system for the FDM process to overcome such drawbacks. In this paper, a data-driven FDM process monitoring and diagnosis system is developed by using two types of sensors - an accelerometer and an acoustic emission (AE) sensor. A large number of experimental data, collected from the accelerometers and AE sensor under healthy and faulty process states, are processed to obtain a critical feature - a root mean square (RMS). The RMS values are then used for training the FDM process monitoring and diagnosis models based on a support vector machine (SVM) algorithm and a k-fold cross validation approach. In particular, the SVM-based models for the odd-and even-numbered layers of one FDM specimen are developed. For a real-time validation in a factory floor, the non-linear SVM-based models using the acceleration signals are used for the software development. The diagnosis accuracy is better than 87.5%, and an applicability of the models is verified.

关键词： Additive manufacturing Data-driven approach Fused deposition modeling Process monitoring and diagnosis Software module support vector machine algorithm

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Microcell prediction model based on support vector machine algorithm

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ANNALS OF TELECOMMUNICATIONS 2014年第1-2期69卷 123-129页

作者： Slavkovic, Vladimir Neskovic, Aleksandar Neskovic, Natasa Univ Belgrade Sch Elect Engn Belgrade 11120 Serbia

A new microcell prediction model for mobile radio environment is presented in this paper. The popular support vector machine algorithm is used as an optimizing tool to build a model. In order to validate the model quality, extensive electric field strength measurements were carried out in the city of Belgrade, for two different test transmitter locations. The analysis of the model has shown that proposed model is fast, accurate (on the order of the local mean measurements uncertainty), reliable, and suitable for computer implementation.

关键词： Microcell Radio propagation Prediction model support vector machine algorithm

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Multi-objective optimization of the Atkinson cycle gasoline engine using NSGA III coupled with support vector machine and back-propagation algorithm

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ENERGY 2023年第PartA期262卷

作者： Li, Yangyang Zhou, Shi Liu, Jingping Tong, Ji Dang, Jian Yang, Fuyuan Ouyang, Minggao Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Hunan Univ State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China

This paper presents an optimization method using Non-dominated Sorting Genetic algorithm (NSGA) III to drive support vector machine (SVM). In the NSGA III algorithm, brake specific fuel consumption (BSFC), NOx and CO2 are optimized by changing the engine control parameters including spark angle, VVT-I (intake), VVT-E (exhaust) and exhaust gas recirculation (EGR). The engine GT-Power physical model is used to generate training data for the SVM, and verify the accuracy of the results of NSGA III algorithm during the optimization process. The SVM with fast calculation speed is used in the calculation of NSGA III fitness evaluation. In addition, enhancing training is utilized to improve the accuracy of the SVM model in this research. When the optimization method is applied to the Atkinson cycle gasoline engine, its high efficiency has been presented. In the three plans obtained by GT-Power physical model with all four parameters optimized, the maximum reduction rates of BSFC, NOx, CO2 and CO (g/kW.h) reached 7.07%, 35.90%, 6.62% and 5.50% respectively. The SVM model is compared with back-propagation algorithm, and the result proves that SVM is more suitable for such problems. Finally, based on the Pareto optimal solution obtained by the optimization method, it significantly promotes the solution of multiobjective optimization problems. Theoretically, the time cost of the optimization method in this paper can reach 1/23 of that for the optimization algorithm directly driving physical model.

关键词： Atkinson cycle gasoline engine NSGA III algorithm support vector machine algorithm Back-propagation algorithm Enhancing training Digital twins

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