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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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Landslide hazard zonation using advanced machine learning algorithms based on statistical models (Case study: Sarvabad, Iran)

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EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-19页

作者： Rastkhadiv, Himan Motamedvaziri, Baharak Javadi, Seyed Akbar Ahmadi, Hassan Islamic Azad Univ Dept Nat Engn Sci & Res Branch Tehran Iran Univ Tehran Dept Reclamat Arid & Mt Reg Karaj Iran

Effectively reducing damages and making decisions on land development policies can be achieved identifying areas that are at high risk. Thus, the aim of the current research is to explore the risk of landslides zonation in Sarvabad by use of advanced machine learning algorithms based on statistical models. At the present research, landslide hazard zonation was conducted by use of hybrid algorithms, including Frequency Ratio-Random Forest (FR-RF), Frequency Ratio-support vector machine (FR-SVM), Weight of Evidence-Random Forest (WoE-RF), and Weight of Evidence-support vector machine (Woe-SVM). First, the point shape files of 166 landslides in Sarvabad, prepared by the Natural Resource Departmens of Kurdistan State, considered as the inventory of landslides map. The data from the landslide points was split into training sets comprising 70% and validation sets comprising 30%. To achieve to conduct the landslides hazard zone, a sum of 16 items were used that are as follows: slope, aspect, elevation, distance to the stream, distance to the road, river density, distance to the fault, fault density, roadway density, precipitation, land utilization, Normalized Difference Vegetation Index (NDVI), lithology, earthquake, Stream Power Index (SPI), and topographic wetness index (TWI). Finally, the performance of the models was evaluated using the ROC curve. Among the FR-RF, WoE-RF, FR-SVM, and WoE-SVM models, the FR-RF model accounted for the most high performance. In the end, it can be concluded that obtaining an accurate and reasonable spatial prediction map can help managers and urban planners identify zones susceptible to landslide occurrence so that they can manage the potential crises of landslide-prone zones.

关键词： Landslide Random forest algorithm support vector machine algorithm Sarvabad

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Identification of key brain networks and functional connectivities of successful aging:A surface-based resting-state functional magnetic resonance study

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World Journal of Psychiatry 2025年第3期15卷 216-226页

作者： Jiao-Jiao Sun Li Zhang Ru-Hong Sun Xue-Zheng Gao Chun-Xia Fang Zhen-He Zhou Department of Psychiatry The Affiliated Mental Health Center of Jiangnan UniversityWuxi 214151Jiangsu ProvinceChina Department of Psychiatry Yangzhou Wutaishan Hospital of Jiangsu ProvinceTeaching Hospital of Yangzhou UniversityYangzhou 225000Jiangsu ProvinceChina Department of Psychiatry Huai’an Third People’s HospitalHuai’an 223300Jiangsu ProvinceChina

BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving *** To explore the potential characteristics of the brain network and functional connectivity(FC)of *** Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by *** support vector machine model was constructed with altered functional connectivities to evaluate the identification value of *** The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE *** FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE *** 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was *** The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.

关键词： Successful aging Resting-state functional magnetic resonance imaging Surface-based brain network analysis Functional connectivity support vector machine algorithm

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Prediction of neurologic deterioration based on support vector machine algorithms and serum osmolarity equations

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BRAIN AND BEHAVIOR 2018年第7期8卷 e01023-e01023页

作者： Lin, Jixian Jiang, Aihua Ling, Meirong Mo, Yanqing Li, Meiyi Zhao, Jing Fudan Univ Minhang Hosp Dept Neurol 170 Xin Song Rd Shanghai Peoples R China Fudan Univ Minhang Hosp Emergency Dept Shanghai Peoples R China Fudan Univ Minhang Hosp Inst Fudan Minhang Acad Hlth Syst Shanghai Peoples R China

Objective: Dehydration on admission is correlated with neurological deterioration (ND). The primary objective of our study was to use support vector machine (SVM) algorithms to identify an ND prognostic model, based on dehydration equations. Methods: This study included a total of 382 patients hospitalized with acute ischemic stroke. The following parameters were recorded: age, sex, laboratory values (serum sodium, potassium, chlorinum, glucose, and urea), and vascular risk factor data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminative performance of the BUN/Cr ratio as well as each of 38 equations for predicting ND. We used the Boruta algorithm for feature selection. After optimizing the SVM kernel parameters, we built an SVM model to predict ND and used the test set to obtain predictive values for assessing model accuracy. Results: In total, 102 of 382 patients (26.7%) with acute ischemic stroke developed ND. In all patients, the BUN/Cr ratio and each of 38 equations were significant predictors of ND. Equation 20 [1.86 x Na+ + glucose + urea + 9] yielded the maximum area under the ROC curve, and faired best in terms of prognostic performance (a cutoff value of 284.49 mM yielded a sensitivity of 94.12% and specificity of 61.43%). Equation 32 predicted ND poststroke across population groups, and worked well in older as well as young adults;(a cutoff value of 297.08 mM yielded a sensitivity of 93.14% and specificity of 60.00%). Feature selection by the Boruta algorithm was used to decrease the number of variables from 18 to 5 in the condition. The specificity of test samples for the SVM prediction model increased from 44.1% to 89.4%, and the AUC increased from 0.700 to 0.927. Conclusions: SVM algorithms can be used to establish a prediction model for dehydration-associated ND, with good classification results.

关键词： dehydration neurological deterioration 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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Identification of suction flow blockages and casing cavitations in centrifugal pumps by optimal support vector machine techniques

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2017年第8期39卷 2957-2968页

作者： Bordoloi, D. J. Tiwari, Rajiv Indian Inst Technol Guwahati Dept Mech Engn Gauhati 781039 India

Centrifugal pumps play vital role in many critical applications in industries and require continuous health monitoring to increase its availability. In centrifugal pumps, inlet pipe blockages and cavitation in the casing cause serious problems, such as the abnormal noise, high vibration, leakage, and decrease in the head capacity and the efficiency. Complexity in such fault is that one leads to other, and as such it falls under the multi-fault condition, which is hardly dealt in literature for pumps. Vibration-based monitoring of machinery faults using machine learning approaches has been one of latest trends. This paper presents the use of one of the machine learning tools, i.e., the support vector machine (SVM), for innovatively using in the diagnosis of blockage levels and the impeding cavitation at diverse pump speeds using statistical features extracted from vibration signature. SVM classifier parameters are optimally selected for better classifications. The prediction of classification is encouraging while taking decision for the pump condition.

关键词： Centrifugal pumps Multi-fault classification Suction blockages Casing cavitation Time domain data support vector machine algorithm

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Gene/protein name recognition based on support vector machine using dictionary as features

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BMC BIOINFORMATICS 2005年第1-Sup期6卷 S8-S8页

作者： Mitsumori, T Fation, S Murata, M Doi, K Doi, H Nara Inst Sci & Technol Grad Sch Informat Sci Nara 6300101 Japan Natl Inst Informat & Commun Technol Kyoto 6190289 Japan

Background: Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. Results: In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. Conclusion: During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

关键词： support vector machine Polynomial Kernel Biomedical Literature support vector machine algorithm GENIA Corpus

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Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM)

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NATURAL HAZARDS 2023年第3期115卷 1997-2030页

作者： Mehrnoor, Shabnam Robati, Maryam Zarkesh, Mir Masoud Kheirkhah Farsad, Forough Baikpour, Shahram Islamic Azad Univ Fac Nat Resources & Environm Dept Environm Sci Sci & Res Branch Tehran Iran Islamic Azad Univ Soil Conservat & Watershed Management Res Inst Sci & Res Branch Agr Res Educ & Extens Org Tehran Iran Islamic Azad Univ Med Geol Res Ctr Sci & Res Branch Tehran Iran

Land subsidence is a morphological phenomenon, which causes negative environmental and economic consequences for human societies. Therefore, identifying the areas prone to subsidence can be one of the necessary measures for reducing the potential risks. This study aims to evaluate the efficiency of the support vector machine (SVM) algorithm and weighted overlay index (WOI) models in zoning the rate of land subsidence hazard in Hashtgerd plain, Iran. First, the 19 criteria include groundwater depletion, groundwater extraction, aquifer thickness, alluvium thickness, aquifer recharge, well density, drainage density, groundwater depth, lithology, bedrock depth, average annual precipitation, average annual temperature, climate type, agricultural use, urban use, industrial use, distance from rivers and streams, distance from roads, distance from faults were considered. Then, the layers were weighed based on the Best-Worst Method (BWM). The results of BWM indicated that the factors of groundwater extraction (0.219), lithology (0.157), and groundwater depletion (0.079) have a greater effect on the potential for subsidence hazard. Moreover, the results of validation by performing ROC curve showed that the accuracy of RBF-SVM, LN-SVM, SIG-SVM, PL-SVM, and WOI were 95.7, 94.3, 94.9, 93.2, and 90%, respectively. Based on the ROC results, all of the models for preparing the subsidence hazard map in Hashtgerd plain exhibit excellent accuracy. Therefore, all of the models used here can predict the areas vulnerable to subsidence properly. In this study, the five land subsidence hazard maps were used as new input factors and integrated using fuzzy gamma-ensemble methods to make better hazard maps. The results of the ensemble model indicated that 19.3% of Hashtgerd plain is in the zone of high to very high sensitivity. The results of this study can help planners in managing and reducing the possible hazards of subsidence.

关键词： Land subsidence support vector machine algorithm Weighted overlay index Best-worst method Hashtgerd plain

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Wiring networks diagnosis using time-domain reflectometry and support vector machines

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2020年第2期14卷 220-224页

作者： Kamel Smail, Mostafa Boubezoul, Abderrahmane Bouchekara, Houssem R. E. H. Sellami, Yamine Inst Polytech Sci Avancees 63 Blvd Brandebourg F-94200 Ivry France UPE IFSTTAR TS2 SIMU&MOTO F-77447 Marne La Vallee France Univ Hafr Al Batin Dept Elect Engn Hafar al Batin 31991 Saudi Arabia

An efficient diagnosis method dedicated to embedded wiring network based on reflectometry technique is developed in this study. The proposed methodology is based on the two complementary steps. In the first step, the time-domain reflectometry (TDR) method is simulated, by RLCG (R: resistance, L: inductance, C: capacitance and G: conductance) circuit model and the numerical finite-difference time-domain method, and at the same time the datasets are created. In the second step, the support vector machine (SVM) algorithm is combined with a principal component analysis to identify the faults on wiring network from the TDR response. Two types of SVM models have been used in the diagnosis procedure: SVM classifiers and SVM regression models. In order to illustrate the performances and the feasibility of the proposed approach, numerical and experimental results are presented.

关键词： support vector machines fault diagnosis regression analysis time-domain reflectometry principal component analysis finite difference time-domain analysis wiring networks diagnosis support vector machines efficient diagnosis method embedded wiring network reflectometry technique complementary steps time-domain reflectometry method numerical finite-difference time-domain method support vector machine algorithm TDR response diagnosis procedure SVM classifiers SVM regression models

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Prediction of β-turn types in protein by using composite vector

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JOURNAL OF THEORETICAL BIOLOGY 2011年第1期286卷 24-30页

作者： Shi, Xiaobo Hu, Xiuzhen Li, Shaobo Liu, Xingxing Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China

Protein secondary structure prediction is an intermediate step in the overall process of tertiary structure prediction. beta-turns are important components of the secondary structure of a protein. Development of an accurate method of prediction of beta-turn types would be helpful for predicting the overall tertiary structure of proteins. In this work, we constructed a database of 2805 protein chains. Our work improved the previous input parameters and used the support vector machine algorithm to predict the beta-turn types: we obtained the overall prediction accuracy of 98.1%, 96.0%, 96.1%, 98.7%, 99.1%, 86.8%, 99.2% and 73.2% with the Matthews Correlation Coefficient values of 0.398, 0.460, 0.043, 0.463, 0.355, 0.172, 0.109 and 0.247, respectively, for types I, II, VIII, I', II', IV, VI and non-beta-turn, respectively. In addition, we also used same method to predict the beta-turn types in three databases of 426, 547 and 823 protein chains and found that our prediction results were better than other predictions. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： support vector machine algorithm Increment of diversity value Position conservation scoring function value Distance value Secondary structure information

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