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IEEE Region 10 Conference (TENCON)

作者： Kolekar, Maheshkumar H. Bharti, Nishant Patil, Priti N. Indian Inst Technol Patna Dept Elect Engn Patna 801103 Bihar India Netaji Subhash Inst Technol Dept Comp Sci Engn Patna Bihar India

ISBN: (纸本)9781509025978

Detection of person climbing the fence is a vital event, taking borders and restricted zones securities into the consideration. In this paper, a model is presented which detects the human crossing a fence. Entire event of climbing a fence involve activities like walking, climbing up and climbing down. Moving person is detected by background subtraction algorithm. Centroid of the blob and centroid variations along the frames are considered as features. support vector machine classifier is used for detection of walking, climbing up and climbing down activities. The experiments show the best results in detection of human crossing a fence compared to the existing state of art methods.

关键词： Fence crossing support vector machine classifier Hidden Markov Model classifier

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Pilot study: electrical impedance based tissue classification using support vector machine classifier

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2014年第6期8卷 579-587页

作者： Grewal, Parvind Kaur Golnaraghi, Farid Simon Fraser Univ Sch Engn Sci Surrey BC V3T 0A3 Canada

Tissue classification using computer aided diagnosis can help automated decision making to aid clinical diagnosis. Classification of breast tissue based on spectral features of impedance loci has frequently been done to classify malignant tissue with further requirement of more complex classification methodologies needed to improve the characterisation. In current study, tissue classification is done using in vivo electrical impedance data of 18 human subjects, from four quadrants of breast, palm, nail, arm, bicep and classified using algorithms involving machine learning methodologies, specifically support vector machines (SVMs) that are supervised learning models. They consist of learning algorithms based on the principal of structural risk minimisation. Two methodologies of SVM have been used in this study: with data binning and data pruning and without data binning and data pruning. Data binning and data pruning have improved the sensitivity of the SVM from 76.76 to 89.23%, but the specificity has decreased from 76.23 to 74.15%. This is a pilot study towards testing the reliability of the developed electrical impedance measuring system and developing a data mining-based decision making system into an electrical impedance spectroscopy system, to help users (physicians) with tissue classification leading to reliable objective decision making.

关键词： pattern classification support vector machines decision making learning (artificial intelligence) data handling data mining electric impedance imaging biological tissues mammography medical diagnostic computing electrical impedance-based tissue classification support vector machine classifier computer aided diagnosis automated decision making clinical diagnosis malignant breast tissue classification impedance loci spectral features machine learning methodologies supervised learning models structural risk minimisation data binning data pruning SVM sensitivity improvement electrical impedance measuring system reliability data mining-based decision making system electrical impedance spectroscopy system

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Condition monitoring of friction stir welding tool with vibration signals using support vector machine classifiers

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷

作者： Krishnamurthy, Balachandar Rakkiyannan, Jegadeeshwaran Gnanasekaran, Sakthivel Thangamuthu, Mohanraj Vellore Inst Technol Sch Mech Engn Chennai Tamil Nadu India Sathyabama Inst Sci & Technol Dept Mechatron Chennai India Vellore Inst Technol Ctr Eautomat Technol Sch Mech Engn Chennai Tamil Nadu India Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore India

Prediction of Friction Stir Welding (FSW) tool conditions has been a major highlight in recent years. Continuous monitoring and predicting tool life is the most difficult task on the industrial side. This is because the occurrence of high temperatures and vibrations during the welding process can affect tool life. Aluminium (Al5083) and magnesium (AZ31B) alloys are selected as a workpiece and H13 tool steel is selected as a tool for the FSW process. The FSW was carried out with different tool conditions namely, good condition, air gap between tool and workpiece, tapered tool, one side lift in base metals, and notch in workpieces. A piezoelectric transducer helps extract the vibration signal from the rotating tool. From the vibration signals, statistical features are extracted, and these features are used as input for the support vector machine (SVM) machine Learning (ML) classifiers. The various SVM algorithms are used to classify the tool condition. A Graphical User Interface (GUI) was developed to monitor the state of the tool, which is an added benefit. Results demonstrate that the SVM model can accurately classify different tool conditions, providing valuable insights into tool health and facilitating timely maintenance interventions. The fine gaussian SVM performs better with 95% and 88.6% accuracy in predicting tool conditions for Al5083 and AZ31B alloy respectively. The proposed SVM-based condition monitoring represents a significant step towards automated, real-time tool condition assessment in FSW, promising enhancements in process reliability and efficiency.

关键词： FSW tool condition monitoring vibration signals data acquisition feature extraction support vector machine classifier

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Color laser printer forensic based on noisy feature and support vector machine classifier

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MULTIMEDIA TOOLS AND APPLICATIONS 2013年第2期67卷 363-382页

作者： Choi, Jung-Ho Lee, Hae-Yeoun Lee, Heung-Kyu Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea Kumoh Natl Inst Technol Sch Comp & Software Engn Gumi Gyeongbuk South Korea

Digital forensics in the ubiquitous era can enhance and protect the reliability of multimedia content where this content is accessed, manipulated, and distributed using high quality computer devices. Color laser printer forensics is a kind of digital forensics which identifies the printing source of color printed materials such as fine arts, money, and document and helps to catch a criminal. This paper present a new color laser printer forensic algorithm based on noisy texture analysis and support vector machine classifier that can detect which color laser printer was used to print the unknown images. Since each printer vender uses their own printing process, printed documents from different venders have a little invisible difference looks like noise. In our identification scheme, the invisible noises are estimated with the wiener-filter and the 2D Discrete Wavelet Transform (DWT) filter. Then, a gray level co-occurrence matrix (GLCM) is calculated to analyze the texture of the noise. From the GLCM, 384 statistical features are extracted and applied to train and test the support vector machine classifier for identifying the color laser printers. In the experiment, a total of 4,800 images from 8 color laser printer models were used, where half of the image is for training and the other half is for classification. Results prove that the presented algorithm performs well by achieving 99.3%, 97.4% and 88.7% accuracy for the brand, toner and model identification respectively.

关键词： Digital forensics Color laser printer Gray level co-occurrence matrix support vector machine classifier

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support vector machine classifierS BY NON-EUCLIDEAN MARGINS

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MATHEMATICAL FOUNDATIONS OF COMPUTING 2020年第4期3卷 279-300页

作者： Lin, Ying Ye, Qi South China Normal Univ Sch Math Sci Guangzhou 510631 Guangdong Peoples R China

In this article, the classical support vector machine (SVM) classifiers are generalized by the non-Euclidean margins. We first extend the linear models of the SVM classifiers by the non-Euclidean margins including the theorems and algorithms of the SVM classifiers by the hard margins and the soft margins. Specially, the SVM classifiers by the 1-norm margins can be solved by the 1-norm optimization with sparsity. Next, we show that the non-linear models of the SVM classifiers by the infinity-norm margins can be equivalently transferred to the SVM in the p-norm reproducing kernel Banach spaces given by the hinge loss, where 1/p + 1/q = 1. Finally, we illustrate the numerical examples of artificial data and real data to compare the different algorithms of the SVM classifiers by the 1-norm margin.

关键词： support vector machine classifier non-euclidean margin reproducing kernel Banach space

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Audio Classification Utilizing a Rule-based approach and the support vector machine classifier

Audio Classification Utilizing a Rule-based approach and the...

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36th International Conference on Telecommunications and Signal Processing (TSP)

作者： Vavrek, Jozef Juhar, Jozef Cizmar, Anton Tech Univ Kosice FEI Dept Elect & Multimedia Commun Kosice Slovakia

ISBN: (纸本)9781479904020;9781479904037

The evaluation of two classification architectures utilizing the rule-based approach and the one-against-one support vector machine (OAO-SVM) is presented in this paper. The classification of the audio stream is carried out in two steps. At first, the rule-based speech/non-speech and music/environment sound discrimination is conducted. The set of adopted features, with a high efficiency in separation of speech and music signals, is implemented in order to find the best discriminator. Consequently, speech segments are classified into pure speech, speech with music and speech with env. sound using the OAO-SVM multi-class classification scheme. Experimental results show that the used classification architecture can decrease the classification error in comparison with OAO-SVM by using MFCC features only.

关键词： Audio content analysis audio classification rule-based discrimination support vector machine classifier

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classifier-based adaptive polynomial chaos expansion for high-dimensional uncertainty quantification

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 422卷

作者： Thapa, Mishal Mulani, Sameer B. Paudel, Achyut Gupta, Subham Walters, Robert W. Univ Alabama Remote Sensing Ctr Dept Aerosp Engn & Mech Tuscaloosa AL 35487 USA Univ Alabama Dept Aerosp Engn & Mech Tuscaloosa AL 35487 USA Virginia Polytech Inst & State Univ Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

A novel approach for the construction of polynomial chaos expansion (PCE) is proposed to facilitate high -dimensional uncertainty quantification (UQ). The current PCE techniques are well-known for UQ;however, they are affected by the curse of dimensionality that leads to over -fitting and tractability issues. Although L1 -minimization can be used to deal with overfitting, it is still ineffective for problems with a large number of independent random inputs or requiring a very high -order PCE. Therefore, a classifier -based PCE (CAPCE) is presented to mitigate the factorial growth of basis terms and prevent over -fitting. The adaptive framework includes four basis selection strategies - enrichment, screening, discovery, and recycling - in the inner loop and the enrichment of the training samples in the outer loop. Mainly, an adaptive classifier is trained on the dominant and discarded basis terms obtained from L1 -minimization during discovery. It then allows the judicious selection of new higher -order basis candidates for L1 -solver in the next iteration, thereby alleviating the effect of the curse of dimensionality. The proposed framework has been tested with analytical problems of varying sizes of independent random inputs and an engineering composite laminate problem. The comparison of CAPCE with the available efficient PCE techniques demonstrated improvements in accuracy using fewer samples due to a faster convergence rate.

关键词： High-dimensionality Adaptive polynomial chaos expansion Uncertainty quantification support vector machine classifier Stochastic nonlinear buckling

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ON THE ANALYTICAL STUDY OF THE SERVICE QUALITY OF INDIAN RAILWAYS UNDER SOFT-COMPUTING PARADIGM

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TRANSPORT 2024年第1期39卷 54-63页

作者： Majumder, Saibal Singh, Aarti Singh, Anupama Karpenko, Mykola Sharma, Haresh Kumar Mukhopadhyay, Somnath Dr. BC Roy Engn Coll Dept Comp Sci & Engn Data Sci Durgapur India FORE Sch Management New Delhi India Birla Inst Management Technol Dept Strateg Environm Management Greater Noida India Vilnius Gediminas Tech Univ Dept Mobile Machinery & Railway Transport Vilnius Lithuania Birla Inst Management Technol Dept Operat Management & Decis Sci Greater Noida India Assam Univ Dept Comp Sci & Engn Silchar India

Indian Railway Catering and Tourism Corporation (IRCTC) is among the busiest railways reservation systems since the Indian Railways (IR) is the vital and economical mode of transportation in India. Hence, rating of the trains seems to be critical aspect for selecting an appropriate train for travelling. In this study, we have considered 7 vital attributes of 500 popular trains and rate their performance based on 7 important related attributes. For this purpose, we have employed 2 different approaches to analyse of the train attributes, which eventually contribute to the overall performance of the trains. Here, we have developed a rule based rough set decision support system to analyse the criticality of the train attributes while rating the train performance. Furthermore, we have also used 3 machine Learning (ML) model estimators: Extra Trees classifier (ETC), support vector machine classifier (SVMC) and Multinomial Naive Bayes classifier (MNBC) and perform their comparative analysis with respect to 7 performance metrics while predicting the overall train rating based.

关键词： rough set theory extra trees classifier support vector machine classifier multinomial naive Bayes classifier performance metrics

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Predicting Power Outages from Ice Storms using machine Learning Models with SHAP Interpretability 56

Predicting Power Outages from Ice Storms using Machine Learn...

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56th North American Power Symposium

作者： Rahman, Farishta Hassan, Tanzim Jim Ranganathan, Prakash Univ North Dakota Sch Elect Engn & Comp Sci SEECS Grand Forks ND 58202 USA

ISBN: (纸本)9798331521042;9798331521035

Ice storms pose significant challenges, causing extensive damage and widespread power outages. Accurate prediction of outages during such events and their impact on distribution infrastructure is critical for mitigation efforts. This study presents a machine learning-based approach to predict the likelihood of distribution line power outages due to ice storms. Utilizing a comprehensive Ice Storm dataset from the cities of Hope and Mayville of North Dakota, containing meteorological and power outage information, features such as temperature, dew point, humidity, wind gust, wind speed, wind direction and precipitation metrics are used in the dataset. Various machine learning (ML) algorithms, including Random Forest classifier (RFC), Logistic Regression classifier (LRC), support vector machine classifier (SVC), and Gradient Boosting classifier (GBC), are used to develop predictive models. The preliminary results indicate that RFC has the highest precision of 1.000. Feature importance using Shapley additive explanations (SHAP) analysis highlights wind gust, and wind direction as critical variables. This work can aid utility companies and emergency management agencies, enabling proactive measures to reduce the impact of ice storms.

关键词： ice storms power transmission lines temperature classifier power outage Random Forest classifier Gradient Boosting classifier Logistic Regression classifier support vector machine classifier

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