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2014年国际计算机科学与软件工程学术会议

作者： Bo TANG Min XIA Sai-Bing QIU Department of Mathematics and Computing Science Hunan City UniversityYiyang 413000China Hunan Arts and Crafts Vocational College Yiyang 413000China

With the development of China's economy,credit scoring has become *** general credit scoring model is to solve the two classification problems,but in real life we often encounter multiple classification *** paper proposes a multi-class support vector machine based on genetic algorithm,which can solve multiple classification problems in the behavior assessment *** algorithm is used for support vector machine's parameters optimization *** parameters of SVM are coded into chromosomes with Gray code *** empirical results show that the algorithm is very practical,and it has good prediction accuracy.

关键词： Credit scoring multi-class support vector machine Genetic algorithm

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GLCM and multi class support vector machine based Automated Skin Cancer classification

GLCM and Multi Class Support Vector Machine based Automated ...

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8th International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Maurya, Ritesh Singh, Surya Kant Maurya, Ashish K. Kumar, Ajeet Shri Ramswaroop Mem Univ Dept Comp Sci & Engn Lucknow Uttar Pradesh India GLA Univ Dept Comp Sci & Engn Mathura India

ISBN: (纸本)9789380544120

It is utmost important to early detect the skin cancers. Proper diagnosis is critical for survival of the patient Biopsy method of detection is much painful. We have proposed an automated system for detection and classification of one of the skin four types of skin cancers: Melanoma,Basal cell carcinoma, actinic Keratosis, Squamous cell carcinoma. There are a certain features of these types of skin cancers, which can be extracted using proper feature extraction algorithm. The features of skin lesions are extracted normalized symmetrical Grey Level Co-occurrence Matrices (GLCM). GLCM based texture features are extracted from each of the four classes and given as input to the multi-class support vector machine which is used for classification purpose. It classifies the given data set into one of the four skin cancer classes. The accuracy of our proposed method is 81.43%.

关键词： Grey Level Co-occurrence Matrices multi-class support vector machine Gray level Texture features Color-coherence vector Global color Histogram

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Hidden fault diagnosis method of digital twin system for crane

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INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING 2025年第1期16卷

作者： Qi, Zhang Hui-Hui, Zhang Xing-Wang, Shen Jin-Song, Bao Dan, Zhang Sen, Wang Donghua Univ Dept Mech Engn Shanghai 201620 Peoples R China Hong Kong Polytech Univ Dept Mech Engn Hong Kong Peoples R China Shanghai Baosight Software Co Ltd Shanghai 201203 Peoples R China

The traditional fault diagnosis approach for cranes often results in unclear control status, protracted fault location times, prolonged equipment downtime, and other related challenges. To address these issues, this paper proposes embedding the stacked denoising auto-encoder, multi-class support vector machine, and fuzzy Bayesian network (SDAE-MCSVM-FBN (SMF)) hidden cascade fault diagnosis method into a digital twin model of the crane. The approach utilizes the digital twin to monitor the crane's state and identify virtual environment faults to make decisions to reduce the direct operation of complex equipment and enhance operational efficiency. This paper first constructs a digital twin model of the crane to describe its composition and interaction behavior within the system. Next, it solves the hidden cascade faults in cranes using the SMF method. Finally, this approach develops a prototype system for a digital twin of cranes with an example in a large steel mill used for verification and analysis purposes. Experimental results demonstrate that compared with traditional manual inspection methods, this paper's proposed hidden cascade fault method reduces repair time by 24.5%-32.8% while decreasing downtime by 20.5%-32.4%. The outcome highlights the effectiveness of our diagnostic approach and demonstrates digital twin technology can minimize frequent maintenance requirements by personnel while protecting crane service life overall.

关键词： Digital twin stacked denoising auto-encoder multi-class support vector machine fuzzy Bayesian network

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Sliding window assisted mutual redundancy-based feature selection for intrusion detection system

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INTERNATIONAL JOURNAL OF AD HOC AND UBIQUITOUS COMPUTING 2022年第1-3期40卷 176-186页

作者： Veeranna, Thotakura Reddy, Kiran Kumar Jawaharlal Nehru Technol Univ Kakinada 533003 Andhra Pradesh India Krishna Univ Dept Comp Sci Machilipatnam 521001 Andhra Pradesh India

In this paper, we propose a sliding windowing based mutual redundancy assisted feature selection (MRFS) algorithm that finds the duplicate features analytically and selects optimal feature for detection. This MRFS evaluates the mutual redundancy between as well as within network traffic connections and then selects an optimal feature subset to represent each connection attribute. After feature selection, they are fed to multi-class support vector machine (MC-SVM) based IDS for classification. The performance of MRFS-IDS is evaluated using a standard intrusion dataset, i.e., NSL-KDD and performance is measured in terms of accuracy and false alarm rate. The simulation results demonstrate that the proposed MRFS-IDS model has gained an accuracy of 95% approximately and false alarm rate of 0.70% which is much better than the counterpart methods.

关键词： intrusion detection data redundancy mutual redundancy sliding windowing mutual information multi-class support vector machine MC-SVM intrusion detection systems IDSs mutual redundancy-based feature selection MRFS

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A real-time FPGA accelerated stream processing for hyperspectral image classification

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GEOCARTO INTERNATIONAL 2022年第1期37卷 52-69页

作者： Gyaneshwar, Dubacharla Nidamanuri, Rama Rao Indian Inst Space Sci & Technol Remote Sensing Lab Dept Earth & Space Sci Thiruvananthapuram 695547 Kerala India

Realizing real-time classification of hyperspectral imagery has attracted researchers across various scientific and engineering disciplines. Several time-critical applications of high-resolution hyperspectral imaging require analysis of hyperspectral imagery with minimal turnaround time. In this regard, field-programmable gate arrays (FPGAs) provide cost-effective performance over the other high-performance reconfigurable computing systems. Developing such efficient systems poses several technical challenges such as designing and validating the FPGA architecture. A rapid prototyping approach offers several benefits in designing and developing FPGA architectures. This paper proposes a rapid prototyping method for the design and implementation of a multi-class support vector machine (MSVM) algorithm for real-time hyperspectral imagery classification using a low-cost FPGA system. We have evaluated design performance in terms of overall accuracy, resource utilization as well as timing requirements using four different datasets containing airborne, drone, and ground-based hyperspectral imagery. Experimental results show that the proposed FPGA design performs classification under strict real-time constraints.

关键词： Hyperspectral imagery real-time processing field-programmable gate array rapid prototyping multi-class support vector machine

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Enhanced tolerance-based intuitionistic fuzzy rough set theory feature selection and ResNet-18 feature extraction model for arrhythmia classification

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multiAGENT AND GRID SYSTEMS 2022年第3-4期18卷 241-261页

作者： Rajeshwari, M. R. Kavitha, K. S. Visvesvaraya Technol Univ Dept Comp Sci & Engn Belagavi 590018 Karnataka India Dayananda Sagar Coll Engn Dept Comp Sci & Engn Bengaluru Karnataka India

Arrhythmia classification on Electrocardiogram (ECG) signals is an important process for the diagnosis of cardiac disease and arrhythmia disease. The existing researches in arrhythmia classification have limitations of imbalance data problem and overfitting in classification. This research applies Fuzzy C-Means (FCM) - Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification. The selected features from FCM-ETIFRST were applied to the multi-class support vector machine (MSVM) for arrhythmia classification. The ResNet18 - Convolution Neural Network (CNN) was applied for feature extraction in input signal to overcome imbalance data problem. Conventional feature extraction along with CNN features are applied for FCM-ETIFRST feature selection process. The FCM-ETIFRST method in arrhythmia classification is evaluated on MIT-BIH and CPCS 2018 dataset. The FCM-ETIFRST has 98.95% accuracy and Focal loss-CNN has 98.66% accuracy on MIT-BIH dataset. The FCM-ETIFRST method has 98.45% accuracy and Explainable Deep learning Model (XDM) method have 93.6% accuracy on CPCS 2018 dataset.

关键词： Arrhythmia classification convolution neural network enhanced tolerance-based intuitionistic fuzzy rough set theory fuzzy c-means multi-class support vector machine

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multi-class support vector machine for Credit Scoring

Multi-Class Support Vector Machine for Credit Scoring

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3rd International Conference on Computing, Control and Industrial Engineering (CCIE 2012)

作者： Tang, Bo Qiu, Saibing Hunan City Univ Math & Comp Sci Dept Yiyang 413000 Peoples R China

The general credit scoring model is to solve the two classification problems, but in real life we often encounter multiple classification problems. This paper proposes a multi-class support vector machine, which can s... 详细信息

ISBN: (纸本)9783037855201

关键词： credit scoring support vector machine multi-class support vector machine

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multi-class support vector machine for Quality Estimation of Black Tea Using Electronic Nose

Multi-class Support Vector Machine for Quality Estimation of...

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6th International Conference on Sensing Technology (ICST)

作者： Saha, Pradip Ghorai, Santanu Tudu, Bipan Bandyopadhyay, Rajib Bhattacharyya, Nabarun Heritage Inst Technol Dept Appl Elect & Instrumentat Engg Kolkata 700107 India Jadavpur Univ Dept Instrumentat & Elect Engn Kolkata 700098 India Ctr Dev Adv Comp Kolkata 700091 India

ISBN: (纸本)9781467322485;9781467327084

Electronic nose (e-nose) is a machine olfaction system that has shown significant possibilities as an improved alternative of human taster as olfactory perceptions vary from person to person. In contrast, electronic noses also detect smells with their sensors, but in addition describe those using electronic signals. An efficient e-nose system should analyze and recognize these electronic signals accurately. For this it requires a robust pattern classifier that can perform well on unseen data. This research work shows the efficient prediction of black tea quality using machine learning algorithm with e-nose. This paper investigates the potential of three different types of multi-class support vector machine (SVM) to build taster-specific computational models. Experimental results show that all the three models offer more than 97% accuracies to predict the considerable variation in tea quality.

关键词： Black tea Electronic nose support vector machine multi-class support vector machine

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Research on Recognition Method of Vehicle Automatic Lane Change Behavior

Research on Recognition Method of Vehicle Automatic Lane Cha...

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International Conference on Intelligent Traffic Systems and Smart City (ITSSC)

作者： Fan, Bo Xu, Yongneng Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China

ISBN: (数字)9781510652071

ISBN: (纸本)9781510652071;9781510652064

Automatic safe lane changing is the key to the realization of unmanned vehicles. To accurately identify the lane changing state of driving vehicles to ensure driving safety, this paper establishes a vehicle automatic lane changing behavior recognition model based on the multi-class support vector machine. This paper selects vehicle trajectory data from the NGSIM data set for classification processing and uses genetic algorithm optimized particle swarm optimization (GA-PSO) to optimize and calibrate the penalty parameter C and the kernel parameter g in the multi-class support vector machine model. Using sample data to train and test lane-changing behavior recognition models and the research shows that the model can well recognize the behavior of the vehicle during the automatic lane changing process and provide support for the study of the vehicle lane changing phase.

关键词： multi-class support vector machine genetic algorithm particle swarm optimization lane changing behavior recognition

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Bearing Faults Diagnosis and classification Using Generalized Gaussian Distribution multiscale Dispersion Entropy Features 30

Bearing Faults Diagnosis and Classification Using Generalize...

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30th European Signal Processing Conference (EUSIPCO)

作者： Dhandapani, Ragavesh Mitiche, Imene McMeekin, Scott Morison, Gordon Natl Univ Sci & Technol Coll Engn Dept Elect & Commun Engn PB 2322 Cpo Seeb 111 Oman Glasgow Caledonian Univ Dept Comp 70 Cowcaddens Rd Glasgow G40 BA Lanark Scotland Glasgow Caledonian Univ Sch Comp Engn & Built Environm 70 Cowcaddens Rd Glasgow G40 BA Lanark Scotland

ISBN: (纸本)9789082797091

Effective fault diagnosis of rolling bearings are vital for the reliable and smooth operation of industrial equipment. Early fault detection and diagnosis of rolling bearings are required to avoid catastrophic failures and financial losses. In this paper, we propose a new sophisticated multiscale Dispersion Entropy (MDE) based feature that uses a nonlinear mapping approach using a Generalized Gaussian Distribution (GGD)-Cumulative Distribution Function (CDF). First of all, the proposed feature extraction method is used to extract the features from a raw 1-D vibration signal and the candidate feature of each vibration signal is selected by analysing the standard deviation of the features. Then, the features are used as input to a multiclass support vector machine (MCSVM) model for categorizing rolling bearing fault conditions. The findings demonstrate that the proposed method is better in terms of classification accuracy, precision, recall and F1-score as compared to other entropy feature driven classification models.

关键词： Dispersion Entropy Bearing Fault classification Generalized Gaussian Distribution multi-class support vector machine multiscale Dispersion Entropy

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