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  • Open Access

    ARTICLE

    A Novel Semi-Supervised Multi-Label Twin Support Vector Machine

    Qing Ai1,2,*, Yude Kang1, Anna Wang2

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 205-220, 2021, DOI:10.32604/iasc.2021.013357

    Abstract Multi-label learning is a meaningful supervised learning task in which each sample may belong to multiple labels simultaneously. Due to this characteristic, multi-label learning is more complicated and more difficult than multi-class classification learning. The multi-label twin support vector machine (MLTSVM) [], which is an effective multi-label learning algorithm based on the twin support vector machine (TSVM), has been widely studied because of its good classification performance. To obtain good generalization performance, the MLTSVM often needs a large number of labelled samples. In practical engineering problems, it is very time consuming and difficult to obtain all labels of all samples… More >

  • Open Access

    ARTICLE

    Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

    Shumaila Shahzadi1, Fahad Ahmad1,*, Asma Basharat1, Madallah Alruwaili2, Saad Alanazi2, Mamoona Humayun2, Muhammad Rizwan1, Shahid Naseem3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2723-2749, 2021, DOI:10.32604/cmc.2021.014594

    Abstract With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to keep the system in the… More >

  • Open Access

    ARTICLE

    A Comprehensive Investigation of Machine Learning Feature Extraction and Classification Methods for Automated Diagnosis of COVID-19 Based on X-ray Images

    Mazin Abed Mohammed1, Karrar Hameed Abdulkareem2, Begonya Garcia-Zapirain3, Salama A. Mostafa4, Mashael S. Maashi5, Alaa S. Al-Waisy1, Mohammed Ahmed Subhi6, Ammar Awad Mutlag7, Dac-Nhuong Le8,9,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3289-3310, 2021, DOI:10.32604/cmc.2021.012874

    Abstract The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor… More >

  • Open Access

    ARTICLE

    Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

    Mangena Venu Madhavan1, Dang Ngoc Hoang Thanh2, Aditya Khamparia1,*, Sagar Pande1, Rahul Malik1, Deepak Gupta3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2939-2955, 2021, DOI:10.32604/cmc.2021.012466

    Abstract Disease recognition in plants is one of the essential problems in agricultural image processing. This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly. The framework utilizes image processing techniques such as image acquisition, image resizing, image enhancement, image segmentation, ROI extraction (region of interest), and feature extraction. An image dataset related to pomegranate leaf disease is utilized to implement the framework, divided into a training set and a test set. In the implementation process, techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features. An image… More >

  • Open Access

    ARTICLE

    Highway Cost Prediction Based on LSSVM Optimized by Intial Parameters

    Xueqing Wang1, Shuang Liu1,*, Lejun Zhang2

    Computer Systems Science and Engineering, Vol.36, No.1, pp. 259-269, 2021, DOI:10.32604/csse.2021.014343

    Abstract The cost of highway is affected by many factors. Its composition and calculation are complicated and have great ambiguity. Calculating the cost of highway according to the traditional highway engineering estimation method is a completely tedious task. Constructing a highway cost prediction model can forecast the value promptly and improve the accuracy of highway engineering cost. This work sorts out and collects 60 sets of measured data of highway engineering; establishes an expressway cost index system based on 10 factors, including main route mileage, roadbed width, roadbed earthwork, and number of bridges; and processes the data through principal component analysis… More >

  • Open Access

    ARTICLE

    Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches

    Syeda Binish Zahra1,2, Muhammad Adnan Khan2,*, Sagheer Abbas1, Khalid Masood Khan2, Mohammed A. Al-Ghamdi3, Sultan H. Almotiri3

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1269-1282, 2021, DOI:10.32604/cmc.2020.012778

    Abstract Biomechanics is the study of physiological properties of data and the measurement of human behavior. In normal conditions, behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style, body movements in walking patterns, writing style and voice tunes. One cannot perform any change in these inputs that make results reliable and increase the accuracy. The aim of our study is to perform a comparative analysis between the marker-based motion capturing system (MBMCS) and the marker-less motion capturing system (MLMCS) using the lower body joint angles of human gait patterns. In both the… More >

  • Open Access

    ARTICLE

    Least-Square Support Vector Machine and Wavelet Selection for Hearing Loss Identification

    Chaosheng Tang1, Deepak Ranjan Nayak2, Shuihua Wang1,3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 299-313, 2020, DOI:10.32604/cmes.2020.011069

    Abstract Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that themultilayer perceptron is a shallowneural network,while the least square support vector machine uses hinge loss function… More >

  • Open Access

    ARTICLE

    Automated and Precise Event Detection Method for Big Data in Biomedical Imaging with Support Vector Machine

    Lufeng Yuan, Erlin Yao, Guangming Tan

    Computer Systems Science and Engineering, Vol.33, No.2, pp. 105-113, 2018, DOI:10.32604/csse.2018.33.105

    Abstract This paper proposes a machine learning based method which can detect certain events automatically and precisely in biomedical imaging. We detect one important and not well-defined event, which is called flash, in fluorescence images of Escherichia coli. Given a time series of images, first we propose a scheme to transform the event detection on region of interest (ROI) in images to a classification problem. Then with supervised human labeling data, we develop a feature selection technique to utilize support vector machine (SVM) to solve this classification problem. To reduce the time in training SVM model, a parallel version of SVM… More >

  • Open Access

    ARTICLE

    A Hybrid Path Planning Method Based on Articulated Vehicle Model

    Zhongping Chen1, Dong Wang1, *, Gang Chen2, Yanxi Ren3, Danjie Du4

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1781-1793, 2020, DOI:10.32604/cmc.2020.010902

    Abstract Due to the unique steering mechanism and driving characteristics of the articulated vehicle, a hybrid path planning method based on the articulated vehicle model is proposed to meet the demand of obstacle avoidance and searching the path back and forth of the articulated vehicle. First, Support Vector Machine (SVM) theory is used to obtain the two-dimensional optimal zero potential curve and the maximum margin, and then, several key points are selected from the optimal zero potential curves by using Longest Accessible Path (LAP) method. Next, the Cubic Bezier (CB) curve is adopted to connect the curve that satisfies the curvature… More >

  • Open Access

    ARTICLE

    Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

    Bao Le Nguyen1, E. Laxmi Lydia2, Mohamed Elhoseny3, Irina V. Pustokhina4, Denis A. Pustokhin5, Mahmoud Mohamed Selim6, Gia Nhu Nguyen7, 8, K. Shankar9, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 87-107, 2020, DOI:10.32604/cmc.2020.011599

    Abstract In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM… More >

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