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  • Open Access

    ARTICLE

    Classifying Abdominal Fat Distribution Patterns by Using Body Measurement Data

    Jingjing Sun1, Bugao Xu1,2,*, Jane Lee3, Jeanne H. Freeland-Graves3

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1189-1202, 2021, DOI:10.32604/cmes.2021.014405

    Abstract This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues (VAT and SAT) measured by magnetic resonance imaging (MRI), to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors (BSDs), and to develop a classifier to predict the fat distribution clusters using the BSDs. In the study, 66 male and 54 female participants were scanned by MRI and a stereovision body imaging (SBI) to measure participants’ abdominal VAT and SAT volumes and the BSDs. A fuzzy c-means algorithm was used to form the inherent grouping clusters of… More >

  • Open Access

    ARTICLE

    Image-Based Lifelogging: User Emotion Perspective

    Junghyun Bum1, Hyunseung Choo1, Joyce Jiyoung Whang2,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1963-1977, 2021, DOI:10.32604/cmc.2021.014931

    Abstract Lifelog is a digital record of an individual’s daily life. It collects, records, and archives a large amount of unstructured data; therefore, techniques are required to organize and summarize those data for easy retrieval. Lifelogging has been utilized for diverse applications including healthcare, self-tracking, and entertainment, among others. With regard to the image-based lifelogging, even though most users prefer to present photos with facial expressions that allow us to infer their emotions, there have been few studies on lifelogging techniques that focus upon users’ emotions. In this paper, we develop a system that extracts users’ own photos from their smartphones… More >

  • Open Access

    ARTICLE

    Timing and Classification of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

    Mai Ramadan Ibraheem1, Jilan Adel2, Alaa Eldin Balbaa3, Shaker El-Sappagh4, Tamer Abuhmed5,*, Mohammed Elmogy6

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 393-409, 2021, DOI:10.32604/cmc.2021.014446

    Abstract Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset,… More >

  • Open Access

    ARTICLE

    Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals

    Seth Aishwarya1, Vaishnav Abeer1,*, Babu B. Sathish1, K. N. Subramanya2

    Journal of Quantum Computing, Vol.2, No.4, pp. 157-170, 2020, DOI:10.32604/jqc.2020.015018

    Abstract The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems. The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body. Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind. One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables, which provide more data density due to its… More >

  • Open Access

    ARTICLE

    Robust Attack Detection Approach for IIoT Using Ensemble Classifier

    V. Priya1, I. Sumaiya Thaseen1, Thippa Reddy Gadekallu1, Mohamed K. Aboudaif2,*, Emad Abouel Nasr3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2457-2470, 2021, DOI:10.32604/cmc.2021.013852

    Abstract Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition.… More >

  • Open Access

    ARTICLE

    A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

    V. Sudha1,*, T. R. Ganeshbabu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 827-842, 2021, DOI:10.32604/cmc.2020.012008

    Abstract Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital… More >

  • Open Access

    ARTICLE

    Anomaly Classification Using Genetic Algorithm-Based Random Forest Model for Network Attack Detection

    Adel Assiri*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 767-778, 2021, DOI:10.32604/cmc.2020.013813

    Abstract Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks. Network-based intrusion detection systems (NIDSs) using machine learning (ML) methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks. Among several ML methods, random forest (RF) is a robust method that can be used in ML-based network intrusion detection solutions. However, the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy. Therefore, optimal parameter selection is a real problem in… More >

  • Open Access

    ARTICLE

    Object Detection and Fuzzy-Based Classification Using UAV Data

    Abdul Qayyum1,*, Iftikhar Ahmad2, Mohsin Iftikhar3, Moona Mazher4

    Intelligent Automation & Soft Computing, Vol.26, No.4, pp. 693-702, 2020, DOI:10.32604/iasc.2020.010103

    Abstract UAV (Unmanned Aerial Vehicle) equipped with remote sensing devices can acquire spatial data with a relevant area of interest. In this paper, we have acquired UAV data for high voltage power poles, urban areas and vegetation/trees near power lines. For object classification, the proposed approach based on the fuzzy classifier is compared with the traditional minimum distance classifier and maximum likelihood classifier on our three defined segments of UAV images. The performance evaluation of all the classifiers was based on the statistics parameters which included the mean, standard deviation and PDF (probability density function) of each object present in the… More >

  • Open Access

    ARTICLE

    Roman Urdu News Headline Classification Empowered with Machine Learning

    Rizwan Ali Naqvi1, Muhammad Adnan Khan2, *, Nauman Malik2, Shazia Saqib2, Tahir Alyas2, Dildar Hussain3

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1221-1236, 2020, DOI:10.32604/cmc.2020.011686

    Abstract Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak Subcontinent. Persons from the subcontinent may speak the same Urdu language but they might be using different scripts for writing. The communication using the Roman characters, which are used in the script of Urdu language on social media, is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information supply. English Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the… More >

  • Open Access

    ARTICLE

    Hardware Design of Codebook‐Based Moving Object Detecting Method for Dynamic Gesture Recognition

    Ching‐Han Chena, Ching‐Yi Chenb, Nai‐Yuan Liua

    Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 375-384, 2019, DOI:10.31209/2019.100000099

    Abstract This study introduces a dynamic gesture recognition system applicable in IPTV remote control. In this system, we developed a hardware accelerator for realtime moving object detection. It is able to detect the position of hand block in each frame at high speed. After acquiring the information of hand block, the system can capture the robust dynamic gesture feature with the moving trail of hand block in the continuous images, and input to FNN classifier for starting recognition process. The experimental results show that our method has a good recognition performance, and more applicable to real gesture-controlled human-computer interactive environment. More >

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