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  • Open Access

    ARTICLE

    An Effective CU Depth Decision Method for HEVC Using Machine Learning

    Xuan Sun1,2,3, Pengyu Liu1,2,3,*, Xiaowei Jia4, Kebin Jia1,2,3, Shanji Chen5, Yueying Wu1,2,3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 275-286, 2021, DOI:10.32604/csse.2021.015255 - 20 July 2021

    Abstract This paper presents an effective machine learning-based depth selection algorithm for CTU (Coding Tree Unit) in HEVC (High Efficiency Video Coding). Existing machine learning methods are limited in their ability in handling the initial depth decision of CU (Coding Unit) and selecting the proper set of input features for the depth selection model. In this paper, we first propose a new classification approach for the initial division depth prediction. In particular, we study the correlation of the texture complexity, QPs (quantization parameters) and the depth decision of the CUs to forecast the original partition depth… More >

  • Open Access

    ARTICLE

    Social Network Rumor Recognition Based on Enhanced Naive Bayes

    Lei Guo*

    Journal of New Media, Vol.3, No.3, pp. 99-107, 2021, DOI:10.32604/jnm.2021.019649 - 13 July 2021

    Abstract In recent years, with the increasing popularity of social networks, rumors have become more common. At present, the solution to rumors in social networks is mainly through media censorship and manual reporting, but this method requires a lot of manpower and material resources, and the cost is relatively high. Therefore, research on the characteristics of rumors and automatic identification and classification of network message text is of great significance. This paper uses the Naive Bayes algorithm combined with Laplacian smoothing to identify rumors in social network texts. The first is to segment the text and More >

  • Open Access

    ARTICLE

    Feature Selection Using Artificial Immune Network: An Approach for Software Defect Prediction

    Bushra Mumtaz1, Summrina Kanwal2,*, Sultan Alamri2, Faiza Khan1

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 669-684, 2021, DOI:10.32604/iasc.2021.018405 - 01 July 2021

    Abstract Software Defect Prediction (SDP) is a dynamic research field in the software industry. A quality software product results in customer satisfaction. However, the higher the number of user requirements, the more complex will be the software, with a correspondingly higher probability of failure. SDP is a challenging task requiring smart algorithms that can estimate the quality of a software component before it is handed over to the end-user. In this paper, we propose a hybrid approach to address this particular issue. Our approach combines the feature selection capability of the Optimized Artificial Immune Networks (Opt-aiNet) More >

  • Open Access

    ARTICLE

    Detection of COVID-19 Using Deep Learning on X-Ray Images

    Munif Alotaibi1,*, Bandar Alotaibi2

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 885-898, 2021, DOI:10.32604/iasc.2021.018350 - 01 July 2021

    Abstract The novel coronavirus 2019 (COVID-19) is one of a large family of viruses that cause illness, the symptoms of which range from a common cold, fever, coughing, and shortness of breath to more severe symptoms. The virus rapidly and easily spreads from infected people to others through close contact in the absence of protection. Early detection of COVID-19 assists governmental authorities and healthcare specialists in reducing the chain of transmission and flattening the curve of the pandemic. The widespread form of the COVID-19 diagnostic test lacks a high true positive rate and a low false… More >

  • Open Access

    ARTICLE

    Machine Learning Based Framework for Maintaining Privacy of Healthcare Data

    Adil Hussain Seh1, Jehad F. Al-Amri2, Ahmad F. Subahi3, Alka Agrawal1, Rajeev Kumar4,*, Raees Ahmad Khan1

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 697-712, 2021, DOI:10.32604/iasc.2021.018048 - 01 July 2021

    Abstract The Adoption of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud services, web-based software systems, and other wireless sensor devices in the healthcare infrastructure have led to phenomenal improvements and benefits in the healthcare sector. Digital healthcare has ensured early diagnosis of the diseases, greater accessibility, and mass outreach in terms of treatment. Despite this unprecedented success, the privacy and confidentiality of the healthcare data have become a major concern for all the stakeholders. Data breach reports reveal that the healthcare data industry is one of the key targets of cyber invaders.… More >

  • Open Access

    ARTICLE

    Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm

    Chenjing Su1, Xiaoyu Li1,*, Mengru Li2, Qinsheng Zhu2, Hao Fu2, Shan Yang3

    Journal of Quantum Computing, Vol.3, No.2, pp. 79-87, 2021, DOI:10.32604/jqc.2021.016651 - 22 June 2021

    Abstract As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this More >

  • Open Access

    ARTICLE

    CNN-Based Voice Emotion Classification Model for Risk Detection

    Hyun Yoo1, Ji-Won Baek2, Kyungyong Chung3,*

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 319-334, 2021, DOI:10.32604/iasc.2021.018115 - 16 June 2021

    Abstract With the convergence and development of the Internet of things (IoT) and artificial intelligence, closed-circuit television, wearable devices, and artificial neural networks have been combined and applied to crime prevention and follow-up measures against crimes. However, these IoT devices have various limitations based on the physical environment and face the fundamental problem of privacy violations. In this study, voice data are collected and emotions are classified based on an acoustic sensor that is free of privacy violations and is not sensitive to changes in external environments, to overcome these limitations. For the classification of emotions… More >

  • Open Access

    REVIEW

    Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

    Faseeha Matloob1, Shabib Aftab1,2, Munir Ahmad2, Muhammad Adnan Khan3,*, Areej Fatima4, Muhammad Iqbal2, Wesam Mohsen Alruwaili5, Nouh Sabri Elmitwally5,6

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 403-421, 2021, DOI:10.32604/iasc.2021.017562 - 16 June 2021

    Abstract Software defect prediction (SDP) is the process of detecting defect-prone software modules before the testing stage. The testing stage in the software development life cycle is expensive and consumes the most resources of all the stages. SDP can minimize the cost of the testing stage, which can ultimately lead to the development of higher-quality software at a lower cost. With this approach, only those modules classified as defective are tested. Over the past two decades, many researchers have proposed methods and frameworks to improve the performance of the SDP process. The main research topics are More >

  • Open Access

    ARTICLE

    Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

    Dah-Jing Jwo*, Jia-Chyi Wu, Kuan-Lin Ho

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 555-574, 2021, DOI:10.32604/cmc.2021.018320 - 04 June 2021

    Abstract This paper investigates performance improvement via the incorporation of the support vector machine (SVM) into the vector tracking loop (VTL) for the Global Positioning System (GPS) in limited satellite visibility. Unlike the traditional scalar tracking loop (STL), the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user. The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error… More >

  • Open Access

    ARTICLE

    Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

    Omer Ali1,2, Mohamad Khairi Ishak1,*, Muhammad Kamran Liaquat Bhatti2

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 747-766, 2021, DOI:10.32604/cmc.2021.018098 - 04 June 2021

    Abstract The COVID-19 virus exhibits pneumonia-like symptoms, including fever, cough, and shortness of breath, and may be fatal. Many COVID-19 contraction experiments require comprehensive clinical procedures at medical facilities. Clinical studies help to make a correct diagnosis of COVID-19, where the disease has already spread to the organs in most cases. Prompt and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease spread. Therefore, clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold, which… More >

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