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  • Open Access

    ARTICLE

    An Online Chronic Disease Prediction System Based on Incremental Deep Neural Network

    Bin Yang1,*, Lingyun Xiang2, Xianyi Chen3, Wenjing Jia4

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 951-964, 2021, DOI:10.32604/cmc.2021.014839

    Abstract Many chronic disease prediction methods have been proposed to predict or evaluate diabetes through artificial neural network. However, due to the complexity of the human body, there are still many challenges to face in that process. One of them is how to make the neural network prediction model continuously adapt and learn disease data of different patients, online. This paper presents a novel chronic disease prediction system based on an incremental deep neural network. The propensity of users suffering from chronic diseases can continuously be evaluated in an incremental manner. With time, the system can predict diabetes more and more… More >

  • Open Access

    ARTICLE

    An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

    Ayesha Jabeen1, Sitara Afzal1, Muazzam Maqsood1, Irfan Mehmood2, Sadaf Yasmin1, Muhammad Tabish Niaz3, Yunyoung Nam4,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1191-1206, 2021, DOI:10.32604/cmc.2021.014598

    Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of… More >

  • Open Access

    ARTICLE

    Image-Based Automatic Diagnostic System for Tomato Plants Using Deep Learning

    Shaheen Khatoon1,*, Md Maruf Hasan1, Amna Asif1, Majed Alshmari1, Yun-Kiam Yap2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 595-612, 2021, DOI:10.32604/cmc.2021.014580

    Abstract Tomato production is affected by various threats, including pests, pathogens, and nutritional deficiencies during its growth process. If control is not timely, these threats affect the plant-growth, fruit-yield, or even loss of the entire crop, which is a key danger to farmers’ livelihood and food security. Traditional plant disease diagnosis methods heavily rely on plant pathologists that incur high processing time and huge cost. Rapid and cost-effective methods are essential for timely detection and early intervention of basic food threats to ensure food security and reduce substantial economic loss. Recent developments in Artificial Intelligence (AI) and computer vision allow researchers… More >

  • Open Access

    ARTICLE

    AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories

    Meenu Gupta1, Rachna Jain2, Simrann Arora2, Akash Gupta2, Mazhar Javed Awan3, Gopal Chaudhary2,*, Haitham Nobanee4,5,6

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 933-950, 2021, DOI:10.32604/cmc.2021.014221

    Abstract The COVID-19 disease has already spread to more than 213 countries and territories with infected (confirmed) cases of more than 27 million people throughout the world so far, while the numbers keep increasing. In India, this deadly disease was first detected on January 30, 2020, in a student of Kerala who returned from Wuhan. Because of India’s high population density, different cultures, and diversity, it is a good idea to have a separate analysis of each state. Hence, this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of… More >

  • Open Access

    ARTICLE

    Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

    Shahan Yamin Siddiqui1,2, Iftikhar Naseer3, Muhammad Adnan Khan4, Muhammad Faheem Mushtaq5, Rizwan Ali Naqvi6,*, Dildar Hussain7, Amir Haider8

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 1033-1049, 2021, DOI:10.32604/cmc.2021.013952

    Abstract Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally. According to clinical statistics, one woman out of eight is under the threat of breast cancer. Lifestyle and inheritance patterns may be a reason behind its spread among women. However, some preventive measures, such as tests and periodic clinical checks can mitigate its risk thereby, improving its survival chances substantially. Early diagnosis and initial stage treatment can help increase the survival rate. For that purpose, pathologists can gather support from nondestructive and efficient computer-aided diagnosis (CAD) systems. This study explores… More >

  • Open Access

    ARTICLE

    Optimal Resource Allocation and Quality of Service Prediction in Cloud

    Priya Baldoss1,2,*, Gnanasekaran Thangavel3

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 253-265, 2021, DOI:10.32604/cmc.2021.013695

    Abstract In the present scenario, cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients. Resources are in self-administration; consequently, clients can adjust their usage according to their requirements. Resource usage is estimated and clients can pay according to their utilization. In literature, the existing method describes the usage of various hardware assets. Quality of Service (QoS) needs to be considered for ascertaining the schedule and the access of resources. Adhering with the security arrangement, any additional code is forbidden to ensure the usage of resources complying with QoS.… More >

  • Open Access

    REVIEW

    Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

    Marrium Anam1, Vasaki a/p Ponnusamy2,*, Muzammil Hussain3, Muhammad Waqas Nadeem2,4, Mazhar Javed3, Hock Guan Goh2, Sadia Qadeer3

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 89-105, 2021, DOI:10.32604/cmc.2021.013159

    Abstract Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles… More >

  • Open Access

    ARTICLE

    A Location Prediction Method Based on GA-LSTM Networks and Associated Movement Behavior Information

    Xingxing Cao1, Liming Jiang1,*, Xiaoliang Wang1, Frank Jiang2

    Journal of Information Hiding and Privacy Protection, Vol.2, No.4, pp. 187-197, 2020, DOI:10.32604/jihpp.2020.016243

    Abstract Due to the lack of consideration of movement behavior information other than time and location perception in current location prediction methods, the movement characteristics of trajectory data cannot be well expressed, which in turn affects the accuracy of the prediction results. First, a new trajectory data expression method by associating the movement behavior information is given. The pre-association method is used to model the movement behavior information according to the individual movement behavior features and the group movement behavior features extracted from the trajectory sequence and the region. The movement behavior features based on pre-association may not always be the… More >

  • Open Access

    ARTICLE

    Building Graduate Salary Grading Prediction Model Based on Deep Learning

    Jong-Yih Kuo*, Hui-Chi Lin, Chien-Hung Liu

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 53-68, 2021, DOI:10.32604/iasc.2021.014437

    Abstract Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a multi-output deep neural network to… More >

  • Open Access

    ARTICLE

    Tyre Inspection through Multi-State Convolutional Neural Networks

    C. Sivamani1, M. Rajeswari2, E. Golden Julie3, Y. Harold Robinson4, Vimal Shanmuganathan5, Seifedine Kadry6, Yunyoung Nam7,*

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 1-13, 2021, DOI:10.32604/iasc.2021.013705

    Abstract Road accident is a potential risk to the lives of both drivers and passers-by. Many road accidents occur due to the improper condition of the vehicle tyres after long term usage. Thus, tyres need to be inspected and analyzed while manufacturing to avoid serious road problems. However, tyre wear is a multifaceted happening. It normally needs the non-linearly on many limitations, like tyre formation and plan, vehicle category, conditions of the road. Yet, tyre wear has numerous profitable and environmental inferences particularly due to maintenance costs and traffic safety implications. Thus, the risk to calculate tyre wear is therefore of… More >

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