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  • Open Access

    ARTICLE

    SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

    Wided Ben Daoud1, Sami Mahfoudhi2,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2727-2742, 2022, DOI:10.32604/cmc.2022.020141

    Abstract The Internet of Thing IoT paradigm has emerged in numerous domains and it has achieved an exponential progress. Nevertheless, alongside this advancement, IoT networks are facing an ever-increasing rate of security risks because of the continuous and rapid changes in network environments. In order to overcome these security challenges, the fog system has delivered a powerful environment that provides additional resources for a more improved data security. However, because of the emerging of various breaches, several attacks are ceaselessly emerging in IoT and Fog environment. Consequently, the new emerging applications in IoT-Fog environment still require novel, distributed, and intelligent security… More >

  • Open Access

    ARTICLE

    An Ensemble Methods for Medical Insurance Costs Prediction Task

    Nataliya Shakhovska1, Nataliia Melnykova1,*, Valentyna Chopiyak2, Michal Gregus ml3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3969-3984, 2022, DOI:10.32604/cmc.2022.019882

    Abstract The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. The open dataset is used for data analysis methods development. The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’ health. Machine learning is associated with many expectations, but its quality is determined by choosing a good algorithm and the proper steps to plan, develop, and implement the model. The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy. Pierson coefficient and Boruta algorithm are used… More >

  • Open Access

    ARTICLE

    User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

    Rahmat Budiarto1,*, Ahmad A. Alqarni1, Mohammed Y. Alzahrani1, Muhammad Fermi Pasha2, Mohamed Fazil Mohamed Firdhous3, Deris Stiawan4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2679-2698, 2022, DOI:10.32604/cmc.2022.019847

    Abstract As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for Early COVID-19 Symptoms Identification

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

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3803-3820, 2022, DOI:10.32604/cmc.2022.019797

    Abstract Symptom identification and early detection are the first steps towards a health condition diagnosis. The COVID-19 virus causes pneumonia-like symptoms such as fever, cough, and shortness of breath. Many COVID-19 contraction tests necessitate extensive clinical protocols in medical settings. Clinical studies help with the accurate analysis of COVID-19, where the virus has already spread to the lungs in most patients. The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography. This is heavily reliant on a larger clinical study and does not emphasize early symptom detection. The aim of this… More >

  • Open Access

    ARTICLE

    Data Analytics for the Identification of Fake Reviews Using Supervised Learning

    Saleh Nagi Alsubari1, Sachin N. Deshmukh1, Ahmed Abdullah Alqarni2, Nizar Alsharif3, Theyazn H. H. Aldhyani4,*, Fawaz Waselallah Alsaade5, Osamah I. Khalaf6

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3189-3204, 2022, DOI:10.32604/cmc.2022.019625

    Abstract Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceive customers, inflict losses, and tarnish the reputation of companies. The present paper… More >

  • Open Access

    ARTICLE

    Applying Apache Spark on Streaming Big Data for Health Status Prediction

    Ahmed Ismail Ebada1, Ibrahim Elhenawy2, Chang-Won Jeong3, Yunyoung Nam4,*, Hazem Elbakry1, Samir Abdelrazek1

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3511-3527, 2022, DOI:10.32604/cmc.2022.019458

    Abstract Big data applications in healthcare have provided a variety of solutions to reduce costs, errors, and waste. This work aims to develop a real-time system based on big medical data processing in the cloud for the prediction of health issues. In the proposed scalable system, medical parameters are sent to Apache Spark to extract attributes from data and apply the proposed machine learning algorithm. In this way, healthcare risks can be predicted and sent as alerts and recommendations to users and healthcare providers. The proposed work also aims to provide an effective recommendation system by using streaming medical data, historical… More >

  • Open Access

    ARTICLE

    Artificial Intelligence in Medicine: Real Time Electronic Stethoscope for Heart Diseases Detection

    Batyrkhan Omarov1,2,*, Nurbek Saparkhojayev2, Shyrynkyz Shekerbekova3, Oxana Akhmetova1, Meruert Sakypbekova1, Guldina Kamalova3, Zhanna Alimzhanova1, Lyailya Tukenova3, Zhadyra Akanova4

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2815-2833, 2022, DOI:10.32604/cmc.2022.019246

    Abstract Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of… More >

  • Open Access

    ARTICLE

    Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

    K. Abhirami1,*, M. K. Kavitha Devi2

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 1127-1144, 2022, DOI:10.32604/csse.2022.020013

    Abstract With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be… More >

  • Open Access

    ARTICLE

    Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data

    G. Maria Jones1,*, S. Godfrey Winster2, P. Valarmathie3

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 963-978, 2022, DOI:10.32604/csse.2022.019483

    Abstract Mobile devices and social networks provide communication opportunities among the young generation, which increases vulnerability and cybercrimes activities. A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters. This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit. We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection. We use forensics techniques, Machine Learning (ML), and Deep Learning (DL) algorithms to exploit suspicious… More >

  • Open Access

    ARTICLE

    IoT Wireless Intrusion Detection and Network Traffic Analysis

    Vasaki Ponnusamy1, Aun Yichiet1, NZ Jhanjhi2,*, Mamoona humayun3, Maram Fahhad Almufareh3

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 865-879, 2022, DOI:10.32604/csse.2022.018801

    Abstract Enhancement in wireless networks had given users the ability to use the Internet without a physical connection to the router. Almost every Internet of Things (IoT) devices such as smartphones, drones, and cameras use wireless technology (Infrared, Bluetooth, IrDA, IEEE 802.11, etc.) to establish multiple inter-device connections simultaneously. With the flexibility of the wireless network, one can set up numerous ad-hoc networks on-demand, connecting hundreds to thousands of users, increasing productivity and profitability significantly. However, the number of network attacks in wireless networks that exploit such flexibilities in setting and tearing down networks has become very alarming. Perpetrators can launch… More >

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