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Search Results (19)
  • Open Access


    Dense-Structured Network Based Bearing Remaining Useful Life Prediction System

    Ping-Huan Kuo1,2, Ting-Chung Tseng1, Po-Chien Luan2, Her-Terng Yau1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 133-151, 2022, DOI: 10.32604/cmes.2022.020350

    Abstract This work is focused on developing an effective method for bearing remaining useful life predictions. The method is useful in accurately predicting the remaining useful life of bearings so that machine damage, production outage, and human accidents caused by unexpected bearing failure can be prevented. This study uses the bearing dataset provided by FEMTO-ST Institute, Besançon, France. This study starts with the exploration of neural networks, based on which the biaxial vibration signals are modeled and analyzed. This paper introduces pre-processing of bearing vibration signals, neural network model training and adjustment of training data. The model is trained by optimizing… More >

  • Open Access


    Secure Dengue Epidemic Prediction System: Healthcare Perspective

    Abdulaziz Aldaej*, Tariq Ahamed Ahanger, Mohammed Yousuf Uddin, Imdad Ullah

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1723-1745, 2022, DOI:10.32604/cmc.2022.027487

    Abstract Viral diseases transmitted by mosquitoes are emerging public health problems across the globe. Dengue is considered to be the most significant mosquito-oriented disease. Conspicuously, the present study provides an effective architecture for Dengue Virus Infection surveillance. The proposed system involves a 4-level architecture for the prediction and prevention of dengue infection outspread. The architectural levels including Dengue Information Acquisition level, Dengue Information Classification level, Dengue-Mining and Extraction level, and Dengue-Prediction and Decision Modeling level enable an individual to periodically monitor his/her probabilistic dengue fever measure. The prediction process is carried out so that proactive measures are taken beforehand. For predictive… More >

  • Open Access


    IoT Based Disease Prediction Using Mapreduce and LSQN3 Techniques

    R. Gopi1,*, S. Veena2, S. Balasubramanian3, D. Ramya4, P. Ilanchezhian5, A. Harshavardhan6, Zatin Gupta7

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1215-1230, 2022, DOI:10.32604/iasc.2022.025792

    Abstract In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN3 techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN) centered BD Mining (BDM)… More >

  • Open Access


    Machine Learning Empowered Software Defect Prediction System

    Mohammad Sh. Daoud1, Shabib Aftab2,3, Munir Ahmad2, Muhammad Adnan Khan4,5,*, Ahmed Iqbal3, Sagheer Abbas2, Muhammad Iqbal2, Baha Ihnaini6,7

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1287-1300, 2022, DOI:10.32604/iasc.2022.020362

    Abstract Production of high-quality software at lower cost has always been the main concern of developers. However, due to exponential increases in size and complexity, the development of qualitative software with lower costs is almost impossible. This issue can be resolved by identifying defects at the early stages of the development lifecycle. As a significant amount of resources are consumed in testing activities, if only those software modules are shortlisted for testing that is identified as defective, then the overall cost of development can be reduced with the assurance of high quality. An artificial neural network is considered as one of… More >

  • Open Access


    An Improved Machine Learning Technique with Effective Heart Disease Prediction System

    Mohammad Tabrez Quasim1, Saad Alhuwaimel2,*, Asadullah Shaikh3, Yousef Asiri3, Khairan Rajab3, Rihem Farkh4,5, Khaled Al Jaloud4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4169-4181, 2021, DOI:10.32604/cmc.2021.015984

    Abstract Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we achieved the component perception measured… More >

  • Open Access


    A Pregnancy Prediction System based on Uterine Peristalsis from Ultrasonic Images

    Kentaro Mori1,*, Kotaro Kitaya2, Tomomoto Ishikawa2, Yutaka Hata3

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 335-352, 2021, DOI:10.32604/iasc.2021.01010

    Abstract In infertility treatment, it is required to improve a success rate of the treatment. A purpose of this study is to develop a prediction system for pregnancy outcomes using ultrasonic images. In infertility treatment, it is typical to evaluate the endometrial shape by using ultrasonic images. The convolutional neural network (CNN) system developed in the current study predicted pregnancy outcome by velocity information. The velocity information has a movement feature of uterine. It is known that a uterine movement is deep related to infertility. Experiments compared the velocity-based and shape-based systems. The shape-based systems predict the optimal uterine features for… More >

  • Open Access


    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


    Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

    Muhammad Adnan Khan1, *, Sagheer Abbas2, Ayesha Atta2, 3, Allah Ditta4, Hani Alquhayz5, Muhammad Farhan Khan6, Atta-ur-Rahman7, Rizwan Ali Naqvi8

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 139-151, 2020, DOI:10.32604/cmc.2020.011416

    Abstract The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases. The most fatal of these is the issue of heart disease that cannot be detected from a naked eye, and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate, body temperature, and blood pressure. The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner, followed by prescribing appropriate treatments and keeping prescription errors to a minimum. In developing countries, the domain of healthcare is progressing day by day using… More >

  • Open Access


    Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive Clustering and Fuzzy Rule-Based Inference Systems

    Sunil Kr. Jha1, Zulfiqar Ahmad2

    CMES-Computer Modeling in Engineering & Sciences, Vol.113, No.4, pp. 443-459, 2017, DOI:10.3970/cmes.2017.113.443

    Abstract Microbial population and enzyme activities are the significant indicators of soil strength. Soil microbial dynamics characterize microbial population and enzyme activities. The present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics, like rock phosphate solubilization, bacterial population, and ACC-deaminase activity. More specifically, optimized subtractive clustering (SC) and Wang and Mendel's (WM) fuzzy inference systems (FIS) have been implemented with the objective to achieve the best estimation accuracy of microbial dynamics. Experimental measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with… More >

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