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  • Open Access


    Performance Improvement through Novel Adaptive Node and Container Aware Scheduler with Resource Availability Control in Hadoop YARN

    J. S. Manjaly, T. Subbulakshmi*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3083-3108, 2023, DOI:10.32604/csse.2023.036320

    Abstract The default scheduler of Apache Hadoop demonstrates operational inefficiencies when connecting external sources and processing transformation jobs. This paper has proposed a novel scheduler for enhancement of the performance of the Hadoop Yet Another Resource Negotiator (YARN) scheduler, called the Adaptive Node and Container Aware Scheduler (ANACRAC), that aligns cluster resources to the demands of the applications in the real world. The approach performs to leverage the user-provided configurations as a unique design to apportion nodes, or containers within the nodes, to application thresholds. Additionally, it provides the flexibility to the applications for selecting and choosing which node’s resources they… More >

  • Open Access


    Deep Learning Model for Big Data Classification in Apache Spark Environment

    T. M. Nithya1,*, R. Umanesan2, T. Kalavathidevi3, C. Selvarathi4, A. Kavitha5

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2537-2547, 2023, DOI:10.32604/iasc.2022.028804

    Abstract Big data analytics is a popular research topic due to its applicability in various real time applications. The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance. Since big data involves numerous features and necessitates high computational time, feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance. This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit (SBOA-OGRU) model for big data classification in Apache Spark. The SBOA-OGRU technique involves the… More >

  • Open Access


    Systematic Survey on Big Data Analytics and Artificial Intelligence for COVID-19 Containment

    Saeed M. Alshahrani1, Jameel Almalki2, Waleed Alshehri2, Rashid Mehmood3, Marwan Albahar2, Najlaa Jannah2, Nayyar Ahmed Khan1,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1793-1817, 2023, DOI:10.32604/csse.2023.039648

    Abstract Artificial Intelligence (AI) has gained popularity for the containment of COVID-19 pandemic applications. Several AI techniques provide efficient mechanisms for handling pandemic situations. AI methods, protocols, data sets, and various validation mechanisms empower the users towards proper decision-making and procedures to handle the situation. Despite so many tools, there still exist conditions in which AI must go a long way. To increase the adaptability and potential of these techniques, a combination of AI and Bigdata is currently gaining popularity. This paper surveys and analyzes the methods within the various computational paradigms used by different researchers and national governments, such as… More >

  • Open Access


    Data Layout and Scheduling Tasks in a Meteorological Cloud Environment

    Kunfu Wang, Yongsheng Hao, Jie Cao*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1033-1052, 2023, DOI:10.32604/iasc.2023.038036

    Abstract Meteorological model tasks require considerable meteorological basis data to support their execution. However, if the task and the meteorological datasets are located on different clouds, that enhances the cost, execution time, and energy consumption of execution meteorological tasks. Therefore, the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations. To the best of our knowledge, this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout. First, we use the FP-Growth-M (frequent-pattern growth for meteorological model datasets) method to mine the relationship… More >

  • Open Access


    Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data

    Baoling Qin*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 255-267, 2023, DOI:10.32604/iasc.2023.037556

    Abstract Although the Internet of Things has been widely applied, the problems of cloud computing in the application of digital smart medical Big Data collection, processing, analysis, and storage remain, especially the low efficiency of medical diagnosis. And with the wide application of the Internet of Things and Big Data in the medical field, medical Big Data is increasing in geometric magnitude resulting in cloud service overload, insufficient storage, communication delay, and network congestion. In order to solve these medical and network problems, a medical big-data-oriented fog computing architecture and BP algorithm application are proposed, and its structural advantages and characteristics… More >

  • Open Access


    An Auxiliary Monitoring Method for Well Killing Based on Statistical Data

    Shuang Liang1,*, Fangyu Luo2, Huihui Yu3, Jian Gao1, Xiaolin Shu1

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.8, pp. 2109-2118, 2023, DOI:10.32604/fdmp.2023.025342

    Abstract In the present study, a large set of data related to well killing is considered. Through a complete exploration of the whole process leading to well-killing, various factors affecting such a process are screened and sorted, and a correlation model is built accordingly in order to introduce an auxiliary method for well-killing monitoring based on statistical information. The available data show obvious differences due to the diverse control parameters related to different well-killing methods. Nevertheless, it is shown that a precise three-fold relationship exists between the reservoir parameters, the elapsed time and the effectiveness of the considered well-killing strategy. The… More >

  • Open Access


    Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

    Mohammed Jasim Mohammed Jasim1, Shakir Fattah Kak2, Zainab Salih Ageed3, Subhi R. M. Zeebaree4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3831-3845, 2023, DOI:10.32604/csse.2023.037580

    Abstract Nowadays, smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature, computational approaches, and discoveries, owing to which a massive quantity of experimental datasets was published and generated (Big Data) for describing and validating such novelties. Drug-drug interaction (DDI) significantly contributed to drug administration and development. It continues as the main obstacle in offering inexpensive and safe healthcare. It normally happens for patients with extensive medication, leading them to take many drugs simultaneously. DDI may cause side effects, either mild or severe health problems. This reduced victims’ quality of life and… More >

  • Open Access


    Big Data Bot with a Special Reference to Bioinformatics

    Ahmad M. Al-Omari1,*, Shefa M. Tawalbeh1, Yazan H. Akkam2, Mohammad Al-Tawalbeh3, Shima’a Younis1, Abdullah A. Mustafa4, Jonathan Arnold5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4155-4173, 2023, DOI:10.32604/cmc.2023.036956

    Abstract There are quintillions of data on deoxyribonucleic acid (DNA) and protein in publicly accessible data banks, and that number is expanding at an exponential rate. Many scientific fields, such as bioinformatics and drug discovery, rely on such data; nevertheless, gathering and extracting data from these resources is a tough undertaking. This data should go through several processes, including mining, data processing, analysis, and classification. This study proposes software that extracts data from big data repositories automatically and with the particular ability to repeat data extraction phases as many times as needed without human intervention. This software simulates the extraction of… More >

  • Open Access


    Quantum Fuzzy Regression Model for Uncertain Environment

    Tiansu Chen1,2, Shi bin Zhang1,2, Qirun Wang3, Yan Chang1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2759-2773, 2023, DOI:10.32604/cmc.2023.033284

    Abstract In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional… More >

  • Open Access


    Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

    R. Sheeba1,*, R. Sharmila2, Ahmed Alkhayyat3, Rami Q. Malik4

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1415-1429, 2023, DOI:10.32604/csse.2023.034321

    Abstract Lately, the Internet of Things (IoT) application requires millions of structured and unstructured data since it has numerous problems, such as data organization, production, and capturing. To address these shortcomings, big data analytics is the most superior technology that has to be adapted. Even though big data and IoT could make human life more convenient, those benefits come at the expense of security. To manage these kinds of threats, the intrusion detection system has been extensively applied to identify malicious network traffic, particularly once the preventive technique fails at the level of endpoint IoT devices. As cyberattacks targeting IoT have… More >

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