T. M. Nithya^1,*, 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 >

Saeed M. Alshahrani¹, Jameel Almalki², Waleed Alshehri², Rashid Mehmood³, Marwan Albahar², Najlaa Jannah², Nayyar Ahmed Khan^1,*

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 >

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 >

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 >

Shuang Liang^1,*, 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 >

Mohammed Jasim Mohammed Jasim¹, Shakir Fattah Kak², Zainab Salih Ageed³, Subhi R. M. Zeebaree^4,*

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 >

Ahmad M. Al-Omari^1,*, 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 >

Tiansu Chen^1,2, Shi bin Zhang^1,2, Qirun Wang³, Yan Chang^1,2,*

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 >

R. Sheeba^1,*, 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 >

Manar Mohamed Hafez^1,*, Essam Eldin F. Elfakharany¹, Amr A. Abohany², Mostafa Thabet³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 943-958, 2023, DOI:10.32604/cmc.2023.034078

Abstract Big data is usually unstructured, and many applications require the analysis in real-time. Decision tree (DT) algorithm is widely used to analyze big data. Selecting the optimal depth of DT is time-consuming process as it requires many iterations. In this paper, we have designed a modified version of a (DT). The tree aims to achieve optimal depth by self-tuning running parameters and improving the accuracy. The efficiency of the modified (DT) was verified using two datasets (airport and fire datasets). The airport dataset has 500000 instances and the fire dataset has 600000 instances. A comparison has been made between the… More >