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Search Results (17)
  • Open Access


    Exploiting Data Science for Measuring the Performance of Technology Stocks

    Tahir Sher1, Abdul Rehman2, Dongsun Kim2,*, Imran Ihsan1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2979-2995, 2023, DOI:10.32604/cmc.2023.036553

    Abstract The rise or fall of the stock markets directly affects investors’ interest and loyalty. Therefore, it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses. In our proposed study, six supervised machine learning (ML) strategies and deep learning (DL) models with long short-term memory (LSTM) of data science was deployed for thorough analysis and measurement of the performance of the technology stocks. Under discussion are Apple Inc. (AAPL), Microsoft Corporation (MSFT), Broadcom Inc., Taiwan Semiconductor Manufacturing Company Limited (TSM), NVIDIA Corporation (NVDA), and… More >

  • Open Access


    A Blockchain-Based Trust Model for Supporting Collaborative Healthcare Data Management

    Jiwon Jeon, Junho Kim, Mincheol Shin, Mucheol Kim*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3403-3421, 2023, DOI:10.32604/csse.2023.036658

    Abstract The development of information technology allows the collaborative business process to be run across multiple enterprises in a larger market environment. However, while collaborative business expands the realm of businesses, it also causes various hazards in collaborative Interaction, such as data falsification, inconstancy, and misuse. To solve these issues, a blockchain-based collaborative business modeling approach was proposed and analyzed. However, the existing studies lack the blockchain risk problem-solving specification, and there is no verification technique to examine the process. Consequently, it is difficult to confirm the appropriateness of the approach. Thus, here, we propose and… More >

  • Open Access


    Introduction to the Special Issue on New Trends in Statistical Computing and Data Science

    Christophe Chesneau1,*, Hassan Doosti2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 981-983, 2023, DOI:10.32604/cmes.2023.028283

    Abstract This article has no abstract. More >

  • Open Access


    Improved Symbiotic Organism Search with Deep Learning for Industrial Fault Diagnosis

    Mrim M. Alnfiai*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3763-3780, 2023, DOI:10.32604/cmc.2023.033448

    Abstract Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems. Both structural and non-structural data of industrial systems are collected, which covers data formats of time-series, text, images, sound, etc. Several researchers discussed above were mostly qualitative, and ceratin techniques need expert guidance to conclude on the condition of gearboxes. But, in this study, an improved symbiotic organism search with deep learning enabled fault diagnosis (ISOSDL-FD) model for gearbox fault detection in industrial systems. The proposed ISOSDL-FD technique majorly concentrates on the identification and classification… More >

  • Open Access


    Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

    Marwa Obayya1, Nadhem NEMRI2, Lubna A. Alharbi3, Mohamed K. Nour4, Mrim M. Alnfiai5, Mohammed Abdullah Al-Hagery6, Nermin M. Salem7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3151-3166, 2023, DOI:10.32604/cmc.2023.032765

    Abstract With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of… More >

  • Open Access


    Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

    T. Edwin Ponraj1,*, J. Charles2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1189-1203, 2023, DOI:10.32604/iasc.2023.033383

    Abstract The mutation is a critical element in determining the proteins’ stability, becoming a core element in portraying the effects of a drug in the pharmaceutical industry. Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential mutations, computational approaches that can reliably anticipate the consequences of amino acid mutations are critical. This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure. Initially, the context in a collection of words is determined using a knowledge More >

  • Open Access


    Big Data Analytics Using Graph Signal Processing

    Farhan Amin1, Omar M. Barukab2, Gyu Sang Choi1,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 489-502, 2023, DOI:10.32604/cmc.2023.030615

    Abstract The networks are fundamental to our modern world and they appear throughout science and society. Access to a massive amount of data presents a unique opportunity to the researcher’s community. As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace. Therefore, this paper initiates a discussion on graph signal processing for large-scale data analysis. We first provide a comprehensive overview of core ideas in Graph signal processing (GSP) and their connection to conventional digital signal… More >

  • Open Access


    Deep Learning Enabled Microarray Gene Expression Classification for Data Science Applications

    Areej A. Malibari1, Reem M. Alshehri2, Fahd N. Al-Wesabi3, Noha Negm3, Mesfer Al Duhayyim4, Anwer Mustafa Hilal5,*, Ishfaq Yaseen5, Abdelwahed Motwakel5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4277-4290, 2022, DOI:10.32604/cmc.2022.027030

    Abstract In bioinformatics applications, examination of microarray data has received significant interest to diagnose diseases. Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes. Microarray data classification incorporates multiple disciplines such as bioinformatics, machine learning (ML), data science, and pattern classification. This paper designs an optimal deep neural network based microarray gene expression classification (ODNN-MGEC) model for bioinformatics applications. The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale. Besides, improved fruit fly optimization (IFFO) based… More >

  • Open Access


    Efficient Data Augmentation Techniques for Improved Classification in Limited Data Set of Oral Squamous Cell Carcinoma

    Wael Alosaimi1,*, M. Irfan Uddin2

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1387-1401, 2022, DOI:10.32604/cmes.2022.018433

    Abstract Deep Learning (DL) techniques as a subfield of data science are getting overwhelming attention mainly because of their ability to understand the underlying pattern of data in making classifications. These techniques require a considerable amount of data to efficiently train the DL models. Generally, when the data size is larger, the DL models perform better. However, it is not possible to have a considerable amount of data in different domains such as healthcare. In healthcare, it is impossible to have a substantial amount of data to solve medical problems using Artificial Intelligence, mainly due to… More >

  • Open Access


    Internal Validity Index for Fuzzy Clustering Based on Relative Uncertainty

    Refik Tanju Sirmen1,*, Burak Berk Üstündağ2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2909-2926, 2022, DOI:10.32604/cmc.2022.023947

    Abstract Unsupervised clustering and clustering validity are used as essential instruments of data analytics. Despite clustering being realized under uncertainty, validity indices do not deliver any quantitative evaluation of the uncertainties in the suggested partitionings. Also, validity measures may be biased towards the underlying clustering method. Moreover, neglecting a confidence requirement may result in over-partitioning. In the absence of an error estimate or a confidence parameter, probable clustering errors are forwarded to the later stages of the system. Whereas, having an uncertainty margin of the projected labeling can be very fruitful for many applications such as… More >

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