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  • Open Access

    ARTICLE

    A Novel Metadata Based Multi-Label Document Classification Technique

    Naseer Ahmed Sajid1, Munir Ahmad1, Atta-ur Rahman2,*, Gohar Zaman3, Mohammed Salih Ahmed4, Nehad Ibrahim2, Mohammed Imran B. Ahmed4, Gomathi Krishnasamy6, Reem Alzaher2, Mariam Alkharraa2, Dania AlKhulaifi2, Maryam AlQahtani2, Asiya A. Salam6, Linah Saraireh5, Mohammed Gollapalli6, Rashad Ahmed7

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2195-2214, 2023, DOI:10.32604/csse.2023.033844 - 09 February 2023

    Abstract From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To… More >

  • Open Access

    ARTICLE

    Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

    D. Viji*, S. Revathy

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 27-42, 2023, DOI:10.32604/csse.2023.030259 - 20 January 2023

    Abstract Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this… More >

  • Open Access

    ARTICLE

    Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

    Bao Rong Chang1, Hsiu-Fen Tsai2,*, Yu-Chieh Lin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128 - 31 August 2022

    Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time More >

  • Open Access

    REVIEW

    Heavy Metal/Metalloid Indexing and Balances in Agricultural Soils: Methodological Approach for Research

    Shahid Hussain*

    Phyton-International Journal of Experimental Botany, Vol.91, No.12, pp. 2687-2697, 2022, DOI:10.32604/phyton.2022.021158 - 29 August 2022

    Abstract Heavy metal(loid) accumulation in agricultural soils is a threat to the soil capacity, quality, and productivity. It also increases human exposure to heavy metal(loid)s via consumption of contaminated plant-based foods. The detrimental effects of soil contamination also deteriorate the environment of plants and animals. For sustainable agriculture, therefore, the soil must be protected from toxic levels of heavy metal(loid)s. Studies on heavy metal(loid) balances in agricultural soils are important in predicting future risks to sustainable production from agro-ecological zones and human exposure to heavy metal(loid)s. The latest and continuous indexing of the problem seems a More >

  • Open Access

    ARTICLE

    Shallow Neural Network and Ontology-Based Novel Semantic Document Indexing for Information Retrieval

    Anil Sharma1,*, Suresh Kumar2

    Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1989-2005, 2022, DOI:10.32604/iasc.2022.026095 - 25 May 2022

    Abstract Information Retrieval (IR) systems are developed to fetch the most relevant content matching the user’s information needs from a pool of information. A user expects to get IR results based on the conceptual contents of the query rather than keywords. But traditional IR approaches index documents based on the terms that they contain and ignore semantic descriptions of document contents. This results in a vocabulary gap when queries and documents use different terms to describe the same concept. As a solution to this problem and to improve the performance of IR systems, we have designed… More >

  • Open Access

    ARTICLE

    Deep Root Memory Optimized Indexing Methodology for Image Search Engines

    R. Karthikeyan1,*, A. Celine Kavida2, P. Suresh3

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 661-672, 2022, DOI:10.32604/csse.2022.018744 - 09 September 2021

    Abstract Digitization has created an abundance of new information sources by altering how pictures are captured. Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing. This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets. Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional,… More >

  • Open Access

    ARTICLE

    SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data

    Kashif Irshad1, Muhammad Tanvir Afzal2, Sanam Shahla Rizvi3, Abdul Shahid4, Rabia Riaz5, Tae-Sun Chung6,*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 877-894, 2021, DOI:10.32604/cmc.2021.014156 - 12 January 2021

    Abstract The growing collection of scientific data in various web repositories is referred to as Scientific Big Data, as it fulfills the four “V’s” of Big Data–-volume, variety, velocity, and veracity. This phenomenon has created new opportunities for startups; for instance, the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs. Traditionally, the content of the papers are compared to list the relevant papers from a repository. The conventional method results in a long list of papers that is often impossible to interpret… More >

  • Open Access

    ARTICLE

    Context Based Adoption of Ranking and Indexing Measures for Cricket Team Ranks

    Raja Sher Afgun Usmani1, Syed Muhammad Saqlain Shah1, *, Muhammad Sher Ramzan2, Abdullah Saad AL-Malaise AL-Ghamdi2, Anwar Ghani1, Imran Khan1, Farrukh Saleem2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1113-1136, 2020, DOI:10.32604/cmc.2020.010789 - 20 August 2020

    Abstract There is an international cricket governing body that ranks the expertise of all the cricket playing nations, known as the International Cricket Council (ICC). The ranking system followed by the ICC relies on the winnings and defeats of the teams. The model used by the ICC to implement rankings is deficient in certain key respects. It ignores key factors like winning margin and strength of the opposition. Various measures of the ranking concept are presented in this research. The proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking metrics. The… More >

  • Open Access

    ARTICLE

    Speech-Music-Noise Discrimination in Sound Indexing of Multimedia Documents

    Lamia Bouafif1, Noureddine Ellouze2

    Sound & Vibration, Vol.52, No.6, pp. 2-10, 2018, DOI:10.32604/sv.2018.02410

    Abstract Sound indexing and segmentation of digital documents especially in the internet and digital libraries are very useful to simplify and to accelerate the multimedia document retrieval. We can imagine that we can extract multimedia files not only by keywords but also by speech semantic contents. The main difficulty of this operation is the parameterization and modelling of the sound track and the discrimination of the speech, music and noise segments. In this paper, we will present a Speech/Music/Noise indexing interface designed for audio discrimination in multimedia documents. The program uses a statistical method based on More >

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