Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (7)
  • Open Access

    ARTICLE

    Deep Learning Based Face Detection and Identification of Criminal Suspects

    S. Sandhya1, A. Balasundaram2,*, Ayesha Shaik1

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2331-2343, 2023, DOI:10.32604/cmc.2023.032715

    Abstract Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network (DNN) model which employs a… More >

  • Open Access

    ARTICLE

    An Automated Word Embedding with Parameter Tuned Model for Web Crawling

    S. Neelakandan1,*, A. Arun2, Raghu Ram Bhukya3, Bhalchandra M. Hardas4, T. Ch. Anil Kumar5, M. Ashok6

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1617-1632, 2022, DOI:10.32604/iasc.2022.022209

    Abstract In recent years, web crawling has gained a significant attention due to the drastic advancements in the World Wide Web. Web Search Engines have the issue of retrieving massive quantity of web documents. One among the web crawlers is the focused crawler, that intends to selectively gather web pages from the Internet. But the efficiency of the focused crawling can easily be affected by the environment of web pages. In this view, this paper presents an Automated Word Embedding with Parameter Tuned Deep Learning (AWE-PTDL) model for focused web crawling. The proposed model involves different processes namely pre-processing, Incremental Skip-gram… More >

  • Open Access

    ARTICLE

    Benchmarking Performance of Document Level Classification and Topic Modeling

    Muhammad Shahid Bhatti1,*, Azmat Ullah1, Rohaya Latip2, Abid Sohail1, Anum Riaz1, Rohail Hassan3

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 125-141, 2022, DOI:10.32604/cmc.2022.020083

    Abstract Text classification of low resource language is always a trivial and challenging problem. This paper discusses the process of Urdu news classification and Urdu documents similarity. Urdu is one of the most famous spoken languages in Asia. The implementation of computational methodologies for text classification has increased over time. However, Urdu language has not much experimented with research, it does not have readily available datasets, which turn out to be the primary reason behind limited research and applying the latest methodologies to the Urdu. To overcome these obstacles, a medium-sized dataset having six categories is collected from authentic Pakistani news… More >

  • Open Access

    ARTICLE

    An Automated System to Predict Popular Cybersecurity News Using Document Embeddings

    Ramsha Saeed1, Saddaf Rubab1, Sara Asif1, Malik M. Khan1, Saeed Murtaza1, Seifedine Kadry2, Yunyoung Nam3,*, Muhammad Attique Khan4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 533-547, 2021, DOI:10.32604/cmes.2021.014355

    Abstract The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teams in making decisions about posting a news article. Article similarity extracted from the articles posted within a small period of time is found to be a useful feature in existing popularity prediction approaches. This work proposes a new approach to estimate the popularity of news articles by adding semantics in the article similarity based approach of popularity estimation. A semantically enriched model is proposed which… 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

    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 productively. Therefore, the need for… More >

  • Open Access

    ARTICLE

    An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

    Muhammad Yahya Saeed1, Muhammad Awais1, Muhammad Younas1, Muhammad Arif Shah2,*, Atif Khan3, M. Irfan Uddin4, Marwan Mahmoud5

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2409-2423, 2021, DOI:10.32604/cmc.2021.014330

    Abstract Text Summarization is an essential area in text mining, which has procedures for text extraction. In natural language processing, text summarization maps the documents to a representative set of descriptive words. Therefore, the objective of text extraction is to attain reduced expressive contents from the text documents. Text summarization has two main areas such as abstractive, and extractive summarization. Extractive text summarization has further two approaches, in which the first approach applies the sentence score algorithm, and the second approach follows the word embedding principles. All such text extractions have limitations in providing the basic theme of the underlying documents.… More >

  • Open Access

    ARTICLE

    PID Tuning Method Using Single-Valued Neutrosophic Cosine Measure and Genetic Algorithm

    Jun Ye

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 15-23, 2019, DOI:10.31209/2018.100000067

    Abstract Because existing proportional-integral-derivative (PID) tuning method using similarity measures of single-valued neutrosophic sets (SVNSs) and an increasing step algorithm shows its complexity and inconvenience, this paper proposes a PID tuning method using a cosine similarity measure of SVNSs and genetic algorithm (GA) to improve the existing PID tuning method. In the tuning process, the step response characteristic values (rising time, settling time, overshoot ratio, undershoot ratio, peak time, and steady-state error) of the control system are converted into the single-valued neutrosophic set (SVNS) by the neutrosophic membership functions (Neutrosophication). Then the values of three appropriate parameters in a PID controller… More >

Displaying 1-10 on page 1 of 7. Per Page