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  • 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

    Modeling Bacterial Species: Using Sequence Similarity with Clustering Techniques

    Miguel-Angel Sicilia1,*, Elena García-Barriocanal1, Marçal Mora-Cantallops1, Salvador Sánchez-Alonso1, Lino González2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 1661-1672, 2021, DOI:10.32604/cmc.2021.015874

    Abstract Existing studies have challenged the current definition of named bacterial species, especially in the case of highly recombinogenic bacteria. This has led to considering the use of computational procedures to examine potential bacterial clusters that are not identified by species naming. This paper describes the use of sequence data obtained from MLST databases as input for a k-means algorithm extended to deal with housekeeping gene sequences as a metric of similarity for the clustering process. An implementation of the k-means algorithm has been developed based on an existing source code implementation, and it has been evaluated against MLST data. Results… More >

  • Open Access

    ARTICLE

    Generalized Normalized Euclidean Distance Based Fuzzy Soft Set Similarity for Data Classification

    Rahmat Hidayat1,2,*, Iwan Tri Riyadi Yanto1,3, Azizul Azhar Ramli1, Mohd Farhan Md. Fudzee1, Ansari Saleh Ahmar4

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 119-130, 2021, DOI:10.32604/csse.2021.015628

    Abstract

    Classification is one of the data mining processes used to predict predetermined target classes with data learning accurately. This study discusses data classification using a fuzzy soft set method to predict target classes accurately. This study aims to form a data classification algorithm using the fuzzy soft set method. In this study, the fuzzy soft set was calculated based on the normalized Hamming distance. Each parameter in this method is mapped to a power set from a subset of the fuzzy set using a fuzzy approximation function. In the classification step, a generalized normalized Euclidean distance is used to determine… More >

  • Open Access

    ARTICLE

    Power Aggregation Operators and Similarity Measures Based on Improved Intuitionistic Hesitant Fuzzy Sets and their Applications to Multiple Attribute Decision Making

    Tahir Mahmood1, Wajid Ali1, Zeeshan Ali1, Ronnason Chinram2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1165-1187, 2021, DOI:10.32604/cmes.2021.014393

    Abstract Intuitionistic hesitant fuzzy set (IHFS) is a mixture of two separated notions called intuitionistic fuzzy set (IFS) and hesitant fuzzy set (HFS), as an important technique to cope with uncertain and awkward information in realistic decision issues. IHFS contains the grades of truth and falsity in the form of the subset of the unit interval. The notion of IHFS was defined by many scholars with different conditions, which contain several weaknesses. Here, keeping in view the problems of already defined IHFSs, we will define IHFS in another way so that it becomes compatible with other existing notions. To examine the… 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

    Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

    Fan Zhang1,2, Fenlin Liu1,2,*, Rui Xu3,4, Xiangyang Luo1,2, Shichang Ding5, Hechan Tian1,2

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 3345-3361, 2021, DOI:10.32604/cmc.2021.014526

    Abstract Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays. However, this principle is often invalid in real Internet environment, which leads to unreliable geolocation results. To improve the accuracy and reliability of locating IP in real Internet, a street-level IP geolocation algorithm based on landmarks clustering is proposed. Firstly, we use the probes to measure the known landmarks to obtain their delay vectors, and cluster landmarks using them. Secondly, the landmarks are clustered again by their latitude and longitude, and the intersection of these two clustering results is taken… 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

    Trade-Off between Efficiency and Effectiveness: A Late Fusion Multi-View Clustering Algorithm

    Yunping Zhao1, Weixuan Liang1, Jianzhuang Lu1,*, Xiaowen Chen1, Nijiwa Kong2

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2709-2722, 2021, DOI:10.32604/cmc.2021.013389

    Abstract Late fusion multi-view clustering (LFMVC) algorithms aim to integrate the base partition of each single view into a consensus partition. Base partitions can be obtained by performing kernel k-means clustering on all views. This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. LFMVC improves computational efficiency to the extent that the computational complexity of each iteration is reduced from O(n3) to O(n) (where n is the number of samples). However, LFMVC also limits the search space of the optimal solution, meaning that… More >

  • Open Access

    ARTICLE

    Lemniscate of Leaf Function

    Kazunori Shinohara*

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 275-292, 2021, DOI:10.32604/cmes.2021.012383

    Abstract A lemniscate is a curve defined by two foci, F1 and F2. If the distance between the focal points of F1F2 is 2a (a: constant), then any point P on the lemniscate curve satisfy the equation PF1 · PF2 = a2. Jacob Bernoulli first described the lemniscate in 1694. The Fagnano discovered the double angle formula of the lemniscate (1718). The Euler extended the Fagnano’s formula to a more general addition theorem (1751). The lemniscate function was subsequently proposed by Gauss around the year 1800. These insights were summarized by Jacobi as the theory of elliptic functions. A… More >

  • Open Access

    ARTICLE

    Thermal Analysis of MHD Non-Newtonian Nanofluids over a Porous Media

    Asad Ejaz1, Imran Abbas1, Yasir Nawaz1, Muhammad Shoaib Arif1, Wasfi Shatanawi2,3,4,*, Javeria Nawaz Abbasi5

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1119-1134, 2020, DOI:10.32604/cmes.2020.012091

    Abstract In the present research, Tiwari and Das model are used for the impact of a magnetic field on non-Newtonian nanofluid flow in the presence of injection and suction. The PDEs are converted into ordinary differential equations (ODEs) using the similarity method. The obtained ordinary differential equations are solved numerically using shooting method along with RK-4. Part of the present study uses nanoparticles (NPs) like TiO2 and Al2O3 and sodium carboxymethyl cellulose (CMC/water) is considered as a base fluid (BF). This study is conducted to find the influence of nanoparticles, Prandtl number, and magnetic field on velocity and temperature profile, however,… More >

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