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  • Open Access


    An Automatic Threshold Selection Using ALO for Healthcare Duplicate Record Detection with Reciprocal Neuro-Fuzzy Inference System

    Ala Saleh Alluhaidan1,*, Pushparaj2, Anitha Subbappa3, Ved Prakash Mishra4, P. V. Chandrika5, Anurika Vaish6, Sarthak Sengupta6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5821-5836, 2023, DOI:10.32604/cmc.2023.033995

    Abstract ESystems based on EHRs (Electronic health records) have been in use for many years and their amplified realizations have been felt recently. They still have been pioneering collections of massive volumes of health data. Duplicate detections involve discovering records referring to the same practical components, indicating tasks, which are generally dependent on several input parameters that experts yield. Record linkage specifies the issue of finding identical records across various data sources. The similarity existing between two records is characterized based on domain-based similarity functions over different features. De-duplication of one dataset or the linkage of multiple data sets has become… More >

  • Open Access


    Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

    Sindhuja Manickavasagam1,*, Poonkuzhali Sugumaran2

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 939-953, 2023, DOI:10.32604/csse.2023.030491

    Abstract The combination of machine learning (ML) approaches in healthcare is a massive advantage designed at curing illness of millions of persons. Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis. Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world. Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs. The previous detection of cancer is not simpler procedure however if it can be detected, it can be curable, also… More >

  • Open Access


    Swarm Optimization and Machine Learning for Android Malware Detection

    K. Santosh Jhansi1,2,*, P. Ravi Kiran Varma2, Sujata Chakravarty3

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6327-6345, 2022, DOI:10.32604/cmc.2022.030878

    Abstract Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential features. The nature-inspired wrapper-based algorithms… More >

  • Open Access


    Moving Multi-Object Detection and Tracking Using MRNN and PS-KM Models

    V. Premanand*, Dhananjay Kumar

    Computer Systems Science and Engineering, Vol.44, No.2, pp. 1807-1821, 2023, DOI:10.32604/csse.2023.026742

    Abstract On grounds of the advent of real-time applications, like autonomous driving, visual surveillance, and sports analysis, there is an augmenting focus of attention towards Multiple-Object Tracking (MOT). The tracking-by-detection paradigm, a commonly utilized approach, connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the similarities of the appearance or the motion between them. For an efficient detection and tracking of the numerous objects in a complex environment, a Pearson Similarity-centred Kuhn-Munkres (PS-KM) algorithm was proposed in the present study. In this light, the input videos were, initially, gathered from the MOT dataset and converted into frames.… More >

  • Open Access


    Incremental Learning Framework for Mining Big Data Stream

    Alaa Eisa1, Nora EL-Rashidy2, Mohammad Dahman Alshehri3,*, Hazem M. El-bakry1, Samir Abdelrazek1

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2901-2921, 2022, DOI:10.32604/cmc.2022.021342

    Abstract At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant… More >

  • Open Access


    Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

    Soukaina Mjahed1,*, Khadija Bouzaachane1, Ahmad Taher Azar2,3, Salah El Hadaj1, Said Raghay1

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 459-494, 2020, DOI:10.32604/cmes.2020.010791

    Abstract This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the “Higgs machine learning challenge 2014” data set. This unsupervised detection goes in this paper analysis through 4 steps: (1) selection of the most informative features from the considered data; (2) definition of the number of clusters based on the elbow criterion. The experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters; (3) proposition of a new approach for hybridization of both hard and fuzzy clustering… More >

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