Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (5)
  • Open Access


    Performance Analysis of Hybrid RR Algorithm for Anomaly Detection in Streaming Data

    L. Amudha1,*, R. PushpaLakshmi2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2299-2312, 2023, DOI:10.32604/csse.2023.031169

    Abstract Automated live video stream analytics has been extensively researched in recent times. Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame. We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). In the first stage, an ensemble of LSTM-RNNs are deployed to generate the anomaly score. The second stage uses the least square method for optimal anomaly score generation. The third stage adopts award-based reinforcement learning to update the… More >

  • Open Access


    A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

    R. Rajakumar1,*, S. Sathiya Devi2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2101-2116, 2023, DOI:10.32604/iasc.2023.028889

    Abstract Due to the advancements in information technologies, massive quantity of data is being produced by social media, smartphones, and sensor devices. The investigation of data stream by the use of machine learning (ML) approaches to address regression, prediction, and classification problems have received considerable interest. At the same time, the detection of anomalies or outliers and feature selection (FS) processes becomes important. This study develops an outlier detection with feature selection technique for streaming data classification, named ODFST-SDC technique. Initially, streaming data is pre-processed in two ways namely categorical encoding and null value removal. In… More >

  • Open Access


    Automated Learning of ECG Streaming Data Through Machine Learning Internet of Things

    Mwaffaq Abu-Alhaija, Nidal M. Turab*

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 45-53, 2022, DOI:10.32604/iasc.2022.021426

    Abstract Applying machine learning techniques on Internet of Things (IoT) data streams will help achieve better understanding, predict future perceptions, and make crucial decisions based on those analytics. The collaboration between IoT, Big Data and machine learning can be found in different domains such as Health care, Smart cities, and Telecommunications. The aim of this paper is to develop a method for automated learning of electrocardiogram (ECG) streaming data to detect any heart beat anomalies. A promising solution is to use medical sensors that transfer vital signs to medical care computer systems, combined with machine learning, More >

  • Open Access


    An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

    Romany F. Mansour1,*, Shaha Al-Otaibi2, Amal Al-Rasheed2, Hanan Aljuaid3, Irina V. Pustokhina4, Denis A. Pustokhin5

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2843-2858, 2021, DOI:10.32604/cmc.2021.016626

    Abstract Big data streams started becoming ubiquitous in recent years, thanks to rapid generation of massive volumes of data by different applications. It is challenging to apply existing data mining tools and techniques directly in these big data streams. At the same time, streaming data from several applications results in two major problems such as class imbalance and concept drift. The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection (MOMBD-CDD) method on High-Dimensional Streaming Data. The presented MOMBD-CDD model has different operational stages such as pre-processing, CDD, and… More >

  • Open Access


    Differentially Private Real-Time Streaming Data Publication Based on Sliding Window Under Exponential Decay

    Lan Sun1, Chen Ge1, Xin Huang1, Yingjie Wu1,*, Yan Gao2

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 61-78, 2019, DOI:10.32604/cmc.2019.03744

    Abstract Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure. The existing research on differential privacy streaming data publication mostly pay close attention to boosting query accuracy, but pay less attention to query efficiency, and ignore the effect of timeliness on data weight. In this paper, we propose an effective algorithm of differential privacy streaming data publication under exponential decay mode. Firstly, by introducing the Fenwick tree to divide and reorganize data items in the stream, we achieve a constant time complexity… More >

Displaying 1-10 on page 1 of 5. Per Page