University of Bahrain
Scientific Journals

Data Stream: Statistics, Challenges, Concept Drift Detector Methods, Applications and Datasets

Show simple item record Al-Khamees, Hussein A. A. Al-A’araji, Nabeel Al-Shamery, Eman S. 2023-04-15T10:09:02Z 2023-04-15T10:09:02Z 2023-04-15
dc.identifier.issn 2210-142X
dc.description.abstract Through real-world applications, the data stream is generated. In contrast to traditional data, data streams have different characteristics; a huge and endless amount, on-line arriving with high speed, single processing as well as the nature of being not static, in the sense that it evolves over time; this is the concept drift. Accordingly, the mining and analysis of the data stream is an arduous and attractive task. Various frameworks for data stream (analysis, mining, etc.) have been proposed over the past years. In the same context, identifying the number of classes of data streams is an important initial step when designing a model for processing the data stream. At present, deep neural networks (DNNs) are a fundamental technique in various applications. DNNs have many structures, including the multilayer perceptron (MLP). In this paper, we propose a deep neural network (DNN) model based on multilayer perceptron (MLP) to classify the streaming datasets and detect their classes as an analysis step for this data type. The proposed model tests different synthetic and real-world stream datasets. The results proved that this model detects the actual number of classes for the given stream dataset. Moreover, this paper presents a systematic review of data stream, its statistics, challenges, concept drift detector methods, data stream applications in different sectors in addition to the streaming datasets. en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Data Stream, Data Stream Challenges, Concept Drift Detector Methods, Streaming Datasets, Deep Neural Network, Multilayer Perceptron. en_US
dc.title Data Stream: Statistics, Challenges, Concept Drift Detector Methods, Applications and Datasets en_US
dc.type Article en_US
dc.volume 13 en_US
dc.issue 1 en_US
dc.pagestart 717 en_US
dc.pageend 728 en_US
dc.contributor.authoraffiliation Software department, Information Technology College, Babylon University, Babil, IRAQ en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

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