Browsing by Author "Dhar, Pranab Kumar"

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    A Comprehensive Review on Big Data for Industries
    (IEEE, 2022-12-26) Sarker, Supriya; Arefin, Mohammad Shamsul; Kowsher, Md.; Bhuiyan, Touhid; Kwon, Oh-Jin; Dhar, Pranab Kumar
    Technological advancements in large industries like power, minerals, and manufacturing are generating massive data every second. Big data techniques have opened up numerous opportunities to utilize massive datasets in several effective ways to improve the efficacy of related industries. This paper presents a review of big data technologies used in the power, mineral, and manufacturing industries for various purposes. We analyze the meta-data of the collected papers before reviewing and selecting papers by applying selection criteria and paper quality assessment strategy. Then we propose a taxonomy of big data application areas in the power, mineral, and manufacturing industries. We have studied current big data architectures and techniques implemented in industry sectors and have uncovered the big data research gaps in industry sectors. To address the gaps, we point out some relevant research questions and, to answer the questions, we make some future research recommendations that might explore interesting research ideas for building a big data-driven industry. As the careful use of big data benefits every other industry sector; hence, supportive big data frameworks need to be developed to speed up the big data analysis process. Proper multi-dimensional big data assessment is also needed to take into account for serving effective data analysis tasks. Industry automation is also heavily influenced by the proper utilization of big data. While an intelligent agent can make many processes and heavy production loads in the manufacturing industry, it can work in a risky environment such as mines efficiently. To train agents for working in a specific environment big data can be used.
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    A Comprehensive Survey on Affinity Analysis, Bibliomining, and Technology Mining
    (Daffodil International University, 2022-05-21) Rahman, Md. Rashadur; Arefin, Mohammad Shamsul; Rahman, Sanjida; Ahmed, Afsana; Islam, Tahsina; Dhar, Pranab Kumar; Kwon, Oh-Jin
    Recent advancements in high-speed communications and high-capacity computing systems have contributed to major growth in the data volume of databases. Data mining is a crucial part of information retrieval; it is often termed as database knowledge discovery. It consists of techniques for examining massive data sets, to find hidden (but possibly important) information. Three interesting fields in data mining are affinity analysis, bibliomining, and technology mining. Affinity analysis provides data mining techniques to determine the similarity among objects; bibliomining is a combination of data mining, bibliometrics, and data warehousing; technology mining is a research topic that is an obstacle to many scientists in the fields of time association, enterprise association, and computer programming. We present a systematic review of the notable research articles in the fields of affinity analysis, bibliomining, and technology mining published between 2000 and December 2021. We provide a systematic analysis of the selected literature by specifying the major contributions, used data sets, performance evaluations, and limitations. Our findings demonstrate that affinity analysis interoperability extends well beyond market basket analysis. We also demonstrate that, in the age of big data, the personalized needs of users are the driving forces behind the evolution of the digital library from a resource-sharing service to a user-centered service. Finally, this article provides insight into major advances and outstanding challenges in the fields of affinity analysis, bibliomining, and technology mining.
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    Moving Object Detection against Sudden Illumination Change Using Improved Background Modeling
    (Faculty of Electrical and Computer Engineering, CUET, 7-Feb-2019) Mohajan, Gowrab; Dhar, Pranab Kumar; Ahmed, Mohammed Toufiq; Shimamura, Tetsuya
    This paper presents a moving object detection method
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    Zebra-Crossing Detection and Recognition Based on Flood Fill Operation and Uniform Local Binary Pattern
    (Faculty of Electrical and Computer Engineering, CUET, 7-Feb-2019) Meem, Mahinul Islam; Dhar, Pranab Kumar; Khaliluzzaman, Md.; Shimamura, Tetsuya
    Zebra-crossing region detection from a zebracrossing