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Trends in Extreme Learning Machine and IOT
Submission DeadlineMay 30, 2020

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Lead Guest Editor
Department of Information Technology, ABES Institute of Technology, Ghaziabad, India
Guest Editors
  • Rizwan Khan
    Department of Computer Science and Engineering, ABES Institute of Technology, Ghaziabad, India
  • Vikash Chaudhary
    Department of Computer Science and Engineering, Jims Department of Engineering Management Technical Campus, Greater Noida, India
  • Shivani Joshi
    Department of Computer Science and Engineering, G.L. Bajaj Institute of Technology & Management, Greater Noida, India
  • Rajnesh Singh
    Department of Computer Science and Engineering, IECCET, Greater Noida, India
  • Abhishek Pandey
    Department of Computer Science and Engineering, Chitkara University, Chandigarh, India
Introduction
The Internet Traffic is predicted to grow by 62% with more than $85 billion market value by 2025. The traffic generates raw data by various sources like-Multimedia streaming applications (Netflix/Amazon etc.), broadcasting channels (IPTV/HDTV), Internet-of-Things (IoT) devices (sensors and actuators), social networks applications (Facebook/Twitter etc.), cloud platforms (Dropbox/Google cloud etc.), and many more. The generated raw data is then stored in centralized servers like cloud or distributed servers at various remote locations over a network. The stored data is mainly unstructured, unformatted, and heterogeneous in nature. Big Data Analytics (BDA) is an emerging field which derives useful and meaningful patterns out of the bulk unstructured data. Although, to achieve this, the data needs to be cleaned and pre-processed before proper analytics can are applied. BDA draws possible conclusions that drives business logics and maximize profits of an organization. Thus, BDA meets the requirements of Industry 4.0- automation, control, security, and faster processing of data among industry stakeholders like- retailers, producers, advertisers, vendors and end-users. The management of big-data is a growing research field which raises concerns regarding data collection through various sources, data preprocessing, analytics, communication network, and security. Modern day research is mainly focused on examining the aforementioned management challenges and issues in BDA. The book aims to bridge the gap between possible research solutions and key technologies related to data analytics to ensure Industry 4.0 requirements and, at the same time ensure proper network communication and security of big data. Researchers have examined the challenges associated with big data and it’s convergence with Machine Learning (ML), Deep Learning (DL) solutions, Social Network Analysis (SNA), and IoT applications in diverse fields. For robust communication infrastructures, researchers examined challenges in network design, 5G technologies, access technologies etc. To address the security concerns, possible cryptographic solutions are presented in cloud as micro services, security protocols, decentralized trust via blockchain, and many more. Thus, we have toxonomized our book literature into three parts. Part one focusses on possible challenges and convergence of BDA with IoT, ML, and DL techniques to preprocess, clean and analyze the data based on standard datasets. Part two of the book focusses on the convergence of BDA with networking infrastructures and technologies to minimize latency and improve Quality-of-Service (QoS) for end-user applications. Part three of the book focusses on the security aspects and issues related to BDA. We identified the opportunities that are presented in convergence of big data with each domain and discuss the importance and possible impacts of such a convergence. Finally, the book presents several open challenges and future research directions.
Aims and Scope:
  1. Big Data Analytics
  2. Supervised Learning
  3. Machine Learning
  4. Deep Learning
  5. Drug Research Using Pharmacocybernetics
  6. 5G Communication and Resource Management
  7. Cloud Security
  8. Micro Services
  9. Distributed Computing
  10. Blockchain
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