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Special Issue on Behaviour Monitoring and Management of Customers, People and Organizations using Deep Learning

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In recent times, it has become increasingly important to identify various aspects of customer behaviour. A reason for people behaviour monitoring is to manage a greater amount of heterogeneous people, with regards to ethnicity, gender and race, working in different environments. For example, customer behaviour monitoring monitors success and failures of online money transfer, customer reviews and experience of a product and people activity in a particular website. Employee behaviour monitoring empowers business managers to track the daily activities of employees. It allows them to understand the information about the employees engaged with work and workplace to improve productivity, attendance and security verification. It is not only monitoring from the business’ point of view, but it measures productivity, resource usage and real experience for employees. The human behaviour monitoring has been implemented using Machine learning algorithms, which consumes more time for feature extraction and classification. But, deep learning extracts and classifies the features automatically and speedily. The application of deep learning techniques range across understanding customer behaviour across domains, analysis on the pattern of transactions, enhanced security based on gesture and video analysis, several types of interactions across different social platforms. Thus, it is imperative to underline the significant footprint of deep learning across building high end artificially intelligent systems for information management.

Deep learning is one of the vital branches of artificial intelligence that provides a computer system the intelligence to classify, predict and make decisions by analyzing the data pertaining to the application where it is deployed.  Deep learning is a special type of machine learning approach used for complex data which can extract the features automatically on large volumes of data.  Compared with the machine learning approaches, deep learning approaches can obtain composite relationship among raw data and analyzing the data according to the context and converting the raw data into useful information, thus paving the way for effective information management. Deep learning, in the recent past, has etched its presence and spread its wings across analyzing digital data across various domains such as medicine, health care, retail, sports, banking, marketing and finance, etc. The voluminous data available is analysed and the essential features are used to extract exciting, essential and knowledgeable patterns that help in enhancing the development of the existing systems.

This special issue on behaviour monitoring using deep learning provides a platform for effective exchange of current and trending ideas towards application of deep learning algorithms to study the behaviour and people and, in turn, convert the data into insightful information. Implementing deep learning can solve the organisational issues and increase the outcomes.

Topics of interest include but are not restricted to:

  • Deep learning to predict customer behaviour routing for enlightening business upshots
  • Deep learning to predict customer experience transformation in future
  • Deep learning for analyzing customer profile big data
  • Workforce optimization using deep learning for improving customer experience
  • Optimizing end user connection and agents using cloud services and HCI
  • Deep learning for active user prediction as better suitable to business
  • Deep learning for user prediction by analysing the Cohort behaviour of individual and group of cohort users
  • Deep learning for people abnormal activity detection and classification
  • Real time behavioral monitoring system using deep learning
  • Deep learning based threat detection by analyzing customer behavior

Important dates

  • Paper Submission Deadline: Aug 1, 2019
  • Author notification: Oct 1, 2019
  • Revised papers submission: Nov 20, 2019
  • Final Acceptance: Jan 10, 2020

Guest Editors:

Dr.-Ing. Heiko Hamann
Professor for Service Robotics, University of Lübeck,
Institute of Computer Engineering, Germany
E-mail: hamann@iti.uni-luebeck.de
Official Web: https://www.iti.uni-luebeck.de/mitarbeiter/prof-dr-ing-heiko-hamann.html
Personal Web: http://heikohamann.de/
Publications: https://scholar.google.com/citations?user=HUoutmwAAAAJ&hl=en

 

Dr.-Ing. Mladen Berekovic
Director, University of Lübeck,
Institute of Computer Engineering, Germany
Email: berekovic@univ-luebeck.de
Official Web: https://www.iti.uni-luebeck.de/mitarbeiter/berekovic.html
Publications: https://scholar.google.de/citations?user=70NvAoYAAAAJ&hl=de

Dr. Yancheng Ji
Associate Professor                                                         
School of Electronics and Information, Nantong University                     
Nantong, Jiangsu 226019 China
Email: jiyancheng@ntu.edu.cn
Publications: https://www.researchgate.net/scientific-contributions/2023656910_Yancheng_Ji