Special Issue "Application of Big Data Analytics in the Management of Business"

Submission Deadline: 09 October 2021 (closed)
Guest Editors
Dr. Abdelli Mohammed El Amine, University of Salamanca, Spain.
Prof. Anjali Awasthi, Concordia University, Canada.
Prof. Enric Serradell-Lopez, Open University of Catalonia, Spain.
Prof. Maite Cancelo, University of Santiago de Compostela, Spain.

Summary

Big Data plays an essential factor in the wheel of management of organizations in the world’s business so that Big Data has been receiving significant attention in a variety of research and application fields as business and management over recent years. The volume of data generated by computers, people, software, and networks along with the related difficulty of the scientific community involves creativity in organizational management. In business, the ability to work with various types of data - management techniques will provide highly technical expertise to the organizations and governments that they need to operate effectively. In addition, the increasing interest in big data analysis and applications in the management activities indicates that the digital domain field is likely to soon become a data-intensive one.

This Special Issue will present selected samples of the recent research on the use of advanced methods in management science in the wide field of Big Data Analytics, technologies, models, strategies, and methodologies. It will address common principles and technical approaches to data analysis and computational methodology used in the business field, and potentially create doors for further study and advancement of innovative analytical tools and techniques in Big Data applications.

This special issue aims to motivate researchers in the field of managing data to publish their latest research, up-to-date challenges, and opportunities. Proposed submissions should be original and unpublished.


Keywords
Big Data; Decision making; Theoretical and computational models for data Analytics; Challenges in Business Intelligence; Big data and enterprise risk management; Big Data and Sustainability; E-entrepreneurship; Entrepreneurship and Digitalization; Data business; Data management and analytics in IoT; Data in the Management; Data analytics in CSR & Accounting; Fintech (Financial technology); Digital transformation.

Published Papers
  • Feature Model Configuration Reuse Scheme for Self-Adaptive Systems
  • Abstract Most large-scale systems including self-adaptive systems utilize feature models (FMs) to represent their complex architectures and benefit from the reuse of commonalities and variability information. Self-adaptive systems (SASs) are capable of reconfiguring themselves during the run time to satisfy the scenarios of the requisite contexts. However, reconfiguration of SASs corresponding to each adaptation of the system requires significant computational time and resources. The process of configuration reuse can be a better alternative to some contexts to reduce computational time, effort and error-prone. Nevertheless, systems’ complexity can be reduced while the development process of systems by reusing elements or components. FMs… More
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  • Dual-Port Content Addressable Memory for Cache Memory Applications
  • Abstract Multicore systems oftentimes use multiple levels of cache to bridge the gap between processor and memory speed. This paper presents a new design of a dedicated pipeline cache memory for multicore processors called dual port content addressable memory (DPCAM). In addition, it proposes a new replacement algorithm based on hardware which is called a near-far access replacement algorithm (NFRA) to reduce the cost overhead of the cache controller and improve the cache access latency. The experimental results indicated that the latency for write and read operations are significantly less in comparison with a set-associative cache memory. Moreover, it was shown… More
  •   Views:408       Downloads:315        Download PDF

  • A New Reward System Based on Human Demonstrations for Hard Exploration Games
  • Abstract The main idea of reinforcement learning is evaluating the chosen action depending on the current reward. According to this concept, many algorithms achieved proper performance on classic Atari 2600 games. The main challenge is when the reward is sparse or missing. Such environments are complex exploration environments like Montezuma’s Revenge, Pitfall, and Private Eye games. Approaches built to deal with such challenges were very demanding. This work introduced a different reward system that enables the simple classical algorithm to learn fast and achieve high performance in hard exploration environments. Moreover, we added some simple enhancements to several hyperparameters, such as… More
  •   Views:381       Downloads:366        Download PDF

  • Data Analytics for the Identification of Fake Reviews Using Supervised Learning
  • Abstract Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceives customers, inflicts losses, and tarnishes the reputation of companies. The present paper… More
  •   Views:463       Downloads:347        Download PDF

  • A Fault-Handling Method for the Hamiltonian Cycle in the Hypercube Topology
  • Abstract Many routing protocols, such as distance vector and link-state protocols are used for finding the best paths in a network. To find the path between the source and destination nodes where every node is visited once with no repeats, Hamiltonian and Hypercube routing protocols are often used. Nonetheless, these algorithms are not designed to solve the problem of a node failure, where one or more nodes become faulty. This paper proposes an efficient modified Fault-free Hamiltonian Cycle based on the Hypercube Topology (FHCHT) to perform a connection between nodes when one or more nodes become faulty. FHCHT can be applied… More
  •   Views:671       Downloads:640        Download PDF

  • Developing a Recognition System for Classifying COVID-19 Using a Convolutional Neural Network Algorithm
  • Abstract The COVID-19 pandemic poses an additional serious public health threat due to little or no pre-existing human immunity, and developing a system to identify COVID-19 in its early stages will save millions of lives. This study applied support vector machine (SVM), k-nearest neighbor (K-NN) and deep learning convolutional neural network (CNN) algorithms to classify and detect COVID-19 using chest X-ray radiographs. To test the proposed system, chest X-ray radiographs and CT images were collected from different standard databases, which contained 95 normal images, 140 COVID-19 images and 10 SARS images. Two scenarios were considered to develop a system for predicting… More
  •   Views:895       Downloads:688        Download PDF

  • Thermodynamic Simulation on the Change in Phase for Carburizing Process
  • Abstract The type of technology used to strengthen the surface structure of machine parts, typically by carbon-permeation, has made a great contribution to the mechanical engineering industry because of its outstanding advantages in corrosion resistance and enhanced mechanical and physical properties. Furthermore, carbon permeation is considered as an optimal method of heat treatment through the diffusion of carbon atoms into the surface of alloy steel. This study presented research results on the thermodynamic calculation and simulation of the carbon permeability process. Applying Fick’s law, the paper calculated the distribution of carbon concentration in the alloy steel after it is absorbed from… More
  •   Views:853       Downloads:575        Download PDF

  • Payload Capacity Scheme for Quran Text Watermarking Based on Vowels with Kashida
  • Abstract The most sensitive Arabic text available online is the digital Holy Quran. This sacred Islamic religious book is recited by all Muslims worldwide including non-Arabs as part of their worship needs. Thus, it should be protected from any kind of tampering to keep its invaluable meaning intact. Different characteristics of Arabic letters like the vowels (), Kashida (extended letters), and other symbols in the Holy Quran must be secured from alterations. The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR) and Embedding Ratio (ER).… More
  •   Views:806       Downloads:737        Download PDF

  • Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning
  • Abstract This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector… More
  •   Views:1988       Downloads:779        Download PDF