Special Issue "Artificial Intelligence and Big Data in Entrepreneurship"

Submission Deadline: 19 January 2021 (closed)
Guest Editors
Dr. Ali Kashif Bashir, Manchester Metropolitan University, UK.
Dr. M. Omair Shafiq, Carleton University, Canada.
Dr. Nawab Muhammad Faseeh Qureshi, Sungkyunkwan University, South Korea.
Dr. Muhammad Shafiq, Guangzhou University, Guangzhou, China.


Entrepreneurship is the process of designing a new business, launching, and running the business. In other words, entrepreneurship is defined as the willingness and capacity of developing new business with any of its risks to reach the profit. The entrepreneurship entirely focuses on launching the industry by analyzing the different risk factors like business start-up due to the lack of investment, economic crisis, wrong business decision, low market demand, etc. During this process, entrepreneurs face several difficulties while recognizing the firm's capital, resources, opportunities, talents for improving business activities. For overcoming this difficulty, Artificial Intelligence (AI) is used. It provides a valuable platform to enhance the entrepreneurship business process because it works according to human characteristics such as discovering, past analysis, generalizing, etc. The AI system performs the business-related task using computer-controlled robots, which is connected with intelligent beings. Moreover, the AI system is programmed to perform the specific task with maximum processing speed, flexibility, and reliable manner compared to the human expert processing criteria. Due to the importance of the AI, the business information is analyzed by using the AI analytic tool.


Although AI gives a useful platform, the entrepreneurship process requires a large volume of information to making the perfect decision. Here, the big data process is utilized because of a variety of information, the quantity of information, veracity, and velocity of information. Moreover, information is collected from different resources with multiple formats that help to analyze the firm's capital, resources, business activities, and talents successfully. The big data collect the business information in various aspects such as user, developer, organizer, etc. that helps to predict the success and failure rate of the business. Based on the AI and big data-based business information analysis, entrepreneurship will be more potent in the future direction. Artificial Intelligence provides different data processing tools for big data techniques with affordability, flexibility, reliability, and scale manner. The AI and Big data tools and techniques are afforded with low also access to the information according to the user demands, which may increase the researcher's interest. Therefore, this special issue focuses on Artificial Intelligence, and big data-based entrepreneurship will serve as the innovative and best platform to discuss and develop a better solution for new beginners. Topics of interest include but are not restricted to:

• Analyzing sustainable entrepreneurship models using AI analytic tools

• Business policies, innovations and business performance towards entrepreneurship

• Big data analytic tool to examine the cooperation between entrepreneurship

• AI and big data for the entrepreneurship ecosystem

• Analyze the entrepreneurship risk factors using Artificial Intelligence

• AI cognitive mechanism in entrepreneurship

• Impact of artificial intelligence on labor in industries

• Analyze minority sectors entrepreneurship economies using Intelligent analytic tools

• AI model for handling entrepreneurship resource decision-making process

Entrepreneurship models, AI analytic tools, big data, cognitive mechanism, decision-making process.

Published Papers
  • Eye Gaze Detection Based on Computational Visual Perception and Facial Landmarks
  • Abstract The pandemic situation in 2020 brought about a ‘digitized new normal’ and created various issues within the current education systems. One of the issues is the monitoring of students during online examination situations. A system to determine the student’s eye gazes during an examination can help to eradicate malpractices. In this work, we track the users’ eye gazes by incorporating twelve facial landmarks around both eyes in conjunction with computer vision and the HAAR classifier. We aim to implement eye gaze detection by considering facial landmarks with two different Convolutional Neural Network (CNN) models, namely the AlexNet model and the… More
  •   Views:46       Downloads:30        Download PDF

  • Recommender System for Configuration Management Process of Entrepreneurial Software Designing Firms
  • Abstract The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide. This problem is addressed by the software development industry by software product line (SPL) practices that employ feature models. However, optimal feature selection based on user requirements is a challenging task. Thus, there is a requirement to resolve the challenges of software development, to increase satisfaction and maintain high product quality, for massive customer needs within limited resources. In this work, we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and… More
  •   Views:311       Downloads:148        Download PDF

  • Social Media and Stock Market Prediction: A Big Data Approach
  • Abstract Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns. The quantity and variety of computer data are growing exponentially for many reasons. For example, retailers are building vast databases of customer sales activity. Organizations are working on logistics financial services, and public social media are sharing a vast quantity of sentiments related to sales price and products. Challenges of big data include volume and variety in both structured and unstructured data. In this paper, we implemented several machine learning models through Spark MLlib using PySpark, which is scalable, fast, easily integrated… More
  •   Views:787       Downloads:294        Download PDF

  • An Efficient Sound and Data Steganography Based Secure Authentication System
  • Abstract The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks. Further, the pin-based security system is an inadequate mechanism for handling such a scenario. The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data. This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound, thereby disregarding the pins’ manual verification. Further, the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches. Firstly, a random signal is encrypted, and then it is transformed into… More
  •   Views:499       Downloads:298        Download PDF

  • An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment
  • Abstract Stock market forecasting is an important research area, especially for better business decision making. Efficient stock predictions continue to be significant for business intelligence. Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices, moving averages, or daily returns. However, major events’ news also contains significant information regarding market drivers. An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market. This research proposes an efficient model for stock market prediction. The current proposed study explores the positive and negative effects of… More
  •   Views:528       Downloads:392        Download PDF

  • SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data
  • Abstract The growing collection of scientific data in various web repositories is referred to as Scientific Big Data, as it fulfills the four “V’s” of Big Data–-volume, variety, velocity, and veracity. This phenomenon has created new opportunities for startups; for instance, the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs. Traditionally, the content of the papers are compared to list the relevant papers from a repository. The conventional method results in a long list of papers that is often impossible to interpret productively. Therefore, the need for… More
  •   Views:355       Downloads:208        Download PDF