Mar'22
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The IUP Journal of Information Technology
ISSN: 0973-2896
A 'peer reviewed' journal indexed on Cabell's Directory,
and also distributed by EBSCO and Proquest Database
It is a quarterly journal that publishes research papers on emerging tools, technologies and paradigms relating to various aspects of IT; Programming and Testing tools; Multimedia tools; Image processing and Communication; Neural networks; Big Data, Data Science; Data Analytics; Cognitive Technologies, Artificial Intelligence, Cloud Computing and Edge Computing; E-Commerce; Cyber Security; 5G Technology, Mobile Applications etc.
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Machine Learning-Based Approach for Classifying the Source Code Using Programming Keywords
The implementation phase is one of the most critical periods in software development. Developers build their source code or reuse old source code functionalities concerning the requirement of the system. Most developers spend more time searching and navigating old source codes than developing them. It is essential to have an efficient method to search source code functionality within a short period. Topic modeling of source code is an approach used to extract topics from source codes. Many topic modeling approaches have been implemented using statistical techniques, which have many setbacks. Those results rely on non-formal code elements such as identifier names, comments, etc. Our novel approach is implemented using a machine-learning algorithm to address these issues. The source code functionality results depend only on the algorithm or the syntax of the source code. Three Java project functionalities, such as prime number, Fibonacci number, and selection sort were evaluated in this study. Java parser library is used to derive the source code elements, and an algorithm is created to take the count matrix of the source code features. Then the dataset was fed to three models-Artificial Neural Network (ANN), Random Forest (RF), and Ensemble Approach. It was found that the Ensemble Approach showed a 96.7% accuracy by surpassing ANN and RF.
Factors Leading to Collusion in Crowdsourced Environments
The popularity of crowdsourcing is on the rise as individuals and organizations seek avenues to solve their problems. The advantages that crowdsourced platforms offer range from outsourcing potentials to reduced costs. There are some challenges with the platforms as workers are likely to collude. Some factors that lead to collusion include communication, nearness to one another and complexity of tasks. This paper presents a survey on crowdsourcing, advantages and disadvantages of crowdsourcing, types of malicious attacks and factors that permit collusion in crowdsourced platforms.
Industry 4.0 and Society 5.0: Drivers and Challenges
The Fourth Industrial Revolution, or Industry 4.0, is the future of today's industry while a few countries are working on ways to face the challenges, there are some countries where the awareness associated with it, is still not sufficient for adoption. A country like Japan, which has implemented the Industry 4.0 concept entirely, is now working on the challenges for fifth revolution. Society 5.0 is an inventive concept derived from Industry 4.0. It seems similar but the difference is that the Fourth Industrial Revolution gives more power to the machine for making decisions. On the other hand, Society 5.0 primarily uses the same technologies, but the center of the focus is humankind. It will help people to live prosperously, and consequently ensure a more fruitful society. This paper presents the essential drivers and the challenges for Industry 4.0 and Society 5.0 based on an exhaustive literature review.
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