SUKAIRAJ HAFIZ IMAM

SUKAIRAJ HAFIZ IMAM

Ph.D (in-view), M.Sc, B.Sc Computer Science

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

The First Corpus for Detecting Fake News in Hausa Language

Published in SPRINGER, 2022

ABSTRACT

The rapid spread of deceptive news especially in Africa has become a global issue in last decade. This triggers the attention of the research community to develop efficient and reliable classification approaches for fake news detection so as to prevent its spread in the community. It has been explored that fake news in regional languages spread with a faster pace as compare to English language in local regions. Hausa is a very common language in Nigeria and some West African countries. So, it opens the challenge to detect the fake news in Hausa language. This paper presents the first corpus for the detection of fake news in Hausa. A dataset has been formed by collecting the labeled real and fake news consists of 2600 articles. In order to classify the fake news in Hausa, six different classifiers have been utilized. The performance of these approaches is then evaluated over different metrics and compared to determine the best model on the Hausa language dataset. The experimental results indicate that support vector machine (SVM) outperformed the other classifiers used by achieving 85% accuracy while AdaBoost happens to emerge as the fair model with 70% accuracy.

Parameter Estimation of Software Reliability Growth Models: A Comparison Between Grey Wolf Optimizer and Improved Grey Wolf Optimizer.

Published in IEEE, 2021

ABSTRACT

In modern era, industries demand for innovative and reliable software solutions. To maintain the reliability level of softwares various software reliability growth models were proposed in last four decades. These models performance relies on parameter estimation approaches utilized to find the optimum values of their unknown model parameters. But, developing an approach that provides the perfect optimum parameter for software reliability growth models (SRGMs) has been the issue of concern within the research community over the decades. This paper adopted the Improved Grey Wolf Optimizer (IGWO) for parameter estimation and compares its accuracy with existing approach Grey Wolf Optimizer (GWO) in estimating the optimum parameters for software reliability growth models. GWO imitates the social leadership pyramid and the hunting methods adopted by grey wolves; IGWO was later proposed to resolve the deficiencies observed in GWO for improved performance. Seven real world failure datasets have been utilized to measure and evaluate the performance of the proposed approach against the existing approach. The results indicate that proposed approach (IGWO) outperform the existing one (GWO).