Jemal Abawajy

Work place: Deakin University, Geelong, Australia



Research Interests: Network Architecture, Network Security, Database Management System, Decision Support System, Data Structures and Algorithms


Dr. Jemal H. Abawajy is a full professor at School of Information Technology, Faculty of Science, Engineering and Built Environment, Deakin University, Australia. He is currently the Director of the Parallel and Distributing Computing Tutorial. He is a Senior Member of IEEE Computer Society; IEEE Technical Committee on Scalable Computing (TCSC); IEEE Technical Committee on Dependable Computing and Fault Tolerance and IEEE Communication Society.
He is actively involved in funded research supervising large number of PhD students, postdoctoral, research assistants and visiting scholar in the area of Cloud Computing, Big Data, Network and System Security, Decision Support System, and E-healthcare. He is the author/co–author of five books, more than 250 papers in conferences, book chapters and journals such as IEEE Transactions on Computers and IEEE Transactions on Fuzzy Systems. He also edited 10 conference volumes. More info at

Author Articles
Detection of False Income Level Claims Using Machine Learning

By Anil Kumar K.M Bhargava S Apoorva R Jemal Abawajy

DOI:, Pub. Date: 8 Feb. 2022

Data driven social security fraud detection has been given limited attention in research. Recently, social schemes have seen significant expansion across many developing countries including India. The fundamental aims of social schemes are to alleviate poverty, enhance the quality of life of the most vulnerable and offer greater chances to those relegated to the fringe of society to engage more enthusiastically in the society. Although governments channel billions of dollars every year in support of these social schemes, quite significant number of the eligible people are excluded from the program mainly through fraud and dishonesty. Although fraud is considered an illegal offence and morally reprehensible, it is unfortunate that the prevalence of fraud in social benefit schemes is rampant and a significant challenge to address. In this paper, we studied the viability of machine learning techniques in identifying fraudulent transactions in the context of social schemes. We focus on the detection of the false income level claims made by the fake beneficiaries to get the privileges of government scheme. We used the standard classifiers like Logistic Regression, Decision Trees, Random Forests, Support Vector Machine (SVM), Multi-Layer Perceptron and Naïve Bayes to identify fake beneficiaries of the government scheme from those deserving people. The results show that the Random Forest Classifier perform best providing an accuracy of 99.3% with F1 score of 0.99. The outcome of this research can be used by the government agencies entrusted with the management of the schemes to wade out the abusers and provide the required benefits to the right and deserving recipients.

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