Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Therefore, this paper attempts to present a systematic literature review (SLR) that systematically reviews and synthesizes the existing literature on machine learning (ML)-based fraud detection. Particularly, the review employed the Kitchenham approach, which uses well-defined protocols to extract and synthesize the relevant articles; it then report the obtained results. Based on the specified search strategies from popular electronic database libraries, several studies have been gathered. After inclusion/exclusion criteria, 93 articles were chosen, synthesized, and analyzed. The review summarizes popular ML techniques used for fraud detection, the most popular fraud type, and evaluation metrics. The reviewed articles showed that support vector machine (SVM) and artificial neural network (ANN) are popular ML algorithms used for fraud detection, and credit card fraud is the most popular fraud type addressed using ML techniques. The paper finally presents main issues, gaps, and limitations in financial fraud detection areas and suggests possible areas for future research.
Cognitive radio (CR) enables spectrum sharing by allowing new unlicensed services from secondary users to operate in pre-allocated bands in an opportunistic manner. TV white space (TVWS) is a portion of the spectrum in ultra-high frequency (UHF) and very-high frequency bands, which is not utilised by primary users in specific time and location. It is envisaged that more spectrum will be available after digital switchover (DSO) TV transmission replaces the current analogue system. Recently, the regulators in the USA and in the UK have given approval for companies to operate new communication systems with the capability of utilising TVWS spectrum. This paper provides a comprehensive overview of the CR technology and quantifies the TVWS status in Malaysia to expose the spectrum reuse opportunities for future secondary user deployment. Taking the 18 major cities in Malaysia into consideration, we find that all seven TV stations utilise from two to seven of the UHF channels. In our analysis, three usage scenarios were considered, where the utilisation of the 40 UHF channels is full, half and a third. For each case, it was discovered that TVWS was 82.5%, 65% and 46.1%, which can be opportunistically utilised by CR users.
In this paper, the effect of environment and altitude on ultrahigh frequency (UHF) band has been studied. Both theoretical study and experimental investigations are conducted in order to model and characterize such complex communication medium. The experimental study is primarily aimed at critically analyzing the detrimental impact of channel condition on received power in hilly environment under different altitudes. The propagated signal strength varies from one place to another due to time varying channel condition as a result of obstacles between the transmitter and receiver. There is dramatic need to experimentally investigate such scenario in order to provide an avenue through which service providers can strategies their policies for effective wireless communication. Developing such channel models is extremely useful in communication systems design and simulation. The proposed model has been compared with ITU-R model for verification of the develop model for propagation loss prediction in hilly environment. The results obtained are compared with different measurement at various altitudes in hilly terrain environment.
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