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.

This Case Study reports the preliminary results of a quantitative survey aimed to identify students' awareness and enthusiasm to learn cybersecurity in Nigerian Universities. The objective of the survey was to see how students in this developing country are aware of cyber-attacks and how they can mitigate the attacks and to find out if cybersecurity awareness program is part of the University program. The preliminary results indicated that the students claimed to have basic cybersecurity knowledge, but are not aware of how to protect their data. It also appears that most Universities do not have an active cybersecurity awareness program to improve students' knowledge on how to protect themselves from any threats. The surveyed students also show interest in learning more about cybersecurity.

Timely knowledge sharing in disaster management (DM) is clearly vital, but it is remains challenging. Roles involved in DM processes often cut across many organizational boundaries and are dynamic. Knowledge involved is enormous and diverse. It includes information related to varieties of disasters, roles descriptions, plans and operations. Alas, practices may also vary across different regions and authorities. This paper makes a crucial contribution to address the knowledge sharing challenge by providing a knowledge based systems approach to facilitate structuring, storing and reusing DM knowledge. The contribution of this paper is three folds: Firstly, it presents a metamodel-based architecture suitable for various distributed knowledge sharing settings; Secondly, it presents an actual implementation of such a system, the Disaster Management Knowledge Repository (DMKR1.0). DMKR facilitates collaboration and DM knowledge sharing using a tailored DM language. This offers a flexible structure to allow the storage and retrieval not only of observed and measured data, but also interpretative and inferred information of the disaster management knowledge. Thirdly, the paper provides disaster management exemplars of how DMKR users can easily instantiate DM models to communicate and to generalize their knowledge for the benefit of sharing it within their community. This presents a compelling evidence of the soundness and the effectiveness of the overall approach to DM knowledge sharing.

Using a face validity approach, this paper provides a validation of the Database Forensic Investigation Metamodel (DBFIM). The DBFIM was developed to solve interoperability, heterogeneity, complexity, and ambiguity in the database forensic investigation (DBFI) field, where several models were identified, collected, and reviewed to develop DBFIM. However, the developed DBFIM lacked the face validity-based approach that could ensure DBFIM’s applicability in the DBFI field. The completeness, usefulness, and logic of the developed DBFIM needed to be validated by experts. Therefore, the objective of this paper is to perform the validation of the developed DBFIM using the qualitative face validity approach. The face validity method is a common way of validating metamodels through subject expert inquiry on the domain application of the metamodel to assess whether the metamodel is reasonable and compatible based on the outcomes. For this purpose, six experts were nominated and selected to validate the developed DBFIM. From the expert review, the developed DBFIM was found to be complete, coherent, logical, scalable, interoperable, and useful for the DBFI field.

Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional–LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin, Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below <10%.