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The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digital landscape of surveillance and supply chain logistics, especially in terrains where such was previously deemed unattainable. Moreover, the adoption of drones has further led to the proliferation of diverse drone types and drone-related criminality, which has introduced a myriad of security and forensics-related concerns. As a step towards understanding the state-of-the-art research into these challenges and potential approaches to mitigation, this study provides a detailed review of existing digital forensic models using the Design Science Research method. The outcome of this study generated in-depth knowledge of the research challenges and opportunities through which an effective investigation can be carried out on drone-related incidents. Furthermore, a potential generic investigation model has been proposed. The findings presented in this study are essentially relevant to forensic researchers and practitioners towards a guided methodology for drone-related event investigation. Ultimately, it is important to mention that this study presents a background for the development of international standardization for drone forensics.
Arafat Al-Dhaqm; Richard Ikuesan; Victor Kebande; Shukor Razak; Fahad Ghabban. Research Challenges and Opportunities in Drone Forensics Models. Electronics 2021, 10, 1519 .
AMA StyleArafat Al-Dhaqm, Richard Ikuesan, Victor Kebande, Shukor Razak, Fahad Ghabban. Research Challenges and Opportunities in Drone Forensics Models. Electronics. 2021; 10 (13):1519.
Chicago/Turabian StyleArafat Al-Dhaqm; Richard Ikuesan; Victor Kebande; Shukor Razak; Fahad Ghabban. 2021. "Research Challenges and Opportunities in Drone Forensics Models." Electronics 10, no. 13: 1519.
This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.
Mehrbakhsh Nilashi; Shahla Asadi; Rabab Abumalloh; Sarminah Samad; Fahad Ghabban; Eko Supriyanto; Reem Osman. Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART). Sustainability 2021, 13, 3870 .
AMA StyleMehrbakhsh Nilashi, Shahla Asadi, Rabab Abumalloh, Sarminah Samad, Fahad Ghabban, Eko Supriyanto, Reem Osman. Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART). Sustainability. 2021; 13 (7):3870.
Chicago/Turabian StyleMehrbakhsh Nilashi; Shahla Asadi; Rabab Abumalloh; Sarminah Samad; Fahad Ghabban; Eko Supriyanto; Reem Osman. 2021. "Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)." Sustainability 13, no. 7: 3870.