We evaluate results of the proposed network on the American University in Cairo (AUC) distracted driver detection dataset as well as Statefarm's dataset on Kaggle and compare the performance with state-of-the-art CNN architectures from literature. We propose a new architecture named as mobileVGG based on depthwise separable convolutions. Hence, we focused on developing computationally efficient CNN while maintaining good accuracy. In the development of safety features for Advanced Driver Assistance Systems, the algorithm not only has to be accurate but also efficient in terms of memory and speed. In this paper, we present a Convolutional Neural Network (CNN) based approach for detecting and classifying the driver distraction. As per the survey of National Highway Traffic Safety Administration (NHTSA), distracted driving is a leading factor in road accidents. Previous GAAR, as well as the use of the purpose test in conjunction with theĪmended abnormality test still result in a GAAR regime that may be an ineffectiveĪccording to the World Health Organization (WHO) report, the number of road traffic deaths have been continuously increasing since last few years though the rate of deaths relative to world's population has stabilized in recent years. That the use of similar (often identical) wording of the purpose test as in the Phase 1 to apply the GAAR to these transactions. Of the cases selected for analysis would have been held in favour of theĬommissioner if they were brought to the courts today on the same grounds that they were attacked at the time and the courts used the instrument developed in The findings of the case studies revealed that, on a balance of probabilities, none If similar such conclusions have been made by other commentators to support The final phase of the study consisted of a literature control to determine Regimes failed to stand up to the rigours of court, thus determining whether theĢ006 amendments to GAAR addressed the weaknesses of the previous GAAR Study this framework was applied to case law in which the previous GAAR Literature study to apply the new GAAR to the cases. In executing this process, an instrument was developed in phase 1 of the Unfavourable judgments for the Commissioner would now be considered TheĬurrent GAAR regime was applied to previous cases to determine if the This study applied a qualitative case study approach to determine if theĢ006 amendments to GAAR have in fact addressed these weaknesses. The current GAAR regime has improved upon the weaknesses identified in the The courts, and thus the use of hindsight is not an appropriate tool to determine if However, in theirĬurrent form (post-2006 amendments) the GAAR have not been presented before
Of hindsight to criticise GAAR and recommend improvements. Legislation fails to stand up to the rigours of court, and has thus used the principle The research on GAAR in South Africa has focused on critical analyses once the Most recent of these amendments are those of 19.
Rules (GAAR) have been amended numerous times to address weaknesses. In view of the above, the general anti-avoidance Minimise their tax burdens necessitate amendments in order to remain at theįorefront of taxpayer compliance. To this struggle and the increasingly innovative ways in which taxpayers seek to Taxpayer compliance while combating tax avoidance.
Tax avoidance has been a concern to revenue authorities throughout the ages,Īnd revenue authorities worldwide are engaged in a constant struggle to ensure