Islamic finance has experienced considerable growth worldwide interms of deposits and assets of Islamic banks over the last 40 years, but showslittle signs of achieving maturity. Almost all Islamic finance products currentlyis use are mere replicas of their conventional counterparts. This study followsthe archival research strategy of Bryman, and analyses the literatureon Islamic finance theory and products and practices in order to ascertainthe reforms needed for the maturity goal. The paper proposes adoption ofa new law regarding money, different policy approaches, (...) new structures offinance for shifting to risk-based financing, new models, procedures and somefresh thought in order to relate banking and finance to the real economy. Itrecommends a leading role for Islamic economists for effective reforms in themonetary and credit system in order to ensure that all financing is based ongoods, services, stocks or securities representing real assets in line withthe Shariʽah principles. The suggested policy measures are expected to leadto social inclusion and harmony in the interests of all stakeholders in businessand finance transactions. (shrink)

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the (...) above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network-based vehicle classification system to improve robustness of vehicle classification in real-time applications. We present a vehicle dataset comprising of 10,000 images categorized into six-common vehicle classes considering adverse illuminous conditions to achieve robustness in real-time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception-v3, VGG, and ResNet are fine-tuned on self-constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine-tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1-score on our self-constructed dataset. (shrink)

Business Ethics, the Environment & Responsibility, EarlyView.

Business Ethics, the Environment & Responsibility, Volume 31, Issue 1, Page 257-271, January 2022.