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This chapter explores the concept of corporate social responsibility (CSR) from an Islamic Shari'ah-compliant perspective. It provides a comprehensive literature review on CSR with an explicit focus on the Islamic perspective of CSR, Islamic models of CSR, CSR practices in conventional and Islamic banks, and the consequences of CSR to Islamic banks. This chapter's main contribution lies in considering the current CSR literature from a Shari'ah perspective. Likewise, it identifies gaps in the current literature and suggests potential areas for future research. This chapter attempts to improve the understanding of how Islamic banks integrate social responsibility into their operations. The insights from this chapter are helpful to practitioners and academic scholars in Islamic finance, accounting, and CSR. This chapter emphasizes the importance of incorporating Islamic values and principles into CSR practices and encourages further research and investigation in this area.

One of the most neglected sources of energy loss is streetlights that generate too much light in areas where it is not required. Energy waste has enormous economic and environmental effects. In addition, due to the conventional manual nature of operation, streetlights are frequently seen being turned ‘ON’ during the day and ‘OFF’ in the evening, which is regrettable even in the twenty-first century. These issues require automated streetlight control in order to be resolved. This study aims to develop a novel streetlight controlling method by combining a smart transport monitoring system powered by computer vision technology with a closed circuit television (CCTV) camera that allows the light-emitting diode (LED) streetlight to automatically light up with the appropriate brightness by detecting the presence of pedestrians or vehicles and dimming the streetlight in their absence using semantic image segmentation from the CCTV video streaming. Consequently, our model distinguishes daylight and nighttime, which made it feasible to automate the process of turning the streetlight ‘ON’ and ‘OFF’ to save energy consumption costs. According to the aforementioned approach, geo-location sensor data could be utilised to make more informed streetlight management decisions. To complete the tasks, we consider training the U-net model with ResNet-34 as its backbone. Validity of the models is guaranteed with the use of assessment matrices. The suggested concept is straightforward, economical, energy-efficient, long-lasting and more resilient than conventional alternatives.