In this study, the implementation of photovoltaic system for the evaluation of the solar energy potential of Turkey is presented in comparative. In the application phase, PV system sizing is performed for a designated area. This area consists of 5 sub-production areas and occupies 180,330 m2. The total nominal installed power of the PV system is 8865 MW. Production and financial performance of the system, provided that the equipment used in the system is the same as the operated personnel; Turkey's socio-economic development has been compared for the cities of Istanbul, Izmir and Ankara. The regions' annual solar radiation data and their respective optimal solar panel angles; The Pvsyst program was accessed using the relevant literature and GEPA data and annual production calculations were made for the specified cities of the system. The results of the production simulations, the area used (parcel) and the equipment were used to perform financial calculations. In the financial analysis part of the study, all costs incurred during the lifetime of the project were calculated. The return on investment and project Payback times were calculated for the 3 alternatives created. For the three cities, comparisons have been made for annual production and financial performance, and the results of the investments have been investigated.

Crime prediction has become a valuable tool for enhancing predictive policing, enabling law enforcement agencies to allocate resources more effectively and implement proactive crime prevention strategies, particularly in high-crime areas. The use of artificial intelligence (AI) has revolutionized this field by analyzing vast amounts of data to identify patterns and anticipate criminal activities with unprecedented accuracy. This paper aims to review the literature on AI-based crime prediction, analyzing 142 studies that focus on crimes against individuals, society, and property. Despite the promising potential of AI in crime prediction, significant challenges remain, particularly regarding the trustworthiness of AI systems, which is essential for their social acceptance. To address these issues, this review explores the explainability of AI-based prediction models, with a specific focus on the role of explainable AI (XAI). The findings highlight the importance of XAI in building trust in these models by offering more transparent and interpretable insights into how AI systems make decisions. However, the review also reveals that the integration of XAI remains underdeveloped in the current literature. By improving the transparency of AI systems, XAI has the potential to lead to more accurate, trustworthy, and fair crime predictions, ultimately facilitating more effective and equitable crime prevention efforts.