Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose an air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino IDE (Integrated Development Environment), and a Wi-Fi module.

The increasing adoption of electric vehicles (EVs) presents significant challenges to power grid management, particularly in balancing demand and supply while ensuring energy efficiency. This project explores a smart Power Allocation and EV Charging System designed to optimize energy distribution, enhance grid stability, and reduce charging costs. The proposed system integrates advanced algorithms for demand response, renewable energy utilization, and predictive analytics to dynamically allocate power to EV charging stations based on real-time data.

Financial planning in the modern, dynamic business world is highly important in strategic decision-making. Conventional financial forecasting tools, combined with ERP systems, have characteristic difficulties in coping with the nonlinear, time-dependent, and unstructured nature of financial data. Deep learning (DL) is a potential solution for businesses, as forecasting tools that have high intelligence and accuracy become more in demand in business. The present paper suggests a hybrid CNN-LSTM model to improve the financial prediction capabilities of an ERP-based business intelligence system.