Optimizing energy management for household appliances is essential for maximizing domestic energy utilization and enabling preventive maintenance. Recent studies indicate that traditional forecasting approaches frequently lack the necessary accuracy and real-time learning capabilities required for effective management of household energy. This study demonstrates the implementation of a comprehensive strategy that integrates Internet of Things (IoT) data, machine learning (ML), and explainable artificial intelligence (XAI) to improve the accuracy and interpretability of predicting energy usage in residential buildings. Our research focuses on the rising issues faced by IoT-based smart systems, partic- ularly the deficiencies in the performance of current solutions. Therefore, as compared to the other 17 models that were examined, polynomial regression demonstrated outstanding performance. Our solution utilizes a non-intrusive sensor to collect data without disrupting its operation. Real-time data collecting is achieved through a Flask-based web page with Ngrok for external access.The efficacy of the proposed system was assessed using many metrics, yielding highly satisfac- tory results: the root mean square error (RMSE) was 0.03, the mean absolute error (MAE) was 0.02, the mean absolute percentage error (MAPE) was 0.04, and the coefficient of determination (R²) was 0.9989. However, modern cutting-edge methods still face considerable hurdles when it comes to interpretability. In order to tackle these problems, we include XAI techniques such as SHAP and LIME. Explainable Artificial Intelligence (XAI) improves the interpretability of the model by elucidating the impact of various variables on energy consumption forecasts. Not only does this increase the effectiveness of the model, but it also promotes comprehension of the data and enables them to identify the elements that influence home energy usage.

Precision agriculture is revolutionizing the agricultural sector by integrating advanced technologies to enhance productivity and sustainability. In aquaculture, precision agriculture can significantly improve fish farming practices through precise monitoring and data-driven decision-making, addressing challenges such as optimizing resource usage and improving fish health. This paper presents the development and implementation of an IoT-based Fish Recommendation System designed to optimize aquaculture practices through a mobile application. This system uses different sensors for extracting data continuously regarding temperature, PH and Turbidity etc. These parameters can be analysed in real-time to help fish farmers make decisions on when or how much the system should feed and aerate, and what approach of water treatment is best for their fishes. This information is stored to create individual datasets, offering researchers valuable insights into optimal conditions for each fish species. This can enhance their survival rates and promote growth. In this study, we evaluate a series of machine learning algorithms for their ability to predict the optimal fish species based on water quality parameters. Among these algorithms, Random Forest demonstrated superior performance, achieving an accuracy of 92.5%, precision of 93%, recall of 93%, and F1-score of 92%. These findings highlight the effectiveness of our approach in integrating machine learning with IoT for precise aquaculture management. Implemented through a user-friendly mobile application, our system enhances accessibility and usability for fish farmers.

To improve surveillance, the proposed patrolling security system employs autonomous mobile robots outfitted with low-cost night vision cameras. Regular patrols, which are essential for discouraging criminal behavior, are typically conducted by security or law enforcement officers with the use of pricey CCTV equipment. The goal of using autonomous robots is to save expenses while enhancing the quality of patrols in particular regions. Using a night vision camera, the late-night guarding robot detects human movement within its assigned zone while following a random path. Its obstacle-detecting sensors help to prevent crashes and guarantee secure navigation. The robot records incidences, takes pictures with its mounted camera, and carefully scans regions for probable incursions. It then sends the data to the user as quickly as it can. This project's primary goal is to draw attention to suspicious activity in hidden areas. 

Smart Vehicle Accident Prevention System is an innovative solution aimed at enhancing road safety and reducing the occurrence of accidents. Leveraging the Internet of Things (IoT) technology, this system combines real-time data acquisition, analysis, and intelligent decision-making algorithms to provide an effective accident prevention mechanism. The Vehicle Accident Prevention System is a com-prehensive project that aims to enhance road safety by utilizing Arduino microcontrollers and various sensors, including an alcohol sensor, temperature sensor, IR sensor and ultrasonic sensor. This report provides a detailed overview of the system’s design, implementation, and functionality.