In today’s world, security has become the most difficult task. With increasing urbanization and the growth of big cities, the crime graph is also on the rise. In order to ensure the security and safety of our home while we are away, we propose the use of Raspberry Pi to implement an IOT-based burglar detection and alert system. IoT involves the improvement of networks to efficiently acquire and inspect statistics from different sensors and actuators, then send the statistics via Wi-Fi connection to a personal smartphone or laptop. The concept of antitheft devices has been around for decades, but most are only CCTVs, IP cameras, or magnetic doorbells. There is a limited amount of work devoted to face recognition and weapon detection. The design of anti-theft protection devices relies primarily on face recognition and remote tracking. Here, our objective is to improve this system by incorporating weapon detection feature by image processing. The system uses Raspberry Pi, in which a person is only permitted access to the house if his/her face is recognized by the proposed system, and if he/she does not carry any weapons. From the standpoint of security, this system is more reliable and efficient. The proposed system is intended to develop a secure access control application based on face recognition along with weapon detection. By using the Telegram app, the proprietor can monitor the digital camera mounted on the door frame. As a means of improving the accuracy and efficiency of our system, we use the Python language and the Open CV library.

The emergence of the sign Language recollection method has a great effect on the day-to-day livings of human beings with hearing disabled individuals utilizing signs to speak with others. Much the same as verbally communicated in dialects, there is no general language as each nation has its communication in language, so every nation has its vernacular of gesture-based communication and in India, they utilize Indian Sign Language (ISL). Over the most recent couple of years, analysts have investigated the computerization of ISL. Here we developed the custom database for 26 English letters and each Letter narrates the 5 times by each person. Train the dataset using 2D CNN and create GUI for recognition. A few endeavors have been made in India and different nations. We attempt to investigate and dissect the ISL that has been made with the mechanization of communication through signing and motion acknowledgment. We attempted to investigate the difficulties that come in the ongoing sign acknowledgment framework. The testing accuracy of the proposed work is 95% and 95% for the validation accuracy.