An emerging topic that has the potential to enhance user experience, reduce crime, and target advertising is human emotion recognition, utilizing DeepFace and Artificial Intelligence (AI). The same feeling may be expressed differently by many individuals. Accurately identifying emotions can be challenging, in light of this. It helps to understand an emotion’s significance by looking at the context in which it is presented. Depending on the application, one must decide which AI technology to employ for detecting human emotions. Because of things like lighting and occlusion, using it in real-world situations can be difficult. Not every human emotion can be accurately detected by technology. Human–machine interaction technology is becoming more popular, and machines must comprehend human movements and expressions. When a machine recognizes human emotions, it gains a greater understanding of human behavior and increases the effectiveness of work. Text, audio, linguistic, and facial movements may all convey emotions. Facial expressions are important in determining a person’s emotions. There has been little research undertaken on the topic of real-time emotion identification, utilizing face photos and emotions. Using an Artificial Intelligence-based DeepFace approach, the proposed method recognizes real-time feelings from facial images and live emotions of persons. The proposed module extracts the facial features from an active shape DeepFace model by identifying 26 facial points to recognize human emotions. This approach recognizes the emotions of frustration, dissatisfaction, happiness, neutrality, and wonder. The proposed technology is unique, in that it implements emotion identification in real-time, with an average accuracy of 94% acquired from actual human emotions.

The regular and continuous request for organic products in the market has increased the need for increase in organic farming. Many of the organic farmers are also small scale and most organic farming can be grown at a small level in homes through Home gardening. The pest infestation in the plants can be prevented and avoided with the usage of organic pesticides. Management of pests in home gardening is an important task. Organic pesticides are eco-friendly and can be applied easily with minimum cost. Organic pesticides can be either homemade or made naturally. The pesticide mixture is a combination of neem, garlic, pepper, milk, and salt solution and all the solutions are kept in separate containers. The management of pesticide includes the accurate amount of pesticide and the time of applying the pesticide and if done automatically, this management system reduces a lot of human effort. An internet of things (IoT) based module for pesticide management automatically controls the valves of the organic pesticide containers. All the data such as environmental conditions, infection in plants at particular seasons and their specific pesticide mixture are to be stored in a database. Prevention of the infections caused by the pathogens and other entities can be effectively done by automatic pesticide dispensing system. This paper analyses the existing systems for organic farming and proposes a framework using an android based application with an in-built mobile module for providing automatic pesticide dispensing.

Unmanned Aerial Vehicles (UAVs) are enabled to be fast and flexible in managing traffic compared to the conventional methods. However, in emergencies, this system takes more time to identify and clear the traffic because of fixed time control. To overcome this problem, an automated intelligent traffic monitoring and controlling system is designed using YOLO V3 neural architecture and implemented to detect the emergency vehicles from video stream data from UAVs using deep Convolution Neural Network (CNN) along with rerouting algorithm to provide the safest alternate route from current position to destination, in a heavy traffic environment. The real-time visual data collected through UAV video cameras are trained using machine learning algorithms to produce statistical profiles that are used continuously as updated inputs to the existing traffic simulation models for improving predictions. The proposed automated system performs exemplary in recognizing emergency vehicles and diverting them to an alternate route for quick transportation in various scenarios.

Nowadays, there is a tremendous increase in the use of online secure payment systems. Even roadside shops all over India and other countries have access to online payment which made each and every person to go cash-free. But there is one important issue we need to consider which is the security during online transactions. There are several new techniques that are being developed nowadays but each has its own pros and cons. In order to enhance the security during online transaction, we have introduced a two-way authentication system which increases the security during online transaction. This system includes facial and proxy detection before entering the UPI pin. The algorithm used in this paper, will further improve the security and efficiency of facial recognition since it embeds 128 feature points of the user’s face thereby increasing the accuracy of the system. While the data is being transmitted for verification, we have included tripleDES encryption to further enhance the security of the work. After the verification is done by the system, the bank balance of the user is verified and the transaction is done. The proposed system has better efficiency when compared to the existing works and also its simple UI makes it easy to use.