Music Genre classification on Neural Network is presented in this article. The research work uses spectrogram images generated from the songs timeslices and given as input to NN to do classification of songs to their respective musical genre. The research work focuses on analyzing the parameters of the model. Using two different datasets and implementing NN technique we have achieved an optimized result. The Convolutional Neural Network model presented in this article classifies 10 classes of Music Genres with the improved accuracy.

Virtual Try-on for Clothes using Deep Neural Networks has been an active area of research in recent years. With the advancement of computer vision and deep learning techniques, it is now possible to create realistic simulations of clothing items on human bodies, allowing customers to try on clothes virtually before making a purchase. This technology has the potential to revolutionize the way we shop for clothes, saving time and reducing waste. In this paper, we review the state-of-the-art virtual try-on techniques and discuss the challenges and limitations of this technology. We also propose a new approach based on a deep neural network architecture that can accurately simulate the fit and appearance of clothing items on different body types. Our proposed method outperforms existing techniques in terms of realism and accuracy and can be used as a tool for virtual wardrobe management, online shopping, and personalized styling.

GPS-based navigation systems have become an integral part of our day-to-day lives. People frequently use these systems to find their way around due to their usability and ease of use. Google Maps is currently the most widely used navigation system in the world. Alongside providing routes and navigation, it also provides information about traffic, weather conditions, and even feedback and reviews of the places you are visiting. But none of the existing navigation systems advises you about the underlying road conditions. Although they suggest the shortest route, it may not be the best route. Many times, you even end up reaching a dead end as a result of relying on these systems. In the midst of all this, a navigation system that can suggest routes not only based on the shortest distance but also based on underlying road conditions with real-time feedback has become a necessity. The goal of this paper is to efficiently search for the most accessible alternative paths in a multi-route navigation system

Nanotechnology has enabled sensors to detect and sense a very small amount of chemical vapors. Sensors play a major role in our daily life. The use of sensors has made human life easy. One such type of sensor is the Gas sensor made up of Semiconducting metal oxides. These sensors have their own unique features which help in the easy monitoring of toxic gases. Out of all the metal oxide present, the gas sensors made up of ZnO nanostructures are mostly used in the gas sensing industry. ZnO has become a research hotspot of gas-sensing material because of the variation in resistance observed on the surface. These resistance changes are observed due to the adsorption & desorption of gases. In this review, we will be discussing the ZnO nanostructures, their preparation and their applications in the sensing of various toxic and flammable gases.