Animal conservation is imperative, and a lot of technology has been used in different ways. The endangered species like tiger and elephant has raised the need for such efforts. Human-Elephant Collision (HEC) has been an active area of research but still, the optimum solution is not found. As trains are widely used transportation medium in Asian countries, the rail track is even laid down through forest areas and hence intervene the wildlife. Elephants due to their bulky size often become victims of trains. Such tragedy is common especially in green belts in southern zones of India. To rectify the problem, we have proposed a deep vision-based model to identify the elephant near-site using implanted video cameras. Four different models are proposed for the identification of elephants in image/video. One novel lightweight CNN based model is proposed. Three Transfer Learning (TL) models, i.e., ResNet50, MobileNet, Inception V3 have been experimented and tuned for elephant detection. These highly accurate and precise models can alarm the trains hence it can save a precious life.

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Background: The outlook and the aura of any place are highly dependent on how a place is decorated and what materials are used in designing it. Granite is such a kind of rock which is vastly used for this purpose. Granite flooring and counters have a major influence on the interior d ́ecor which is essential to set the mood and ambience of a house. A system is needed to help the end users differentiate between granites, which enhance the grandeur of their house and also check the frauds of different color granite being sent by the merchant as compared to what was selected by the end user. Several models have been developed for this cause using CNN and other image processing techniques. However, a solution for this purpose must be precise and computationally efficient. Methods: For this purpose,researchers in this work developed a machine learning based granite classifier using Edge Computing and a website to help users in choosing which granite would go well with their d ́ecor is also built. The developed system consists of a color sensor [TCS3200] integrated with an ESP8266 board. The data pertaining to RGB contrasts of different rocks is acquired by using the color sensor from a dealership.This data is used to train a Machine Learning algorithm to classify the rock into different granite types from a granite dealer and yield the category prediction. Results: The proposed system yields a result of 94% accuracy when classified using Random Forest Algorithm. Conclusion: Thus, this system provides an upper hand for the end users in differentiating between different types of granites.

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