Sahana V

Work place: Department of ISE, JSS Academy of Technical Education, Bangalore, Karnataka, India



Research Interests:


Sahana V has completed MTech in 2015 from Department of Computer science and Engineering, RNSIT, Bengaluru under Visveswaraya Technological University.
She is currently working as an Assistant Professor in the Department of Information Science and Engineering at JSSATE, Bengaluru-560060

Author Articles
IOT Based Burglar Detection and Alarming System Using Raspberry Pi

By Sahana V Shashidhar R Bindushree R Chandana A N

DOI:, Pub. Date: 8 Dec. 2023

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.

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A Survey on Hybrid Recommendation Engine for Businesses and Users

By Spurthy Mutturaj Shwetha B Sangeetha P Shivani Beldale Sahana V

DOI:, Pub. Date: 8 Jun. 2021

Various techniques have been used over the years to implement recommendation systems. In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system. To build a recommender system, we require abundant amount of data which comprises of a spectrum of reviews and sentiments from all user domains. Websites like Yelp and TripAdvisor, allow users to post reviews for various businesses, products and services. In this work we have two objectives 1) Recommend restaurants to user based on user reviews in Yelp dataset and 2) Suggest improvements to business based on user reviews. In the first scenario, we will use the comments and ratings available in the Yelp dataset to generate restaurant recommendations and personalize them with user profile data. In the second scenario, we intend to suggest improvements to businesses based on various user reviews and provide them with a ranked list of predefined parameters to help them understand where they stand with respect to their competitors and where they should improve to do better. For both scenarios, we will perform two major steps to achieve our objective 1) Sentiment Analysis and 2) Content Based Recommendation. The first step gives us the - sentiment, quality, subject of discussion relevant to product and in the second step we use the outcomes of first step for personalizing and ranking our results. We came across Gensim and Latent Dirichlet Allocation which seemed to be interesting and was tailored to our requirements. In the yelp dataset, user comments are a mixture of various topics which are extracted by the algorithm (LDA) to provide accurate recommendation for all the users. A prototype of this method provided us with 93% accuracy.

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