Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Collaborative Filtering for Book Recommendation System(Springer, 2020) Ramakrishnan, G.; Saicharan, V.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.V.Collaborative filtering is one of the most important techniques in the market nowadays. It is prevalent in almost every aspect of the internet, in e-commerce, music, books, social media, advertising, etc., as it greatly grasps the needs of the user and provides a comfortable platform for the user to find what they like without searching. This method has a few drawbacks; one of them being, it is based only on the explicit feedback given by the user in the form of a rating. The real needs of a user are also demonstrated by various implicit indicators such as views, read later lists, etc. This paper proposes and compares various techniques to include implicit feedback into the recommendation system. The paper attempts to assign explicit ratings to users depending on the implicit feedback given by users to specific books using various algorithms and thus, increasing the number of entries available in the table. © 2020, Springer Nature Singapore Pte Ltd.Item Review of techniques for automatic text summarization(Springer, 2020) Prakash, B.S.; Sanjeev, K.V.; Prakash, R.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.V.Summarization refers to the process of reducing the textual components such as words and sentences but conveying most of the information in the input text. Research in summarization is very prominent in the current scenario where the textual data available is enormous and contains valuable information. People have been interested in summarization since time immemorial. The methods adopted in the past relied on manually reading the text and based on one’s understanding of the text, manually generating the summary. In the current world, due to the explosion of data from Internet and social media, the manual process is very tedious and time-consuming. As a result, there is a great need to automate the process of summarization. In this paper, we summarize most of the researches in the field of summarization which is unique and path-breaking. © Springer Nature Singapore Pte Ltd. 2020.Item Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe(Institute of Electrical and Electronics Engineers Inc., 2020) Jain, M.; Singh, S.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.In the present-day scenario, several clothing recommender systems have been developed for the online e-commerce industry. However, when it comes to recommending clothes that a person already possesses, i.e, from their personal wardrobe, there are very few systems that have been proposed to perform the task. In this paper, we tackle the latter issue, and perform experimental analysis of the various Machine Learning techniques that can be used for carrying out the task. Since the recommendations must be made from a user's personal wardrobe, the recommender system doesn't follow a traditional approach. This is explained in detail in the following sections. Further, the paper contains a complete description of the results obtained from the experiments conducted, and the best approach is specified, with appropriate justification for the same. © 2020 IEEE.Item On Feature Models of Home Automation Systems towards Smart Sensing(Institute of Electrical and Electronics Engineers Inc., 2020) Patil, P.; Aparna, R.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.V.The growing need for innovation in software products and an increase in their complexity has demanded an evolution in the software industry in recent years. Domain Analysis assists software developers in understanding the domains leading to significant improvement in their software development. Domain Analysis can be easily carried out using Feature Modelling by identifying the commonalities and variations among attributes of the domain. On the other hand, Home Automation is a developing domain and is now seeing an increase in the number and complexity of products. The heterogeneity of the home automation system elements introduced a lot of complexity to the developers. The commonly observable features and variations arising from HA systems allow us to visualize Home Automation (HA) systems as a Software Product Line (SPL). In this paper, we aim to adopt the Feature Modelling technique for Domain Analysis of the HA system. The efficacy and validity of this approach is evaluated with the help of proof of concepts. © 2020 IEEE.Item A Study on Depth Estimation from Single Image Using Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2022) Shree, R.; Madagaonkar, S.B.; Singh, M.; Chandra, M.T.A.; Rathnamma, M.V.; Venkataramana, V.; Chandrasekaran, K.Depth estimation is fundamental in upcoming technology advancements like scene understanding, robot vision, intelligent driver assistance systems, and many new technologies. Estimating the depth of objects from a viewport can be achieved using various mathematical, geometrical, and stereo concepts, but the process is unaffordable and erroneous. Depth estimation from a single can be accurately done using neural networks. Although this is a challenging task, researchers around the globe have published various works. The works include different neural network standards like CNN, GANs, Encoder-Decoder. The paper analyses and examines famous works in this field of study. Later in the paper, a comparative survey of depth estimation approaches using neural networks is done. © 2022 IEEE.