2. Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/1/7
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Item VoteChain: A Blockchain Based E-Voting System(2019) Pandey A.; Bhasi M.; Chandrasekaran K.In the past, electronic voting systems have not seen widespread adoption due to data privacy concerns. Previously proposed e-voting systems make use of a central database to store data, resulting in the servers used to store these databases being a single point of failure. These systems have also been found to be vulnerable to DoS attacks, leading to concerns over their reliability.Blockchains have been used to build secure and scalable distributed systems which have shown several benefits over centralized systems. They have seen uses in sectors ranging from finance and healthcare to food and energy.In this paper, we present VoteChain, a blockchain based voting system to help bring transparency and security to polls. We report on our implementation of VoteChain, as well as the results obtained in testing the system in a real-world poll which prove that such a system can be used in practice for large-scale elections. © 2019 IEEE.Item Towards a Domain Specific Modeling Tool for Home Automation Systems(2020) Patil P.; Aparna R.; Chandrasekaran K.There is a steadily growing market for applications enabling home automation system connectivity to control various applications and home appliances for managing and security purposes. Development of Home Automation (HA) systems involves a lot of complexity resulting from the heterogeneous nature of the system and its need to be able to provide data security and authenticity. The software development life cycle can be shortened if there exists a solution that enables a suitable transformation from design to crude implementation. There are less known integrated framework for HA system development from model to implementation. This solution can be obtained through Model-driven Development. This paper aims to propose a Model-driven Development based solution which models the Home automation domain using meta-modeling technique and a platform-independent Domain-specific Modelling Language (DSML) which can be used for developing platform dependant DSML to achieve automated code generation using a model. The DSML proposed has been validated with the help of a proof of concept, using case studies. This approach can shorten the development duration and more emphasis can be given for the better design of the system. © 2020 IEEE.Item Singlow: Simulator for General Network Flow Problems(2020) Raghavan S.; Bhagtya P.; Chandrasekaran K.Simulation is an important process and an inevitable part of engineering. There are several applications of simulation with one of the important being visualization of complex methods and processes. This paper aims at creating a simulator for general network flow optimization problems. This work uses a modular approach for creating a simulator. A simulator in this area is necessary for several reasons. The main reason for requirement is its usefulness in explaining the problems to the people/students who might use these kinds of network optimization methods to solve several variety of problems. This simulator can simulate standard problems namely transportation problems with various methods, network flow problem and some popular problems in that area. This simulator will be helpful for educational institutions to teach the students about the standard problems on network flow optimization. Here this paper proposes a framework i.e. Singlow for the above mentioned purpose. This paper explains the framework with the flow of execution by keeping in mind a general simulation software. The Simulator has been designed and implemented using Processing 3.4, a software which facilitates designing graphical user interfaces. © 2020 IEEE.Item Review of techniques for automatic text summarization(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 Optimized diet plan using unbounded knapsack Algorithm(2020) Bobade P.; Kumar P.; Chandrasekaran K.; Usha D.Cholesterol, hypertension and diabetes are the three major chronic diseases from which most of the people suffers and these peoples often use search engines to acquire related information about these problems. But, almost every information related to diet on the internet isn't suitable for people to gather information about the diet suggestions. A system for diet suggestion which can advocate a prudent diet for such peoples is suggested in this paper. We designed a system that recommends a proper diet which has the adequate knowledge of three above mentioned highly chronic diseases. We propose a solution to the menu recommending problem using the optimization algorithm known as unbounded knapsack. We designed a model which satisfies the nutritional requirements of individuals while imposing the 'Laws of Nutrition', a set of hypothesis used by almost all Latin America's nutrition scientists. This prototype corresponds to a numerical optimization problem with constraints. We design a menu items generator application model to set up a convenient menu for a user with different properties. © 2020 IEEE.Item Offline Character recognition on Segmented Handwritten Kannada Characters(2019) Joe K.G.; Savit M.; Chandrasekaran K.Optical character recognition (OCR) is the conversion of pictures of typed or handwritten characters into machine encoded characters. We chose to work on a subfield of OCR, namely offline learning of handwritten characters. Kannada script is agglutinative, where simple shapes are concatenated horizontally to form words. This paper presents a comparative study between different machine learning and deep learning models on Kannada characters. A Convolutional Neural Network (CNN) was chosen to show that handcrafted features are not required for recognizing classes to which characters belong to. The CNN beats the accuracy score of previous models by 5%. © 2019 IEEE.Item On Feature Models of Home Automation Systems towards Smart Sensing(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 Middleware Frameworks for Mobile Cloud Computing, Internet of Things and Cloud of Things: A Review(2020) Debbarma T.; Chandrasekaran K.Mobile cloud computing (MCC) is an extension of cloud computing (CC) technologies. It provides seamless access of different cloud services to smart mobile devices (SMDs). There is no denying that CC can be scaled to a great extent in terms of computing, storage and other services, but the SMDs used for accessing those services are limited on battery capacity, storage and computing power due to their small form factors. The limitations of SMDs can be minimised/resolved by using MCC platforms. Though MCC is advantageous in many ways, it has its own inherent challenges and issues due to the heterogeneous hardware and software platforms used by SMDs and CC platforms, which makes it difficult to have interoperable services and the development of applications for those devices. This paper studied recently (from 2012 onwards) proposed/developed middlewares for the Internet of things (IoT), cloud of things (CoT), context-aware middlewares (CaMs) and mobile cloud middlewares (MCMs). Different middleware architectures are chosen, as in many cases, these technologies converge in terms of features, functions and services they provide. The study finds that the present middlewares lack in providing an integrated solution that complies with interoperability, portability, adaptability, context awareness, security and privacy, service discovery, fault tolerance requirements. At the end of the paper, the challenges pertaining to achieve portability, interoperability, context awareness, security are discussed and identify the gaps in the existing approaches in MCC interoperability and context adaptability. © 2020, Springer Nature Singapore Pte Ltd.Item Machine Learning Models with Optimization for Clothing Recommendation from Personal Wardrobe(2020) Jain M.; Singh S.; Chandrasekaran K.; Rathnamma M.V.; Venkata 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 Improving Job Recommendation Using Ontological Modeling and User Profiles(2019) Rimitha S.R.; Abburu V.; Kiranmai A.; Marimuthu C.; Chandrasekaran K.The recommendation system uses prior obtained information about the user to present user inteseted data. Personalized results aim to provide relevant information to the user based on the user's basic information or activity with the system. The user's basic information can be modeled into a user profile using ontology. Ontology is the systematic representation of various entities in a domain and the relationships between them. In this paper, we aim to present the conceptual model for a job recommendation system that uses ontology-based user profiles. The system collects basic information and models into a user profile. The dynamic aspects such as favorite jobs list and recently viewed jobs are then used as a source of data for the system. The recommendation algorithm works on the input given to present the list of relevant jobs to the user. © 2019 IEEE.