Conference Papers
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Item Capturing important information from an audio conversation(Institute of Electrical and Electronics Engineers Inc., 2017) Roshan, S.; Vinay Kumar, S.; Kumar, M.A conversation between people can involve exchange of important information. Often in an ongoing conversation, someone has to manually keep on taking notes of important points. This is a tedious process and can involve errors. In today's era of technology, there is a need for an automated process for extracting important information from a conversation, with minimal manual efforts. In this paper, we propose methods of transcribing a conversation between two people. We will consider two major modes of conversation-when the conversation is happening over a phone call, and when the conversation is happening in person. In addition to transcribing a conversation, this paper will also suggest ways to extract important parts of a conversation. We will extract important information from a conversation, using three different approaches-noun phrase extraction, named entity extraction and open information extraction method. Since current mobile operating systems provide limited support for transcribing a phone call, we will suggest ways of transcribing a call, and extracting important information from it. © 2017 IEEE.Item Project spear: Reporting human trafficking using crowdsourcing(Institute of Electrical and Electronics Engineers Inc., 2017) Roshan, S.; Vinay Kumar, S.; Kumar, M.Human trafficking is a severe crime which is prevailing in the society. It is hard to track and report incidents of human trafficking to the concerned authorities, due to the complex nature of this crime. This paper introduces a crowdsourcing approach to report incidents of human trafficking, using a mobile application. Crowdsourcing is the practice of obtaining information from a large number of people. Information about human trafficking incidents reported using this mobile application is forwarded to the concerned authorities of the country where the crime has been reported. A user can also upload crime scene photos and provide details of the crime location using Global Positioning System. The mobile application introduced in this paper currently focuses on top ten countries which have the highest rates of human trafficking in the world, while very basic support for the rest of the countries. Since the process of reporting human trafficking incidents to the authorities can be complex, time consuming and dangerous, the mobile application allows users to stay anonymous. The aim of this mobile application is to create huge impact in the fight against human trafficking, by using the collective power of the crowd to report incidents of human trafficking. Also, a section of the mobile application is dedicated to educating the user about basic knowledge of human trafficking, its types and measures taken by various governments to fight against it. © 2017 IEEE.Item Performance evaluation of web browsers in iOS platform(Institute of Electrical and Electronics Engineers Inc., 2017) Roshan, S.; Vinay Kumar, S.; Kumar, M.The number of mobile users have grown tremendously in the past few years. Nowadays, mobile phones are not just used for the purpose of making calls, rather they have become an important modern gadget which helps people in various aspects of their lives. With this increase, a lot of time on mobile is spent in browsing the internet websites through mobile web browsers. Hence, mobile web browsers actually play an important role in the mobile market, and in the lives of the users. In this paper, we have analyzed the performance of six web browsers present in the iOS platform. These web browsers are selected based on their market share in the iOS platform and their popularity. We have evaluated them on different parameters such as browser features, speed, security, performance benchmark tests, plugin support and video streaming. We propose the best web browser based on each parameter used for performance evaluation. Also, we recommend the best web browser based on the overall performance evaluation. © 2017 IEEE.Item Study on fracture toughness of carbon-carbon composites at low temperatures(Elsevier Ltd, 2022) Sunil Kumar, B.V.S.; Neelakantha, N.V.; Kumar, M.; Lokesha, M.; Vasantha Kumar, S.N.; Surendranathan, A.O.Carbon-carbon composites (C-CC), employed as composites in space and other industries for their outstanding properties. In extreme temperatures, the C-CC has proved to be the most efficient material. C-CC is one of the top thermal quality high-temperature materials such as high-temperature stability, excellent thermal conductivity, and low-temperature expansion coefficients. C-CC brake disks are highly demanded in aviation, trains, trucks, even race vehicles. Although C-CC is normally utilized at very high service temperatures, recently it has been necessary to explore these in low-temperature circumstances as components must also pass through low-temperature conditions in modern applications. In developing engineering structures, materials and systems for their technical safety, durability, and reliability, fractures and damage prevention and evaluation have an important role to play. Fracture toughness means quantifying the resistance of the fracture when a crack occurs. The present experimental study explores the influence of low temperature on the fracture toughness of C-CC. The low temperatures test of the samples has been done at a temperature between -10 °C and -40 °C. The results demonstrate that the fracture toughness value consistently raised as the temperature dropped. The Fluctuation began at a -10 °C from 2 % with a forecast of -40 °C to 32 %. © 2022 Elsevier Ltd. All rights reserved.Item Machine Learning-Based Technique for Phishing URLs Detection from TLS 1.2 and TLS 1.3 Traffic Without Decryption(Springer Science and Business Media Deutschland GmbH, 2023) Kumar, M.; Pais, A.R.; Rao, R.S.Phishing is one of the major leading cyberattack leading to huge financial loss and sensitive information loss such as account information, card details, password, login credentials. Existing techniques for phishing URL detection are unable to efficiently classify them. The use of TLS 1.2 and TLS 1.3 for client/server applications to communicate over the Internet securely has also contributed to the increase in these attacks. TLS 1.2 and TLS 1.3 traffic is encrypted, so detecting phishing URLs from encrypted traffic without decryption is a challenging task. In this paper, a machine learning (ML)-based technique is proposed for the detection of phishing URLs from encrypted traffic. The features are extracted from TLS 1.2 and TLS 1.3 traffic and based on the extracted features URLs are classified using ML algorithms. The dataset has been prepared for legitimate and phishing sites based on the features extracted from TLS 1.2 and TLS 1.3 traffic. Based on the experimental results, it is observed that the proposed model achieved promising results in the detection of phishing URLs from the encrypted traffic with an accuracy of 89.6%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.