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Research Paper

Smart trash picking vehicle for waste management

This project aims to design an Automatic trash picking vehicle and is done without human intervention. Garbage collection and management is a large issue undergoing extensive research, and solutions are being proposed accurately. Thanks to an exponential rise in population, there is an increased production of waste, and also a significant amount of litter consisting of plastic, medical waste, paper, and other waste materials are thrown out and dispersed in public. Thus, the need for a most effective waste management strategy is essential. In this project an automated system is provided for segregating types of wastes as biodegradable, non-biodegradable, and metallic wastes. The project is built in such a way that, when it is started it will move on the path defined in the program. When it encounters the obstacle, depending on the conditions applied in the program the robot proceeds with further motion and then robot picks up the garbage. Once it senses the color of trash using color sensor, these sensed values are given to the Arduino UNO, and thus based on the inputs from the controller the system will operate and through the operation of various drivers used it will pick up the trash with robotic arm and thus segregation of wastes into Bio-degradable, Nonbiodegradable can be done by dropping waste into the respective bins. Once the bin gets filled the user will get notified.

Published by: A. Mounika, V. Gayathri, Amal Xavier, G. Kullai Reddy, A. Archana

Author: A. Mounika

Paper ID: V7I3-2124

Paper Status: published

Published: June 25, 2021

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Research Paper

Four-wheel steering mechanism

Nowadays most of the vehicle using two-wheel steering mechanism as the main handling system. But the efficiency of the two wheels steering vehicle is proven to be low as compared to the four-wheel steering vehicle Four-wheel steering can be adopted to increase the steering response, increase vehicle stability at a certain speed to decrease the turning radius. If a car could automatically compensate for an understeer/oversteer problem, the driver would enjoy nearly neutral steering under varying operating conditions. Four-wheel is a serious effort on the part of automotive design engineers to provide near-neutral steering. Also in situations like low-speed cornering, vehicle paring, and driving in city conditions with heavy traffic in tight spaces, driving would be very difficult due to the vehicle’s large wheelbase and track width. Hence there is a requirement of the mechanism which results in less turning radius and it can be achieved by implementing four-wheel mechanisms. Instead of regular two-wheel steering.

Published by: Virendrakumar Hubalal Patel, Nikhil Banne, Yash Patil, Abhijit Mandal

Author: Virendrakumar Hubalal Patel

Paper ID: V7I3-2004

Paper Status: published

Published: June 25, 2021

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Review Paper

High-speed download cost-effective version control compression on cloud

Traditional techniques for image retrieval are not supported for the ever-expansive image database. These downsides can be removed by utilizing contents of the image for image retrieval. This sort of image retrieval can be accomplished with the help of cross batch redundancy detection (CBRD). When the cross batch redundancy detection (CBRD) is combinedly work with Bandwidth Energy Efficient sharing (BEES) has very precise result other than any commonly known feature detector or traditional method that are usually utilized in vision programming. In this paper we mainly focus in texture, colour ,shape and resolution of an image matching with superior accuracy and we also review some of the futuristic tool developed on CBRD

Published by: Hari Krishnan M., Jayaraman N., Mahendran P., Dr. N. Kanya

Author: Hari Krishnan M.

Paper ID: V7I3-2117

Paper Status: published

Published: June 25, 2021

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Research Paper

An authentication & data sharing of the medical report using blockchain security

The health care services industry is always showing payments. Blockchain 2.0 focused on decentralizing the entire market and is employed to transform assets through smart signs of change and supporting new advancements and advances. One of the predominant requirements in today’s health care systems is to protect the patient's medical report against potential attackers. Hence, it is basic to have secure information that can just approve people can get to the patient's medical report. So, we have proposed Blockchain technology as a disbursed approach to grant security in accessing the medical report of a patient. It’s composed of three phases Authentication, Encryption, and Data Retrieval using BlockChain technology. For authentication – Quantum Cryptography, for Encryption – AES, and for Data Retrieval– SHA algorithms are used to resist the frequent attacks. This proposed framework may likewise ensure the protection of the patients and moreover keeps up the security and trustworthiness of the health care system.

Published by: Komal Suresh Raut

Author: Komal Suresh Raut

Paper ID: V7I3-2116

Paper Status: published

Published: June 25, 2021

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Research Paper

Network intrusion detection system

With the fast development of PC utilization and PC network, the security of the PC framework has turned out to be very significant. Businesses are looking for consistently new sorts of assaults. As the danger turns into a genuine chapter year by year, interruption discovery advancements are essential for organization and PC security. An assortment of interruption recognition approaches be available to determine this severe condition; in any case, the fundamental problem is execution. It is imperative to increment the discovery rates and lessen bogus alert rates in the space of interruption recognition. To recognize the interruption, different methodologies have been created and proposed over the most recent decade. This paper effectively compares the accuracy of different classification algorithms, like algorithms like Support Vector Machine (SVM), Naive Bayes, KNN, Decision Tree[15]. This study aims to perform a comparative analysis of these different machine learning algorithms on datasets available to predict which model best suits intrusion detection.

Published by: Kj Bhoomika, Impana N., Priya D.

Author: Kj Bhoomika

Paper ID: V7I3-2102

Paper Status: published

Published: June 25, 2021

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Review Paper

A review on Deep Learning for content-based image retrieval

Learning effective feature representations and similarity measures are crucial to the retrieval performance of a content-based image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of real-world CBIR systems. The key challenge has been attributed to the well-known “semantic gap” issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning techniques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements in CBIR tasks can be achieved by exploring the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Specifically, we investigate a framework of deep learning with application to CBIR tasks with an extensive set of empirical studies by examining a state-of-the-art deep learning method (Convolution Neural Networks) for CBIR tasks under varied settings. From our empirical studies, we find some encouraging results and summarize some important insights for future research.

Published by: Nikhil Nandkishor Kela, S. R. Tayde

Author: Nikhil Nandkishor Kela

Paper ID: V7I3-2050

Paper Status: published

Published: June 25, 2021

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