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Research Paper

Segmentation of customers using Machine Learning algorithm

The separation of the market into a discrete customer group of customers with comparable characteristics is known as customer segmentation. Client segmentation may be an effective tool for identifying unmet customer demands. The advent of a slew of new rivals and entrepreneurs has created a lot of friction among competing firms as they try to gain new customers while keeping the ones they already have. The k-means clustering technique is utilized in this research for this purpose. Segmentation of customers allows a business to personalize its interactions with consumers, much as we do in our daily lives. We discovered that each customer's behavior and demands had similar features when we segmented them. Then they're grouped together to meet needs using diverse ways. Doing segmentation manually can be exhausting. That is why we have implemented Machine Learning Algorithm to do for us.

Published by: Souvik Biswas, Neha Bhattacharya, Ananya Samanta, Nitin Gupta, Dr. Sangita Roy

Author: Souvik Biswas

Paper ID: V8I3-1351

Paper Status: published

Published: May 28, 2022

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Research Paper

Visualization of the shape index profile of a 2D Astronomical image in 3D

Astronomical data provides assertions about the nature of life. This paper focuses on data and noise reduction of the provided Moon fit files, to analyze the mountains on five different terminator positions of the Moon. Autostakkert and ds9 tools are used respectively for noise reduction as well as pixel distance calculation. The main focus of this paper is to provide 3D visualization of the shape index profile of the input image using python script.

Published by: Yazhini Gopalakrishnan, Prishita Omesh Mirchandani Budhrani

Author: Yazhini Gopalakrishnan

Paper ID: V8I3-1350

Paper Status: published

Published: May 27, 2022

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Research Paper

The effects of bionics on mankind and the challenges faced by the biomedical industry

The purpose of this paper is to discuss the advancements in the field of biomedical engineering and the effects of prosthetic implants and 3D skin on humans. This research aims to explore possibilities related to bioengineering and prosthetics. It also includes information on the potential challenges the industry might face and how it can advance further, to be more beneficial to humanity. The following is a detailed study of the uses of artificial organs, and 3D printers, and how we might come up with artificial skin that is able to comprehend sensations and mimic natural skin more accurately.

Published by: Nandini Ghosh Dastidar

Author: Nandini Ghosh Dastidar

Paper ID: V8I3-1318

Paper Status: published

Published: May 26, 2022

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Research Paper

Waste Stratification using Deep Learning

Waste management is one of the most serious problems that practically every country faces. Its goal is to offer sanitary, efficient, and costeffective solid waste storage, collection, transportation, treatment, and disposal without harming the environment, land, or water system. Waste sorting errors can result in incorrect disposal and the loss of opportunity to recycle or reuse items. Monitoring by hand is inefficient and costly. As a result, deep learning and machine learning play a critical role in categorizing and sorting garbage and rubbish, effectively distinguishing between different types of waste. Many tasks, such as garbage collection and waste classification/ segregation, are part of waste management. One of the most important operations is garbage classification, which is carried out in a variety of ways based on the nature of the material.

Published by: Hanumanth K., S. Vidhayini, Pawan Sahu

Author: Hanumanth K.

Paper ID: V8I3-1341

Paper Status: published

Published: May 26, 2022

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Research Paper

Online Exam Portal

Online Examination Portal (OEP) is an electronic assessment framework wherever assessments are given on the online either through the net or utilizing the computer framework. The elemental aim of the web communication device is to properly assess the students via a processed device that saves the specified time and provides speedy and correct outcomes. Advances in innovation in recent and ages have upheld the viability of an online assessment and have rested the cycle of an online assessment throughout the long run. However, several institutes, particularly in higher institutional companies, have standardized written examination contrivance ways in growing countries. The web assessment framework improves the approach toward leading tests and compellingly making ready the outcomes. This work talks about the electronic online assessment framework and a safer instrument to steer AN assessment. AN OEP could be a viable and nice declare a mass coaching assessment.

Published by: Aditya P. Singh, Surendra Singh Chauhan

Author: Aditya P. Singh

Paper ID: V8I3-1334

Paper Status: published

Published: May 26, 2022

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Review Paper

Prediction of traffic congestion using swarm-based long short term memory

The vehicular Adhoc network is a new research area in the smart transport system that offers critical information to the network's cars. Road accidents affect about 150 thousand individuals, and VANET must improve safety. Traffic congestion forecasting is critical for reducing road accidents and enhancing traffic management for people. The dynamic behavior of the cars in the network, on the other hand, reduces the accuracy of deep learning models in predicting traffic congestion on roadways. This research introduces a new hybrid boosted long short-term memory ensemble and convolutional neural network (CNN) model to solve the congestion problem. to effectively handle the vehicle's dynamic behavior and estimate traffic congestion on routes The suggested BLSTME trains and improves the weak classifiers for the prediction of congestion, while the CNN collects characteristics from traffic photos. The suggested model is built with Tensor flow Python modules and evaluated in a real-world traffic situation with SUMO and OMNeT++. Extensive testing is done, and the model's performance is assessed using the performance criteria likely prediction accuracy, precision, and recall. As a consequence, the experimental result indicates 98 percent precision, 96 percent recall, and 94 percent accuracy. The findings show that the suggested model outperforms other current algorithms by 10% in terms of stability and performance when compared to deep learning methods.

Published by: B. Naveen Kumar, K. Sushma, Y. Bharath Reddy, B. Tejaswari, P. Babu Reddy

Author: B. Naveen Kumar

Paper ID: V8I3-1312

Paper Status: published

Published: May 25, 2022

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