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Online interview based on facial expression

Facial expressions of humans carry more information visually than they do verbally. Human-machine interaction is a crucial part of facial expression recognition. The automated facial expression reputation system can be used for many purposes, including detection of intellectual issues and human behavior information. It is still difficult to recognize facial expressions using computers with high recognition charges. The most well-known techniques used in automatic FER systems are based on look and geometry. Normally, facial expression recognition works in four stages, which include preprocessing, face identification, Feature extraction, and Classification. We also used feature extraction and expression classification to identify the seven key human emotions.

Published by: Kamal Raj T., Sourav P Kachwahe, Tejas S., Shashank C., Sanjay H. S.

Author: Kamal Raj T.

Paper ID: V7I4-1518

Paper Status: published

Published: July 27, 2021

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Research Paper

Soil quality monitoring, automated irrigation system using machine learning and Blynk

India Ranks the second country in the world in farm output of 64% of cultivated land which depends on monsoons. Irrigation accounts for Fifty-five to seventy-five percent of water usage In the World. Also, nearly sixty percent of this water while irrigation is wasted. So now we have to conserve the water by making use of soil moisture sensors resulting in smart water management Another is an issue is people always focus on the crop yield whereas before the crop yield the other process such as soil quality and soil fertility, which crop to be grown and what fertilizers needed plays a very important role in the yield of the crop. So in our project, we have focused on these factors such as irrigation, prediction for fertilizer, and which crop to be grown. This project takes real-time data from the deployed sensors such as temperature, moister, NPK and ph values into account and predicts the output in the IoT machine learning environment. The system implemented will be introduced to the semi-supervised learning model where we will be applying algorithms such as KNN and random forest and SVM to predict fertility and whereas for the crop along with this we have considered other factors such as season and place.

Published by: Kamal Raj T., Kavya G. S., Firdose Tabassum, Reddy Nagadurga, Keerthana Prakash Nayak

Author: Kamal Raj T.

Paper ID: V7I4-1536

Paper Status: published

Published: July 27, 2021

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Research Paper

Design of wheel rim by using design of experiments

There are many failures that occurred such as Defects occurred during manufacturing, Initiation of Crack, over-tightening of bolts, and Corrosion. Some of the people have used FEM methods in their Research. They all have worked only on the negative offset and zero offset wheel rims. In this paper, we are focusing more on the positive offset wheel rim as they consume less material as compared to negative offset and positive offset wheel rims. In this paper, we know the natural frequency and deformation of the original rim and we are going to design more than 2-3 designs for the wheel rim. Using those designs and analysis types such as Static, Modal, and mainly the Design of experiments we are going to produce the alternate design for the wheel rim which will be more sustainable and lesser in cost.

Published by: Mauli Vasant Jadhav, V.C.MALI

Author: Mauli Vasant Jadhav

Paper ID: V7I4-1540

Paper Status: published

Published: July 27, 2021

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Research Paper

Imbalanced data handling using Machine Learning

Machine learning algorithm applications still control Internet trade with their seemingly endless options for customization. Great fast data is continuously passed through socially important forecasts to improve online shopping. In the absence of analytical instruments to manage homogeneous data sets and outlines, unforeseen occurrences of data known as imbalanced data are unfortunately still overlooked. Rare cases of substance use are therefore still ignored, causing costly losses or even tragic circumstances. A number of methods have been successfully implemented to meet this challenge over the past 10 years. In many cases, however, there are significant disadvantages due to the non-uniformity of the relevant data when used for diverse application domains.

Published by: Kamal Raj T., Bhavana K., Chandana M. R.

Author: Kamal Raj T.

Paper ID: V7I4-1514

Paper Status: published

Published: July 27, 2021

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Research Paper

Breast Cancer Prediction using Machine Learning model

Day by day the number of cancer cases around the world is increasing and it has become a common health issue. One of the most commonly seen cancer is Breast cancer. It has become one of the leading causes of death. in women Breast cancer can be mainly seen in women, but it can be seen in men too. There is a belief that men cannot get breast cancer, but it is not true. Cancer can be treated only if it is detected in the initial stages, if not then it will spread to various parts of the body and its effect is irreversible and it will cause a threat to life. But most of the time it is difficult to detect cancer and also sometimes people ignore the symptoms. In this matter, artificial intelligence can be of great help. Here we have collected a set and then we have built a prediction model to detect stroke based on the different algorithms that are available on machine learning.

Published by: Sanath Kumar A.

Author: Sanath Kumar A.

Paper ID: V7I4-1539

Paper Status: published

Published: July 26, 2021

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Research Paper

Network packet sniffing

Packet sniffing is a technique for tapping each packet as it streams across the organization. It is a method in which a client sniffs information having a place with different clients of the organization. It is successful in both exchanged organizations and non-exchanged organizations. Packet sniffers can work as an authoritative apparatus or for vindictive purposes. It relies upon the client's goal. This paper examines how sniffing can be done if there should arise an occurrence of the center point and exchanged organization, different packet sniffing strategies, various techniques that AntiSniff uses to identify these sniffing programs are additionally talked about.

Published by: Md Haroon, Dr. Ramveer Singh, Vishant Sharma, Lakshya Kaushik, Alok Kumar Mishra

Author: Md Haroon

Paper ID: V7I4-1507

Paper Status: published

Published: July 26, 2021

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