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Research Paper

Explore the factors affecting students’ success in the first- year of college

The United Arab Emirates invests great resources in the education of their youth to ensure they can continue and improve the legacy of the nation’s founding fathers. As such, the government pumps in billions of dirhams annually to build an innovative, learned, and globally competitive society (MOE Website, 2021). However, at a crucial transition point, something is missing. Studies found that in some parts of the country up to 45% of high-school seniors, and young Emirati nationals are not interested in pursuing higher education (Bayoumi et al., 2016). This and probably other elements contribute to a disturbingly common phenomenon of Emirati students struggling to succeed in their first year of college. In light of this problem, this exploratory research seeks to analyze the factors that affect freshmen year success among Emirati students in particular. A qualitative method is suggested to identify the main challenging factors that hinder the success of the demographic studied and address those challenges to maximize success.

Published by: Malika Elmelyany

Author: Malika Elmelyany

Paper ID: V8I3-1279

Paper Status: published

Published: May 16, 2022

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Research Paper

Cursor Movement on Object Motion

This paper proposes sanewapproachforcontrolling mouse movement using a real-time camera. Most of the existing approaches involve changing mouse parts such as adding more buttons or changing the position of the tracking ball. Instead, it proposes to change the hardware design. The method uses a camera and computer vision technology, such as image segmentation and gesture recognition, to control mouse tasks (left-click and right-click, selection, scroll, and drag). Hand gestures are acquired using a Web camera based on color detection. In this research work, three colourtapes are used on fingers. The tapes will be used for clicking events on the mouse.ThroughaWebcamera, the real-time video is captured. Image processing is performed in each frame of that video to detect the color and mouse tasks are performed.

Published by: Davuluri Manikanta, K. Harith, S. Sameer Basha, M. Sumanth Reddy

Author: Davuluri Manikanta

Paper ID: V8I3-1162

Paper Status: published

Published: May 16, 2022

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Research Paper

IP traffic classification of 4G network using Machine Learning techniques

In today's world, the number of online services and users is growing rapidly. This leads to a huge increase in internet traffic. Therefore, the task of separating IP traffic is approx. it is important for Internet service providers or ISPs, as well as a variety of government and the private sector for better network management and security. IP traffic separation includes identifying user activity using network traffic flowing into the system. This will also help to improve the network performance. The use of traditional IP traffic Classification methods based on the evaluation of packet capacity and hole numbers dropped significantly because there are so many online apps today that use naturally incorrect port numbers than well-known port numbers. Also, there are several encryption strategies today as a result of when testing the package payload is blocked. Currently, various machine reading techniques are commonly used to differentiate IP traffic. However, not much research has been done on IP fragmentation 4G network traffic. During this study, we did a new database by downloading real-time Internet traffic packets 4G network data using a tool called Wireshark. After that, we released the considered features of the packaged packages using the python script. Then we used five typewriters models, namely, Decision Tree, Vector Support Equipment, K Very Near Neighbors, Random Forest, and Naive Bayes IP splitting traffic. It was noted that Random Forest offered the best almost 87% accuracy

Published by: S. Mahammad Rafi, T. Lavanya, B. Shamitha, S. Phaneeswar, N. Chandu

Author: S. Mahammad Rafi

Paper ID: V8I3-1178

Paper Status: published

Published: May 16, 2022

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Research Paper

Traffic Sign Classification using Federated Deep Learning model

For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of federated deep learning-based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network ResNet34 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The results we found are efficient, which emphasizes the effectiveness of our method

Published by: Shaik Mahammad Rafi, Maddina Nikhil, T. Sathish Kumar Reddy, K. Kanchana, M. Ravali

Author: Shaik Mahammad Rafi

Paper ID: V8I3-1211

Paper Status: published

Published: May 16, 2022

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Research Paper

Prognosticate Diabetic Mellitus in Women by using Performance Evaluation and Classification Algorithms

Diabetes is a persistent disorder that takes place while the pancreas does now no longer produces sufficient insulin or while the frame can’t use the insulin it produces Diabetes is known as one of the deadliest and most chronic diseases that cause blood sugar levels to rise. Many headaches arise if diabetes stays untreated and unidentified. Early prediction of diabetes can save a life. In our undertaking, prediction of diabetes for women between the ages of 30 and 80 through the use of classification algorithms. We used various Machine Learning classification algorithms like Logistic Regression, Decision Tree, and Random Forest on various attributes like Glucose, Blood Pressure, Skin thickness, Insulin, BMI, Diabetes pedigree function, Age, Pregnancies, and discover goal variable i.e., outcome. Finally, different classification algorithms along with their comparison of performances with the use of Confusion Matrix, Accuracy, F-Measure, and Recall.

Published by: Patnayakuni Pragathi, Komali Yasudha, Maddila Suresh Kumar

Author: Patnayakuni Pragathi

Paper ID: V8I3-1251

Paper Status: published

Published: May 16, 2022

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Research Paper

Retailer’s perception towards Edible oil in and around Coimbatore

This research was made to analyze the factors that are needed to be strengthened for the product’s sales growth, it helped to determine the retailer perception towards edible oil brands, and also it helps to specify the reason for the specify Edible product which is not sold in the shop. These are the objective of this research. The research area was conducted in Coimbatore particularly in developing areas in Coimbatore like Eachanari, Malumichampatti, Othakalmandapam, and Kinathukadavu. The sample collected for this research is 181. The research design of the project is descriptive in nature. By using a structured questionnaire, the primary data was collected. Secondary data is collected from various journals, books, literatures, websites, and magazines. Convenient sampling is the sampling method used for the study. Chi-square test, Regression analysis,r correlation, and Cronbach Alpha test are the tools used for analyzing the data which are collected in the survey. From this study, it determines that the Majority of the retailers prefer that lowering the price can influence the new customer. In various retail outlets, customers are not specifying the brand while they are buying the product is the main reason for the reduction in sales. Because the majority of the retailers are not having awareness of Sim Sim oil which contributes to the reduction in sales due to not having awareness.

Published by: Mugunthan P., Ivan Kenny Raj L.

Author: Mugunthan P.

Paper ID: V8I3-1263

Paper Status: published

Published: May 16, 2022

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