Implementation of artificial intelligence on the identification of smart attendance with real-time face recognition using CNN
In the changing world, automatic face recognition (AFR) systems have achieved numerous advancements. Smart Attendance with Real-Time Face Recognition is a practical option for dealing with day-to-day tasks. Attendance system for students Attendance is based on the recognition of faces. This is a method of recognizing a student's face in preparation for a test. Face biometrics based on high-definition images are used to track attendance. keep an eye on video and other forms of information technology in front of my face, A computer system will be able to find and recognize people as part of the recognition project. In photos or videos, distinguish human faces quickly and precisely are being filmed on video by a surveillance system. Numerous Improved algorithms and methodologies have been developed for Face recognition performance, but the concept to be Deep Learning is used in this case. It aids conversion.
Published by: Chintapalli Mohan Kumar, Dr. Vanitha Kakollu
Author: Chintapalli Mohan Kumar
Paper ID: V8I3-1256
Paper Status: published
Published: May 18, 2022
Women workers in construction sector of Kerala –An insight into their economic condition
The economic development of a country depends on the growth of Industry and technological development. The transition from an agriculture-oriented economy to an industry-based seems to be a major landmark. Building construction is a concomitant factor of industrial development. Construction activities create larger employment opportunities for a considerable number of workers. The construction workers are mainly working in informal or unorganized sectors. Basic problems faced in this field include disparity in wages, health issues, discrimination, economic problems, etc. The main objective of the study is to analyze the economic and social conditions of women working in the construction sector. The study reveals the weaker economic conditions of women construction workers. Lack of fixed income and varying working hours continues to haunt them. Their income is found insufficient for their subsistence. The study also revealed that out of the sampled women construction workers most of them depend on loans for meeting their needs of existence. They are not getting work on all days of the month and hence occupational safety is found to be lacking.
Published by: Dr. T. Shameerdas
Author: Dr. T. Shameerdas
Paper ID: V8I3-1294
Paper Status: published
Published: May 18, 2022
Part Presence Detection and Counting using Advanced Image Processing
Part presence detection, part counting, and part inspection are major steps involved during a quality inspection in various stages of production in any production line. Quality inspection in most industries is done by quality inspectors or operators depending on the stage of production. Visual inspections by humans can cause variations in accuracy and results due to differences among workers. Part presence can be detected by using electronic/electrical sensors. But in cases where there is a large number of components/parts, the number of sensors to be used also increases. This leads to the development of complex systems. As the need for Factory automation has increased in recent times, there is a need to increase the rate of production. Replacing manual intervention with an automated system is an ideal solution. This can be successfully achieved by introducing machine vision and image processing technologies in the automated process. Since machine vision offers high image transmission and high image processing it is easier to achieve reliable and accurate results. Systems that provide these solutions are already available in the market. But these systems are highly expensive and only target large-scale customers that can afford such solutions. This arises a need for solutions that are cost-effective and reliable to target customers with simpler image processing requirements. The main objective of this project is to develop and provide simple low-cost machine vision solutions to customers.
Published by: Jhanavi Trilok, Shivkumar A., Dr. Babu Rao, Dr. S Saravana Kumar
Author: Jhanavi Trilok
Paper ID: V8I3-1287
Paper Status: published
Published: May 16, 2022
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
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
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
