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Research Paper

Car logo detection and classification approaches: A review

Vehicle identification systems rely on logo recognition to identify vehicles (VLRS). Convolutional Neural Networks are used to automatically learn characteristics for car logo recognition (CNNs). However, CNN struggles with rotated or noisy pictures. CNN's Random Forest classification technique is used to create an image recognition system. Random forest decision tree ensemble and train. AI has overcome object recognition problems. Convolutional Neural Networks (CNNs) is a popular type utilized to address object recognition issues due to their complicated structure and hidden layers. Logo recognition is often solved using CNN-derived techniques. The creators of used pre-trained CNNs to recognize logos. These technologies are also computationally expensive. This limits the use of computationally expensive alternatives. A logo's recognition accuracy with little computing burden remains a mystery.

Published by: Sushil Kumar, Ms. Bhuvneshwari

Author: Sushil Kumar

Paper ID: V7I5-1391

Paper Status: published

Published: October 28, 2021

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Research Paper

What are the main differences between the economic and military dimensions of the United States and China’s cybersecurity policies?

The world's two most powerful economies, China and the United States rely on the global digital domain for economic productivity, societal well-being, and national security. At the same time, governments and companies alike are becoming increasingly concerned about the security and reliability of their information systems, even as there remains significant ambiguity about the true nature of the threats and the most effective ways of addressing them. In recent years, the security of global information networks has been a contentious issue in US-China relations. Against this backdrop, this paper seeks to appraise and analyze the variations in strategies and methods used by the United States and China to pursue their military and economic interests in the cyber domain. This paper concludes by outlining the threat landscape that characterizes US-China relations in cyberspace, as well as maps the way forward for bridging gaps in trust and strengthening international cooperation frameworks.

Published by: Rajveer Batra

Author: Rajveer Batra

Paper ID: V7I5-1238

Paper Status: published

Published: October 26, 2021

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Review Paper

Nanotechnology-A promising approach in the prevention and treatment of coronavirus infection

COVID-19 has emerged as one of the most challenging pandemics that has increased at an alarming rate over the past decade. The critical need for the designing of vaccines with high-efficiency rates has been emphasized to prevent viral infection. Therefore, it is necessary to rely on high sensitivity and selectivity diagnostics kits, and effective antiviral and protective therapeutics to eliminate and combat the viral load and also to counter the side effects derived from tissue damages. In this regard, the use of nanotechnology has shown new opportunities for developing novel strategies for the prevention, diagnosis, and treatment of COVID-19 and other viral infections. Thus non-toxic antiviral nanoparticles (NPs) have been promising in providing nano vaccines against infections from viruses. In this review, the potential use of nanotechnology for COVID-19 virus management by the development of nanoparticles as a drug and the advanced strategies to detect viruses using nanoparticles are widely discussed.

Published by: Reshma R.

Author: Reshma R.

Paper ID: V7I5-1387

Paper Status: published

Published: October 26, 2021

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Review Paper

Facial emotion recognition using Convolutional Neural Networks for Autism and Dementia-related diseases

Transfer research findings are important for mouth-based emotional recognition since there are limited data sets and most contain virtual actor emotional gestures rather than real-world categorization. Throughway of transfer learning, we can use less training data than training an entire network from scratch, thereby improving the network efficiency with emotional data and improving the overall output accuracy of the neuronal network within the desired area. The suggested solution seeks to dynamically enhance the understanding of feelings, taking into consideration not just new situations, but changed contexts, because even though the face in whole is apparent in an unfavorable perspective, the picture of the mouth can be accessible. Typical applications include automatic monitoring of vital bedridden patients in a hospital management system and wearable applications assisting people with conditions with facial expressions challenging to see or understand. This achievement takes advantage of previous work on mouth-based deep-learning emotional recognition and has also been validated and compared to a variety of other networks utilizing an extensive dataset for facial emotional recognition, well-known in publication. The exact recognition of the mouth was also contrasted to that of total emotional knowledge; we find that the lack of precision is mostly offset by continuous success in the area of visual awareness of emotions. Therefore we may conclude that in the dynamic phase of emotional recognition our system shows the value of mouth detection.

Published by: Hanshul Bahl

Author: Hanshul Bahl

Paper ID: V7I5-1370

Paper Status: published

Published: October 26, 2021

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Technical Notes

Web Application for E-Vehicle marketplace using Django

The worldwide electric vehicle market size is projected to develop from 4,093 thousand units in 2021 to 34,756 thousand units by 2030, at a CAGR of 26.8%. India has effectively demonstrated its strong fascination to be a significant piece of this car outlook change. Adding to that, India has as of now set forward the craving to turn into the greatest center point for electric vehicles later. Industry pioneers consider electric vehicles to be a promising choice.

Published by: Durbhakula Sai Praneeth, Hari Sarathi M.

Author: Durbhakula Sai Praneeth

Paper ID: V7I5-1388

Paper Status: published

Published: October 26, 2021

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Research Paper

Image classifying AI Chatbot

We have built the bot by using HTML and CSS for a simple front-end display and JavaScript for the application tier. The neural network is constructed using Brain.js, a GPU accelerated library for handling neural networks in JavaScript. Brain.js consists of multiple neural network implementations mainly to foster the ability of different neural nets to perform different tasks well. The object detection feature is built using ML5.js. Training of the bot by exposure to both specific and generic questions would be crucial. Also, whenever a user isn’t satisfied with the answer given by the bot, he can raise an issue, and the answer for that question would be answered correctly in future updates. The bot also learns with each question the user asks, thereby making it better and better with each iteration. The bot also has an image classifier feature for detecting objects.

Published by: Pritham Sriram G., Prasana Venkatesh S.

Author: Pritham Sriram G.

Paper ID: V7I5-1378

Paper Status: published

Published: October 26, 2021

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