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Plant Afflict Perception using Deep Learning

Plant disease detection is a critical task in agriculture to prevent significant losses due to disease spread. The manual examination process used in traditional disease detection methods takes a lot of time and labor. This research presents a plant disease detection system using deep learning, specifically leveraging the InceptionV3 architecture, a type of Convolutional Neural Network (CNN). Our approach demonstrates improved accuracy and speed in identifying plant diseases, contributing to more efficient agricultural practices. The model achieved a validation accuracy of 96%.

Published by: G Amrutha Swapna Tulasi

Author: G Amrutha Swapna Tulasi

Paper ID: V10I4-1182

Paper Status: published

Published: August 8, 2024

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Research Paper

Gender Pay Gap in the Two Most Popular Sports; A Comprehensive View of the Current Position of Soccer Players and Cricketers

This research paper discusses the differences between the experiences of women and men in sports, largely focusing on soccer. This paper takes a comprehensive look at factors that prevent women from reaching an equal pay gap, acts in certain countries to prevent pay gap discrimination that is ineffective, comparison of the salaries of men vs women in particular sports, the large difference in media coverage and sponsorships received by women vs men, whether the popularity of a sport impacts the pay gap, challenges women face, recognition of male vs female athletes, and the politicizing of women in sports.

Published by: Kimaya Jhala Gulati

Author: Kimaya Jhala Gulati

Paper ID: V10I4-1180

Paper Status: published

Published: August 5, 2024

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Research Paper

Histopathological Pancreatic Cancer Detection

We demonstrate a successful use of quantum machine learning in the medical domain. This study focuses on a classification issue employing quantum transfer learning for the identification of histopathological pancreatic cancer. numerous transfer learning models, including VGG-16, ResNet18, AlexNet, Inception-v3, and numerous highly expressible variational quantum circuits (VQC), are used in this work model instead of a single one. Consequently, we offer a comparative evaluation of the models, highlighting the top-performing transfer learning model for histopathological cancer diagnosis, which has a prediction AUC of around 0.93. Additionally, we noticed that Classical (HQC) and Hybrid Quantum offered a little higher accuracy (0.885) than classical (0.88) for 1000 photos using Resnet18.

Published by: Anandhi R, Jayabhargavi B, Esther Jasmine C, Angelin Pabitha N S

Author: Anandhi R

Paper ID: V10I4-1187

Paper Status: published

Published: August 5, 2024

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Research Paper

Comparative Drying Study and Quality Evaluation of Cauliflower Leaf Powder

This study presents the comparative analysis of drying methods and quality evaluation of cauliflower leaf powder. The project focused on cauliflower leaves, often discarded as waste despite being highly nutritious. This project was aimed to assess the effectiveness of different drying techniques in preserving the quality attributes of cauliflower leaf powder for potential food and nutritional applications. Cauliflower leaves were dried using hot air oven drying and tray drying techniques. The quality parameters were evaluated for fresh and stored powder. The drying curves obtained from both methods revealed distinct drying behaviours, with the hot air oven exhibiting a higher initial drying rate, while the tray dryer achieved a shorter drying time i.e., 1hr 45min. The moisture content of the cauliflower leaf powder decreased significantly with drying time in both methods. Quality evaluation of the dried cauliflower leaf powder included analyses such as colour, moisture, ash, iron, calcium, vitamin C, microbe load. The fresh tray dried powder was high in moisture 4.725%, ash 14.6%, iron 15.23±5.491mg/100g, vitamin C 2.403mg/100g and hot air oven dried powder was high in calcium 40mg/100g. The stored tray dried powder was high in ash 13.8%, iron 9.369±5.98mg/100g, vitamin C 1.455mg/100g, whereas the 3 months stored hot air oven powder was high in moisture 9.831% and calcium 23.255mg/100g. The microbe load which was observed for 3 months indicated the microbe load was higher in tray dried powder than hot air oven dried powder. In conclusion, by considering the yield, drying time, drying rate, quality analysis tray dryer technique was found to be better than hot air oven technique for the preparation of cauliflower leaf powder.

Published by: Tatineni Kushala, Dr. R Renu

Author: Tatineni Kushala

Paper ID: V10I4-1178

Paper Status: published

Published: July 31, 2024

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Research Paper

Medical Waste Management in India: Trends, Regulations and Technological Innovations

Effective medical waste management is essential to mitigate environmental and public health risks. This study examines the evolution of medical waste management practices in India, highlighting historical trends, regulatory frameworks, technological advancements, and ongoing challenges. The research identifies a significant shift from rudimentary disposal methods to regulated practices, particularly following the Biomedical Waste (Management and Handling) Rules of 1998 and their 2016 amendments through literature review, policy analysis, and interviews with healthcare professionals. Despite these regulations, challenges such as inadequate infrastructure, insufficient training, and inconsistent compliance persist. Technological advancements like autoclaving and waste-to-energy processes offer promising improvements. The study underscores the necessity for stricter enforcement, enhanced training, and the integration of advanced technologies to overcome these challenges and advance sustainable healthcare practices.

Published by: Niharika Gupta

Author: Niharika Gupta

Paper ID: V10I4-1175

Paper Status: published

Published: July 30, 2024

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Research Paper

The Impact of Artificial Intelligence on Decision-Making, Motivation, and Safety in Education

Abstract Artificial Intelligence (AI) is being more and more incorporated into educational institutions, providing the possibility of important changes in different aspects. This research studies the influence of artificial intelligence (AI) on decision-making, motivation, and safety in the academic sector. Artificial intelligence (AI) improves decision-making by offering insights based on data and personalized learning opportunities. However, it also raises concerns about the possible decline of critical thinking and human judgment. In addition, although AI tools have the potential to enhance student motivation by providing personalized learning routes and interactive information, there is a concern that they may promote reliance on technology and diminish intrinsic motivation. AI-powered surveillance and predictive analytics can greatly enhance the safety and welfare of pupils. However, it is important to weigh the advantages of these benefits against the need to protect privacy and ensure ethical usage. Gaining a comprehensive understanding of these effects is essential for educators, administrators, and policymakers toutilize the benefits of AI while minimizing its drawbacks. This will eventually promote a more efficient and secure educational setting.

Published by: Dr.Kamma Ramanjaneyulu1, Dr. S.Aneel Amrutha Raju2, Mrs. B. Rasheeda3

Author: Dr.Kamma Ramanjaneyulu1

Paper ID: V10I4-1173

Paper Status: published

Published: July 30, 2024

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