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Skin Cancer Type Detection using Deep Learning

The research "Detection of Skin Cancer Types Using Deep Learning" addresses the serious issue of skin cancer. There's an urgent need for early diagnosis to help patients get better treatment. Skin cancer, especially melanoma, can be hazardous and often leads to high death rates when not caught early. Traditionally, doctors mainly rely on visual checks, which can vary from person to person. This can lead to misdiagnoses and delayed treatments. So, we decided to use a technology called Convolutional Neural Networks (CNNs) to create a machine that recognizes different types of skin cancer using specialized images. We did a thorough review of current methods and identified their limitations. This will help us build our approach while also making it easier for places with fewer resources to access. By studying things like color, texture, shape, and size in dermoscopy images—and using fresh techniques like transfer learning—we hope to boost accuracy and efficiency in diagnosis. Ultimately, we look forward to helping improve skin cancer treatments.

Published by: Abhijeet Gopal Roy, Anuja Jadhav, Disha Shende, Kishor Khandait, Sakshi Barde, Dr. Smita Nirkhi

Author: Abhijeet Gopal Roy

Paper ID: V10I6-1399

Paper Status: published

Published: December 12, 2024

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Research Paper

Campus Navigator

This paper introduces Campus Navigator, a web-based application developed using Python, Django, and map APIs to assist in navigating a university campus with multiple colleges. The system enables new students and visitors to easily locate specific destinations, such as buildings, classrooms, or faculty cabins. By integrating an intuitive interface with real-time map functionality, Campus Navigator ensures users can efficiently find their way around the campus. The platform also provides administrators with tools to manage and update campus data, ensuring accuracy and scalability.

Published by: Vikas Anil Choudhary, Shantanu Barhate , Kartiki Pranjale

Author: Vikas Anil Choudhary

Paper ID: V10I6-1370

Paper Status: published

Published: December 11, 2024

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Research Paper

A Hyperparameter Tuning Optimized Convolutional Neural Network for Classification of Fruit Type and Quality through Computer Vision

Food poisoning is a problem affecting people on a global scale, killing 420,000 people a year as of 2022. This problem is exacerbated by the distribution of already-rotten food from farms to vendors and can be mitigated by preventing infected food from ever leaving farms in the first place, or by identifying rotten foods before vendors sell them. Thus, this study summarizes the building of a hyperparameter-optimized deep learning model that uses a Convolutional Neural Network (CNN) to identify the kind of fruit and its quality by looking at an image of a fruit, a simple process. This automation of food classification and safety allows lower-income farmers and vendors to escape the time and monetary cost of manually verifying whether or not each fruit they distribute/sell is safe to eat.

Published by: Aarav Kodathala Reddy

Author: Aarav Kodathala Reddy

Paper ID: V10I6-1395

Paper Status: published

Published: December 10, 2024

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Research Paper

Sustainable Solutions for the Disposal of End-of-Life Solar Panels

Solar PV recycling methods currently in use are elaborated in this paper. This paper explains in detail the recycling methods of crystalline, CdTe, and CIGS solar panels which are being currently used in the industries. As there are a lot of recycling methods this paper finds you the best recycling method with a high recovery rate of recycled products up to 99%. The use of plant-based materials to create solar PV modules is also suggested as one of the main innovations needed to fight used solar PV disposal. Because of this, there is still a great deal of effort to be made to develop the subject of solar PV recycling by material scientists and other related professionals.

Published by: Aishwarya S, Karthikeyan S, Sathiyamurthi P, Vijayanand P S

Author: Aishwarya S

Paper ID: V10I6-1393

Paper Status: published

Published: December 10, 2024

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Research Paper

Marital Rape in India: The Intersection of Law, Society, and Structural Inequality

Rape is a serious crime which violates the body and spirit of the victim. Marital rape remains an issue in India where the legal framework fails to protect women from the physical, emotional, and psychological trauma attempted by the husband. After the Nirbhaya Case, there was a lot of media attention on the topic of rape which further led to the implementation of the Criminal Law (Amendment) Act, of 2013. But marital rape is still a problem as the law states specific circumstances in which the husband can be held accountable for committing the crime of raping his wife without consent. This research paper aims to explore and elaborate on the legal framework in place to protect women from the horrific crime of spousal rape. For example how the constitution has laws that contradict themself, how the sacred bond of marriage and the sanctity of the family is more important than a woman facing abuse from her husband, how the law undermines marital rape reports and lets the perpetrator (husband) walk off free, how rape affects a women's body and mind. This paper will answer all these questions and also aim to unfold all the harsh realities that many women in India face every day in the name of marital rape.

Published by: Seerat Khanna

Author: Seerat Khanna

Paper ID: V10I6-1359

Paper Status: published

Published: December 9, 2024

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Research Paper

Assessing the Knowledge and Skills Regarding Partograph Among Nursing Students

The partograph is a critical tool for monitoring labor progress and ensuring maternal and fetal well-being. However, nursing students often face challenges in effectively understanding and utilizing it, influencing labor management outcomes. This cross-sectional descriptive study assessed the knowledge and skill levels regarding partograph use among 125 final-year DGNM and BSc Nursing students in a Chennai college. Data collection involved a 35-item questionnaire for knowledge and case-based scenarios for skill evaluation. Results showed that 66% of students had inadequate knowledge, 33% had moderately adequate knowledge, and only 1% demonstrated sufficient knowledge. Similarly, 67% had inadequate skills, 23% had moderately adequate skills, and 10% demonstrated adequate skills. Significant associations were found between knowledge and skills with demographic variables such as age and educational qualifications (p<0.05), while no significant associations were observed for other variables like religion and participation in in-service education programs. The findings highlight the need for simulation-based training and increased clinical exposure to enhance nursing students' competency in partograph use.

Published by: Blessy Little Christy P

Author: Blessy Little Christy P

Paper ID: V10I6-1386

Paper Status: published

Published: December 9, 2024

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