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e-ISSN: 2455-6491 | Published by Global Advanced Research Publication House (GARPH)

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Archives of International Journal of Advanced Innovative Technology in Engineering(IJAITE)

Volume 9 Issue 1 January 2024



1. Predicting Kidney Disease Using Deep Neural Networks

AUTHOR NAME : Anushree Anand Pande

ABSTRACT : A deep neural network, known as DNN, is very important in machine learning research. Its used in many areas to solve difficult problems. In health, its helping to find diseases like cancer and diabetes in medical images. Kidney disease is a serious problem too. If one kidney stops working, death can happen within a few days. Ignoring kidney problems can lead to a serious disease that causes death slowly over time. Sometimes, symptoms of this kidney disease are not noticed for a long time. The doctors are concerned because many people have this kidney disease. They tried to find a way to solve it, but they have not succeeded yet. They collected information from 400 patients at General Hospital. They used a DNN model to guess if a person had kidney disease or not. The model was right about 98% of the time. They also figured out which information was most important for predicting kidney disease. They found that Creatinine and Bicarbonate were the most important in predicting kidney disease.

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2. E-Waste Identification and Management Using IoT And Deep Learning

AUTHOR NAME : Prof. Prashant Govardhan, Richal Meshram, Prathmesh Birelly, Yash Ramteke

ABSTRACT : E-waste is a rapidly growing global concern due to its hazardous components and environmental impact. Recognizing the gravity of this issue, a groundbreaking solution has been devised in the form of a smart e-waste management system that leverages the power of IoT and Deep Learning object detection. The primary objective of this smart e-waste management system is to address the burgeoning e-waste crisis efficiently. By employing advanced algorithms, the system can accurately identify and categorize different types of electronic waste. The implementation of DL ensures precision and reliability in classifying e-waste items, mitigating the risks associated with manual sorting and disposal. By adopting this smart e-waste management system, it can actively contribute to green initiatives and promote sustainability. The real-time monitoring and efficient sorting of e-waste not only reduce health and environmental risks but also pave the way for the recovery of valuable materials, fostering a circular economy.

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