AICE23: Artificial Intelligence and Computer Vision in E-waste Management |
Website | https://scholar.google.com/citations?user=YR5LZ6kAAAAJ&hl=en |
Submission link | https://easychair.org/conferences/?conf=aice23 |
Abstract registration deadline | August 31, 2022 |
Submission deadline | November 30, 2022 |
Artificial Intelligence and Computer Vision in E-waste Management (AICE23)
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
- Full papers: This book would be a great learning resource for academic and industry researchers in medical data analysis, and for graduate students taking courses on deep learning for computer vision and managing E-waste technologies. The proposed book would fill the gap between popular oriented, often hyperbolic introductions to electronics in waste management for buddy researchers, and textbooks for professionals. Computer vision is the subfield of Artificial intelligence. The main objective is to enable the machine to understand and interpret the object and classify them. Considering the waste management scenario Electronic wastes are predominantly increasing the problems in the environment by polluting the atmosphere and affecting human health. For managing the waste the computer vision might help the machine to sort out the wastes. In this work the application of artificial intelligence for the investigation of the hazardous pollutants in e-waste is discussed and its effects no the atmosphere and human health are discussed in detail. The strategies developed for the managing of the e-wastes might be discussed in detail. The processing of the e-wastes, its recycling procedures and its economic importance is discussed in detail.
List of Topics
Artificial intelligence-based E-waste Management.
Mobile collection of E-waste using Artificial Intelligence.
Computational Techniques for the segregation of the E-wastes.
Recycling of E-wastes using Artificial Intelligence.
E-waste management using Computer Vision
Sorting of e-wastes using Computer Vision.
Retrieval Algorithms for E-waste Classification.
Waste collection planning using Deep Learning.
Computer vision-based sustainable E-waste collection.
IoT-based E-waste Management system using Computer Vision.
Social impact on the recent methods of E-waste collection
Mobile solution for E-waste collection from Households
Application of Deep learning for the identification of Waste equipment.
E-waste Treatment: Opportunities and Challenges
Indexing: Google Scholar, DOJA, WoS. NO PROCESSING CHARGE
Committees
Organizing Committee
- T. Ananth Kumar
- K. Suresh Kumar
- Sunday A Ajagbe
- Christo Ananth
Publication
AICE23 Book
by Nova Science Publishers
Contact
All questions about submissions should be emailed to: aicenovabook23@gmail.com