An extensive photographic dataset to classify laptop components for automating e-waste management by recycling old laptops.

Automatic electronic waste management needs to classify components specifically from laptops of different models and brands so that parts can be recycled efficiently using AI-enabled robots. To achieve this goal, a good dataset plays a significant role as the systems that operate e waste management machines will learn from this dataset and act accordingly. The lack of proper datasets that are available publicly related to components of any type of device can be a barrier to the work process.

CottonFabricImageBD: An image dataset characterized by the percentage of cotton in a fabric for computer vision-based garment recycling.

The utilization of computer vision techniques has significantly enhanced the automation processes across various industries, including textile manufacturing, agriculture, and information technology. Specifically, in the domain of textile manufacturing, these techniques have revolutionized the detection of fiber defects and the quantification of cotton content in fabrics. Traditionally, the assessment of cotton percentages was a labor-intensive and time-consuming process that relied heavily on manual testing methods.

ArsenicSkinImageBD: A comprehensive image dataset to classify affected and healthy skin of arsenic-affected people.

Compared to other popular research domains, dermatology got less attention among machine learning researchers. One of the main concerns for this problem is an inadequate dataset since collecting samples from the human body is very sensitive. In recent years, arsenic has emerged as a significant issue for dermatologists.