People are preoccupied with their own duties in today’s environment, making it tough to oversee and care for disabled people. Paralyzed and disabled people, on the other hand, have a powerful desire to move around freely. Different initiatives have been taken in the past to improve and preserve the self-esteem of such people, and various technologies have also been created to assist them in a better way. The primary advantage of a vision-based autonomous wheelchair is that it can provide greater independence to users with limited mobility. The methodology of other smart wheelchairs varies depending on the specific technology used. Some smart wheelchairs may use sensors to detect obstacles and provide feedback to the user, while others may incorporate GPS and other navigation technologies to assist with indoor and outdoor navigation. Our study proposes a revolutionary implementation of an autonomous system for fully handicapped people, allowing them to drive wheelchairs using just their eyes. The wheelchair’s orientation will be determined by the orientation of the eye. The camera will follow the movement of the eyes. Furthermore, sensors will be installed on the front side of the wheelchair to identify any obstacles along its path. The calibration of the camera with the human eye, without obstructing its vision, is the most challenging task in this research, besides controlling the wheels of the wheelchair.

Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model: The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93, 95.40 precision, 96.26 recall, and 95.75 F1-score in classifying the silicon wafer defects based on the wafer map images.