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4. Framework for Automatic Blood Group Identification and Notification Alert System.

Author

Dewal, Boby, Memon, Madeha, Rathi, Mahaveer, Memon, Yasra, and Fatima, Knooz

Subjects
BLOOD grouping & crossmatching, BLOOD groups, BLOOD transfusion, BLOOD group antigens, IMAGE processing, GLOBAL Positioning System
Abstract

Image Processing has assisted researchers in a variety of ways, especially in the areas of security and medicine fields. Identifying blood types in emergencies or far-off places and regions where experts have not been available is a present-day challenge. Therefore, we have developed an automatic system that will detect the blood group and notify an alert system using GSM and various image processing methods. Prior to any treatment or operation, it is necessary to determine the blood type for a transfusion of blood, even in an emergency. Currently, technicians manually conduct these tests, which can cause human mistakes. Different systems have been created to automate these tests; none have been successful in completing the analysis in time for emergencies. This project intends to create an automated system to do these tests quickly, adjusting to urgent circumstances. Initially, the slide test is performed to collect the blood images. Furthermore, various image processing methods have been performed for processing images using the PI camera. Subsequently, an alert with the patient's blood group is then generated and sent to the concerned patient or the hospital to immediately consult the patient. Unit testing and load testing were performed on 950 images at a time which yielded 97% accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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5. An automated system for traffic sign recognition using convolutional neural network

Author

Narejo, Sanam, Talpur, Shahnawaz, Memon, Madeha, Rahoo, Amna, Narejo, Sanam, Talpur, Shahnawaz, Memon, Madeha, and Rahoo, Amna

Abstract

TSR (Traffic Sign Recognition) represents an important feature of advanced driver assistance system, contributing to the safety of the drivers, autonomous vehicles as well and to increase driving comfort. In today’s world road conditions drastically improved as compared with past decades. Obviously, vehicle’s speed increased. So, on driver’s point of view there might be chances of neglecting mandatory road signs while driving. This paper explores the system to helps the driver about recognition of road signs to avoid road accidents. TSR is challenging task, while its accuracy depends on two aspects: feature extractor and classifier. Current popular algorithms mainly deploy CNN (Convolutional Neural Network) to execute both feature extraction and classification. In this paper, we implement the traffic sign recognition by using CNN, the CNN will be trained by using the dataset of 43 different classes of traffic signs along with TensorFlow library. The results will show the 95% accuracy.

Published
2020

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