Sir Syed University of Engineering and Technology

About: Sir Syed University of Engineering and Technology is a education organization based out in Karachi, Pakistan. It is known for research contribution in the topics: Wireless sensor network & Computer science. The organization has 455 authors who have published 398 publications receiving 1890 citations.

Automatic Number Plate Recognition System for Vehicle Identification Using Optical Character Recognition

Abstract: Automatic Number Plate Recognition (ANPR) is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. The system is implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e.g. Parliament, Supreme Court etc. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is extracted using the image segmentation in an image. Optical character recognition technique is used for the character recognition. The resulting data is then used to compare with the records on a database so as to come up with the specific information like the vehicle’s owner, place of registration, address, etc. The system is implemented and simulated in Matlab, and it performance is tested on real image. It is observed from the experiment that the developed system successfully detects and recognize the vehicle number plate on real images.

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions.

Abstract: With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

An overview of various kinds of wind effects on unmanned aerial vehicle

Abstract: Attitude, speed, and position of unmanned aerial vehicles are susceptible to wind disturbance. The types, characteristics, and mathematical models of the wind, which have great influence on unmanne...

Usage of social media, student engagement, and creativity: The role of knowledge sharing behavior and cyberbullying

Abstract: Despite the increasing use of social media, very little is known about its consequences for students of graduate research training. The purpose of this study is to look into the outcomes of social media usage while answering when and how the use of social media may lead to students' creativity and engagement. Primary data were collected from 383 research students studying in different universities in eastern China. Findings reveal that students’ use of social media is related to their creativity and engagement in graduate research training through knowledge sharing behavior. In addition, cyberbullying was found to play the role of boundary condition such that the mediated relationships are weak for the students facing more cyberbullying. Limitations and future directions, as well as implications for research and practice, are discussed.

Automatic Fish Species Classification Using Deep Convolutional Neural Networks

Abstract: In this paper, we presented an automated system for identification and classification of fish species. It helps the marine biologists to have greater understanding of the fish species and their habitats. The proposed model is based on deep convolutional neural networks. It uses a reduced version of AlexNet model comprises of four convolutional layers and two fully connected layers. A comparison is presented against the other deep learning models such as AlexNet and VGGNet. The four parameters are considered that is number of convolutional layers and number of fully-connected layers, number of iterations to achieve 100% accuracy on training data, batch size and dropout layer. The results show that the proposed and modified AlexNet model with less number of layers has achieved the testing accuracy of 90.48% while the original AlexNet model achieved 86.65% over the untrained benchmark fish dataset. The inclusion of dropout layer has enhanced the overall performance of our proposed model. It contain less training images, less memory and it is also less computational complex.