This research paper explores the use of Chat GPT in solving programming bugs. The paper examines the characteristics of Chat GPT and how they can be leveraged to provide debugging assistance, bug prediction, and bug explanation to help solve programming problems. The paper also explores the limitations of Chat GPT in solving programming bugs and the importance of using other debugging tools and techniques to validate its predictions and explanations. The paper concludes by highlighting the potential of Chat GPT as one part of a comprehensive debugging toolkit, and the benefits of combining its strengths with the strengths of other debugging tools to identify and fix bugs more effectively.
First outbreak of COVID-19 was in the city of Wuhan in China in Dec.2019 and then it becomes a pandemic disease all around the world. World Health Organization (WHO) confirmed more than 5.5 million cases and 341,155 deaths from the disease till the time of writing this paper. This new worldwide disease forced researchers to make more precise way to diagnose COVID-19. In the last decade, medical imaging techniques show its efficiency in helping radiologists to detect and diagnose the diseases. Deep learning and transfer learning algorithms are good techniques to detect disease from different image source types such as X-Ray and CT scan images. In this work we used a deep learning technique based on Convolution Neural Network (CNN) to detect and diagnose COVID-19 disease using Chest X-ray images. Moreover, the modified AlexNet architecture is proposed in different scenarios were differing from each other in terms of the type of the pooling layers and/or the number of the neurons that have used in the second fully connected layer. The used chest X-ray images are gathered from two COVID-19 X-ray image datasets and one dataset includes large number of normal and pneumonia X-ray images. With the proposed models we obtained the same or even better result than the original AlexNet with having a smaller number of neurons in the second fully connected layer.
Automation security is one of the main concerns of modern times. A safe and reliable identity verification system is in great demand. A biometric verification system can represent a reliable method of identifying an individual. The knuckle pattern is considered to be one of the emerging hand biometrics because it has the potential to identify individuals. This presented work explores the possibility of using 3D middle finger knuckle for biometric identification. The study provides a new simple, trained from scratch but effective deep convolutional neural network model that designed for 3D figure knuckle print recognition. To the best of the author's knowledge, this is the first successful attempt to develop 3D middle finger knuckle based authentication system using Convolutional Neural Network (CNN). Extensive experiment was carried out using (HKPolyU) the 3D knuckle image database of Hong Kong Polytechnic University. The performance of the proposed CNN model has been evaluated using two session data obtained from different camera lenses. The model designed to be implemented in a real-time system. It can be deployed in a small-scale environment like offices, houses, or personal devices, where the training is very easy. The experimental results were very encouraging and showed the potential of using the 3D middle finger knuckle pattern for biometric applications. The results confirmed that despite various challenges compared with other studies in the same field, the proposed method provides an optimistic solution with an accuracy of 71%.
A trustworthy and secure identity verification system is in great demand nowadays. The automatic recognition of the 3D middle finger knuckle is a new biometric identifier that could offer a precise, practical, and efficient alternative for personal identification. According to earlier studies, deep learning algorithms could be used for biometric identification. However, the accuracy of the current 3D middle finger knuckle recognition model is relatively low. Motivated by this fact, in this study, seven deep learning neural networks have been modified and trained to identify 3D middle finger knuckle patterns using transfer learning. Using the Hong Kong Polytechnic University’s 3D knuckle image dataset, an extensive experiment was performed. Two sessions of data from different camera lenses were used to assess the performance of the suggested deep learning model. The results show that the InceptionV3 method significantly enhanced the recognition of 3D middle finger knuckle patterns with 99.07% accuracy, followed by Xception, NasNetMobile, and DenseNet201 (97.35%, 92.92%, and 92.59%, respectively), which is superior to the current middle finger knuckle recognition model. This accurate, fast, and automatic middle finger knuckle identification will help to be implemented in real-time and small-scale settings like offices, schools, or personal devices like laptops and smartphones, where training is simple.
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