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As cloud computing continues to evolve, efficiently managing resource allocation and resolving system bottlenecks remain pivotal challenges. This paper explores the application of Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), to these critical issues. We employ the MNIST dataset, typically used for image classification, as a proxy to model and analyze cloud resource bottlenecks. This approach allows us to simulate complex pattern recognition scenarios that are analogous to identifying and resolving bottlenecks in cloud environments. Our study assesses various DL architectures, including Recurrent Neural Networks Residual Networks (ResNets), and CNNs, with our proposed CNN model demonstrating superior performance. It achieved an accuracy of 99.61% within just 10 epochs and excelled in managing unpredictable cloud performance issues caused by resource bottlenecks. These findings underscore the robust potential of CNNs to enhance cloud applications and emphasize the crucial role of DL in addressing cloud computing challenges. Further research is recommended to tailor DL architectures specifically for optimizing cloud resource allocation and system performance. [ABSTRACT FROM AUTHOR]

The surging popularity of generative adversarial networks (GANs) has ignited a wave of innovation in the realm of computer vision, a highly explored subfield of deep learning. GANs are revolutionizing the area of machine learning because they use a game-based training technique. This is in contrast to traditional approaches to machine learning, which center on feature learning and picture production. Several subfields of computer vision have seen tremendous progress thanks to the integration of numerous processing approaches, including image processing, dynamic processing, text, audio, and video processing, as well as generalized generative adversarial networks (GANs). Nevertheless, despite the fact that GANs have made great progress, they still offer promise that has not been fully realized and space for additional development. GANs have a wide range of applications within computer vision, including data augmentation, displacement recording, dynamic modeling, and image processing. This article digs into recent advances made by GAN researchers working in the realm of AI-based security and defense and discusses their accomplishments. In particular, we investigate how well image optimization, image processing, and image stabilization are incorporated into GAN-driven picture training. We want to achieve our goal of providing a complete overview of the present status of GAN research by carefully evaluating research articles that have been subjected to peer review. [ABSTRACT FROM AUTHOR]

The rise in traffic congestion today has necessitated growing research and development in parking management systems to provide real-time indications of the occupancy of indoor and outdoor parking spaces. The main challenge has been developing affordable detection methods based on images to substitute the more expensive sensor-based techniques deployed in indoor environments. With the advancement in computer vision and deep learning, we aim to harness the remarkable performance of convolutional neural networks for carrying out image category recognition tasks to develop a robust parking occupancy detection framework. The classifier was modeled and evaluated with the help of the features learned by the model from the PKLot dataset under varied illuminance and weather conditions. These two models used for parking space detection and classification include – Resnet50 (combined with support vector machine) and VGG16 (combined with OpenCV functionalities). An accuracy of 98.9% was reported with the ResNet and support vector machine model. The model using VGG16 reported an accuracy of 93.4%. Thereby, a low-cost and reliable solution to parking occupancy systems in outdoor systems was arrived at. [ABSTRACT FROM AUTHOR]

Handwritten digit recognition is a complex problem that has stumped even the brilliant minds of this century. Getting precise results from different handwritten samples has been a challenge that needs to be addressed due to the occurrence of this issue in several sectors like document verification, post mail, deciphering, etc. Hence, we introduce our paper as a response to the requirement of an accurate model that can acutely recognize and then predict the handwriting of a variety of individuals with ease. Our model aims to do number recognition through the implementation of neural networks. We tested out different models with each optimizer to verify which model provided the best performance and with which optimizer. Optimizers are an inherent part of Deep learning, and they are used to upgrade the weights, so the model can learn accordingly and get a more accurate system. Instead of just comparing the performance of various optimizers with only one model, we compared different model performances, while trying to select the optimizer that would best suit that learning model. Rigorous training and experimentalizing have resulted in an accuracy of 98.55% for an ANN model employed with an Adagrad optimizer. [ABSTRACT FROM AUTHOR]

This paper develops an integrity protection technique based on image demosaicking. To provide the reversible property, the rebuilt components in color pixels are used for secret data embedding. The random value and the hashed result of the most significant high-order bits of the color components are employed to generate the authentication code for each rebuilt component. In addition, the optimal single bit map block truncation coding (OSBMBTC) technique is adopted for generating recovery codes. A block-based image recovery procedure is designed to reconstruct the modified areas. Experimental results reveal that the illegally tampered objects in the test images can be found in these tests even when 87.5% pixels are modified. From the results, the image quality of the recovery image is acceptable when 50% of pixels are modified. Moreover, the demosaicked image can be reversibly constructed when the embedded image is not tampered. [ABSTRACT FROM AUTHOR]