Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With the advancements in the technologies, deep learning (DL) is a technology that can be trained to mimic human intelligence to recognize various image patterns, audio files and text for accuracy. A deep learning framework empowers correct processing of multiple images for object identification and quick decision abilities without human interventions. Here Convolution Neural Network (CNN) model has been implemented to detect and identify types of noise in the given image. Over the multiple internal iterations to optimize the results, the identified noise is classified with 99.25% accuracy using the Proposed System Architecture (PSA) compared with AlexNet, Yolo V5, Yolo V3, RCNN and CNN. The proposed model in this study proved to be suitable for the classification of mural images on the basis of every performance parameter. The precision, accuracy, f1-score and recall of the PSA are 98.50%, 99.25%, 98.50% and 98.50%, respectively. This study contributes to the development of mural art recovery.
Social media is a platform where people communicate, share content, and build relationships. Due to the current pandemic, many people are turning to social networks such as Facebook, WhatsApp, Twitter, etc., to express their feelings. In this paper, we analyse the sentiments of Indian citizens about the COVID-19 pandemic and vaccination drive using text messages posted on the Twitter platform. The sentiments were classified using deep learning and lexicon-based techniques. A lexicon-based approach was used to classify the polarity of the tweets using the tools VADER and NRCLex. A recurrent neural network was trained using Bi-LSTM and GRU techniques, achieving 92.70% and 91.24% accuracy on the COVID-19 dataset. Accuracy values of 92.48% and 93.03% were obtained for the vaccination tweets classification with Bi-LSTM and GRU, respectively. The developed models can assist healthcare workers and policymakers to make the right decisions in the upcoming pandemic outbreaks.
Using Eucalyptus Systems' private cloud solution, an institute can build Virtual Computing Lab (VCL) that can satisfy the requirements of an institute, but it is assumed that infinite computing resources are available on demand thereby eliminating the need for cloud computing users to plan far ahead for provisioning.. However, due to extensive usage of computational resources, cluster controller (CC) component of Eucalyptus becomes a bottleneck, hampering performance of cloud computing environment. To overcome the drawbacks of Eucalyptus, diffused cloud approach is proposed based on master-slave concept where one master and multiple slaves serve the resources to the clients. This approach improves the performance of the server and would allow cloud servers to extend their computational power by dynamic resource discovery over the network. This architecture allows new clients to request virtual machines, and the server makes the choice of running the requested virtual machine either on previously available slaves, or on the clients who are recently registered into a set of slaves. Thus this architecture reduces the probability of occurrence of network bottlenecks and ensures that sufficient resources are always available to the end users, thus implementing the concept "Cloud never Dies". In order to demonstrate the performance of this novel architecture we provide and interpret several experimental results.
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