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The COVID-19 pandemic has placed an enormous strain on healthcare systems worldwide, leading to a need for more efficient methods of identifying the severity of COVID-19 patients to efficiently allocate resources. Existing Xray processing models for identification of COVID-19 are either highly complicated or showcase lower efficiency when applied for real-time scenarios. To overcome these issues, this paper presents a novel approach for identifying the severity of COVID-19 patients using an augmented multimodal X-ray feature representation model. The proposed model combines X-ray images, clinical data, and demographic information to create a robust representation of individual patient condition. The collected information is converted into multidomain feature sets, including frequency, Gabor, Wavelet and entropy components. A customized deep neural network is trained on this representation to predict the severity level of COVID-19 patients. To evaluate the performance of the proposed model, we used a dataset of X-ray images and clinical data from COVID-19 patients. Our results demonstrate that the proposed model outperforms existing methods for identifying COVID-19 severity levels, achieving an accuracy of 98.5% on multiple dataset samples. The proposed model's performance was observed to be promising in terms of precision, recall and delay, thus has the potential to aid in the early identification and effective management of severe COVID-19 cases, thus contributing to the global effort to combat the COVID-19 pandemic under clinical use cases.

After the price swings of crypto-currencies in past years, it has been considered as an asset. As crypto-currency is unpredictable, there arises the requirement of crypto-currency price prediction with greater level of accuracy. For this many researchers uses variety of ML and DL algorithms and are applying them to build a model which will predict crypto-currency price with improved accuracy. To build successful investment plan, accurate prediction is needed. The proposed method uses combination of LSTM and GRU for the bitcoin price prediction in order to find the closing price of bitcoin

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The essential requirement in MANET is now group communication or multicasting since it is used in applications such as network news dissemination, collaborative computing, disaster relief operation, sensor network, military services. In this type of application reliability plays an important role. Designing a reliable multicast protocol in MANET is challenging task due to the dynamic topology, limited bandwidth, constraints of node capability, and frequent disconnections in MANET. In this paper, we propose a scheme called Congestion Control Anonymous Gossip(CCAG) to improve the reliable packet delivery of multicast routing protocols and decrease the variation in the number of packets received by the different nodes. It also consider the issues of reliability, low end to end delay, control overhead and packet delivery in mobile ad-hoc networks. The propose scheme works in two phases. In the first phase any suitable protocol is used to multicast the message to the group, while in second phase, the gossip protocol tries to recover lost messages.