Noise in computed tomography (CT) images may occur due to low radiation doses. Hence, the main aim of this paper is to reduce the noise from low-dose CT images so that the risk of high radiation dose can be reduced. Background: The novel coronavirus outbreak has ushered in different new areas of research in medical instrumentation and technology. Medical diagnostics and imaging are one of the ways in which the area and level of infection can be detected. Objective: COVID-19 attacks people with less immunity, so infants, kids, and pregnant women are more vulnerable to the infection. So, they need to undergo CT scanning to find the infection level. But the high radiation diagnostic is also fatal for them, so the intensity of radiation needs to be reduced significantly, which may generate the noise in the CT images. Method: This paper introduces a new denoising technique for low-dose Covid-19 CT images using a convolution neural network (CNN) and noise-based thresholding method. The major concern of the methodology for reducing the risk associated with radiation while diagnosing. Results: The results are evaluated visually and using standard performance metrics. From comparative analysis, it was observed that proposed works give better outcomes. Conclusions: The proposed low-dose COVID-19 CT image denoising model is therefore concluded to have a better potential to be effective in various pragmatic medical image processing applications in noise suppression and clinical edge preservation.

Big data entails massive cloud resources for data processing and analysis, which consumes more energy to run. The resources and tasks are increasing exponentially in the cloud environment for the processing of big data, which results in an increment in power consumption to run the cloud data center. So, there is always a scope for optimizing the energy utilization in cloud data centers. This paper presents a visionary architecture in a cloud environment for big data with a proposed energy-efficient strategy based on LSTM-DQN (long-short-term memory-deep Q network) using reinforcement learning (RL). The traditional techniques are not so efficient when the tasks are allocated dynamically, and the generic RL strategies are not able to store the data iterated in the last cycles of processing, so the LSTM is considered for this purpose. In the proposed model, integration of DPSO and DQN is used for better estimation and rectification of the curse of dimensionality. The proposed strategy is compared with different variants of PSO (particle swarm optimization) such as DPSO and QoS-PSO. The improvement in results through proposed model is recoded over the algorithm such as load aware (8.01%), DQN (13.36%), EA-DQN (34.16%), L-No-DEAF (15.62%), DPSO (62.68%), QoS-PSO (72.69%), FFO-EVSM (75.42%), and MIMT (76.39%) on the parameter of energy efficiency, tasks completion time, and energy consumption over the timeline. So, the proposed model is encouraging in the energy-efficient cloud environment for big data with the challenges that the technological world is facing and the emergence of deep learning as one propitious field.

Chronic diseases are the most severe health concern today, and heart disease is one of them. Coronary artery disease (CAD) affects blood flow to the heart, and it is the most common type of heart disease which causes a heart attack. High blood pressure, high cholesterol, and smoking significantly increase the risk of heart disease. To estimate the risk of heart disease is a complex process because it depends on various input parameters. The linear and analytical models failed due to their assumptions and limited dataset. The existing studies have used medical data for classification purposes, which help to identify the exact condition of the patient, but no one has developed any correlation equation which can be directly used to identify the patients. In this paper, mathematical models have been developed using the medical database of patients suffering from heart disease. Curve fitting and artificial neural network (ANN) have been applied to model the condition of patients to find out whether the patient is suffering from heart disease or not. The developed curve fitting model can identify the cardiac patient with accuracy, having a coefficient of determination (R²-value) of 0.6337 and mean absolute error (MAE) of 0.293 at a root mean square error (RMSE) of 0.3688, and the ANN-based model can identify the cardiac patient with accuracy having a coefficient of determination (R²-value) of 0.8491 and MAE of 0.20 at RMSE of 0.267, it has been found that ANN provides superior mathematical modeling than curve fitting method in identifying the heart disease patients. Medical professionals can utilize this model to identify heart patients without any angiography or computed tomography angiography test.

Renewable energy generation is a key to a sustainable future. Efforts are continuously being made to develop new technologies that improve energy harnessing in a more sustainable way. Additionally, existing systems are being improved with modern practices to increase their resilience and efficiency. However, the transition from conventional to digital energy systems brings significant cybersecurity challenges due to the increase in complexity and interconnectedness of energy systems. This paper provides a comprehensive review of cybersecurity challenges in digitizing and integrating renewable energy systems. The paper highlights the intertwined nature of cyber and physical threats, emphasizing how cyberattacks can lead to grid instability and disruptions in power infrastructure. It also provides an understanding of the evaluation of mitigation strategies, including advanced encryption, real-time anomaly detection, and standardized cyber security protocols. The study provides a roadmap for enhancing the security of renewable energy infrastructures, ensuring a reliable and resilient transition to digital energy systems. The findings suggest that while advancements in cybersecurity have improved certain aspects of energy security, significant gaps remain. Therefore, real-time monitoring, regulatory compliance, and industry-wide cooperation are essential for addressing cybersecurity threats.

The revolution in utility computing paradigm is brought by Cloud computing which replaces the traditional software installation. The cloud computing service models are basically classified into three parts i.e. software as a service (Saas), Platform as a service (Paas) and Infrastructure as a service (Iaas). There are four deployment models followed by Cloud computing i.e. Public Cloud, Private Cloud, Hybrid Cloud and Community Cloud. The proposed system comes under the Iaas which provides memory and processor as a resource. Every user wanted to get the services of cloud as they submit the job immediately. In this situation the allocation of resource at real time is a challenging issue. Cloud services are efficiently and optimally allocated to satisfy the requirements of customer. The focus of this paper is detailed and comparative study on different resource allocation strategies by preserving the service level agreement (SLA). The paper brief the algorithms used by the cloud service provider to allocate resources when the multiple jobs arrive with different burst time and different resource requirement.