Blockchain is a recent revolutionary technology primarily associated with cryptocurrencies. It has many unique features including its acting as a decentralized, immutable, shared, and distributed ledger. Blockchain can store all types of data with better security. It avoids third-party intervention to ensure better security of the data. Deep learning is another booming field that is mostly used in computer applications. This work proposes an integrated environment of a blockchain-deep learning environment for analyzing the Electronic Health Records (EHR). The EHR is the medical documentation of a patient which can be shared among hospitals and other public health organizations. The proposed work enables a deep learning algorithm act as an agent to analyze the EHR data which is stored in the blockchain. This proposed integrated environment can alert the patients by means of a reminder for consultation, diet chart, etc. This work utilizes the deep learning approach to analyze the EHR, after which an alert will be sent to the patient's registered mobile number.

Today, humans live in the era of rapid growth in electronic devices that are based on artificial intelligence, including the significant growth in the manufacture of machines that perform intelligent human tasks to solve complex situations. Artificial intelligence will significantly influence the development of many domains, especially the medical domain, which relies heavily on artificial intelligence techniques in diagnosing disease data and manufacturing drugs and vaccines. Artificial intelligence has unexpectedly advanced in helping physicians and healthcare workers save many lives, especially during the spread of the COVID-19 virus. This article reviews some literature that have applied deep learning techniques to detect COVID-19 based on chest x-rays and CT-scans images. This article concluded that deep learning techniques have a fundamental and significant role in diagnosing a big dataset of images and assisting specialists in determining whether a person is infected (positive cases).

Owing to the latest advancements in networking devices and functionalities, there is a need to build future intelligent networks that provide intellectualization, activation, and customization. Software-defined networks (SDN) are one of the latest and most trusted technologies that provide a method of network management that provides network virtualization. Although traditional networks still have a strong presence in the industry, software-defined networks have begun to replace them at faster rates. When network technologies emerge at a steady rate, SDN will be implemented at higher rates in the upcoming years in all fields. Although SDN technology removes the complexity of tying control and data plane together over traditional networks, certain aspects such as security, controllability, and economy of network resources are vulnerable. Among these aspects, security is one of the main concerns that are to be viewed seriously as far as the applications of SDN are concerned. This paper presents the most recent security issues SDN environment followed by preventive mechanisms. This study focuses on Internet control message protocol (ICMP) attacks in SDN networks. This study proposes a security policy protocol (SPP) to detect attacks that target devices such as switches and the SDN controller in the SDN networks. The mechanism is based on ICMP attacks, which are the main source of flooding attacks in the SDN networks. The proposed model focuses on two aspects: security policy process verification and client authentication verification. Experimental results shows that the proposed model can effectively defend against flooding attacks in SDN network environments.

On-demand computing ability and efficient service delivery are the major benefits of cloud systems. The limitation in resource availability in single data centers causes the extraction of additional resources from the cloud providers group. The federation scheme dynamically increases resource availability in response to service requests. The dynamic increase in resource count leads to excessive energy consumption, maximum cost, and carbon footprints emission. Hence, the reduction of resources is the major requirement to construct the optimized cloud source models for profit maximization without considering energy mix and CO2. This paper proposes the novel migration method to reduce carbon emissions and energy consumption. The initial stage in the proposed work is the categorization of data centers based on the MIPS and cost prior to job allocation offers scalable and efficient services and resources to the cloud user. Then, the job with the maximum size is allotted to the VM only if its capacity is less than the cumulative capacity of data centers. A novel migration based on overutilized and underutilized levels provides the services to the user even if the particular VM fails. The proposed work offers efficient maintenance of resource availability and maximizes the profit of the cloud providers associated with the federated cloud environment. The comparative analysis of the proposed algorithm with the existing methods regarding the response time, accuracy, profit, carbon emission, and energy consumption assures the effectiveness in a confederated cloud environment.

Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. Therefore, in this article, a tree hierarchical deep convolutional neural network (T-CNN) optimized with sheep flock optimization algorithm based work load prediction is proposed for sustainable cloud data centers. Initially, the historical data from the cloud data center is preprocessed using kernel correlation method. The proposed T-CNN approach is used for workload prediction in dynamic cloud environment. The weight parameters of the T-CNN model are optimized by sheep flock optimization algorithm. The proposed COSCO2 method has accurately predicts the upcoming workload and reduces extravagant power consumption at cloud data centers. The proposed approach is evaluated utilizing two benchmark datasets:(i) NASA, (ii) Saskatchewan HTTP traces. The simulation of this model is implemented in java tool and the parameters are calculated. From the simulation, the proposed method attains 20.