This paper surveys and examines how computer-aided techniques can be deployed in detecting pneumonia. It also suggests a hybrid model that can effectively detect pneumonia while using the real-time medical image data in a privacy-preserving manner. This paper will explore how various preprocessing techniques such as X-rays can detect and classify multiple diseases. The survey also examines how different machine learning technologies like convolution neural network (CNN), k-nearest neighbor (KNN), RESNET, CheXNet, DECNET and artificial neural network (ANN) can be used in detecting pneumonia disease. In this article, we have performed a comprehensive review of the literature to find how we can combine hospitals and medical institutions to train the machine learning models from their datasets so that the ML algorithms can detect disease more efficiently and correctly. We have proposed the future work of using transfer learning combined with federated knowledge that could help the medical institutions and hospitals form a combined approach of performing medical image detection using real-time datasets. We have also explored the scope, future work and limitations of the proposed solution.

Malaria is one of the life-threatening diseases caused by the parasite known as Plasmodium falciparum, affecting the human red blood cells. Therefore, it is an important to have an effective computer-aided system in place for early detection and treatment. The visual heterogeneity of the malaria dataset is highly complex and dynamic, therefore higher number of images are needed to train the machine learning (ML) models effectively. However, hospitals as well as medical institutions do not share the medical image data for collaboration due to general data protection regulations (GDPR) and the data protection act (DPA). To overcome this collaborative challenge, our research utilised real-time medical image data in the framework of federated learning (FL). We have used state-of-the-art ML models that include the ResNet-50 and DenseNet in a federated learning framework. We have experimented both models in different settings on a malaria dataset constituting 27,560 publicly available images and our preliminary results showed that the DenseNet model performed better in accuracy (75%) in contrast to ResNet-50 (72%) while considering eight clients, while the trend was observed as common in four clients with the similar accuracy of 94%, and six clients showed that the DenseNet model performed quite well with the accuracy of 92%, while ResNet-50 achieved only 72%. The federated learning framework enhances the accuracy due to its decentralised nature, continuous learning, and effective communication among clients, as well as the efficient local adaptation. The use of federated learning architecture among the distinct clients for ensuring the data privacy and following GDPR is the contribution of this research work.

The Blockchain is an ingenious and remarkable invention. This technology created the backbone of a new type of internet. It was originally designed for the digital currency, Bitcoin, in which you can send digital money to anyone, even a stranger. Originally, it transmits bitcoin. It is considered as a revolutionary technology. Blockchain helps in creating the record whose authenticity can be verified by the entire community. To make centralized and distributed systems it stores the data over a network of computers, which anyone can use this system, no central organization or person owns a system. It contains a number of blocks that contain data. The database of Blockchain is not stored at a single location, it is an open source database which can be accessed by anyone and it is easily verifiable. No one owns the blockchain technology. As the block chain’s nature is distributed, there must be a simple way by which all the nodes reach an agreement; Consensus agreement is a way by which all the participants agree on the contents of the blockchain. Here we will tell you about the basic principles and characteristics of the Consensus Algorithms so that you can decide which of the algorithm the best one to work with is. In this paper, we will research on the number of technical aspects of Blockchain technology, the pace in which this technology is booming and most importantly its implementation on the Bitcoin cryptocurrency which has totally revolutionized the financial infrastructure of the World. Along with this, we will also consider a number of security concerns, challenges and other technical vulnerabilities associated with the Bitcoin technology. The decentralized mechanism, distributed mechanism, scripted mechanism and the mechanism of the password related to Blockchain has opened a new view on the rapidly developing internet technology. There is no need for participants or any third party who are needed to be known to each other. The responsibility is included in the recording, transmission and the activities regarding transferring the storage by distributed technology. That is how the assurance is guaranteed by keeping these aloof from tampering and forged. With the assistance of asymmetric cryptographic algorithm, each and every participant can reach towards consensus on the information of the blockchain. The blockchain technology has the potential to play a key role in case of the information security technology. Hence, in this paper we will also cover the impact of blockchain will ensure the expansion in the sphere of information security.

The world is getting into the practice of implementing 5G network on a business scale; there is research work that is covering the limits beyond 5G (B5G) and 6G. 6G network with the concept of more efficiency and better performance. The demand for network efficiency was always heightened, which is followed by the quality of service and availability. The technology and concept of 6G network are considered by the researchers while taking into account the progression and advancement in mobile applications. This paper will explore the various trend followed by 6G network in terms of its operation and efficiency. It also considers the use of its architectural design which is supported by the combination of emerging technologies notably, Machine Learning and artificial intelligence, 3D featured coverage and advanced protocols to promise the higher efficiency.

A city situated in an urban area that aces in different sectors of the economy, people, governing, strength in human capital, the strong infrastructure of ICT, is said to be a smart city. This smart city term is fairly a recent approach to manage the difficulties of the city life that can mark up the efficiency, minimise the expenses and develops the quality of life of the citizens in the city. This paper is based on the applications of smart cities that can be applied as domains in case for smart transportation, smart recreation and tourism, health, prevention of the crimes and the safety of the community, management of disaster, management of environment, refusal of collection as well as it undertakes the city for management and the smart house and energy as well. The applications of the smart cities have conformance with the vision that is aimed at exploring ICTs such as the Internet of Things (IoT) Technologies to deliver the services. Moreover, the report suggests technical solutions to control energy as well as comfort at homes for the sake of the application of a smart city.

Malaria is one of the life-threatening disease caused by the Anopheles mosquitoes affecting the human red blood cells. It is the global health concern which is quite common in tropical as well as sub-tropical parts of the World. Therefore, it is an important to have an effective computer aided system in place for early detection and treatment. State of the art machine learning techniques are used to detect the infected malaria cells from the pool of images. As the visual heterogeneity of the malaria dataset is highly complex and dynamic, therefore higher number of images are needed to train the machine learning models effectively. However, hospitals as well as medical institutions do not share the medical image data for collaboration due to general data protection regulation (GDPR) and data protection act (DPA). To overcome this collaborative challenge, our research is inspired to use the real-time medical image data while using the framework of federated learning (FL) framework that will ensure the data privacy while mutual collaboration of hospitals and medical institutes. We have used the state of the art machine learning models that include the Resnet50 and densenet in a federated learning framework. We have experimented both models in different settings and our preliminary results showed that the densenet model performed better in accuracy (75%) in contrast to resnet50 (72%) while considering 8 clients, while the trend is observed common in 4 clients with the similar accuracy of 94% and 6 client showed that the densenet model performed quite well with the accuracy of 92% while resnet50 achieving only 72%.