Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times.

Open Source Software are gaining popularity over licensed software and Docker is one of them. Docker has many advantages when compared with virtual machines environment. This attracts developers for building micro services that will run over containers. The demand in the market for docker containers is rapidly increasing. Light weight and speed are the factors due to which docker outperforms virtual machine environment. The time required for starting and running a docker container is very less when compared to virtual machine. Different docker containers shares the resources provided by the operating system via kernel. On the top of the operating system kernel, the entire container is executed in a confined way. This paper considers the advantages of docker and proposed a docker deployed applications.

With the advent of time, the fast progress in the communication technologies and evolvement of Internet of Things (IoT), the modern world is able to invisibly intertwine with sensors and different computational devices while sustaining constant network connectivity. The constant connected modern world with computational devices and actuators develop a smart environment. Smart environmental monitoring is a systematic sampling that helps to understand the natural environment such as air, water, biota, and land. In general form, environmental monitoring is the application of IoT. It normally uses the sensor to help in the protection of environment via parameters for monitoring such as quality of air, quality of water etc, and it can include area like monitoring the condition of wildlife. This paper explores IoT architecture in the smart environment as well as a comparison between technologies used in environmental monitoring. It basically emphasizes on various issues and challenges involved in smart environment monitoring. A layered framework of the smart environment is also presented. It is an informative paper for the researchers that suggest effective research in this domain.

The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations.