Background and aims COVID 19 (Coronavirus) pandemic has created surge demand for essential healthcare equipment, medicines along with the requirement for advance information technologies applications. Industry 4.0 is known as the fourth industrial revolution, which has the potential to fulfil customised requirement during COVID-19 crisis. This revolution has started with the applications of advance manufacturing and digital information technologies. Methods A detailed review of the literature is done on the technologies of Industry 4.0 and their applications in the COVID-19 pandemic, using appropriate search words on the databases of PubMed, SCOPUS, Google Scholar and Research Gate. Results We found several useful technologies of Industry 4.0 which help for proper control and management of COVID-19 pandemic and these have been discussed in this paper. The available technologies of Industry 4.0 could also help the detection and diagnosis of COVID-19 and other related problems and symptoms. Conclusions Industry 4.0 can fulfil the requirements of customised face masks, gloves, and collect information for healthcare systems for proper controlling and treating of COVID-19 patients. We have discussed ten major technologies of Industry 4.0 which help to solve the problems of this virus. It is useful to provide day to day update of an infected patient, area-wise, age-wise and state-wise with proper surveillance systems. We also believe that the proper implementation of these technologies would help to enhance education and communication regarding public health. These Industry 4.0 technologies could provide a lot of innovative ideas and solution for fighting local and global medical emergencies.

Industry 4.0 technologies and their applications in fighting COVID-19 pandemic.

The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0

Enabling technologies for fog computing in healthcare IoT systems

A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system

In-Mapper combiner based MapReduce algorithm for processing of big climate data

Display Omitted Novel graph clustering algorithms (kNAS) is proposed, for overlapping community detection in large graph by combining the topological and attribute similarity that partitions the large graph into m clusters having high intracluster and low intercluster similarity.The core node in the graph is identified using Local Outlier Factor. Structural Similarity is based on grouping of nodes based on the neighbourhood of the core node and Attribute Similarity is achieved using Similarity Score.An objective function is defined for the faster convergence of the clustering algorithm.Density and Tanimoto coefficient are the validation measures used to define the effectiveness and quality of the proposed algorithm with the existing algorithms. A simple and novel approach to identify the clusters based on structural and attribute similarity in graph network is proposed which is a fundamental task in community detection. We identify the dense nodes using Local Outlier Factor (LOF) approach that measures the degree of outlierness, forms a basic intuition for generating the initial core nodes for the clusters. Structural Similarity is identified using k-neighbourhood and Attribute similarity is estimated through Similarity Score among the nodes in the group of structural clusters. An objective function is defined to have quick convergence in the proposed algorithm. Through extensive experiments on dataset (DBLP) with varying sizes, we demonstrate the effectiveness and efficiency of our proposed algorithm k-Neighbourhood Attribute Structural (kNAS) over state-of-the-art methods which attempt to partition the graph based on structural and attribute similarity in field of community detection. Additionally, we find the qualitative and quantitative benefit of combining both the similarities in graph.

Graph clustering using k-Neighbourhood Attribute Structural similarity

The rapid development of data generation sources such as digital sensors, networks, and smart devices along with their extensive use is leading to create huge database and coins the term Big Data. Cloud Computing enables computing resources such as hardware, storage space and computing tools to be provided as IT services in a pay-as-you-go fashion with high efficiency and effectiveness. Cloud-based technologies with advantages over traditional platforms are rapidly utilized as potential hosts for big data. However, privacy and security is one of major issue in cloud computing due to its availability with very limited user-side control. This chapter proposes security architecture to prevent and secure the data and application being deployed in cloud environment with big data technology. This chapter discuss the security issues for big data in cloud computing and proposes Meta Cloud Data Storage architecture to protect big data in cloud computing environment. Big Data Security Framework for Distributed Cloud Data Centers Chandu Thota Infosys Ltd., India Gunasekaran Manogaran VIT University, India Daphne Lopez VIT University, India Vijayakumar V. VIT University Chennai, India Big Data Security Framework for Distributed Cloud Data Centers

Big Data Security Framework for Distributed Cloud Data Centers

Infectious diseases are threatening the people’s life because it spreads easily and its impacts are more dangerous. Government should take a necessary action to control the spread of the diseases and implement the prevention policies to secure people. Agent based and compartment simulation models are used for the epidemic disease outbreak. Compartment models are better than the agent based model for quick estimating and require less computer resources. The compartment model is consists of ordinary differential equations, which is used to analyse people behaviours and the spread of the disease. The goal of this study is to develop a new SIR based model for epidemic diseases. A proposed model extended the idea of SELMAHRD model. The model idea is to combine SIjRS and SELMAHRD models. In addition, Multi-hidden layer neural network with non-multiplier is also used in this paper to learn the spread of disease of disease dynamics.

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Modelling the H1N1 influenza using mathematical and neural network approaches

Graph Clustering techniques are widely used in detecting densely connected graphs from a graph network. Traditional Algorithms focus only on topological structure but mostly ignore heterogeneous vertex properties. In this paper we propose a novel graph clustering algorithm, SANS (Structural Attribute Neighborhood Similarity) algorithm, provides an efficient trade-off between both topological and attribute similarities. First, the algorithm partitions the graph based on structural similarity, secondly the degree of contribution of vertex attributes with the vertex in the partition is evaluated and clustered. An extensive experimental result proves the effectiveness of SANS cluster with the other conventional algorithms.

Daphne Lopez

Papers

In-Mapper combiner based MapReduce algorithm for processing of big climate data

Graph clustering using k-Neighbourhood Attribute Structural similarity

Big Data Security Framework for Distributed Cloud Data Centers

Modelling the H1N1 influenza using mathematical and neural network approaches

Graph clustering based on Structural Attribute Neighborhood Similarity (SANS)