Cyber-physical system (CPS) is an integration of physical processes with computation and communication. It has the ability to add more intelligence to social life. Wireless sensor networks (WSN) can be a vital part of CPS as strong sensing capability is one of the major driving factors for CPS applications. CPS is still considered to be a nascent technology, and there are many challenges yet to be addressed. A few CPS applications in healthcare have been proposed to date, and they lack the flexibility of technology integration, such as integration of computing resources with sensor networks. This paper presents a survey of CPS in healthcare applications that have been proposed to date by academia as well as industry. A comprehensive taxonomy is also provided that characterizes and classifies different components and methods that are required for the application of CPS in healthcare. The taxonomy not only highlights the similarities and differences of the state-of-the-art technologies utilized in CPS for healthcare from the perspective of WSN and Cloud Computing but also identifies the areas that require further research. It is expected that this taxonomy and its mapping to relevant systems will be highly useful for further development of CPS for healthcare.

https://journals.sagepub.com/doi/pdf/10.1155/2014/217415

Review of Cyber-Physical System in Healthcare

http://www.phorminx.de/Publications_files/Fabian_2014_Collaborative_and_Secure_Sharing_of_Healthcare_Data_in_Multi-Clouds.pdf

Collaborative and secure sharing of healthcare data in multi-clouds

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

The Indian Peninsula in general and its southern part in particular has been thought to be a stable shield area and hence inert to younger earth movements and seismicities However, in addition to fast relapsing seismicities, the studies carried out by earlier workers during the past three decades indicate possible pulsatory tectonism, at least since the Jurassics The present study is a newer attempt to identify, analyse, and spatially amalgamate a large number of anomalies visibly displayed by the tectonic, fluvial, coastal, and hydrological systems in remote sensing and ground based datasets/observations, and to finally paint a fair picture on the active tectonic scenario of South India The study reveals that the phenomena, viz extensive soil erosion, reservoir siltation, sediment dump into the ocean, preferential migration of rivers, restricted marine regression, shrinkage of back waters, withdrawal of creeks, fall of groundwater table, etc, indicate two E–W trending ongoing tectonic (Cymatogenic) 

Remote sensing and active tectonics of South India

Green processes are very important for the implementation of green technologies in production to achieve positive sustainability outcomes in the Industry 4.0 era. The scope of the paper is to review how conventional green processes as a part of Industry 4.0 provide sustainability outcomes in manufacturing. The paper is based on the methodology of systematic literature review through the content analysis of literary resources. Twenty-nine studies were included in our content analysis. The results show the main focus of current literature related to Industry 4.0, sustainability outcomes and green processes. The authors present a conceptual Sustainability Green Industry 4.0 (SGI 4.0) framework that helps to structure and evaluate conventional green processes in relation to Industry 4.0 and sustainability. The study summarizes which technologies (big data, cyber-physical systems, Industrial Internet of Things and smart systems) and green processes (logistics, manufacturing and product design) are important for achieving a higher level of sustainability. The authors found that the most often common sustainability outcomes are energy saving, emission reduction, resource optimalization, cost reduction, productivity and efficiency and higher economic performance, human resources development, social welfare and workplace safety. The study suggests implications for practice, knowledge and future research.

https://www.mdpi.com/2071-1050/12/15/5968/pdf

Sustainability Outcomes of Green Processes in Relation to Industry 4.0 in Manufacturing: Systematic Review

Over the the past decade, social networking services (SNS) have proliferated on the web. The nature of such sites makes identity deception easy, providing a fast means for creating and managing identities, and then connecting with and deceiving others. Fake users are those accounts specifically created for purposes such as stalking or abuse of another user, for slander, or for marketing. The current system for detecting deception depends on behavioral, non-behavioral and user-generated content (UGC) information gathered from users. Although these methods have high detection accuracy, they cannot be implemented in databases with massive volumes of data. To address this issue, this paper proposes an enhanced graph-based semi-supervised learning algorithm (EGSLA) to detect fake users from a large volume of Twitter data. The proposed method encompasses four modules: data collection, feature extraction, classification and decision making. Data collected from Twitter using Scrapy is utilized for the evaluation. The performance of the proposed algorithm is tested with existing game theory, k-nearest neighbor (KNN), support vector machine (SVM) and decision tree techniques. The results show that the proposed EGSLA algorithm achieves 90.3% accuracy in spotting fake users.

An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter

Predicting and Identifying behavioral analysis in social media using big data analytics is exceptionally repetitive. Since data in motion are difficult to capture and process with existing innovation. In spite of the fact that there are numerous frameworks that have executed for user behavior analysis, it’s as yet an upcoming and unexplored market that has more prominent potential for better advancements. So with the help of Hadoop framework, a new approach of designing a communal framework is proposed and it’s used for predicting and identifying user’s behavioral analysis in a community. The proposed work can be applied in the community-based environment where the prediction and identification of user behavior analysis has to be made with the semantic web approach. In addition to that, a suitable model for the communally accountable software objects where these objects would observe online communal network information is designed. Further, these objects in online communal network, assess them from the viewpoint of communally accountable performance based on the relation modeling conceptions. The location of communally accountable mediators is grounded in diverse methods. Diverse illustrations are grabbed from the analysis with microblog assessments, community semantic web, higher relations for communal web and social network sourced big information assessments. In the proposed framework, the analysis is based on the assessment/observation of the communally accountable performance of the communal media big data and design of higher-level relations as the model for the above mentioned assessments.

Designing a Framework for Communal Software: Based on the Assessment Using Relation Modelling

A hospital is a health care organization providing patient treatment by expert physicians, surgeons and equipments. A report from a health care accreditation group says that miscommunication between patients and health care providers is the reason for the gap in providing emergency medical care to people in need. In developing countries, illiteracy is the major key root for deaths resulting from uncertain diseases constituting a serious public health problem. Mentally affected, differently abled and unconscious patients can't communicate about their medical history to the medical practitioners. Also, Medical practitioners can't edit or view DICOM images instantly. Our aim is to provide palm vein pattern recognition based medical record retrieval system, using cloud computing for the above mentioned people. Distributed computing technology is coming in the new forms as Grid computing and Cloud computing. These new forms are assured to bring Information Technology (IT) as a service. In this paper, we have described how these new forms of distributed computing will be helpful for modern health care industries. Cloud Computing is germinating its benefit to industrial sectors especially in medical scenarios. In Cloud Computing, IT-related capabilities and resources are provided as services, via the distributed computing on-demand. This paper is concerned with sprouting software as a service (SaaS) by means of Cloud computing with an aim to bring emergency health care sector in an umbrella with physical secured patient records. In framing the emergency healthcare treatment, the crucial thing considered necessary to decide about patients is their previous health conduct records. Thus a ubiquitous access to appropriate records is essential. Palm vein pattern recognition promises a secured patient record access. Likewise our paper reveals an efficient means to view, edit or transfer the DICOM images instantly which was a challenging task for medical practitioners in the past years. We have developed two services for health care. 1. Cloud based Palm vein recognition system 2. Distributed Medical image processing tools for medical practitioners.

Cloud Based Emergency Health Care Information Service in India

The social media offers the firms the capability to assess the feelings towards the contexts and the actions related to them in the real time. Additionally, the initial demarche of the sentiment assessment is the pre-processing of information gathered from the social media. Several prevailing analysis is focused on dealing with the social media by mining fresh characteristics in relation to the sentiment. The objective is to offer the employment of Twitter in a number of designed topics which is the immense social networking sites where the Twitter information is constantly escalating at immense ratio every day which regards it as Big Data source. Followed by which the mechanism is Big Data scheme like infosphere big vision permits the processing of information which is initially gathered from the social networks by Apache Flume and hoarded in Hadoop storage. Moreover, the intention is to combine the perceptions of these assessment outcomes employing big sheets. The assessment based on these investigations approves the Big Data platform creates improved outcomes in terms of assessing the social media.

Envisioning Social Media Information for Big Data Using Big Vision Schemes in Wireless Environment

Health services research provides a multi-disciplinary area of scientific exploration in relation to financial systems, social factors, organizational processes, and health technologies. With the help of big data, the huge amount of data can well be stored and handled effectively for diagnosis and also proper treatment of diseases can be monitored with these emerging technologies. In recent years, Diabetes Mellitus is non-transmittable illnesses that are a matter of concern in most of the developing countries. This paper proposes a model of a statistical assessment, healthcare information system for Diabetes Analysis employing big data. The performance metric such as accuracy and F-measure for the proposed statistical assessment model is evaluated by Hadoop framework, the results are comparatively higher than existing methods.

N. Karthikeyan

Papers

An enhanced graph-based semi-supervised learning algorithm to detect fake users on Twitter

Designing a Framework for Communal Software: Based on the Assessment Using Relation Modelling

Cloud Based Emergency Health Care Information Service in India

Envisioning Social Media Information for Big Data Using Big Vision Schemes in Wireless Environment

Designing statistical assessment healthcare information system for diabetics analysis using big data