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Über dieses Buch

This book provides a range of application areas of data and knowledge management and their solutions for the fields related to the convergence of information and communication technology (ICT), healthcare, and telecommunication services. The authors present approaches and case studies in future technological trends and challenges in the aforementioned fields. The book acts as a scholarly forum for researchers both in academia and industry.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Development of an Interoperable-Integrated Care Service Architecture for Intellectual Disability Services: An Irish Case Study

Abstract
The Center for eIntegrated Care (CeIC) in Dublin City University is a research centre with a mission to advance eIntegrated care in order to improve citizen health and wellbeing. The core objective of the CeIC is to inform, develop and advance knowledge on integrated care at the national and international level to support eHealth practices, empower citizens and practitioners. This chapter will provide a summary overview using a case study focused on development of an ontology underpinned by a published International Standard ISO 13940 entitled Health Informatics Systems of Concepts for Continuity of Care (Contsys). The development work focuses on semantic interoperability to support service improvement initiatives to inform the development of core infrastructure for shared intellectual disabilities care services. Based on user defined and agreed needs, the authors illustrate phase one of preliminary development work using a dedicated application to support COVID-19 clients in residential care units. This initial work is used to test an emerging conceptual framework underpinned by the state-of-the-art health informatics standards for knowledge discovery and data integration systems. Involving a scholarship group of intellectual disability service staff and users, a co-participatory design approach has been used to conduct the following methodology.
Subhashis Das, Pamela Hussey

Chapter 2. Big Data Analytics for Healthcare Information System: Field Study in an US Hospital

Abstract
In each year, huge investments on Healthcare Information Systems (HIS) and Health Information Technology (HIT) are being made all over the world. These systems incur huge costs on the hospitals, yet the overall impacts of HISs on hospital performance have not been deeply assessed. Some recent studies attempted to establish the link between the implementation of Healthcare Information Systems and the performance of organizations. However, some questions remain unclear. Do HISs influence different hospitals the same way? How to understand and explain the mechanism that HIS/HIT improves the performance of hospitals? To address these questions, we identify the bottlenecks of the current healthcare systems which affect the operation efficiency, and conduct a field study to expose issues of current healthcare systems and the value of the HIS/HIT and to identify the factors that affect the performance of different hospitals.
Liuliu Fu, Wenlu Zhang, Lusi Li

Chapter 3. Smart City: An Intelligent Automated Mode of Transport Using Shortest Time of Travel Using Big Data

Abstract
The twenty-first century ushered with it the age of smart and predictive technology with the optimum use of resources to make life easier and take preventive measures against predicted mishaps. In accordance to this trend, the proposal is an automated (Society of Automotive Engineers level 5) mode of transport using a modified Dijkstra’s algorithm with concepts of Ant Colony Optimization, big data analytics and cloud computing for number crunching to arrive at the destination based on the traffic and the number of drivers on the road, in the shortest time possible instead of the usual shortest distance algorithms. The analysis of the time saved by the drivers for different map types is recorded and compared, and the results are found to be more efficient with respect to time as compared to the time taken by the original Dijkstra’s algorithm.
Mashrin Srivastava, Suvarna Saumya, Maheswari Raja, Mohana Natarajan

Chapter 4. Context Awareness for Healthcare Service Delivery with Intelligent Sensors

Abstract
The industrialized countries are confronting substantial complications regarding the quality of service and increasing cost incurred in the healthcare imparting sectors. Such complications often get exacerbated with an increase in population that eventually translates into a swarm of chronic and viral diseases. Based upon the symptoms and severity of the ailments, this ultimately calls for tremendous demand for advancing the healthcare services with context-aware wireless sensors. This urgency for sustainable healthcare delivery transforms into a wide range of challenges in the technological domain that could assist in improving traditional well-being services with present societal circumstances. Our research surveys through various applications of a wide variety of medical sensors, especially to control the outbreak of communicable diseases. The usage of intelligent sensors in the healthcare sector is targeted to improve the accessibility of medical amenities, hence expediting medication facilities. The application of smart sensors equipped with context-awareness will revolutionize the healthcare domain and offer sustainable solutions to medical authorities towards designing effective ways to combat life-threatening diseases, irrespective of the geographic widespread.
Shikha Singhal, Adwitiya Sinha, Buddha Singh

Chapter 5. Optimization of Training Data Set Based on Linear Systematic Sampling to Solve the Inverse Kinematics of 6 DOF Robotic Arm with Artificial Neural Networks

Abstract
The amount of data that can be represented in the workspace of a robotic manipulator can be a factor that has a decisive influence on the processing time and that ensures the success of the knowledge extraction algorithms. In this study, two data sets were generated by analyzing the direct kinematics of a six-degree-of-freedom robotic manipulator. The first set was generated with a size greater than 4 billion data and the second set with a quantity greater than 350 thousand data. To solve the data volume problem, a data reduction filtering algorithm based on the linear systematic sampling technique was implemented. To validate the filtering algorithm, the training of two neural network architectures was performed, measuring the performance and generalizability in both networks due to the application of the filter on the data. The network architectures used were a back propagation neural network and a generalized regression neural network. For the first network, the optimal parameters were determined by applying a robust design methodology based on the Taguchi philosophy applied to the design of neural networks. For the second, a comparative performance model was used to determine the best constant propagation value for network training. In the results obtained, an increase in the generalizability was observed when using the data set previously treated by the filter in both network architectures. In the testing stage, a chi-square statistical analysis of less than 5% was considered to validate the application of the filtering algorithm, managing to maintain a prediction of 83% of the test data within the same margin of error.
Ma. del Rosario Martínez-Blanco, Teodoro Ibarra-Pérez, Fernando Olivera-Domingo, José Manuel Ortiz-Rodríguez

Chapter 6. Smart Farming Prediction System Embedded with the Internet of Things

Abstract
Automation and the Internet of Things are the two rising innovations changing the present reality. Because agriculture is a significant, possibly the most important, part of India’s economy, it needs to be connected at the hip with the innovation to save water and enhance efficiency. Water is crucial to life, and its significance has received increased attention because of population growth; the amount of fresh usable water is diminishing. A lack of rain resulting in the shortage of surface and groundwater has likewise brought about a decrease in the volume of water on the planet. The abatement in the water supply has led to food insufficiency, and this is a significant worry worldwide. In the agriculture industry, sensors (for example, those used to recognize soil pH value) are used in the field. To overcome the issues facing irrigation systems, we proposed a system to sense humidity, soil moisture, and diffusive temperature through the sensors. LoRa technology was used to collect data from the sensors. The parameters used included a soil moisture sensor, temperature and humidity sensor DHT11, an LDR light sensor, and the web server – NRF24L01 was used as the transmitter and receiver. The system supports water management decisions and monitors the whole system using a GSM(RS-232) module. A brief study was carried out on the different sensors to check humidity and soil pH value in an automated irrigation system. In addition, the data collected from these sensors can be used to analyze the system, appraise water use, and predict farming risk factors.
R. Mallikka, S. S. Manikandasaran, K. S. Karthick

Backmatter

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