Curriculum guide to teach computed radiography at El Camino College

The purpose of the project was to design a curriculum guideline for educators to teach computed radiography. This project can be used as a stand-alone course, or integrated into existing radiologic technology courses

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

An Integrated and Distributed Framework for a Malaysian Telemedicine System (MyTel)

The overall aim of the research was to produce a validated framework for a Malaysian integrated and distributed telemedicine system. The framework was constructed so that it was capable of being useful in retrieving and storing a patient's lifetime health record continuously and seamlessly during the downtime of the computer system and the unavailability of a landline telecommunication network. The research methodology suitable for this research was identified including the verification and validation strategies. A case study approach was selected for facilitating the processes and development of this research. The empirical data regarding the Malaysian health system and telemedicine context were gathered through a case study carried out at the Ministry of Health Malaysia (MOHM). The telemedicine approach in other countries was also analysed through a literature review and was compared and contrasted with that in the Malaysian context. A critical appraisal of the collated data resulted in the development of the proposed framework (MyTel) a flexible telemedicine framework for the continuous upkeep o f patients' lifetime health records. Further data were collected through another case study (by way of a structured interview in the outpatient clinics/departments of MOHM) for developing and proposing a lifetime health record (LHR) dataset for supporting the implementation of the MyTel framework. The LHR dataset was developed after having conducted a critical analysis of the findings of the clinical consultation workflow and the usage o f patients' demographic and clinical records in the outpatient clinics. At the end of the analysis, the LHR components, LHR structures and LHR messages were created and proposed. A common LHR dataset may assist in making the proposed framework more flexible and interoperable. The first draft of the framework was validated in the three divisions of MOHM that were involved directly in the development of the National Health JCT project. The division includes the Telehealth Division, Public and Family Health Division and Planning and Development Division. The three divisions are directly involved in managing and developing the telehealth application, the teleprimary care application and the total hospital information system respectively. The feedback and responses from the validation process were analysed. The observations and suggestions made and experiences gained advocated that some modifications were essential for making the MyTel framework more functional, resulting in a revised/ final framework. The proposed framework may assist in achieving continual access to a patient's lifetime health record and for the provision of seamless and continuous care. The lifetime health record, which correlates each episode of care of an individual into a continuous health record, is the central key to delivery of the Malaysian integrated telehealth application. The important consideration, however, is that the lifetime health record should contain not only longitudinal health summary information but also the possibility of on-line retrieval of all of the patient's health history whenever required, even during the computer system's downtime and the unavailability of the landline telecommunication network

Satellite Networks: Architectures, Applications, and Technologies

Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

General Course Catalog [2022/23 academic year]

General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp