RESUMEN
The Internet of Things (IoT) and its relevant advances have attracted significant scholarly, governmental, and industrial attention in recent years. Since the IoT specifications are quite different from what the Internet can deliver today, many groundbreaking techniques, such as Mobile Ad hoc Networks (MANETs) and Wireless Sensor Networks (WSN), have gradually been integrated into IoT. The Routing Protocol for Low power and Lossy network (RPL) is the de-facto IoT routing protocol in such networks. Unfortunately, it is susceptible to numerous internal attacks. Many techniques, such as cryptography, Intrusion Detection System (IDS), and authorization have been used to counter this. The large computational overhead of these techniques limits their direct application to IoT nodes, especially due to their low power and lossy nature. Therefore, this paper proposes a Trust-based Hybrid Cooperative RPL protocol (THC-RPL) to detect malicious Sybil nodes in an RPL-based IoT network. The proposed technique is compared and evaluated with state-of-the-art and is found to outperform them. It detects more attacks while maintaining the packet loss ratio in the range of 15-25%. The average energy consumption of the nodes also remains in the ratio of 60-80 mj. There is approximately 40% more energy conservation at node level with an overall 50% increase in network lifetime. THC-RPL has 10% less message exchange and 0% storage costs.
Asunto(s)
Redes de Comunicación de Computadores , Internet de las Cosas , Algoritmos , Confianza , Tecnología InalámbricaRESUMEN
This paper discusses the challenge of modeling in-flight startle causality as a precursor to enabling the development of suitable mitigating flight training paradigms. The article presents an overview of aviation human factors and their depiction in fuzzy cognitive maps (FCMs), based on the Human Factors Analysis and Classification System (HFACS) framework. The approach exemplifies system modeling with agents (causal factors), which showcase the problem space's characteristics as fuzzy cognitive map elements (concepts). The FCM prototype enables four essential functions: explanatory, predictive, reflective, and strategic. This utility of fuzzy cognitive maps is due to their flexibility, objective representation, and effectiveness at capturing a broad understanding of a highly dynamic construct. Such dynamism is true of in-flight startle causality. On the other hand, FCMs can help to highlight potential distortions and limitations of use case representation to enhance future flight training paradigms.
Asunto(s)
Cognición , Lógica Difusa , Análisis Factorial , HumanosRESUMEN
Weight-loss is an integral part of Huntington's disease (HD) that can start before the onset of motor symptoms. Investigating the underlying pathological processes may help in the understanding of this devastating disease as well as contribute to its management. However, the complex behavior and associations of multiple biological factors is impractical to be interpreted by the conventional statistics or human experts. For the first time, we combine a clinical dataset, expert knowledge and machine intelligence to model the multi-dimensional associations between the potentially relevant factors and weight-loss activity in HD, specifically at the premanifest stage. The HD dataset is standardized and transformed into required knowledge base with the help of clinical HD experts, which is then processed by the class rule mining and self-organising maps to identify the significant associations. Statistical results and experts' report indicate a strong association between severe weight-loss in HD at the premanifest stage and measures of certain cognitive, psychiatric functional ability factors. These results suggest that the mechanism underlying weight-loss in HD is, at least partly related to dysfunction of certain areas of the brain, a finding that may have not been apparent otherwise. These associations will aid the understanding of the pathophysiology of the disease and its progression and may in turn help in HD treatment trials.
Asunto(s)
Enfermedad de Huntington/diagnóstico , Movimiento/fisiología , Pérdida de Peso/fisiología , Adulto , Anciano , Encéfalo/fisiopatología , Conjuntos de Datos como Asunto , Progresión de la Enfermedad , Diagnóstico Precoz , Femenino , Humanos , Enfermedad de Huntington/fisiopatología , Masculino , Persona de Mediana Edad , Pruebas Neuropsicológicas/estadística & datos numéricosRESUMEN
In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.
Asunto(s)
Comercio/tendencias , Redes Neurales de la Computación , Simulación por Computador , Modelos Lineales , Modelos EconómicosRESUMEN
Recent epidemiological studies have shown an increased incidence of diabetes worldwide. In United Arab Emirates (UAE), where this study on Emirati children was conducted, over 20% of the population has diabetes. With such a high incidence, there is a need to raise the awareness of the disease in order to reduce the growing number of cases and manage the disease more effectively. This research aims to identify the needs of affected children and proposes a design for an E-learning prototype that can pedagogically raise their awareness and knowledge of the disease. The use of a prototype was chosen in order to validate and refine the usability of the system, and to quickly evaluate user-interface designs without the need for an expensive working model to help refine and develop the system design. The system requirements were identified through a set of interviews with kindergarten teachers, curriculum design experts in UAE, and diabetes nutrition specialists and clinicians. The rationale behind the interview was to identify the optimal age group, describe the appropriate level of the instructional materials and activities, and propose a suitable learning approach that could facilitate and improve diabetes awareness among this age group. The prototype was evaluated by children, teachers, parents (or guardians) and nutrition specialists. We followed a three-stage software development based on a user-informed approach model for stepwise refinement that ranged from prototype to final design. The evaluation results indicate that the proposed computer-supported learning approach can generate positive learning and behavior in children while reducing the time needed to complete awareness tasks when compared to traditional methods; thus making learning more engaging and allowing children to learn at their own pace.
Asunto(s)
Instrucción por Computador , Diabetes Mellitus , Educación a Distancia , Educación en Salud/métodos , Programas Informáticos , Enseñanza/métodos , Preescolar , Femenino , Promoción de la Salud , Humanos , Masculino , Encuestas y Cuestionarios , Emiratos Árabes Unidos , Interfaz Usuario-ComputadorRESUMEN
One of the major concerns of research in integrated healthcare information systems is to enable decision support among clinicians across boundaries of organizations and regional workgroups. A necessary precursor, however, is to facilitate the construction of appropriate awareness of local clinical practices, including a clinician's actual cognitive capabilities, peculiar workplace circumstances, and specific patient-centered needs based on real-world clinical contexts across work settings. In this paper, a user-centered study aimed to investigate clinical practices across three different geographical areas-the U.K., the UAE and Nigeria-is presented. The findings indicate that differences in clinical practices among clinicians are associated with differences in local work contexts across work settings, but are moderated by adherence to best practice guidelines and the need for patient-centered care. The study further reveals that an awareness especially of the ontological, stereotypical, and situated practices plays a crucial role in adapting knowledge for cross-boundary decision support. The paper then outlines a set of design guidelines for the development of enterprise information systems for e-health. Based on the guidelines, the paper proposes the conceptual design of CaDHealth, a practice-centered framework for making sense of clinical practices across work settings for effective cross-boundary e-health decision support.