A Context-Aware Architecture for Personalized Elderly Care in Smart Environments

Much research has focused recently on the development of smart environments and services for human-centered applications for personalized care and improved quality of life. This is especially relevant to support the elderly to lead an active and independent life. Recent efforts exploited the state of art development in the Internet of Things, Smart Sensors grid, Embedded and Wearable systems as well as Cloud Computing to build mathematical models of personal behavior and lifestyle largely driven by big data analytics. In order to overcome the range of challenges associated with the size and heterogeneity of the related data, hardware and software, as well as of the human and social factors involved, a context-aware architecture appropriate for smart environments is needed. This paper describes ACTiVAGE (ACTiVe AGeing sErvices), a conceptual framework for developing Personalized Elderly Care services that leverage big data analytics for context-awareness in smart environments

A Markovian-Genetic Algorithm Model for Predicting Pavement Deterioratio

Pavement structures are constantly deteriorating due to many distresses, for instance cracks and rutting that are initiated and expanded. Deterioration models of pavement structures is an important component of pavement management systems (PMS). The deterioration of pavements has been extensively modeled using Markov chains. This paper aims at formulating a more efficient deterioration model to predict the condition of pavement sections. It is proposed to accomplish this by developing a Markovian deterioration model coupled with a meta-heuristic search optimization method, namely genetic algorithms (GA). An essential component of the Markov chain model is the transition probability matrix. In the proposed model, a standard percentage prediction method was used to calculate the transition probabilities. This is then calibrated by integrating the GA method with the Markov chain. The model is based on the historical international roughness index (IRI) data retrieved from the long-term pavement performance (LTPP) database. To test the validity of the method, a real-life case study is used and the performance of the developed model was assessed using both validation and testing data. For predicting pavement conditions, this study concluded that calibrating calculated transition probabilities using meta-heuristic optimization results in better performance than developing the transition probabilities using classical methods. The Markovian-GA model developed in the present study can be used to predict the future condition of pavement facilities in order to assist engineers in planning the optimum maintenance and rehabilitation (M&R) actions

Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications

© 2018 Elsevier Inc. The over-reliance of today\u27s world on information and communication technologies (ICT) has led to an exponential increase in data production, network traffic, and energy consumption. To mitigate the ecological impact of this increase on the environment, a major challenge that this paper tackles is how to best select the most energy efficient services from cross-continental competing cloud-based datacenters. This selection is addressed by our Cloud-SEnergy, a system that uses a bin-packing technique to generate the most efficient service composition plans. Experiments were conducted to compare Cloud-SEnergy\u27s efficiency with 5 established techniques in multi-cloud environments (All clouds, Base cloud, Smart cloud, COM2, and DC-Cloud). The results gained from the experiments demonstrate a superior performance of Cloud-SEnergy which ranged from an average energy consumption reduction of 4.3% when compared to Based Cloud technique, to an average reduction of 43.3% when compared to All Clouds technique. Furthermore, the percentage reduction in the number of examined services achieved by Cloud-SEnergy ranged from 50% when compared to Smart Cloud and average of 82.4% when compared to Base Cloud. In term of run-time, Cloud-SEnergy resulted in average reduction which ranged from 8.5% when compared to DC-Cloud, to 28.2% run-time reduction when compared to All Clouds

Smart Home Systems Security

© 2018 IEEE. Due to the increase of the Smart Home System market, it has become important to outline and understand the direction and progress needed to ensure that, as Smart Home Systems become more common, the security and functionality of these systems. This research sheds light on what has been done in the field and Smart Home System owners feel currently about the systems they already have, the reasons behind using it as well as what could be done differently to improve its security. The results are presented from feedback received from the questionnaire to provide knowledge and understanding of how a Smart Home System can be improved, and what the main paths of future progress in this area. The ultimate aims of this work are to identify the risks associated with Smart Home Systems and investigate how the risks can be mitigated

Forecasting Natural Events Using Axonal Delay

The ability to forecast natural phenomena relies on understanding causality. By definition this understanding must include a temporal component. In this paper, we consider the ability of an emerging class of neural network, which encode temporal information into the network, to perform the difficult task of Natural Event Forecasting. The Axonal Delay Network (ADN) models axonal delay in order to make predictions about sunspot activity, the Auroral Electrojet (AE) index and daily temperatures during a heatwave. The performance of this network is benchmarked against older types of neural networks; including the Multi-Layer Perceptron (MLP) network and Functional Link Neural Network (FLNN). The results indicate that the inherent temporal characteristics of the Axonal Delay Network make it well suited to the processing and prediction of natural phenomena

Financial time series prediction using spiking neural networks

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. © 2014 Reid et al

A constraint-based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants

© 2018 Elsevier B.V. The loss of coolant accident (LOCA) of a nuclear power plant (NPP) is a severe accident in the nuclear energy industry. Nowadays, neural networks have been trained on nuclear simulation transient datasets to detect LOCA. This paper proposes a constraint-based genetic algorithm (GA) to find optimised 2-hidden layer network architectures for detecting LOCA of a NPP. The GA uses a proposed constraint satisfaction algorithm called random walk heuristic to create an initial population of neural network architectures of high performance. At each generation, the GA population is split into a sub-population of feature subsets and a sub-population of 2-hidden layer architectures to breed offspring from each sub-population independently in order to generate a wide variety of network architectures. During breeding 2-hidden layer architectures, a constraint-based nearest neighbor search algorithm is proposed to find the nearest neighbors of the offspring population generated by mutation. The results showed that for LOCA detection, the GA-optimised network outperformed a random search, an exhaustive search and a RBF kernel support vector regression (SVR) in terms of generalization performance. For the skillcraft dataset of the UCI machine learning repository, the GA-optimised network has a similar performance to the RBF kernel SVR and outperformed the other approaches

Understanding Clinical Work Practices for Cross-Boundary Decision Support in e-Health

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