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The paper aims to be to identify how dashboards can be used to help Information and Communications Technology (ICT) production support teams to manage systems better and giving business stability and performance to be able to be more competitive. This includes forecasting potential problems that can occur as well as prescribe possible solutions before there are any major system outages. Dashboards will track and monitor behaviour of systems to allow for faster turnaround time in resolving issues when it occurs. The analysis and design show how the technologies work together to create a prescriptive dashboard, e.g. alerts sent through to mobile devices (phones) which create a quick reaction time and staff not on site (vendors, remote locations, etc.) can be informed and reduce downtime to the business. The proposed method provides knowledge management requests/incidents/problems and existing monitoring tools can all be integrated to provide the support staff with possible solutions. This paper will explain how dashboards can assist ICT, with the focus on production support areas, to help resolve ICT incidents, problems, and requests. A few concerns were highlighted however these concerns form part of the continuous improvements to the prescriptive dashboards. The system model will help minimize the run around between various ICT teams for troubleshooting. The flow diagram shows how it would provide early warnings and recommended solutions based on calculations of previous ICT incidents and problems. This model is complementary in the sense that it will work/integrate with existing system monitoring tools. Various types of technologies have been identified and researched through the review of the literature. The research was conducted to identify the feel for dashboards, how they can be used, and the various technologies that can complement each other to get the success of a prescriptive dashboard.

This work proposes a novel multivariate-multiscale approach for computing the spectral and temporal entropies from the multichannel electroencephalogram (EEG) signal. This facilitates the recognition of three human emotions: positive, neutral, and negative. The proposed approach is based on the application of the Fourier-Bessel series expansion based empirical wavelet transform (FBSE-EWT). We have extended the existing FBSE-EWT method for multichannel signals and derived FBSE-EWT based multivariate Hilbert marginal spectrum (MHMS) for computing spectral Shannon and K-nearest neighbor (K-NN) entropies. The multivariate FBSE-EWT decomposes the multichannel EEG signals into narrow band subband signals. The multiscaling operation adapted in the spectral domain is based on the selection of successive joint instantaneous amplitude and frequency functions of the subband signals. On the other hand, the time domain multiscale K-NN entropy is computed from the cumulatively added multidimensional subband signals. The extracted spectral and temporal entropy features are smoothed and fed to the sparse autoencoder based random forest (ARF) classifier architecture for emotion classification. The proposed approach is tested using multichannel EEG signals available in a public database (SJTU emotion EEG dataset (SEED)). The bivariate EEG signals from different channel pairs with distinct spatial locations over the scalp are considered as input to our proposed system. The obtained overall classification accuracy of 94.4% reveals that the proposed approach is useful in classifying human emotions. The method is also validated using DREAMER emotion EEG public database. The method outperforms the existing state-of-the-art methods evaluated in these databases.

In several countries, the ageing population contour focuses on high healthcare costs and overloaded health care environments. Pervasive health care monitoring system can be a potential alternative, especially in the COVID-19 pandemic situation to help mitigate such problems by encouraging healthcare to transition from hospital-centred services to self-care, mobile care and home care. In this aspect, we propose a pervasive system to monitor the COVID’19 patient’s conditions within the hospital and outside by monitoring their medical and psychological situation. It facilitates better healthcare assistance, especially for COVID’19 patients and quarantined people. It identifies the patient’s medical and psychological condition based on the current context and activities using a fuzzy context-aware reasoning engine based model. Fuzzy reasoning engine makes decisions using linguistic rules based on inference mechanisms that support the patient condition identification. Linguistics rules are framed based on the fuzzy set attributes belong to different context types. The fuzzy semantic rules are used to identify the relationship among the attributes, and the reasoning engine is used to ensure precise real-time context interpretation and current evaluation of the situation. Outcomes are measured using a fuzzy logic-based context reasoning system under simulation. The results indicate the usefulness of monitoring the COVID’19 patients based on the current context.

According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semiautomated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research develops a deep convolutional neural network (deep CNN) models for predicting the reason behind the driver’s distraction. The deep CNN models are trained using numerous images of distracted drivers. The performance of deepCNNmodels, namely theVGG16,ResNet, and Xception network, is assessed based on the evaluation metrics, such as the precision score, the recall/sensitivity score, the F1 score, and the specificity score. The ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’ activities.

Internet of Things (IoT), Information and Communication Technology (ICT), and data mining technologies are three prime pillars of any smart city project. The intelligent urban transportation system is an indispensable part of any smart city and acts as a major contributor to a city's vitality. An extensive amount of vehicular information is being amassed by Wireless Sensor Networks (WSNs), Vehicular Ad Hoc Networks (VANETs), and Radio-Frequency Identification (RFID) based Intelligent Transportation System (ITS), which can provide an insightful image of various ITS issues concerning safety, security, and management. Merely depending on WSN and RFID predicated technologies for the construction of ITS is not adequate to realise smart city dream in developing countries due to technological and monetary barriers. Considering this, an Intelligent Vigilare System (IVS) for ITS is proposed, which sanctions network implementation without disrupting the current vehicular infrastructure through multifaceted modeling.

In an uncontrolled environment, many of the face detection algorithms lack robustness due to their design. The present research on unconstrained face detection is focused on handcrafted and visual features in isolation. We propose a novel approach to use handcrafted as well as visual features together for improvement in face detection to achieve robustness. The algorithm uses a side-view face detector, which divides the problem space into two: side view face detection and frontal face detection. For frontal faces, Discrete Wavelet Transform (DWT) followed by the encoding of Eyes like landmarks (ELL) pixels is proposed in this work. A Human trait that helps to make decisions even better when they are taken together with the help of more than one decision makers is modeled in this work. To achieve this, a combination of handcrafted and visual features is used. Further to improve classification and to provide a better decision, a faster second stage classification scheme is introduced. The result shows an improvement when handcrafted and visual features are combined instead of using them separately.

Blockchain, a form of distributed ledger technology has attracted the interests of stakeholders across several sectors including healthcare. Its’ potential in the multi-stakeholder operated sector like health has been responsible for several investments, studies, and implementations. Electronic Health Records (EHR) systems traditionally used for the exchange of health information amongst healthcare stakeholders have been criticised for centralising power, failures and attack-points with exchange data custodians. EHRs have struggled in the face of multi-stakeholder and system requirements while adhering to security, privacy, ethical and other regulatory constraints. Blockchain is promising amongst others to address the many EHR challenges, primarily trustless and secure exchange of health information amongst stakeholders. Many blockchain-in-healthcare frameworks have been proposed; some prototyped and/or implemented. This study leveraged the PRISMA framework to systematically search and evaluate the different models proposed; prototyped and/or implemented. The bibliometric and functional distribution of all 143 articles from this study were presented. This study evaluated 61 articles that discussed either prototypes or pilot or implementations. The technical and architectural analysis of these 61 articles for privacy, security, cost, and performance were detailed. Blockchain was found to solve the trust, security and privacy constraints of traditional EHRs often at significant performance, storage and cost trade-offs.

This study aims at understanding how a user's emotions fluctuate when undertaking certain tasks on a social media platform such as Facebook or other software products which may have emotional effects on its user. Specifically, we explored the difference in the usability aspect of Facebook concerning frequent and new Facebook users. The study involves a qualitative study on eighteen participants, nine of whom were Facebook users and nine non-Facebook users who had never used Facebook before participating in this study. During the testing procedure, users were asked to complete several tasks on Facebook, while the electrophysiological activity of their brain was recorded using an EEG (electroencephalogram) acquisition system. Certainly, this study can be applied to any software product, before its release, to improve its user interface by acquiring insight into how user-friendly it is for new users when compared to frequent users. Additionally, a correlation in user friendliness between new users and frequent users is investigated. Furthermore, the study will help us discern which parts of the brain had the most significant difference between groups and discuss the motives behind an individual's emotional state, concerning user experience. Based on the analysis of the power spectrum of the characteristic brain waves, this research establishes that there is a substantial statistical difference between new and frequent Facebook users. Also, it resulted that there is a significant difference between the central, temporal and occipital lobes of new and frequent users. These results will assist developers in creating optimal and user-friendly software products.

Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain's trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.

Since the early 1990s, there has been a lot of enthusiasm for using high-speed connectivity to develop local community links through education, employment possibilities, fostering community events, and enhancing overall sociability within a local region. 5G is the 5th iteration of a broadband network operating on cellular systems. 5G is not only for mobile phones, but it is also the foundation for virtual reality (VR); the internet of things (IoT); and autonomous transport, immersive services, and public infrastructure; and connecting many electronic devices to the internet. In this chapter, first, the authors have discussed the evolution of 1G network to 6G networks by focussing on its potential impact on the quality of life. Further, 5G applications in IoT, autonomous transport, immersive services, and public infrastructure have been discussed. Then the chapter discusses popular advantages, limitations in the current technologies, implementations, and future perspective.

5G is a new universal wireless standard, a new form of mobile network engineered to bring everyone and everything virtually together. 5G is not only for mobile phones, but it is also the foundation for virtual reality (VR), the internet of things (IoT), and autonomous driving, connecting many electronic devices to the internet. Having good healthcare is very important as it affects all parts of human life and social well-being. Moreover, it is crucial to have a great healthcare system if we want economic growth, workforce productivity, and society to advance. Despite all the hard work done by scientists and medical professionals, today's healthcare is mainly inefficient, and a significant overhaul is required. This chapter discusses the primary advantages, including the 5G's main features in healthcare and their limitations and probable solutions and applications to the latest scenario.

This paper reviews the existing technologies for the development of cross-platform applications. Both Server-side as well as client-side applications for web, desktop and mobile devices are considered which mainly consists of smart phones and tablets. Further, based on the review process and research issues a web-based development approach is recommended for the development of truly cross-platform applications across devices and operating systems.

Cloud computing is not associated with a specific technology, instead it as an alternative method to deliver technology as a service. This article investigates current cloud computing adoption in the United States (USA) and United Kingdom (UK) hedge fund industry. Hedge fund technologists, prime service consultants, technology service providers, industry application vendors, investors and an independent information security consultant participated were surveyed for this article. The article acknowledges the growth of cloud computing in the hedge fund sector. This research work also highlights that the private cloud definition is vague and requires further classification, elaborating on the variants of private cloud. This is important as the variants of private cloud computing offer varying benefits and risk which the hedge fund sector has proven to be sensitive. Equally, this article argues that some of the current security concerns are over-stated and perhaps reflect a conservative decision making framework rather than a realistic consideration of the options.

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

The Internet of Things (IoT), a booming technology, will change the way we look at the world today. IoT-based solutions facilitate business by improving and simplifying tasks. The IoT consists of a huge number of sensors, each with a unique address, grouped to form a network entity that exchanges data internally or with other internetworks and intranetworks, using different internet protocols. The main aim of the proposed system is to design a Wi-Fi controlled robotic car that can be used for surveillance applications. A complete description of the block diagram, the flow chart, and the working of the proposed system are discussed in the following sections. Geared DC motors are used to direct the movement of the spy-bot. The HTML scripting language is used to design our webpage, a platform through which we can access the spy-bot. The spy-bot can be compliant for military purposes in remotely monitoring intrusions along the border.

Monitoring the environment plays a significant role in the evolution of a smart city with enhanced safety, comfort, and security. One promising technology for smart environment monitoring is the radar-based moving object tracking and classification. Radar-based technology is known to provide better solutions compared to vision-based systems as the latter is prone to the adverse effects of lighting and weather conditions. Radars operating in mm-wave range are preferred due to small size, good angular resolution, small apertures, and low cost. The signature waveform received from the moving object is used to estimate the range and the velocity of multiple targets based on Doppler drift. Also, the micro-Doppler effect on the return signal that occurs due to the micromotion dynamics such as vibrations or rotations can be used to identify the specific type of the target. This book chapter provides an overview of different types of radars and their respective signal processing aspects used for detecting the human and animal activity. Moreover, this chapter explains the deep learning algorithms used for detection and classification of human and animal activity. Thus the intelligent radar systems add greater value to the smart environment compared to other non-radar methods.

It has always been a human desire to see how far up into the sky their creation can reach. It is like humanity to push further than ever and create things better than anyone before they only dreamt of. This complexity of achievements achieved by human beings, however, brings forward other challenges that need to be faced by present scientists.

Cloud computing is spreading its scope to the different industries, where sensitive data are being maintained. The healthcare industry is facing the challenge of data integrity, confidentiality and huge operational cost of the data. Hence, there is a need to adopt the cloud-based clinical decision support system (CCDSS) in order to maintain the Electronic Medical Records (EMRs), where patients and healthcare professional can easily have access to their legitimate records irrespective of the geographical location, time and cost-effectively through a decision support system. The main objective of this research work is to build a clinical decision support system (DSS) through the deployment of a private Cloud-based System Architecture. This research work explored the pros & cons, challenges that could be faced during implementing a (CCDSS) in a Healthcare for optimization of the resources with time, money, and efforts along with the patient data security. A quantitative survey was conducted to gather the opinions of healthcare stakeholders. Based on the findings of the survey, a rule-based DSS was developed using a Data Flow Modelling tool that facilitated the Cloud deployment of applications and in-House private cloud deployment to ensure the security of data, protection of data, and the regulatory compliance policies in the health organizations. In order to evaluate the proposed system, the User Acceptance Testing was carried out to determine how well the proposed CCDSS meets the requirements.

Life becomes exceptionally less difficult in all stages with the improvement of automation innovation. Today, programmed techniques are being picked over manual strategies. The Web has turned into a part of life, and the Internet of things (IoT) is the cutting edge technology developing web expertise in accordance with the rapid increase in the quantity of web clients. From mechanical apparatus to client, the IoT is creating a system of regular articles that can share information and complete obligations when you are occupied with different happenings. In this chapter, we present an IoT-based environment monitoring framework utilizing the WSN innovation. The primary goal of the framework is to give environmental parameters at remote areas utilizing the web. This proposed framework speaks to the environmental parameter monitor utilizing wireless sensors associated with the Internet. There are two distinctive sensor nodes in the framework. Clients can observe the information by means of the web application from anyplace on the Internet. In the event that the sensor node information surpasses the designed range in the web application, a notice message is sent to clients to improve environmental conditions.

Video searching in search engines uses metadata information to find the relevant videos according to the search queries. Metadata information mainly comprises the title and description of the video. The major drawback of this approach is that it overlooks whether or not the content of the video is genuine or not. Since the metadata information is provided by the uploader, the person may provide false information about it. Therefore, there is a need of improving the results of video searched. The proposed work classifies the video in different categories and then compares the tag provided to each video with the tags that were extracted from the metadata of the video. The other factor like views count, likes and dislikes, comments is also considered for the ranking of the video searched. It improves the genuineness of the content of the video searched.

Background Years of health information system investment in many countries have facilitated service delivery, surveillance, reporting, and monitoring. Electricity, computing hardware, and internet networks are vital for health facility–based information systems. Availability of these infrastructures at health facilities is crucial for achieving national digital health visions. Objective The aim of this study was to gain insight into the state of computing hardware, electricity, and connectivity infrastructure at health facilities in Sierra Leone using a representative sample. Methods Stratified sampling of 72 (out of 1284) health facilities distributed in all districts of Sierra Leone was performed, factoring in the rural-urban divide, digital health activity, health facility type, and health facility ownership. Enumerators visited each health facility over a 2-week period. Results Among the 72 surveyed health facilities, 59 (82%) do not have institutionally provided internet. Among the 15 Maternal and Child Health Posts, as a type of primary health care unit (PHU), 9 (60%) use solar energy as their only electricity source and the other 6 (40%) have no electricity source. Similarly, among the 13 hospitals, 5 (38%) use a generator as a primary electricity source. All hospitals have at least one functional computer, although only 7 of the 13 hospitals have four or more functional computers. Similarly, only 2 of the 59 (3%) PHUs have one computer each, and 37 (63%) of the PHUs have one tablet device each. We consider this health care computing infrastructure mapping to be representative with a 95% confidence level within an 11% margin of error. Two-thirds of the PHUs have only alternate solar electricity, only 10 of the 72 surveyed health facilities have functional official internet, and most use suboptimal computing hardware. Overall, 43% of the surveyed health facilities believe that inadequate electricity is the biggest threat to digitization. Similarly, 16 (22%) of the 72 respondents stated that device theft is a primary hindrance to digitization. Conclusions Electricity provision for off-electricity-grid health facilities using alternative and renewable energy sources is emerging. The current trend where GSM (Global System for Mobile Communication) service providers provide the internet to all health facilities may change to other promising alternatives. This study provides evidence of the critical infrastructure gaps in health facilities in Sierra Leone.

The development of new lighting sources, such as light emitting diode (LED), induction, and plasma, presented more possible cost effective ways for roadway lighting. A study was therefore conducted for the Indiana Department of Transportation (INDOT) to evaluate the performance and effectiveness of some selected new lighting devices in roadway lighting. This paper describes the field evaluation process and presents the evaluation results. A number of LEDs, plasma and induction luminaires from various manufacturers were selected to replace the existing high pressure sodium (HPS) lamps in conventional and high mast lightings. Illuminance values were measured over a period of 12 months on the existing and new light sources. Light performance metrics, including illuminance level and uniformity ratios, were calculated to make quantitative comparisons of the HPS and new types of light devices. Based on the evaluation in terms of lighting performance and life cycle costs, it was concluded that LED luminaires should be utilized in roadway lighting in place of HPS luminaires. The results of this study will be useful to state highway and city street agencies in making decisions on their lighting policies and developing technical specifications for use of the new lighting technologies in roadway and street lightings. The study provides a basis for manufacturers to improve their luminaire design and integration to better fit the needs of roadway and street lightings. Keywords: Roadway lighting, Luminaires, Illuminance, Field tests, LED, HPS

Due to its interpretability and intuitiveness, survival tree based analysis is a powerful Artificial Intelligence method for modelling longitudinal survival data, its relationship with covariates and the interrelationship between covariates. Furthermore, it is being increasingly used for a range of applications including clustering, prognostication and classification. Phase type survival tree methods have been demonstrated to have important applications, including clustering patients into clinically meaningful groups, patient pathway prognostication and forecasting bed requirements. In this article, we critically investigate and assess several selection information criteria with regards to their suitability and limitations when used as splitting criteria in phase type survival tree construction. As shown in Table 12, the results of this analysis are compared and discussed. Furthermore, a text mining approach is utilised to further assess correlations, which have been extracted from hospital data, between the three underlying diseases and the two different types of population groups, namely age and gender groups. Its aim is to provide further investigative tools. In fact, due to its ability to analyse large volumes of textual data, text mining can provide a useful approach to this research area.

This research paper is all about a system Kitchen Genie an intelligent kitchen that proposes a system that would make the household, especially the kitchen, intelligent. This will be done by keeping track of its inventory automatically, without the user having to interact with the products. This system would not only be useful in a household but it can also be adapted on a larger scale such as a restaurant kitchen or in a supermarket. It could be very useful for those families that both parents work and when they get home have to do all the housework besides going shopping, etc. Therefore, Kitchen Genie would save a lot of time. It could also save a lot of money for those people who have a large storage space. This makes it hard to keep track of what items they have and end up throwing them away because they are past their expiry date. This system does all that is mentioned above with the use of a NEX-6810 Ultra-High Frequency Mid-Range Radio Frequency Identification Reader and Higgs3 stickers. We have also designed a hardware (Circuit Diagram) and tested it to show the results.

Emotion recognition from electroencephalography (EEG) signals is a very cost-effective method to monitor the general well-being of an individual, an employee of an organization, or to cater to mental health patients. But it is a challenging task owing to the non-stationarity of the EEG signals. Extracting relevant features through signal processing techniques that can be used to classify patterns in the EEG signal leading to different emotions is a difficult task. A dataset for emotion analysis with physiological signals DEAP [1] consists of EEG signals of 32 participants are categorized on the quadrant of valence, arousal, dominance, and liking, which signifies how they are associated with different emotions. In this paper, an efficient classifier for emotion/quadrant recognition from EEG signals with exceptional accuracy is presented. The data preprocessing strategy adapted is Empirical Mode Decomposition (EMD), which decomposes the signals into several oscillatory Intrinsic Mode Functions (IMF). Features are extracted from Second order difference plots (SODP) are area, mean, and central tendency measure of the elliptical region. Wilcoxson Test was performed to ensure the statistical significance of the extracted features with p

We provide a framework for simulating the entire patient journey across different phases (such as diagnosis, treatment, rehabilitation and long-term care) and different sectors (such as GP, hospita...

This paper reviews the literature to understand the current state of Corporate Social Responsibility (CSR) communication. The authors analyze not only the importance of CSR communication for businesses but also review the literature that deals with both digital and traditional tools adopted for CSR communication. This is followed by a discussion on how today's businesses are more aware of the importance of communicating CSR to their stakeholders. Furthermore, the literature review attempts to investigate how businesses are implementing both digital and traditional tools in a complementary way for their CSR communication strategy.

Access to standard and economical health care delivery, availability of significant health information are viewed as some of the most functional public health interventions in present-day history. Despite that, current information obtained from the WHO regarding Nigeria’s health condition shows that the average existence expectancy is at 54 years and maternal mortality at 608 per 100,000 live births as a result of poor health care services. Several aspects of health informatics have been applied to solve these challenges such as the transformation of records from manual to electronic. Among these are the telemedicine and socialized healthcare, which have been barely adopted in developing nations. This work thus proposes an architectural framework for a cloud-supported socialized healthcare system. In order to achieve this; a web-based application software was designed and implemented through the use of cloud computing technology platforms and server side scripting tools. This study proves that socialized healthcare will really go a long way in defeating barriers of viable human access and delivery., peer-reviewed

The aim of this paper is to demonstrate that small dataset can be used in machine learning for seizure monitoring and detection using smart organization of multichannel EEG sensor data. This reduces training time and improves computational performance in terms of space and time complexities on hardware implementations. The proposed approach has been tested and validated using CHB-MIT dataset containing EEG recordings of 24 clinically verified seizure and non-seizure pediatric patients. The predictability is discussed in terms of the latency and the required length of data for the proposed approach over the state-of-the-art method in the field of EEG-based seizure prediction.

Business has come to a point whereby there is a substantial reliance on computing devices. People using machines need to be identified by who they are and not what they know or have. Real identity is the individual. Biometrics is the way to go if this is to be achieved. The biometric is a statistical measurement of biological data; be it physical, chemical or electric. Brain Computer Interface (BCI) is a maturing technology, becoming more affordable, easier to use and integrate with computing devices. With the increasing need of the internet based services (web and cloud services), it has become paramount for identification technologies to get close to natural means of identification, and biometrics offers this mechanism because it is a genuine identity of an individual. Therefore, this work aims to explore the feasibility that computer systems can use brain signals as a biometric method for authentication for web and cloud services users through a brain-computer interface (BCI). Though the main aim of this study is not to create a perfect solution, yet use the prototype as a suggestion to spur and provoke identification mechanisms to catch on in this direction. This study has been validated using the electroencephalography (EEG) scans using the Emotiv’s 5-channel EEG headset and recording devices and the survey of the 52 volunteers participated in the study. As the same brain signals authentication can be used for all services irrespective of service or provider, therefore, it can be an ideal candidate for the Biometrics-as-a-Service (BaaS). The brain scan can be stored once at the BaaS provider and would be used by all service providers for all services. It can also be combined with other authentication methods to have multi-factor authentication (MFA) for improved security.

Effective resource requirement forecasting is necessary to reduce the escalating cost of care by ensuring optimum utilization and availability of scarce health resources. Patient hospital length of stay (LOS) and thus resource requirements depend on many factors including covariates representing patient characteristics such as age, gender, and diagnosis. We therefore propose the use of such covariates for better hospital capacity planning. Likewise, estimation of the patient's expected destination after discharge will help in allocating scarce community resources. Also, probable discharge destination may well affect a patient's LOS in hospital. For instance, it might be required to delay the discharge of a patient so as to make appropriate care provision in the community. A number of deterministic models such as ratio-based methods have failed to address inherent variability in complex health processes. To address such complexity, various stochastic models have therefore been proposed. However, such models fail to consider inherent heterogeneity in patient behavior. Therefore, we here use a phase-type survival tree for groups of patients that are homogeneous with respect to LOS distribution, on the basis of covariates such as time of admission, gender, and disease diagnosed; these homogeneous groups of patients can then model patient flow through a care system following stochastic pathways that are characterized by the covariates. Our phase-type model is then extended by further growing the survival tree based on covariates representing outcome measures such as treatment outcome or discharge destinations. These extended phase-type survival trees are very effective in modeling interrelationship between a patient's LOS and such outcome measures and allow us to describe patient movements through an integrated care system including hospital, social, and community components. In this paper, we first propose a generalization of the Coxian phase-type distribution to a Markov process with more than one absorbing state; we call this the multi-absorbing state phase-type distribution. We then describe how the model can be used with the extended phase-type survival tree for forecasting hospital, social, and community care resource requirements, estimating cost of care, predicting patient demography at a given time in the future, and admission scheduling. We can, thus, provide a stochastic approach to capacity planning across complex heterogeneous care systems. The approach is illustrated using a five year retrospective data of patients admitted to the stroke unit of the Belfast City Hospital.

Stroke disease places a heavy burden on society, incurring long periods of hospital and community care. Also stroke is a highly complex disease with heterogeneous outcomes and multiple strategies for therapy and care. In this article we develop a modeling framework that clusters patients with respect to their length of stay (LOS); phase-type models are then used to describe patient flows for each cluster. In most cases, there are multiple outcomes, such as discharge to normal residence, nursing home, or death. We therefore derive a novel analytical model for the distribution of LOS in such situations. A model of the whole care system is developed, based on Poisson admissions to hospital, and results obtained for expected numbers in different states of the system at any time. We can thus describe the whole integrated system of stroke patient care, which will facilitate planning of services. We also use the basic model to build a discrete-event simulation, which incorporates back-up queues to model delayed discharge. Based on stroke patients' data from the Belfast City Hospital, various scenarios are explored with a focus on the potential efficiency gains if LOS, prior to discharge to a private nursing home, can be reduced. Predictions for bed occupancy are also provided. The overall modeling framework characterizes the behavior of stroke patient populations, with a focus on integrated system-wide planning, encompassing hospital and community services. Within this general framework we can develop either analytic or simulation models that take account of patient heterogeneity and multiple care options.

While literature reviews with a large-scale scope are nowadays becoming a staple element of modern research practice, there are many challenges in taking on such an endeavour, yet little evidence of previous studies addressing these challenges exists. This paper introduces a practical and efficient review framework for extremely large corpora of literature, refined by five parallel implementations within a multi-disciplinary project aiming to map out the research and practice landscape of modelling, simulation, and management methods, spanning a variety of sectors of application where such methods have made a significant impact. Centred on searching and screening techniques along with the use of some emerging IT-assisted analytic and visualisation tools, the proposed framework consists of four key methodological elements to deal with the scale of the reviews, namely: (a) an incremental and iterative review structure, (b) a 3-stage screening phase including filtering, sampling and sifting, (c) use of visualisation tools, and (d) reference chasing (both forward and backward). Five parallel implementations of systematically conducted literature search and screening yielded a total initial search result of 146 087 papers, ultimately narrowed down to a final set of 1383 papers which was manageable within the limited time and other constraints of this research work., peer-reviewed

Sequential pattern mining has been a popular data mining technique for extracting useful information from large databases and has successfully been used for numerous industrial and commercial problems. This paper presents a new mathematical modelling application to healthcare, providing important information to health service managers and policy makers to help them identify sequential patterns which require attention for efficiently managing scarce healthcare resources and developing effective healthcare management policies. In healthcare, these sequential patterns are analogous to the patient pathways. We present a non-homogeneous Markov model for identifying not only patient pathways which have high probability but also for identifying pathways which incur high cost or time. In order to have a more realistic model, we also consider time-dependent covariates and their impact on the pathways. An algorithm based on branch and bound global optimization is presented which can efficiently extract a required number of such patient pathways of interest. The approach is illustrated using historical data on geriatric patients from an administrative database of a London hospital.

Advances in mobile computing offer the potential to deliver better solutions in the growing health problem of obesity. Poor nutrition and a lack of exercise have led to a situation where obesity is rapidly on the rise. As a result, mobile health interventions in the area of nutrition have been a great asset into changing the lives of many through the use of their devices. Smartphones and other mobile devices can be transformed into effective nutrition tracking tools that consumers can easily make use of to help improve their lives and wellbeing. The academic community has conducted a vast amount of research in the area of mobile nutrition however a number of challenges still exist in the area of human-computer interaction (HCI). HCI has been found to be a key contributing factor when developing systems that constantly and directly interact with their respective users. Effective HCI is required to bridge the communication barrier between a user and the device, thus ensuring a seam-less approach. This research paper highlights the efforts done by academics in the area of mobile HCI within the nutrition landscape. Every study will be discussed in detailed by highlighted the key benefits together with a number of challenges that still exist. As a result, this review of literature will identify key lacunae that still exist in the field of mobile HCI within the nutrition landscape.

Questionnaires are often used to understand the quality of life of patients, treatment and disease burden and to obtain their feedback on the provided health care. However, a common problem with questionnaires is missing data. Some level of missing data is common and unavoidable. For example, patients may elect to leave one or more items unanswered either inadvertently or because they feel inhibited in responding to items dealing with a sensitive topic. Such missing data may lead to biased parameter estimates and inflated errors. In this paper, we propose an innovative collaborative filtering technique to complete missing data in medical questionnaires. The proposed technique is based on canonical tensor decomposition (CANDECOMP) and parallel factor decomposition (PARAFAC). It is very fast and effective especially with repeated medical questionnaires. To assess the different algorithms and our methods, we used SLEQOL questionnaires (“systemic lupus erythematosus-specific quality-of-life instrument”) completed by one hundred patients from TTSH and hospitals in China and Vietnam. Our results demonstrate that the tensor decomposition based method provides significant improvement on many existing methods and overcome their limitations in terms of various statistical measures.

Activity recognition is an important component of patient management in smart homes where high level activities can be learned from low level sensor data. Such activity recognition utilises sensor ID, task order and time of activation to learn about patient behavior, detect anomalies and provide prompts or other interventions. In this paper we use the sensor activation times to calculate durations and then investigate several model-based clustering approaches with a view to discretising the duration data and using such data to improve activity prediction. We explore several popular approaches to characterising such duration data, namely Coxian phase type distributions and Gaussian mixture distributions. We then show how we can utilise the learned clustering components for discretisation. Finally we use simulated data, based on a real smart kitchen deployment, to compare these approaches and evaluate the discretisation results with regard to activity prediction.

In our previous work we proposed a phase type survival tree method for clustering patients into clinically meaningful patient groups. We also discussed its extension which uses the survival tree based analysis for patient pathway prognostication and to examine the relationship between length of stay in hospital and destination on discharge. The current paper illustrates how phase type survival tree based clustering can be used for better forecasting of bed requirements and cost in a stroke unit by analyzing the effects of individual clusters and their interactions with the whole patient care unit. The paper then describes how this approach can be used for better estimation of patient demography and cost of care at a given time in the future. An application of the approach is illustrated using 5 years' retrospective data of patients admitted to the Belfast City Hospital with a diagnosis of stroke.

Gaussian mixture distributions and Coxian phase type distributions have been popular choices model based clustering of patients' length of stay data. This paper compares these models and presents an idea for a mixture distribution comprising of components of both of the above distributions. Also a mixed distribution survival tree is presented. A stroke dataset available from the English Hospital Episode Statistics database is used as a running example.

We have previously used Markov models to describe movements of patients between hospital states. The distribution of costs at any time and in a given time interval were also previously evaluated and expressions found for the corresponding means and variances. In this paper we extend our previous model to a system that includes on-off costs of making a transition from one state to another; previously costs were per day for the appropriate state. Such transition costs allow us to evaluate the overall costs of therapy or a clinical intervention where an operation or other intervention may be an option. This model can be used to determine costs for the entire system for different strategies thus facilitating a systems approach to the planning of healthcare and a holistic approach to costing. Such models can also help us to assess the complex relationship between hospital and community care where there may be possible trade-offs between hospital treatment costs and community care costs. Such a scenario is commonly encountered in stroke disease where care may include a long period of rehabilitation or residence in a nursing home.

Markov chain modelling has been previously used for hospital and community care systems, where the states in hospital care are described as phases, such as acute, rehabilitation, or long-stay and likewise social care in the community may be modelled using phases such as dependent, convalescent, or nursing home. This approach allows us to adopt a unified approach to health and community care modelling and management rather than focusing on the improvement of part of the system to the possible detriment of other components. We here extend this approach to show how the non-homogeneous Markov framework can be used to extract various metrics of interest. In particular, we use time-dependent covariates to obtain the mean and variance of the number of spells spent by a patient in hospital and in the community, and the expected total lengths of time in hospital and in the community., peer-reviewed