Disease diagnosis in smart healthcare: Innovation, technologies and applications

To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer’s disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed

Unsupervised-Learning Assisted Artificial Neural Network for Optimization

Innovations in computer technology made way for Computational Fluid Dynamics (CFD) into engineering, which supported the development of new designs by reducing the cost and time by lowering the dependency on experimentation. There is a further need to make the process of development more efficient. One such technology is Artificial Intelligence. In this thesis, we explore the application of Artificial Intelligence (AI) in CFD and how it can improve the process of development. AI is used as a buzz word for the mechanism which can learn by itself and make the decision accordingly. Machine learning (ML) is a subset of AI which learns any method without the need for any explicit algorithm. Deep Learning is another subset of ML, which is different in its composition. Deep Learning, or Neural Networks (NN), is made up of nodes like the neurons and works on the principle of the human brain. NN can be exploited for any problem without the need for any explicit algorithm for the task. It can be achieved by analyzing and inferring from the observations. Artificial Neural Network (ANN) is used for data analysis and Convolutional Neural Networks (CNN) for image analysis. Our area of interest herein is ANN and its application for a medical equipment called Convective Polymerase Chain Reaction (cPCR) device. Many have relied on engineering experimentation to develop an optimized PCR device, which requires high cost and time. That makes the use of PCR devices less cost-effective as a commonplace for healthcare. We optimize a convective PCR reactor using a high-fidelity CFD-based surrogate model to find an economical and performance-effective one. We plan numerous possible design combinations, evaluating DNA doubling time. Based on these results, an accurate surrogate model is developed for optimization using Deep Learning. We produce two kinds of surrogate models using ANN; one by directly employing ANN and another by using unsupervised learning called, k-Means-Clustering-Assisted ANN, and then compare the results from these two methods. For developing a suitable model of ANN to fit our data, we carry out the analysis of model accuracy and obtain the best design by using a differential evolution method. The best designs obtained by the two methods are verified with the corresponding result obtained from CFD. This shows an effective way of designing an optimized device by reducing the number of CFD simulations required for the development. Consequently, the computational results demonstrate that the convective PCR device can be efficiently developed using our proposed methodology, making it viable for point-of-care applications

Exploring ICMetrics to detect abnormal program behaviour on embedded devices

Execution of unknown or malicious software on an embedded system may trigger harmful system behaviour targeted at stealing sensitive data and/or causing damage to the system. It is thus considered a potential and significant threat to the security of embedded systems. Generally, the resource constrained nature of Commercial off-the-shelf (COTS) embedded devices, such as embedded medical equipment, does not allow computationally expensive protection solutions to be deployed on these devices, rendering them vulnerable. A Self-Organising Map (SOM) based and Fuzzy C-means based approaches are proposed in this paper for detecting abnormal program behaviour to boost embedded system security. The presented technique extracts features derived from processor's Program Counter (PC) and Cycles per Instruction (CPI), and then utilises the features to identify abnormal behaviour using the SOM. Results achieved in our experiment show that the proposed SOM based and Fuzzy C-means based methods can identify unknown program behaviours not included in the training set with 90.9% and 98.7% accuracy

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830 A-TIC-608-UGR20 B-TIC-402-UGR18European Commission B-TIC-402-UGR1

Unsupervised clustering of wildlife necropsy data for syndromic surveillance

<p>Abstract</p> <p>Background</p> <p>The importance of wildlife disease surveillance is increasing, because wild animals are playing a growing role as sources of emerging infectious disease events in humans. Syndromic surveillance methods have been developed as a complement to traditional health data analyses, to allow the early detection of unusual health events. Early detection of these events in wildlife could help to protect the health of domestic animals or humans. This paper aims to define syndromes that could be used for the syndromic surveillance of wildlife health data. Wildlife disease monitoring in France, from 1986 onward, has allowed numerous diagnostic data to be collected from wild animals found dead. The authors wanted to identify distinct pathological profiles from these historical data by a global analysis of the registered necropsy descriptions, and discuss how these profiles can be used to define syndromes. In view of the multiplicity and heterogeneity of the available information, the authors suggest constructing syndromic classes by a multivariate statistical analysis and classification procedure grouping cases that share similar pathological characteristics.</p> <p>Results</p> <p>A three-step procedure was applied: first, a multiple correspondence analysis was performed on necropsy data to reduce them to their principal components. Then hierarchical ascendant clustering was used to partition the data. Finally the k-means algorithm was applied to strengthen the partitioning. Nine clusters were identified: three were species- and disease-specific, three were suggestive of specific pathological conditions but not species-specific, two covered a broader pathological condition and one was miscellaneous. The clusters reflected the most distinct and most frequent disease entities on which the surveillance network focused. They could be used to define distinct syndromes characterised by specific post-mortem findings.</p> <p>Conclusions</p> <p>The chosen statistical clustering method was found to be a useful tool to retrospectively group cases from our database into distinct and meaningful pathological entities. Syndrome definition from post-mortem findings is potentially useful for early outbreak detection because it uses the earliest available information on disease in wildlife. Furthermore, the proposed typology allows each case to be attributed to a syndrome, thus enabling the exhaustive surveillance of health events through time series analyses.</p