Machine Learning for Classification of Imbalanced Big Data

The problem of classification of imbalanced datasets is a critical one. With an increase in the number of application domains that rely on classification, extensive research has been carried out in this field; with focus directed towards the problem of poor classification accuracy. Of late, the rise in significance of Big Data has forced industries to search for better techniques to handle massive and unstructured datasets; this has led to a need for robust classification algorithms that deal with unbalanced Big Data. This paper surveys the current algorithms provided by Machine Learning for unbalanced dataset classification and considers their possible use for larger or unstructured datasets

Fault detection, diagnosis, and prognosis of a process operating under time-varying conditions

In the industrial panorama, many processes operate under time-varying conditions. Adapt¬ing high-performance diagnostic techniques under these relatively more complex situations is ur¬gently needed to mitigate the risk of false alarms. Attention is being paid to fault anticipation, requiring an in-depth study of prediction techniques. Predicting remaining life before the occurrence of faults allows for a comprehensive maintenance management protocol and facilitates the wear management of the machine, avoiding faults that could permanently compromise the integrity of such machinery. This study focuses on canonical variate analysis for fault detection in processes operating under time-varying conditions and on its contribution to the diagnostic and prognostic analysis, the latter of which was performed with machine learning techniques. The approach was validated on actual datasets from a granulator operating in the pharmaceutical sector

An Application of Gaussian Processes for Analysis in Chemical Engineering

Industry 4.0 is transforming the chemical engineering industry. With it, machine learning (ML) is exploding, and a large variety of complex algorithms are being developed. One particularly popular ML algorithm is the Gaussian Process (GP), which is a full probabilistic, non-parametric, Bayesian model. As a blackbox function, the GP encapsulates an engineering system in a cheaper framework known as a surrogate model. GP surrogate models can be confidently used to investigate chemical engineering scenarios. The research conducted in this thesis explores the application of GPs to case studies in chemical engineering. In many chemical engineering scenarios, it is critical to understand how input uncertainty impacts an important output. A sensitivity analysis does this by characterising the input-output relationship of a system. ML encapsulates a large system into a cheaper framework, enabling a Global Sensitivity Analysis (GSA) to be conducted. The GSA considers the model behaviour over the entire range of inputs and outputs. The Sobol’ indices are recognised as the benchmark GSA method. To achieve a satisfactory precision level, the variance-based decomposition method requires a significant computational burden. Thus, one exciting application of GPs is to reduce the number of model evaluations required and efficiently calculate the Sobol’ indices for large GSA studies. The first three case studies used GPs to perform GSA’s in chemical engineering. The first examined the effects of thermal runaway (TR) abuse on lithium-ion batteries. To calculate time-dependent Sobol’ indices, this study created an accurate surrogate model by training individual GPs at each time step. This work used GPs to help develop a complex chemical engineering simulation model. The second GSA calibrated a high-shear wet granulation model using experimental data. This work developed a methodology, linking two GSA studies, to substantially reduce the experimental effort required for model-driven design and scale-up of model processes. This enabled the creation of a targeted experimental design that reduced the experimental effort by 42%. The third case study developed a novel reduced order model (ROM) for predicting gaseous uptake of metal-organic framework (MOF) structures using GPs. Based on previous GSA research, the Active Subspaces were located using the Sobol’ indices of each pore property for the MOF structures. The novel ROM was shown to be a viable tool to identify the top-performing MOF structures showing its potential to be a universal MOF exploration model. The final two case studies applied GPs as a tool in novel techniques that combined ML algorithms. First, GPs are seldom used for mid-term electricity price forecasting because of their inaccuracy when extrapolating data. This research aimed to improve GP prediction accuracy by developing a GP-based clustering hybridisation method. The proposed hybridisation method outperformed other GP-based forecasting techniques, as demonstrated by the Diebold-Mariano hypothesis test. In the final case study, ML models were used to develop an effective maintenance strategy. The work compares ML algorithms for predictive maintenance and maintenance time estimation on a cyber-physical process plant to find the best for the maintenance workflow. The best algorithms for this case study were the Quadratic Discriminant Analysis model and the GP. The overall plant maintenance costs were found to be reduced by combining predictive maintenance with maintenance time estimation into a workflow. This research could help improve maintenance tasks in Industry 4.0. This thesis focused on using GPs to enhance collaborative efforts and demonstrate the enormous impact that ML can have in both research and industry. By proposing several novel ideas and applications, it is shown that GPs can be an efficient and effective tool for the analysis of chemical engineering systems

Age Estimation with Decision Trees: Testing the Relevance of 94 Aging Indicators on the William M. Bass Donated Collection

Anthropologists have been estimating ages-at-death of skeletons for a long time. A variety of different age indicators has been studied and age estimation methods have been developed in an attempt to standardize the process. Even with all the work that has gone into developing age estimation methods, age estimation of mature skeletons is still very imprecise. This research investigates various age indicator definitions and their performance on an elderly skeletal sample. Using 176 individuals from the William M. Bass Donated Collection curated in the Department of Anthropology at the University of Tennessee, Knoxville, data were collected on age indicators gathered from fifteen age estimation methods. Ninety-four variables were tested with various decision trees to show patterns among the variables. Regression equations were built using the same variables as the decision trees, and the performance between the two methodologies were compared. The decision trees performed slightly better, with a mean absolute error of prediction of around five years. Variable occurrence was tabulated across various decision tree models. The most common variables are pit shape of the sternal rib end morphology and the phase of the auricular phase. These two variables, along with others commonly selected, present best candidates for building an age estimation method that pertains to older populations

Foam dressings for treating pressure ulcers

Background: Pressure ulcers, also known as pressure injuries and bed sores, are localised areas of injury to the skin or underlying tissues, or both. Dressings made from a variety of materials, including foam, are used to treat pressure ulcers. An evidence-based overview of dressings for pressure ulcers is needed to enable informed decision-making on dressing use. This review is part of a suite of Cochrane Reviews investigating the use of dressings in the treatment of pressure ulcers. Each review will focus on a particular dressing type. Objectives: To assess the clinical and cost effectiveness of foam wound dressings for healing pressure ulcers in people with an existing pressure ulcer in any care setting. Search methods: In February 2017 we searched: the Cochrane Wounds Specialised Register; the Cochrane Central Register of Controlled Trials (CENTRAL); Ovid MEDLINE (including In-Process & Other Non-Indexed Citations); Ovid Embase; EBSCO CINAHL Plus and the NHS Economic Evaluation Database (NHS EED). We also searched clinical trials registries for ongoing and unpublished studies, and scanned reference lists of relevant included studies as well as reviews, meta-analyses and health technology reports to identify additional studies. There were no restrictions with respect to language, date of publication or study setting. Selection criteria: Published or unpublished randomised controlled trials (RCTs) and cluster-RCTs, that compared the clinical and cost effectiveness of foam wound dressings for healing pressure ulcers (Category/Stage II or above). Data collection and analysis: Two review authors independently performed study selection, risk of bias and data extraction. A third reviewer resolved discrepancies between the review authors. Main results: We included nine trials with a total of 483 participants, all of whom were adults (59 years or older) with an existing pressure ulcer Category/Stage II or above. All trials had two arms, which compared foam dressings with other dressings for treating pressure ulcers. The certainty of evidence ranged from low to very low due to various combinations of selection, performance, attrition, detection and reporting bias, and imprecision due to small sample sizes and wide confidence intervals. We had very little confidence in the estimate of effect of included studies. Where a foam dressing was compared with another foam dressing, we established that the true effect was likely to be substantially less than the study's estimated effect. We present data for four comparisons. One trial compared a silicone foam dressing with another (hydropolymer) foam dressing (38 participants), with an eight-week (short-term) follow-up. It was uncertain whether alternate types of foam dressing affected the incidence of healed pressure ulcers (RR 0.89, 95% CI 0.45 to 1.75) or adverse events (RR 0.37, 95% CI 0.04 to 3.25), as the certainty of evidence was very low, downgraded for serious limitations in study design and very serious imprecision. Four trials with a median sample size of 20 participants (230 participants), compared foam dressings with hydrocolloid dressings for eight weeks or less (short-term). It was uncertain whether foam dressings affected the probability of healing in comparison to hydrocolloid dressings over a short follow-up period in three trials (RR 0.85, 95% CI 0.54 to 1.34), very low-certainty evidence, downgraded for very serious study limitations and serious imprecision. It was uncertain if there was a difference in risk of adverse events between groups (RR 0.88, 95% CI 0.37 to 2.11), very low-certainty evidence, downgraded for serious study limitations and very serious imprecision. Reduction in ulcer size, patient satisfaction/acceptability, pain and cost effectiveness data were also reported but we assessed the evidence as being of very low certainty. One trial (34 participants), compared foam and hydrogel dressings over an eight-week (short-term) follow-up. It was uncertain if the foam dressing affected the probability of healing (RR 1.00, 95% CI 0.78 to 1.28), time to complete healing (MD 5.67 days 95% CI -4.03 to 15.37), adverse events (RR 0.33, 95% CI 0.01 to 7.65) or reduction in ulcer size (MD 0.30 cm2 per day, 95% CI -0.15 to 0.75), as the certainty of the evidence was very low, downgraded for serious study limitations and very serious imprecision. The remaining three trials (181 participants) compared foam with basic wound contact dressings. Follow-up times ranged from short-term (8 weeks or less) to medium-term (8 to 24 weeks). It was uncertain whether foam dressings affected the probability of healing compared with basic wound contact dressings, in the short term (RR 1.33, 95% CI 0.62 to 2.88) or medium term (RR 1.17, 95% CI 0.79 to 1.72), or affected time to complete healing in the medium term (MD -35.80 days, 95% CI -56.77 to -14.83), or adverse events in the medium term (RR 0.58, 95% CI 0.33 to 1.05). This was due to the very low-certainty evidence, downgraded for serious to very serious study limitations and imprecision. Reduction in ulcer size, patient satisfaction/acceptability, pain and cost effectiveness data were also reported but again, we assessed the evidence as being of very low certainty. None of the included trials reported quality of life or pressure ulcer recurrence. Authors' conclusions: It is uncertain whether foam dressings are more clinically effective, more acceptable to users, or more cost effective compared to alternative dressings in treating pressure ulcers. It was difficult to make accurate comparisons between foam dressings and other dressings due to the lack of data on reduction of wound size, complete wound healing, treatment costs, or insufficient time-frames. Quality of life and patient (or carer) acceptability/satisfaction associated with foam dressings were not systematically measured in any of the included studies. We assessed the certainty of the evidence in the included trials as low to very low. Clinicians need to carefully consider the lack of robust evidence in relation to the clinical and cost-effectiveness of foam dressings for treating pressure ulcers when making treatment decisions, particularly when considering the wound management properties that may be offered by each dressing type and the care context.The NHMRC has provided funding for this review from its Centre of Research Excellence Scheme, which funds one or more of the authors. Griffith University, Australia. The National Institute for Health Research (NIHR), UK

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as taxonomy, biology, pharmacy,finance, agriculture, ecology, health and architecture

Artificial Intelligence-Powered Chronic Wound Management System: Towards Human Digital Twins

Artificial Intelligence (AI) has witnessed increased application and widespread adoption over the past decade. AI applications to medical images have the potential to assist caregivers in deciding on a proper chronic wound treatment plan by helping them to understand wound and tissue classification and border segmentation, as well as visual image synthesis. This dissertation explores chronic wound management using AI methods, such as Generative Adversarial Networks (GAN) and Explainable AI (XAI) techniques. The wound images are collected, grouped, and processed. One primary objective of this research is to develop a series of AI models, not only to present the potential of AI in wound management but also to develop the building blocks of human digital twins. First of all, motivations, contributions, and the dissertation outline are summarized to introduce the aim and scope of the dissertation. The first contribution of this study is to build a chronic wound classification and its explanation utilizing XAI. This model also benefits from a transfer learning methodology to improve performance. Then a novel model is developed that achieves wound border segmentation and tissue classification tasks simultaneously. A Deep Learning (DL) architecture, i.e., the GAN, is proposed to realize these tasks. Another novel model is developed for creating lifelike wounds. The output of the previously proposed model is used as an input for this model, which generates new chronic wound images. Any tissue distribution could be converted to lifelike wounds, preserving the shape of the original wound. The aforementioned research is extended to build a digital twin for chronic wound management. Chronic wounds, enabling technologies for wound care digital twins, are examined, and a general framework for chronic wound management using the digital twin concept is investigated. The last contribution of this dissertation includes a chronic wound healing prediction model using DL techniques. It utilizes the previously developed AI models to build a chronic wound management framework using the digital twin concept. Lastly, the overall conclusions are drawn. Future challenges and further developments in chronic wound management are discussed by utilizing emerging technologies

An independent review of monitoring measures undertaken in Neath Port Talbot in respect of particulate matter (PM10)

The purpose of this Executive Summary is not to provide a comprehensive summation of all the observations and conclusions identified during this study but rather provide a synopsis of the main findings of this independent review. The points raised in this Executive Summary are supported by in-depth discussion and data analysis in the main document and therefore the reader should not draw any conclusions without reading the main document in detail.The Air Quality Management Resource Centre (AQMRC), University of the West of England, Bristol (UWE) was appointed by the Welsh Assembly Government following a competitive tendering process to undertake a project entitled ‘An Independent Review of Monitoring Measures Undertaken in Neath Port Talbot in Respect of Particulate Matter (PM10) - Contract Number RPP0001/2009’. Within the Tender Specification prepared by the Welsh Assembly Government, clear project aims have been highlighted as follows:- Provide an independent amalgamation and review of the monitoring, modelling, source apportionment and atmospheric particle characterisation work undertaken in respect of PM10 pollution in the Neath Port Talbot area since 2000;- Draw upon the projects undertaken by, and experiences of, relevant stakeholders including Neath Port Talbot County Borough Council (NPTCBC), contracted consultants, WAG, the Environment Agency Wales (EAW), the Port Talbot Steelworks site operators and several university researchers;- Provide advice to WAG on further measures to pinpoint sources of particulate matter within the area; and- Assist the Welsh Minister’s understanding of the issues and implementation of actions in the affected area to ensure that concentrations of PM10 attain the air quality standards as set out in the Air Quality Standards (Wales) Regulations 2007.Following the Environment Act 1995 all local authorities have a statutory duty to review and assess air quality within their administrative area. NPTCBC have undertaken their review and assessment duties since the commencement of Round 1 in 1998. In Round 1 the Council identified an exceedence of the PM10 24-hour air quality objective and the Taibach Margam Air Quality Management Area for PM10 (24-hour objective) was declared on the 1st of July 2000.Subsequently, as required by the Environment Act 1995, NPTCBC undertook a Stage 4 / Further Assessment of air quality in which their source apportionmentstudy identified the Port Talbot Steelworks as the primary source of PM10 emissions. As required by the legislation NPTCBC has developed the Taibach Margam Air Quality Management Area (PM10) Air Quality Action Plan (NPTCBC AQAP) in collaboration with various stakeholders including the site operators and Environment Agency Wales (EAW) and has subsequently continued with their statutory Local Air Quality Management (LAQM) duties. A synopsis of all the key conclusions and recommendations from this study are provided below and they have been categorised according to the primary objectives of the project tender specifications as outlined in Section 2.1

Soil carbon modelling as a tool for carbon balance studies in forestry

Soils represent a remarkable stock of carbon, and forest soils are estimated to hold half of the global stock of soil carbon. Topical concern about the effects of climate change and forest management on soil carbon as well as practical reporting requirements set by climate conventions have created a need to assess soil carbon stock changes reliably and transparently. The large spatial variability of soil carbon commensurate with relatively slow changes in stocks hinders the assessment of soil carbon stocks and their changes by direct measurements. Models therefore widely serve to estimate carbon stocks and stock changes in soils. This dissertation aimed to develop the soil carbon model YASSO for upland forest soils. The model was aimed to take into account the most important processes controlling the decomposition in soils, yet remain simple enough to ensure its practical applicability in different applications. The model structure and assumptions were presented and the model parameters were defined with empirical measurements. The model was evaluated by studying the sensitivities of the model results to parameter values, by estimating the precision of the results with an uncertainty analysis, and by assessing the accuracy of the model by comparing the predictions against measured data and to the results of an alternative model. The model was applied to study the effects of intensified biomass extraction on the forest carbon balance and to estimate the effects of soil carbon deficit on net greenhouse gas emissions of energy use of forest residues. The model was also applied in an inventory based method to assess the national scale forest carbon balance for Finland’s forests from 1922 to 2004. YASSO managed to describe sufficiently the effects of both the variable litter and climatic conditions on decomposition. When combined with the stand models or other systems providing litter information, the dynamic approach of the model proved to be powerful for estimating changes in soil carbon stocks on different scales. The climate dependency of the model, the effects of nitrogen on decomposition and forest growth as well as the effects of soil texture on soil carbon stock dynamics are areas for development when considering the applicability of the model to different research questions, different land use types and wider geographic regions. Intensified biomass extraction affects soil carbon stocks, and these changes in stocks should be taken into account when considering the net effects of forest residue utilisation as energy. On a national scale, soil carbon stocks play an important role in forest carbon balances.Metsien maaperän hiilivarastolla on merkittävä rooli metsien hiilitaseessa. Hakkuutähteiden keruu hakkuiden jälkeen vähentää puustosta maaperään siirtyvää hiilen määrää ja tämä hiilivarastomuutos on merkittävä verrattuna muihin hakkuutähteiden energiakäytön aiheuttamiin kasvihuonekaasupäästöihin. Metsien maaperä on merkittävä hiilen varasto. Ilmastonmuutos ja erilaiset metsänkäsittelyt vaikuttavat paitsi puuston biomassan myös maaperän hiilivarastoon. Näitä vaikutuksia ei kuitenkaan vielä täysin tunneta. Kansainvälinen ilmastosopimus kuitenkin velvoittaa sopijamaat raportoimaan myös maaperän hiilivarastossa tapahtuvat muutokset. Maaperän hiilivaraston muutosten arviointi mittaamalla on hyvin vaikeaa ja työlästä, koska varaston spatiaalinen vaihtelu on suurta verrattuna ajallisiin muutoksiin. Tämän vuoksi hiilivaraston ja sen muutosten arvioinnissa käytetään usein malleja. Tässä väitöskirjassa kehitettiin ja testattiin kivennäismaiden metsien orgaanisen aineen hajoamista ja maaperän hiilivaraston dynamiikkaa kuvaava YASSO-malli. Mallilla pyrittiin kuvaamaan tärkeimmät hiilivaraston dynamiikkaan vaikuttavat tekijät, mutta silti pitämään malli niin yksinkertaisena, että sen toimintaperiaatteiden ymmärtäminen ja käyttö sovelluksissa olisi helppoa. Mallin toimintaa arvioitiin tarkastelemalla mallitulosten herkkyyttä mallin parametriarvojen muutoksille, tutkimalla mallitulosten tarkkuutta epävarmuusanalyysin avulla ja vertaamalla mallituloksia mitattuihin havaintoihin ja toisen maamallin antamiin tuloksiin. Epävarmuus- ja herkkyysanalyysien mukaan YASSO-mallin hiilivarastoarviot ovat epävarmoja. Hiilivarastomuutosten arviot sen sijaan ovat verrattain tarkkoja. Testit mitattuja aineistoja vastaan vastaan osoittivat, että malli onnistuu kohtalaisesti kuvaamaan erilaisten karikkeiden hajoamisen erilaisissa ilmasto-olosuhteissa ja maaperän kokonaishiilivaraston erilaisissa suomalaisissa metsiköissä. Mallilla tutkittiin hakkuutähteiden talteenoton ja energiakäytön vaikutusta maaperän hiilivarastoon ja maaperän roolia Suomen metsien hiilitaseessa. Mallin dynaaminen lähestymistapa osoittautui tehokkaaksi sovelluksissa, joissa se yhdistettiin metsikkömalliin tai inventointitietoihin ja biomassa- ja karikemalleihin