Machine Learning and other Computational-Intelligence Techniques for Security Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Wrist-based Phonocardiogram Diagnosis Leveraging Machine Learning

With the tremendous growth of technology and the fast pace of life, the need for instant information has become an everyday necessity, more so in emergency cases when every minute counts towards saving lives. mHealth has been the adopted approach for quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this research is to diagnose the heart condition based on phonocardiogram (PCG) analysis using Machine Learning techniques assuming limited processing power, in order to be encapsulated later in a mobile device. The diagnosis of PCG is performed using two techniques; 1. parametric estimation with multivariate classification, particularly discriminant function. Which will be explored at length using different number of descriptive features. The feature extraction will be performed using Wavelet Transform (Filter Bank). 2. Artificial Neural Networks, and specifically Pattern Recognition. This will also use decomposed version of PCG using Wavelet Transform (Filter Bank). The results showed 97.33% successful diagnosis using the first technique using PCG with a 19 dB Signal-to-Noise-Ratio. When the signal was decomposed into four sub-bands using a Filter Bank of the second order. Each sub-band was described using two features; the signal’s mean and covariance. Additionally, different Filter Bank orders and number of features are explored and compared. Using the second technique the diagnosis resulted in a 100% successful classification with 83.3% trust level. The results are assessed, and new improvements are recommended and discussed as part of future work.Teknologian valtavan kehittymisen ja nopean elämänrytmin myötä välittömästi saatu tieto on noussut jokapäiväiseksi välttämättömyydeksi, erityisesti hätätapauksissa, joissa jokainen säästetty minuutti on tärkeää ihmishenkien pelastamiseksi. Mobiiliterveys, eli mHealth, on yleisesti valjastettu käyttöön nopeaksi diagnoosimenetelmäksi mobiililaitteiden avulla. Käyttö on kuitenkin ollut haastavaa korkean datan laatuvaatimuksen ja suurten tiedonkäsittelyvaatimuksien, nopean laskentatehon ja sekä suuren virrankulutuksen vuoksi. Tämän tutkimuksen tavoitteena oli diagnosoida sydänsairauksia fonokardiogrammianalyysin (PCG) perusteella käyttämällä koneoppimistekniikoita niin, että käytettävä laskentateho rajoitetaan vastaamaan mobiililaitteiden kapasiteettia. PCG-diagnoosi tehtiin käyttäen kahta tekniikkaa 1. Parametrinen estimointi käyttäen moniulotteista luokitusta, erityisesti signaalien erotteluanalyysin avulla. Tätä asiaa tutkittiin syvällisesti käyttäen erilaisia tilastotieteellisesti kuvailevia piirteitä. Piirteiden irrotus suoritettiin käyttäen Wavelet-muunnosta ja suodatinpankkia. 2. Keinotekoisia neuroverkkoja ja erityisesti hahmontunnistusta. Tässä menetelmässä käytetään myös PCG-signaalin hajoitusta ja Wavelet-muunnos -suodatinpankkia. Tulokset osoittivat, että PCG 19dB:n signaali-kohina-suhteella voi johtaa 97,33% onnistuneeseen diagnoosiin käytettäessä ensimmäistä tekniikkaa. Signaalin hajottaminen neljään alikaistaan suoritettiin käyttämällä toisen asteen suodatinpankkia. Jokainen alikaista kuvattiin käyttäen kahta piirrettä: signaalin keskiarvoa ja kovarianssia, näin saatiin yhteensä kahdeksan ominaisuutta kuvaamaan noin yhden minuutin näytettä PCG-signaalista. Lisäksi tutkittiin ja verrattiin eriasteisia suodattimia ja piirteitä. Toista tekniikkaa käyttäen diagnoosi johti 100% onnistuneeseen luokitteluun 83,3% luotettavuustasolla. Tuloksia käsitellään ja pohditaan, sekä tehdään niistä johtopäätöksiä. Lopuksi ehdotetaan ja suositellaan käytettyihin menetelmiin uusia parannuksia jatkotutkimuskohteiksi.fi=vertaisarvioitu|en=peerReviewed

What are the factors influencing GPs in the recognition and referral of suspected lung cancer?

Background - Lung cancer is the second most common cancer diagnosed in the UK after breast cancer. Around 41,000 people were diagnosed with lung cancer in the UK in 2008 (or 112 people every day). Patients with symptoms suggestive of lung cancer usually present to their GP first who operate a gatekeeping role for referrals into secondary care.Aims - This study aimed to identify factors influencing GPs in the recognition and referral of patients with suspected lung cancer and to identify potential modifiable factors in order to develop interventions in the future to enable GPs to recognise and refer patients with lung cancer appropriately.Methods - Thirty six in-depth interviews with GPs were conducted across Hull, East Riding of Yorkshire, North East Lincolnshire and North Yorkshire. The interviews were in two parts: first, a number of clinical case scenarios were presented to the participants and think aloud methodology applied to establish insights into GPs cognitive processes in decision making. The second part was an in-depth exploration of the process of recognition and referral of patients with suspected lung cancer symptoms. Thematic analysis was used for the development of key themes.Findings - Data analysis identified four key themes from the data: [1] the ways in which GPs make decisions and in particular how they deal with challenging or unusual presentations, [2] understanding the differences in how GPs run their practices and how this may impact upon decision making, [3] the complexity of general practice and [4] the pressures faced by general practitioners. The findings from the think aloud method emphasised the focus participants placed on symptoms, context and patient factors in the development of a clinical hypothesis. It was then a process of seeking to prove or disprove a hypothesis by working through a list of differential diagnoses and complexity within the time constraints and context of the consultation. The open-ended interviews added reliability by corroborating some of the think aloud findings regarding knowledge and compliance of lung cancer guidelines but also introduced a broader perspective about practice factors involving internal organisational culture, structures and processes not mentioned in think aloud but which may influence participants in consultations.Conclusion - The study has learned that the recognition and referral of lung cancer is complex. The research findings highlight a range of factors which help understand what makes it easier to recognise and refer lung cancer and also what are the barriers to recognising and referring lung cancer in general practice and how this may potentially impact on GP consultations

Covid-19, Law and Human Rights : Essex Dialogues. A Project of the School of Law and Human Rights Centre

Covid-19 presents one of the gravest, acute challenges our world has faced for many years. The pandemic impacts a vast array of areas of life across the globe. It also raises a multitude of very urgent questions for law and human rights. This volume provides a series of scholarly responses to many of the questions Covid-19 raises for the theory and practice of law and human rights. The assembled papers in this volume collectively seek to engage with academic and practitioner communities alike and the volume aims to positively contribute to our collective attempts to “build back better” once a globally available vaccine for Covid-19 has been produced and distributed. The volume emerged from a hastily convened Zoom meeting of over thirty colleagues based within the Human Rights Centre and the School of Law at the University of Essex. The purpose of the meeting was to gauge ongoing research related to Covid-19 and the breadth and array of responses led to this project. It quickly became apparent that many academic colleagues were extremely interested in contributing their expertise on a very broad range of multidisciplinary Covid-19 related topics and issues. The combination of contributors’ enthusiasm for the project and our editorial efforts has enabled us to produce this volume in a very timely manner. A mere three months has elapsed from the first meeting to the final publication! The contents of this volume span a very comprehensive range of topics, questions and expertise. The volume is purposefully multidisciplinary. It is also intended to be accessible to a relatively broad readership who, one imagines, is nevertheless united by an interest in the role which expertise has to play in confronting and overcoming the very many legal, social, philosophical and political challenges which Covid-19 entails

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios