69 research outputs found
White learning methodology: a case study of cancer-related disease factors analysis in real-time PACS environment
Bayesian network is a probabilistic model of which the prediction accuracy may not be one of the highest in the machine learning family. Deep learning (DL) on the other hand possess of higher predictive power than many other models. How reliable the result is, how it is deduced, how interpretable the prediction by DL mean to users, remain obscure. DL functions like a black box. As a result, many medical practitioners are reductant to use deep learning as the only tool for critical machine learning application, such as aiding tool for cancer diagnosis. In this paper, a framework of white learning is being proposed which takes advantages of both black box learning and white box learning. Usually, black box learning will give a high standard of accuracy and white box learning will provide an explainable direct acyclic graph. According to our design, there are 3 stages of White Learning, loosely coupled WL, semi coupled WL and tightly coupled WL based on degree of fusion of the white box learning and black box learning. In our design, a case of loosely coupled WL is tested on breast cancer dataset. This approach uses deep learning and an incremental version of Naïve Bayes network. White learning is largely defied as a systemic fusion of machine learning models which result in an explainable Bayes network which could find out the hidden relations between features and class and deep learning which would give a higher accuracy of prediction than other algorithms. We designed a series of experiments for this loosely coupled WL model. The simulation results show that using WL compared to standard black-box deep learning, the levels of accuracy and kappa statistics could be enhanced up to 50%. The performance of WL seems more stable too in extreme conditions such as noise and high dimensional data. The relations by Bayesian network of WL are more concise and stronger in affinity too. The experiments results deliver positive signals that WL is possible to output both high classification accuracy and explainable relations graph between features and class. [Abstract copyright: Copyright © 2020. Published by Elsevier B.V.
Investigation of Low-Cost Wearable Internet of Things Enabled Technology for Physical Activity Recognition in the Elderly
Technological advances in mobile sensing technologies has produced new opportunities for the monitoring of the elderly in uncontrolled environments by researchers. Sensors have become smaller, cheaper and can be worn on the body, potentially creating a network of sensors. Smart phones are also more common in the average household and can also provide some behavioural analysis due to the built-in sensors. As a result of this, researchers are able to monitor behaviours in a more naturalistic setting, which can lead to more contextually meaningful data. For those suffering with a mental illness, non-invasive and continuous monitoring can be achieved. Applying sensors to real world environments can aid in improving the quality of life of an elderly person with a mental illness and monitor their condition through behavioural analysis. In order to achieve this, selected classifiers must be able to accurately detect when an activity has taken place. In this thesis we aim to provide a framework for the investigation of activity recognition in the elderly using low-cost wearable sensors, which has resulted in the following contributions: 1. Classification of eighteen activities which were broken down into three disparate categories typical in a home setting: dynamic, sedentary and transitional. These were detected using two Shimmer3 IMU devices that we have located on the participants’ wrist and waist to create a low-cost, contextually deployable solution for elderly care monitoring. 2. Through the categorisation of performed Extracted time-domain and frequency-domain features from the Shimmer devices accelerometer and gyroscope were used as inputs, we achieved a high accuracy classification from a Convolutional Neural Network (CNN) model applied to the data set gained from participants recruited to the study through Join Dementia Research. The model was evaluated by variable adjustments to the model, tracking changes in its performance. Performance statistics were generated by the model for comparison and evaluation. Our results indicate that a low epoch of 200 using the ReLu activation function can display a high accuracy of 86% on the wrist data set and 85% on the waist data set, using only two low-cost wearable devices
An Ensemble Self-Structuring Neural Network Approach to Solving Classification Problems with Virtual Concept Drift and its Application to Phishing Websites
Classification in data mining is one of the well-known tasks that aim to construct a
classification model from a labelled input data set. Most classification models are
devoted to a static environment where the complete training data set is presented to the
classification algorithm. This data set is assumed to cover all information needed to
learn the pertinent concepts (rules and patterns) related to how to classify unseen
examples to predefined classes. However, in dynamic (non-stationary) domains, the set
of features (input data attributes) may change over time. For instance, some features
that are considered significant at time Ti might become useless or irrelevant at time Ti+j.
This situation results in a phenomena called Virtual Concept Drift. Yet, the set of
features that are dropped at time Ti+j might return to become significant again in the
future. Such a situation results in the so-called Cyclical Concept Drift, which is a direct
result of the frequently called catastrophic forgetting dilemma. Catastrophic forgetting
happens when the learning of new knowledge completely removes the previously
learned knowledge.
Phishing is a dynamic classification problem where a virtual concept drift might occur.
Yet, the virtual concept drift that occurs in phishing might be guided by some
malevolent intelligent agent rather than occurring naturally. One reason why phishers
keep changing the features combination when creating phishing websites might be that
they have the ability to interpret the anti-phishing tool and thus they pick a new set of
features that can circumvent it. However, besides the generalisation capability, fault
tolerance, and strong ability to learn, a Neural Network (NN) classification model is
considered as a black box. Hence, if someone has the skills to hack into the NN based
classification model, he might face difficulties to interpret and understand how the NN
processes the input data in order to produce the final decision (assign class value).
In this thesis, we investigate the problem of virtual concept drift by proposing a
framework that can keep pace with the continuous changes in the input features. The
proposed framework has been applied to phishing websites classification problem and
it shows competitive results with respect to various evaluation measures (Harmonic
Mean (F1-score), precision, accuracy, etc.) when compared to several other data mining
techniques. The framework creates an ensemble of classifiers (group of classifiers) and it
offers a balance between stability (maintaining previously learned knowledge) and
plasticity (learning knowledge from the newly offered training data set). Hence, the
framework can also handle the cyclical concept drift. The classifiers that constitute the
ensemble are created using an improved Self-Structuring Neural Networks algorithm
(SSNN). Traditionally, NN modelling techniques rely on trial and error, which is a
tedious and time-consuming process. The SSNN simplifies structuring NN classifiers
with minimum intervention from the user. The framework evaluates the ensemble
whenever a new data set chunk is collected. If the overall accuracy of the combined
results from the ensemble drops significantly, a new classifier is created using the SSNN
and added to the ensemble. Overall, the experimental results show that the proposed
framework affords a balance between stability and plasticity and can effectively handle
the virtual concept drift when applied to phishing websites classification problem. Most
of the chapters of this thesis have been subject to publicatio
Balance-guaranteed optimized tree with reject option for live fish recognition
This thesis investigates the computer vision application of live fish recognition, which
is needed in application scenarios where manual annotation is too expensive, when
there are too many underwater videos. This system can assist ecological surveillance
research, e.g. computing fish population statistics in the open sea. Some pre-processing
procedures are employed to improve the recognition accuracy, and then 69 types of
features are extracted. These features are a combination of colour, shape and texture
properties in different parts of the fish such as tail/head/top/bottom, as well as
the whole fish. Then, we present a novel Balance-Guaranteed Optimized Tree with
Reject option (BGOTR) for live fish recognition. It improves the normal hierarchical
method by arranging more accurate classifications at a higher level and keeping the
hierarchical tree balanced. BGOTR is automatically constructed based on inter-class
similarities. We apply a Gaussian Mixture Model (GMM) and Bayes rule as a reject
option after the hierarchical classification to evaluate the posterior probability of being
a certain species to filter less confident decisions. This novel classification-rejection
method cleans up decisions and rejects unknown classes. After constructing the tree
architecture, a novel trajectory voting method is used to eliminate accumulated errors
during hierarchical classification and, therefore, achieves better performance. The proposed
BGOTR-based hierarchical classification method is applied to recognize the 15
major species of 24150 manually labelled fish images and to detect new species in
an unrestricted natural environment recorded by underwater cameras in south Taiwan
sea. It achieves significant improvements compared to the state-of-the-art techniques.
Furthermore, the sequence of feature selection and constructing a multi-class SVM
is investigated. We propose that an Individual Feature Selection (IFS) procedure can
be directly exploited to the binary One-versus-One SVMs before assembling the full
multiclass SVM. The IFS method selects different subsets of features for each Oneversus-
One SVM inside the multiclass classifier so that each vote is optimized to discriminate
the two specific classes. The proposed IFS method is tested on four different
datasets comparing the performance and time cost. Experimental results demonstrate
significant improvements compared to the normal Multiclass Feature Selection (MFS)
method on all datasets
Cyber-Physical Threat Intelligence for Critical Infrastructures Security
Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well
Cyber-Physical Threat Intelligence for Critical Infrastructures Security
Modern critical infrastructures can be considered as large scale Cyber Physical Systems (CPS). Therefore, when designing, implementing, and operating systems for Critical Infrastructure Protection (CIP), the boundaries between physical security and cybersecurity are blurred. Emerging systems for Critical Infrastructures Security and Protection must therefore consider integrated approaches that emphasize the interplay between cybersecurity and physical security techniques. Hence, there is a need for a new type of integrated security intelligence i.e., Cyber-Physical Threat Intelligence (CPTI). This book presents novel solutions for integrated Cyber-Physical Threat Intelligence for infrastructures in various sectors, such as Industrial Sites and Plants, Air Transport, Gas, Healthcare, and Finance. The solutions rely on novel methods and technologies, such as integrated modelling for cyber-physical systems, novel reliance indicators, and data driven approaches including BigData analytics and Artificial Intelligence (AI). Some of the presented approaches are sector agnostic i.e., applicable to different sectors with a fair customization effort. Nevertheless, the book presents also peculiar challenges of specific sectors and how they can be addressed. The presented solutions consider the European policy context for Security, Cyber security, and Critical Infrastructure protection, as laid out by the European Commission (EC) to support its Member States to protect and ensure the resilience of their critical infrastructures. Most of the co-authors and contributors are from European Research and Technology Organizations, as well as from European Critical Infrastructure Operators. Hence, the presented solutions respect the European approach to CIP, as reflected in the pillars of the European policy framework. The latter includes for example the Directive on security of network and information systems (NIS Directive), the Directive on protecting European Critical Infrastructures, the General Data Protection Regulation (GDPR), and the Cybersecurity Act Regulation. The sector specific solutions that are described in the book have been developed and validated in the scope of several European Commission (EC) co-funded projects on Critical Infrastructure Protection (CIP), which focus on the listed sectors. Overall, the book illustrates a rich set of systems, technologies, and applications that critical infrastructure operators could consult to shape their future strategies. It also provides a catalogue of CPTI case studies in different sectors, which could be useful for security consultants and practitioners as well
Advances in Robotics, Automation and Control
The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man
Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches
Introduction:
Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design.
Methods:
Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay.
Results:
i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”.
ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively.
iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features.
Conclusions:
Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people
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