Combining Spatial and Telemetric Features for Learning Animal Movement Models

Abstract We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to data and demonstrate its effectiveness via asymptotic analysis and synthetic experiments. We also apply our model to real datasets, and show that it outperforms the most popular radio telemetry software package used in ecology. We conclude that integration of different data sources under a single statistical framework, coupled with appropriate parameter and state estimation procedures, produces both accurate location estimates and an interpretable statistical model of animal movement

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Neural Network Analysis of Bone Vibration Signals to Assesses Bone Density

Osteoporosis is a systemic disease, characterised by low bone mineral density (BMD) with a consequent increase in bone fragility. The most commonly used method to examine BMD is dual energy X-ray absorptiometry (DXA). However DXA cannot be used reliably in children less than 5 years old because of the limitations in the availability of required normative data. Vibration analysis is a well-established technique for analysing physical properties of materials and so it has the potential for assessing BMD. The overall purpose of this study was development and evaluation of low frequency vibration analysis as a tool to assess BMD in children. A novel portable computer-controlled system that suitably vibrated the bone, acquired, stored, displayed and analysed the resulting bone vibration responses was developed and its performance was investigated by comparing it with DXA-derived BMD values in children. 41 children aged between 7 and 15 years suspected of having abnormal BMD were enrolled. The ulna was chosen for all tests due to the ease with which it could be vibrated and responses measured. Frequency spectra of bone vibration responses were obtained using both impulse and continuous methods and these plus the participants’ clinical data were processed by a multilayer perceptron (MLP) artificial neural network. The correlation coefficient values between MLP outputs and DXA-derived BMD values were 0.79 and 0.86 for impulse and continuous vibration methods respectively. It was demonstrated that vibration analysis has potential for assessing fracture ris

Epidemiology of pemphigus in Turkey: One-year prospective study of 220 cases

Pemphigus is a group of rare and life-threatening autoimmune blistering diseases of the skin and mucous membranes. Although they occur worldwide, their incidence shows wide geographical variation, and prospective data on the epidemiology of pemphigus are very limited. Objective of this work is to evaluate the incidence and epidemiological and clinical features of patients with pemphigus in Turkey. All patients newly diagnosed with pemphigus between June 2013 and June 2014 were prospectively enrolled in 33 dermatology departments in 20 different provinces from all seven regions of Turkey. Disease parameters including demography and clinical findings were recorded. A total of 220 patients were diagnosed with pemphigus during the 1-year period, with an annual incidence of 4.7 per million people in Turkey. Patients were predominantly women, with a male to female ratio of 1:1.41. The mean age at onset was 48.9 years. Pemphigus vulgaris (PV) was the commonest clinical subtype (n=192; 87.3%), followed by pemphigus foliaceus (n=21; 9.6%). The most common clinical subtype of PV was the mucocutaneous type (n=83; 43.2%). The mean Pemphigus Disease Area Index was 28.14±22.21 (mean ± Standard Deviation).  The incidence rate of pemphigus in Turkey is similar to the countries of South-East Europe, higher than those reported for the Central and Northern European countries and lower than the countries around the Mediterranean Sea and Iran. Pemphigus is more frequent in middle-aged people and is more common in women. The most frequent subtype was PV, with a 9-fold higher incidence than pemphigus foliaceus.   </p

Applications of Machine Learning to Location Data

Positioning devices are generating location data at an unprecedented pace. Coupled with the right software, these data may enable a virtually unlimited number of valuable services. However, to build such software, there is a need for sophisticated algorithms that can extract the relevant information from location data. In this thesis, we use machine learning to develop such algorithms for three fundamental location-based problems. First, we introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of spatial features. We apply our model to real datasets and show that it outperforms the most popular radio telemetry software package used in ecology, produces accurate location estimates, and yields an interpretable model of animal movement. Second, we develop a novel collaborative ranking framework called Collaborative Local Ranking (CLR), which is designed to solve a ranking problem that occurs frequently in the real-world but has not received enough attention in the scientific community. In this setting, users provide their affinity for items via local preferences among a subset of items instead of global preferences across all items. We justify CLR with a bound on its generalization error and derive an alternating minimization algorithm with runtime guarantees. We apply CLR to a venue recommendation task and demonstrate it outperforms state-of-the-art collaborative ranking methods on real datasets. Third, we design two Bayesian probabilistic graphical models that predict users' future geographic coordinates based on sparse observations of their past geographic coordinates. Our models intelligently share information across users to infer their locations at any future weekhour, determine the number of significant places and the spatial characteristics of these places, and compute the conditional distributions that describe how users spend their time at these places. We apply our models to real location datasets and demonstrate that, despite the sparsity, they provide accurate representations of users' places and outperform existing methods in estimating users' future locations