Exploring Emotion Recognition for VR-EBT Using Deep Learning on a Multimodal Physiological Framework

Post-Traumatic Stress Disorder is a mental health condition that affects a growing number of people. A variety of PTSD treatment methods exist, however current research indicates that virtual reality exposure-based treatment has become more prominent in its use.Yet the treatment method can be costly and time consuming for clinicians and ultimately for the healthcare system. PTSD can be delivered in a more sustainable way using virtual reality. This is accomplished by using machine learning to autonomously adapt virtual reality scene changes. The use of machine learning will also support a more efficient way of inserting positive stimuli in virtual reality scenes. Machine learning has been used in medical areas such as rare diseases, oncology, medical data classification and psychiatry. This research used a public dataset that contained physiological recordings and emotional responses. The dataset was used to train a deep neural network, and a convolutional neural network to predict an individual’s valence, arousal and dominance. The results presented indicate that the deep neural network had the highest overall mean bounded regression accuracy and the lowest computational time

Análise discriminante sobre variáveis qualitativas

Este estudo insere-se no campo da Análise Discriminante Discreta (ADD) propondo uma combinação de modelos, uma vez que se tem verificado que, em geral, a sua aplicação conduz a métodos mais estáveis e robustos. O trabalho que se apresenta é particularmente focado no caso em que se dispõe de classes a priori mal separadas e/ou amostras de pequena ou moderada dimensão, situações em que a tarefa de ADD é mais difícil. Procura-se com esta contribuição, ultrapassar a dificuldade de estimação de um grande número de parâmetros em ADD e encontrar classificadores que melhor se ajustem aos dados em estudo, uma vez que os erros de classificação obtidos por vários modelos não ocorrem sobre os mesmos objetos (Sousa Ferreira, 2000; Brito, 2002 e Brito et al., 2006). Com este objetivo, propusemos uma combinação de dois modelos com especificidades diferentes, o Modelo de Independência Condicional (Goldstein and Dillon, 1978) e o Modelo Gráfico Decomponível (Celeux and Nakache, 1994; Pearl, 1988). Tendo-nos deparado, em diversas aplicações do modelo proposto, com um número demasiado elevado de variáveis explicativas face à dimensão da amostra considerada, direcionámos o trabalho na procura de métodos de seleção de variáveis de forma a reduzir a complexidade dos dados a analisar. Houve, ainda, necessidade de avaliar o impacto de alguns fatores no desempenho dos classificadores propostos, nomeadamente: relação entre as variáveis explicativas intra-classes; grau de separabilidade entre as classes; classes balanceadas ou não balanceadas; número de estados omissos e dimensão da amostra.This study falls within the scope of Discrete Discriminant Analysis (DDA) and proposes a combination of models since, overall, its application has been found to lead to more stable and robust methods. The work focuses particularly on the case where there are poorly separated a priori classes and/or small or moderate-sized samples which tend to present more difficulties for the DDA task. This contribution sets out to overcome the difficulty of estimating a large amount of DDA parameters and to find classifiers which are better suited to the data under study, given that the classification errors obtained by diverse models do not occur on the same objects (Sousa Ferreira, 2000; Brito, 2002 and Brito et al., 2006). To this end, we have proposed a combination of two models with different specificities, the First-order Independence Model (Goldstein and Dillon, 1978) and the Dependence Tree Model (Celeux and Nakache, 1994; Pearl, 1988). In several applications of the proposed model, we were confronted with an excessive number of explanatory variables in relation to the sample size under study. Therefore, our work has been geared towards seeking variable selection methods, so as to reduce the complexity of the data to be analysed. It was also necessary to evaluate the impact of certain factors on the performance of the proposed combined model, namely the relationship among intra-class explanatory variables; the degree of separation between classes; balanced or unbalanced classes; number of missing states and sample size

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Longwall Guidance and Control Development

The longwall guidance and control (G&C) system was evaluated to determine which systems and subsystems lent themselves to automatic control in the mining of coal. The upper coal/shale interface was identified as the reference for a vertical G&C system, with two sensors (the natural backgound and the sensitized pick) being used to locate and track this boundary. In order to insure a relatively smooth recession surface (roof and floor of the excavated seam), a last and present cut measuring instrument (acoustic sensor) was used. Potentiometers were used to measure elevations of the shearer arms. The intergration of these components comprised the vertical control system (pitch control). Yaw and roll control were incorporated into a face alignment system which was designed to keep the coal face normal to its external boundaries. Numerous tests, in the laboratory and in the field, have confirmed the feasibility of automatic horizon control, as well as determining the face alignment

Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs

An ensemble of intelligent water drop algorithm for feature selection optimization problem

Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed

Physical Activity Classification with Conditional Random Fields

In this thesis we develop methods for classifying physical activity using accelerometer recordings. We cast this as a problem of classification in time series with moderate to high dimensional observations at each time point. Specifically, we observe a vector of summary statistics of the accelerometer signal at each point in time, and we wish to use these observations to estimate the type and intensity of physical activity the individual engaged in as it changes over time. Our methods are based on Conditional Random Fields, which allow us to capture temporal dependence in an individual’s physical activity type without requiring us to model the distribution of the observed features at each point in time. We develop three novel estimation strategies for Conditional Random Fields, evaluate their performance on classification tasks through simulation studies and demonstrate their use in applications with real physical activity data sets