Fusing dynamic deep learned features and handcrafted features for facial expression recognition

The automated recognition of facial expressions has been actively researched due to its wide-ranging applications. The recent advances in deep learning have improved the performance facial expression recognition (FER) methods. In this paper, we propose a framework that combines discriminative features learned using convolutional neural networks and handcrafted features that include shape- and appearance-based features to further improve the robustness and accuracy of FER. In addition, texture information is extracted from facial patches to enhance the discriminative power of the extracted textures. By encoding shape, appearance, and deep dynamic information, the proposed framework provides high performance and outperforms state-of-the-art FER methods on the CK+ dataset

Machine Learning Approaches for Heart Disease Detection: A Comprehensive Review

This paper presents a comprehensive review of the application of machine learning algorithms in the early detection of heart disease. Heart disease remains a leading global health concern, necessitating efficient and accurate diagnostic methods. Machine learning has emerged as a promising approach, offering the potential to enhance diagnostic accuracy and reduce the time required for assessments. This review begins by elucidating the fundamentals of machine learning and provides concise explanations of the most prevalent algorithms employed in heart disease detection. It subsequently examines noteworthy research efforts that have harnessed machine learning techniques for heart disease diagnosis. A detailed tabular comparison of these studies is also presented, highlighting the strengths and weaknesses of various algorithms and methodologies. This survey underscores the significant strides made in leveraging machine learning for early heart disease detection and emphasizes the ongoing need for further research to enhance its clinical applicability and efficacy

Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning

This study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Melody Informatics: Computational Approaches to Understanding the Relationships Between Human Affective Reasoning and Music

Music is a powerful and complex medium that allows people to express their emotions, while enhancing focus and creativity. It is a universal medium that can elicit strong emotion in people, regardless of their gender, age or cultural background. Music is all around us, whether it is in the sound of raindrops, birds chirping, or a popular song played as we walk along an aisle in a supermarket. Music can also significantly help us regain focus while doing a number of different tasks. The relationship between music stimuli and humans has been of particular interest due to music's multifaceted effects on human brain and body. While music can have an anticonvulsant effect on people's bodily signals and act as a therapeutic stimulus, it can also have proconvulsant effects such as triggering epileptic seizures. It is also unclear what types of music can help to improve focus while doing other activities. Although studies have recognised the effects of music in human physiology, research has yet to systematically investigate the effects of different genres of music on human emotion, and how they correlate with their subjective and physiological responses. The research set out in this thesis takes a human-centric computational approach to understanding how human affective (emotional) reasoning is influenced by sensory input, particularly music. Several user studies are designed in order to collect human physiological data while they interact with different stimuli. Physiological signals considered are: electrodermal activity (EDA), blood volume pulse (BVP), skin temperature (ST), pupil dilation (PD), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Several computational approaches, including traditional machine learning approaches with a combination of feature selection methods are proposed which can effectively identify patterns from small to medium scale physiological feature sets. A novel data visualisation approach called "Gingerbread Animation" is proposed, which allows physiological signals to be converted into images that are compatible with transfer learning methods. A novel stacked ensemble based deep learning model is also proposed to analyse large-scale physiological datasets. In the beginning of this research, two user studies were designed to collect physiological signals from people interacting with visual stimuli. The computational models showed high efficacy in detecting people's emotional reactions. The results provided motivation to design a third user study, where these visual stimuli were combined with music stimuli. The results from the study showed decline in recognition accuracy comparing to the previous study. These three studies also gave a key insight that people's physiological response provide a stronger indicator of their emotional state, compared with their verbal statements. Based on the outcomes of the first three user studies, three more user studies were carried out to look into people's physiological responses to music stimuli alone. Three different music genres were investigated: classical, instrumental and pop music. Results from the studies showed that human emotion has a strong correlation with different types of music, and these can be computationally identified using their physiological response. Findings from this research could provide motivation to create advanced wearable technologies such as smartwatches or smart headphones that could provide personalised music recommendation based on an individual's physiological state. The computational approaches can be used to distinguish music based on their positive or negative effect on human mental health. The work can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research

Output Effect Evaluation Based on Input Features in Neural Incremental Attribute Learning for Better Classification Performance

[[abstract]]Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can obtain lower classification error rates than a conventional batch training approach. Yet the reason for that is still not very clear. In this study, the feasibility of IAL is verified by mathematical approaches. Moreover, experimental results derived by IAL neural networks on benchmarks also confirm the mathematical validation.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]電子

Automatic BIRAD scoring of breast cancer mammograms

A computer aided diagnosis system (CAD) is developed to fully characterize and classify mass to benign and malignancy and to predict BIRAD (Breast Imaging Reporting and Data system) scores using mammographic image data. The CAD includes a preprocessing step to de-noise mammograms. This is followed by an active counter segmentation to deforms an initial curve, annotated by a radiologist, to separate and define the boundary of a mass from background. A feature extraction scheme wasthen used to fully characterize a mass by extraction of the most relevant features that have a large impact on the outcome of a patient biopsy. For this thirty-five medical and mathematical features based on intensity, shape and texture associated to the mass were extracted. Several feature selection schemes were then applied to select the most dominant features for use in next step, classification. Finally, a hierarchical classification schemes were applied on those subset of features to firstly classify mass to benign (mass with BIRAD score 2) and malignant mass (mass with BIRAD score over 4), and secondly to sub classify mass with BIRAD score over 4 to three classes (BIRAD with score 4a,4b,4c). Accuracy of segmentation performance were evaluated by calculating the degree of overlapping between the active counter segmentation and the manual segmentation, and the result was 98.5%. Also reproducibility of active counter 3 using different manual initialization of algorithm by three radiologists were assessed and result was 99.5%. Classification performance was evaluated using one hundred sixty masses (80 masses with BRAD score 2 and 80 mass with BIRAD score over4). The best result for classification of data to benign and malignance was found using a combination of sequential forward floating feature (SFFS) selection and a boosted tree hybrid classifier with Ada boost ensemble method, decision tree learner type and 100 learners’ regression tree classifier, achieving 100% sensitivity and specificity in hold out method, 99.4% in cross validation method and 98.62 % average accuracy in cross validation method. For further sub classification of eighty malignance data with BIRAD score of over 4 (30 mass with BIRAD score 4a,30 masses with BIRAD score 4b and 20 masses with BIRAD score 4c), the best result achieved using the boosted tree with ensemble method bag, decision tree learner type with 200 learners Classification, achieving 100% sensitivity and specificity in hold out method, 98.8% accuracy and 98.41% average accuracy for ten times run in cross validation method. Beside those 160 masses (BIRAD score 2 and over 4) 13 masses with BIRAD score 3 were gathered. Which means patient is recommended to be tested in another medical imaging technique and also is recommended to do follow-up in six months. The CAD system was trained with mass with BIRAD score 2 and over 4 also 4 it was further tested using 13 masses with a BIRAD score of 3 and the CAD results are shown to agree with the radiologist’s classification after confirming in six months follow up. The present results demonstrate high sensitivity and specificity of the proposed CAD system compared to prior research. The present research is therefore intended to make contributions to the field by proposing a novel CAD system, consists of series of well-selected image processing algorithms, to firstly classify mass to benign or malignancy, secondly sub classify BIRAD 4 to three groups and finally to interpret BIRAD 3 to BIRAD 2 without a need of follow up study

Ancestry-independent osteometric sex estimation from selected postcranial skeletal elements of South Africans: a machine learning approach

Sex estimation, as part of a biological profile, has the power to halve the number of possible identities of unidentified skeletal remains. Postcranial elements have been studied in South Africa (SA) for the purpose of sex estimation and have often proven to be more accurate than the cranium. Estimation techniques using postcranial elements in SA almost exclusively utilise discriminant analysis to evaluate sex, but international publications have shown success using alternative machine learning (ML) algorithms. SA methods and standards are often restricted by limited sample size, lack of robust statistical techniques in older publications and, the prerequisite of known or estimated ancestry. Most methods are specific to SA African, European or, more recently, Mixed ancestry groups and are unreliable when ancestry is unknown. The aim of this study was to apply a series of ML algorithms to train ancestry independent sex classification models using postcranial osteometric measurements from the cadaveric skeletal remains of modern South Africans, focussing on long bone joints. The study consisted of a roughly demographically representative, pooled sample, of 650 South Africans (325 male, 325 female). 12 osteometric measurements were taken from available left- and, or right-sided bones for each individual. All 12 mensurations were sexually dimorphic and differences between left- and right-sided bones were negligible. The dataset was subjected to ML algorithm training using univariate and multivariate predictor combinations. The best performing ML algorithm, given the sample size and available predictors was discriminant function analysis. Univariate model accuracies ranged from 80.5-89.1% and multivariate model accuracies ranged from 84.5%, using 2 predictors, to 92.8%, using 12 predictors. An optimised 3-predictor model was able to predict sex with 92.7% accuracy. Results from this study were comparable to those using ancestry-specific models and non-ancestry-specific models, where available. Findings from this study suggest that the inclusion of ancestry, when predicting sex using the elements examined, is not necessary as it does not significantly improve prediction accuracy