Disrupted Asymmetry of Inter- and Intra-Hemispheric Functional Connectivity at Rest in Medication-Free Obsessive-Compulsive Disorder

Disrupted functional asymmetry of cerebral hemispheres may be altered in patients with obsessive-compulsive disorder (OCD). However, little is known about whether anomalous brain asymmetries originate from inter- and/or intra-hemispheric functional connectivity (FC) at rest in OCD. In this study, resting-state functional magnetic resonance imaging was applied to 40 medication-free patients with OCD and 38 gender-, age-, and education-matched healthy controls (HCs). Data were analyzed using the parameter of asymmetry (PAS) and support vector machine methods. Patients with OCD showed significantly increased PAS in the left posterior cingulate cortex, left precentral gyrus/postcentral gyrus, and right inferior occipital gyrus and decreased PAS in the left dorsolateral prefrontal cortex (DLPFC), bilateral middle cingulate cortex (MCC), left inferior parietal lobule, and left cerebellum Crus I. A negative correlation was found between decreased PAS in the left DLPFC and Yale–Brown Obsessive-compulsive Scale compulsive behavior scores in the patients. Furthermore, decreased PAS in the bilateral MCC could be used to distinguish OCD from HCs with a sensitivity of 87.50%, an accuracy of 88.46%, and a specificity of 89.47%. These results highlighted the contribution of disrupted asymmetry of intra-hemispheric FC within and outside the cortico-striato-thalamocortical circuits at rest in the pathophysiology of OCD, and reduced intra-hemispheric FC in the bilateral MCC may serve as a potential biomarker to classify individuals with OCD from HCs

Improved scheme of e-mail spam classification using meta-heuristics feature selection and support vector machine

With the technological revolution in the 21st century, time and distance of communication are decreased by using electronic mail (e-mail). Furthermore, the growing use of e-mail has led to the emergence and further growth problems caused by unsolicited bulk e-mails, commonly referred to as spam e-mail. Many of the existing supervised algorithms like the Support Vector Machine (SVM) were developed to stop the spam e-mail. However, the problem of dealing with large data and high dimensionality of feature space can lead to high execution-time and low accuracy of spam e-mail classification. Nowadays, removing the irrelevant and redundant features beside finding the optimal (or near-optimal) subset of features significantly influences the performance of spam e-mail classification; this has become one of the important challenges. Therefore, in order to optimize spam e-mail classification accuracy, dimensional reduction issues need to be solved. Feature selection schemes become very important in order to reduce the dimensionality through selecting a proper subset feature to facilitate the classification process. The objective of this study is to investigate and improve schemes to reduce the execution time and increase the accuracy of spam e-mail classification. The methodology of this study comprises of four schemes: one-way ANOVA f-test, Binary Differential Evolution (BDE), Opposition Differential Evolution (ODE) and Opposition Particle Swarm Optimization (OPSO), and combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The four schemes were used to improve the spam e-mail classification accuracy. The classification accuracy of the proposed schemes were 95.05% with population size of 50 and 1000 number of iterations in 20 runs and 41 features. The experiment of the proposed schemes were carried out using spambase and spamassassin benchmark dataset to evaluate the feasibility of proposed schemes. The experimental findings demonstrate that the improved schemes were able to efficiently reduce the number of features as well as improving the e-mail classification accuracy

Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets

Die Qualitätskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die Überwachung von Aktivitäten, um zu überprüfen, ob die Produkte den gewünschten Qualitätsstandard erfüllen. Viele Ansätze für die Qualitätskontrolle verwenden spezialisierte Bildverarbeitungssoftware, die auf manuell entwickelten Merkmalen basiert, die von Fachleuten entwickelt wurden, um Objekte zu erkennen und Bilder zu analysieren. Diese Modelle sind jedoch mühsam, kostspielig in der Entwicklung und schwer zu pflegen, während die erstellte Lösung oft spröde ist und für leicht unterschiedliche Anwendungsfälle erhebliche Anpassungen erfordert. Aus diesen Gründen wird die Qualitätskontrolle in der Industrie immer noch häufig manuell durchgeführt, was zeitaufwändig und fehleranfällig ist. Daher schlagen wir einen allgemeineren datengesteuerten Ansatz vor, der auf den jüngsten Fortschritten in der Computer-Vision-Technologie basiert und Faltungsneuronale Netze verwendet, um repräsentative Merkmale direkt aus den Daten zu lernen. Während herkömmliche Methoden handgefertigte Merkmale verwenden, um einzelne Objekte zu erkennen, lernen Deep-Learning-Ansätze verallgemeinerbare Merkmale direkt aus den Trainingsproben, um verschiedene Objekte zu erkennen. In dieser Dissertation werden Modelle und Techniken für die automatisierte Erkennung von Defekten in lichtmikroskopischen Bildern von materialografisch präparierten Schnitten entwickelt. Wir entwickeln Modelle zur Defekterkennung, die sich grob in überwachte und unüberwachte Deep-Learning-Techniken einteilen lassen. Insbesondere werden verschiedene überwachte Deep-Learning-Modelle zur Erkennung von Defekten in der Mikrostruktur von Lithium-Ionen-Batterien entwickelt, von binären Klassifizierungsmodellen, die auf einem Sliding-Window-Ansatz mit begrenzten Trainingsdaten basieren, bis hin zu komplexen Defekterkennungs- und Lokalisierungsmodellen, die auf ein- und zweistufigen Detektoren basieren. Unser endgültiges Modell kann mehrere Klassen von Defekten in großen Mikroskopiebildern mit hoher Genauigkeit und nahezu in Echtzeit erkennen und lokalisieren. Das erfolgreiche Trainieren von überwachten Deep-Learning-Modellen erfordert jedoch in der Regel eine ausreichend große Menge an markierten Trainingsbeispielen, die oft nicht ohne weiteres verfügbar sind und deren Beschaffung sehr kostspielig sein kann. Daher schlagen wir zwei Ansätze vor, die auf unbeaufsichtigtem Deep Learning zur Erkennung von Anomalien in der Mikrostruktur von gesinterten NdFeB-Magneten basieren, ohne dass markierte Trainingsdaten benötigt werden. Die Modelle sind in der Lage, Defekte zu erkennen, indem sie aus den Trainingsdaten indikative Merkmale von nur "normalen" Mikrostrukturmustern lernen. Wir zeigen experimentelle Ergebnisse der vorgeschlagenen Fehlererkennungssysteme, indem wir eine Qualitätsbewertung an kommerziellen Proben von Lithium-Ionen-Batterien und gesinterten NdFeB-Magneten durchführen

Automatic Speech Emotion Recognition- Feature Space Dimensionality and Classification Challenges

In the last decade, research in Speech Emotion Recognition (SER) has become a major endeavour in Human Computer Interaction (HCI), and speech processing. Accurate SER is essential for many applications, like assessing customer satisfaction with quality of services, and detecting/assessing emotional state of children in care. The large number of studies published on SER reflects the demand for its use. The main concern of this thesis is the investigation of SER from a pattern recognition and machine learning points of view. In particular, we aim to identify appropriate mathematical models of SER and examine the process of designing automatic emotion recognition schemes. There are major challenges to automatic SER including ambiguity about the list/definition of emotions, the lack of agreement on a manageable set of uncorrelated speech-based emotion relevant features, and the difficulty of collected emotion-related datasets under natural circumstances. We shall initiate our work by dealing with the identification of appropriate sets of emotion related features/attributes extractible from speech signals as considered from psychological and computational points of views. We shall investigate the use of pattern-recognition approaches to remove redundancies and achieve compactification of digital representation of the extracted data with minimal loss of information. The thesis will include the design of new or complement existing SER schemes and conduct large sets of experiments to empirically test their performances on different databases, identify advantages, and shortcomings of using speech alone for emotion recognition. Existing SER studies seem to deal with the ambiguity/dis-agreement on a “limited” number of emotion-related features by expanding the list from the same speech signal source/sites and apply various feature selection procedures as a mean of reducing redundancies. Attempts are made to discover more relevant features to emotion from speech. One of our investigations focuses on proposing a newly sets of features for SER, extracted from Linear Predictive (LP)-residual speech. We shall demonstrate the usefulness of the proposed relatively small set of features by testing the performance of an SER scheme that is based on fusing our set of features with the existing set of thousands of features using common machine learning schemes of Support Vector Machine (SVM) and Artificial Neural Network (ANN). The challenge of growing dimensionality of SER feature space and its impact on increased model complexity is another major focus of our research project. By studying the pros and cons of the commonly used feature selection approaches, we argued in favour of meta-feature selection and developed various methods in this direction, not only to reduce dimension, but also to adapt and de-correlate emotional feature spaces for improved SER model recognition accuracy. We used rincipal Component Analysis (PCA) and proposed Data Independent PCA (DIPCA) by training on independent emotional and non-emotional datasets. The DIPCA projections, especially when extracted from speech data coloured with different emotions or from Neutral speech data, had comparable capability to the PCA in terms of SER performance. Another adopted approach in this thesis for dimension reduction is the Random Projection (RP) matrices, independent of training data. We have shown that some versions of RP with SVM classifier can offer an adaptation space for Speaker Independent SER that avoid over-fitting and hence improves recognition accuracy. Using PCA trained on a set of data, while testing on emotional data features, has significant implication for machine learning in general. The thesis other major contribution focuses on the classification aspects of SER. We investigate the drawbacks of the well-known SVM classifier when applied to a preprocessed data by PCA and RP. We shall demonstrate the advantages of using the Linear Discriminant Classifier (LDC) instead especially for PCA de-correlated metafeatures. We initiated a variety of LDC-based ensembles classification, to test performance of scheme using a new form of bagging different subsets of metafeature subsets extracted by PCA with encouraging results. The experiments conducted were applied on two benchmark datasets (Emo-Berlin and FAU-Aibo), and an in-house dataset in the Kurdish language. Recognition accuracy achieved by are significantly higher than the state of art results on all datasets. The results, however, revealed a difficult challenge in the form of persisting wide gap in accuracy over different datasets, which cannot be explained entirely by the differences between the natures of the datasets. We conducted various pilot studies that were based on various visualizations of the confusion matrices for the “difficult” databases to build multi-level SER schemes. These studies provide initial evidences to the presence of more than one “emotion” in the same portion of speech. A possible solution may be through presenting recognition accuracy in a score-based measurement like the spider chart. Such an approach may also reveal the presence of Doddington zoo phenomena in SER