Mental and behavioural disorders such as bipolar disorder and depression are critical healthcare issues that affected approximately 45 and 264 million people around the world, respectively in 2020. Early detection and intervention are crucial for limiting the negative effects that these illnesses can have on people’s lives. Although the symptoms for different mental disorders vary, they generally are characterized by a combination of abnormal behaviours, thoughts, and emotions. Mental disorders can affect one’s ability to relate to others and function every day. To assess symptoms, clinicians often use structured clinical interviews and standard questioners. However, there is a scarcity of automated or technology-assisted tools that can simplify the diagnostic process. The main objective of this thesis is to investigate, develop, and propose automated methods for mental disorder detection. We focus in our research on bipolar disorder and depression as they are two of the most common and debilitating mental illnesses. Bipolar disorder is one of the most prevalent mental illnesses in the world. Its principal indicator is the extreme swings in the mood ranging from the manic to depressive states. We propose automatic ternary classification models for the bipolar disorder manic states. We employ a dataset that uses the Young Mania Recall Scale to distinguish the manic states of patients as: Mania, Hypo- Mania, and Remission. The dataset comprises audio-visual recordings of bipolar disorder patients undergoing a structured interview. We propose three bipolar disorder classification solutions. The first approach uses a hybrid LSTM-CNN model. We apply a CNN model to extract facial features from video signals. We supply the features’ sequence to an LSTM model to resolve the bipolar disorder state. Our solution achieved promising results on the development and test set of the Turkish Audio-Visual Bipolar Disorder Corpus with the Unweighted Average Recall of 60.67% and 57.4%, respectively. The second solution employs additional features from the structured interview recordings. We acquire visual representations along with audio and textual cues. We capture Mel-Frequency Cepstral Coefficients and Geneva Minimalistic Acoustic Parameter Set as audio features. We compute linguistic and sentiment features for each subject’s transcript. We present a stacked ensemble classifier to classify all fused features after feature selection. A set of three homogeneous CNNs and an MLP constitute the first and second levels of the stacked ensemble classifier respectively. Moreover, we use reinforcement learning to optimize the networks and their hyperparameters. We show that our stacked ensemble solution outperforms existing models on the Turkish Audio-Visual Bipolar Disorder corpus with a 59.3% unweighted average unit on the test set. To the best of our knowledge, this is the highest performance achieved on this dataset. The Turkish Audio-Visual Bipolar Disorder dataset comprises a relatively small number of videos. Moreover, the labels for the testing set are kept confidential by the dataset provider. Hence, this motivated us to train a classifier using a semi-supervised ladder network for the third solution. This network benefits from unlabeled data during training. Our goal was to investigate whether a bipolar disorder states classifier can be trained using a mix of labelled and unlabelled data. This would alleviate the burden of labelling all the videos in the training set. We collect informative audio, visual, and textual features from the recordings to realize a multi-model classifier of the manic states. The third proposed model achieved a 53.7% and 60.0% unweighted average unit on the test and development sets, respectively. There is a growing demand for automated depression detection system to control the subjective bias in diagnosis. We propose an automated depression severity detection model that uses multi- modal fusion of audio and textual information. We train the model on the E-DAIC corpus, which labels the individual’s depression level with patient health questionnaire score. We use MFCCs and eGeMAPs as audio representations and Word2Vec embeddings for the textual modality. Then, we implement a stacked ensemble regressor to detect depression severity. The proposed model achieves a concordance correlation coefficient 0.49 on the test set. To the best of our knowledge, this is the highest performing model on this dataset.

The presence of the ordered $\gamma^{\prime}$ phase ($\text{Ni}_{3}\text{Al}$) in Ni-base superalloys is fundamental to the performance of engineering components such as turbine disks and blades which operate at high temperatures and loads. Hence for these alloys it is important to optimize their microstructure and phase composition. This is typically done by varying their chemistry and heat treatment to achieve an appropriate balance between $\gamma^{\prime}$ content and other constituents such as carbides, borides, oxides and topologically close packed phases. In this work we have set out to investigate the onset of $\gamma^{\prime}$ ordering in Ni-Al single crystals and in Ni-Al bicrystals containing coincidence site lattice grain boundaries (GBs) and we do this at high temperatures, which are representative of typical heat treatment schedules including quenching and annealing. For this we use the atomistic simulation methods of molecular dynamics (MD) and density functional theory (DFT). In the first part of this work we develop robust Bayesian classifiers to identify the $\gamma^{\prime}$ phase in large scale simulation boxes at high temperatures around 1500 K. We observe significant \gamma^{\prime} ordering in the simulations in the form of clusters of $\gamma^{\prime}$-like ordered atoms embedded in a $\gamma$ host solid solution and this happens within 100 ns. Single crystals are found to exhibit the expected homogeneous ordering with slight indications of chemical composition change and a positive correlation between the Al concentration and the concentration of $\gamma^{\prime}$ phase. In general, the ordering is found to take place faster in systems with GBs and preferentially adjacent to the GBs. The sole exception to this is the $\Sigma3 \left(111\right)$ tilt GB, which is a coherent twin. An analysis of the ensemble and time lag average displacements of the GBs reveals mostly `anomalous diffusion' behaviour. Increasing the Al content from pure Ni to Ni 20 at.% Al was found to either consistently increase or decrease the mobility of the GB as seen from the changing slope of the time lag displacement average. The movement of the GB can then be characterized as either `super' or `sub-diffusive' and is interpreted in terms of diffusion induced grain boundary migration, which is posited as a possible precursor to the appearance of serrated edge grain boundaries. In the second part of this work we develop a method for the training of empirical interatomic potentials to capture more elements in the alloy system. We focus on the embedded atom method (EAM) and use the Ni-Al system as a test case. Recently, empirical potentials have been developed based on results from DFT which utilize energies and forces, but neglect the electron densities, which are also available. Noting the importance of electron densities, we propose a route to include them into the training of EAM-type potentials via Bayesian linear regression. Electron density models obtained for structures with a range of bonding types are shown to accurately reproduce the electron densities from DFT. Also, the resulting empirical potentials accurately reproduce DFT energies and forces of all the phases considered within the Ni-Al system. Properties not included in the training process, such as stacking fault energies, are sometimes not reproduced with the desired accuracy and the reasons for this are discussed. General regression issues, known to the machine learning community, are identified as the main difficulty facing further development of empirical potentials using this approach.

This work deals with the prediction of numerical series whose application is suitable for prediction of stock prices. They explain the procedures for analysis and works with price charts. Also explains the methods of machine learning. Knowledge is used to build a program that finds patterns in numerical series for estimation.