Feature Ranking Techniques For 3D ATS Drug Molecular Structure Identification

Existing laboratory analysis techniques of ATS drug identification have their challenges which include the cost of training expert operators, the cost of acquired materials, and the dangers involved in operating the experiments. Furthermore, with the constantly emerging of the new ATS drugs design into the illicit market, it serves as a challenge to the comprehensive analytical method to detect and validate these compounds. This research is aimed to propose a computational intelligence approach in assisting the analysis phase of ATS drug identification process. The dataset namely ATS drugs 3D molecular structure representation dataset was analyzed. It consists of 7212 sample records associated with 1185 features. This research has investigated numerous complexities and uncertainties that have embedded in the dataset in the form of high dimensionality and existence of irrelevant and noisy features. These challenges motivated this research to tackle these problems by reduce the dimensionality of the dataset and selecting the significant subset of features from the dataset. Hence, this led to the proposal of a feature selection approach for removing the irrelevant and noisy data and selecting a feature subset which best represent the ATS drug and produce a better identification performance. The proposed feature selection approach has a simple algorithmic framework and makes use of the existing feature selection techniques to cater different variety of data issues, namely Ensemble Filter-Embedded Feature Ranking Approach (FEFR). This proposed approach is performed in two main phases. The first phase is to carry out a thorough analysis of the effectiveness and capability of various feature ranking techniques in ATS drug identification. Six feature ranking techniques were used: Information Gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), Support vector machine based recursive feature elimination (SVM-RFE), and Variable Importance based random forest (VI-RF). The selected feature subset by each of the selected feature ranking technique were run through five different popular classifiers: Random forest (RF), Naïve Bayes (NB), IBK, Sequential Minimal Optimization (SMO), J48, and their performances were analyzed and compared. Experiments on the dataset showed that ReliefF and VIRF performed the best among the other techniques in retaining the significant features and eliminate the irrelevant features. For the second phase, the results of these two top performers in the analysis will be selected and aggregate to gain benefit from their advantages whilst minimize their shortcomings to yield a more reliable result. All the performance is evaluated in term of the number of features selected and classification accuracy. Paired t-test also carry out to further validated the quality of the FEFR based on the classification accuracy performance metric. The results show that the feature subset selected by the FEFR feature selection approach is either superior or at least as adequate as those subsets that selected by the individual feature ranking method and the original dataset

Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work

Learning to Generate and Refine Object Proposals

Visual object recognition is a fundamental and challenging problem in computer vision. To build a practical recognition system, one is first confronted with high computation complexity due to an enormous search space from an image, which is caused by large variations in object appearance, pose and mutual occlusion, as well as other environmental factors. To reduce the search complexity, a moderate set of image regions that are likely to contain an object, regardless of its category, are usually first generated in modern object recognition subsystems. These possible object regions are called object proposals, object hypotheses or object candidates, which can be used for down-stream classification or global reasoning in many different vision tasks like object detection, segmentation and tracking, etc. This thesis addresses the problem of object proposal generation, including bounding box and segment proposal generation, in real-world scenarios. In particular, we investigate the representation learning in object proposal generation with 3D cues and contextual information, aiming to propose higher-quality object candidates which have higher object recall, better boundary coverage and lower number. We focus on three main issues: 1) how can we incorporate additional geometric and high-level semantic context information into the proposal generation for stereo images? 2) how do we generate object segment proposals for stereo images with learning representations and learning grouping process? and 3) how can we learn a context-driven representation to refine segment proposals efficiently? In this thesis, we propose a series of solutions to address each of the raised problems. We first propose a semantic context and depth-aware object proposal generation method. We design a set of new cues to encode the objectness, and then train an efficient random forest classifier to re-rank the initial proposals and linear regressors to fine-tune their locations. Next, we extend the task to the segment proposal generation in the same setting and develop a learning-based segment proposal generation method for stereo images. Our method makes use of learned deep features and designed geometric features to represent a region and learns a similarity network to guide the superpixel grouping process. We also learn a ranking network to predict the objectness score for each segment proposal. To address the third problem, we take a transformation-based approach to improve the quality of a given segment candidate pool based on context information. We propose an efficient deep network that learns affine transformations to warp an initial object mask towards nearby object region, based on a novel feature pooling strategy. Finally, we extend our affine warping approach to address the object-mask alignment problem and particularly the problem of refining a set of segment proposals. We design an end-to-end deep spatial transformer network that learns free-form deformations (FFDs) to non-rigidly warp the shape mask towards the ground truth, based on a multi-level dual mask feature pooling strategy. We evaluate all our approaches on several publicly available object recognition datasets and show superior performance

Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.

Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs