Exploring multinomial naïve Bayes for Yorùbá text document classification

The recent increase in the emergence of Nigerian language text online motivates this paper in which the problem of classifying text documents written in Yorùbá language into one of a few pre-designated classes is considered. Text document classification/categorization research is well established for English language and many other languages; this is not so for Nigerian languages. This paper evaluated the performance of a multinomial Naive Bayes model learned on a research dataset consisting of 100 samples of text each from business, sporting, entertainment, technology and political domains, separately on unigram, bigram and trigram features obtained using the bag of words representation approach. Results show that the performance of the model over unigram and bigram features is comparable but significantly better than a model learned on trigram features. The results generally indicate a possibility for the practical application of NB algorithm to the classification of text documents written in Yorùbá language. Keywords: Supervised learning, text classification, Yorùbá language, text mining, BoW Representatio

Torso detection and tracking using a 2D laser range finder

Detecting and tracking people in populated environments has various applications including, robotics, healthcare, automotive, security and defence. In this paper, we present an algorithm for people detection and tracking based on a two dimensional laser rage finder (LRF). The LRF was mounted on a mobile robotic platform to scan a torso section of a person. The tracker is designed to discard spurious targets based on the log likelihood ratio and can effectively handle short term occlusions. Long term occlusions are considered as new tracks. Performance of the algorithm is analysed based on experiments, which shows appealing results

Speaker gender recognition system

Abstract. Automatic gender recognition through speech is one of the fundamental mechanisms in human-machine interaction. Typical application areas of this technology range from gender-targeted advertising to gender-specific IoT (Internet of Things) applications. It can also be used to narrow down the scope of investigations in crime scenarios. There are many possible methods of recognizing the gender of a speaker. In machine learning applications, the first step is to acquire and convert the natural human voice into a form of machine understandable signal. Useful voice features then could be extracted and labelled with gender information so that are then trained by machines. After that, new input voice can be captured and processed and the machine is able to extract the features by pattern modelling. In this thesis, a real-time speaker gender recognition system was designed within Matlab environment. This system could automatically identify the gender of a speaker by voice. The implementation work utilized voice processing and feature extraction techniques to deal with an input speech coming from a microphone or a recorded speech file. The response features are extracted and classified. Then the machine learning classification method (Naïve Bayes Classifier) is used to distinguish the gender features. The recognition result with gender information is then finally displayed. The evaluation of the speaker gender recognition systems was done in an experiment with 40 participants (half male and half female) in a quite small room. The experiment recorded 400 speech samples by speakers from 16 countries in 17 languages. These 400 speech samples were tested by the gender recognition system and showed a considerably good performance, with only 29 errors of recognition (92.75% accuracy). In comparison with previous speaker gender recognition systems, most of them obtained the accuracy no more than 90% and only one obtained 100% accuracy with very limited testers. We can then conclude that the performance of the speaker gender recognition system designed in this thesis is reliable

Line Based Multi-Range Asymmetric Conditional Random Field For Terrestrial Laser Scanning Data Classification

Terrestrial Laser Scanning (TLS) is a ground-based, active imaging method that rapidly acquires accurate, highly dense three-dimensional point cloud of object surfaces by laser range finding. For fully utilizing its benefits, developing a robust method to classify many objects of interests from huge amounts of laser point clouds is urgently required. However, classifying massive TLS data faces many challenges, such as complex urban scene, partial data acquisition from occlusion. To make an automatic, accurate and robust TLS data classification, we present a line-based multi-range asymmetric Conditional Random Field algorithm. The first contribution is to propose a line-base TLS data classification method. In this thesis, we are interested in seven classes: building, roof, pedestrian road (PR), tree, low man-made object (LMO), vehicle road (VR), and low vegetation (LV). The line-based classification is implemented in each scan profile, which follows the line profiling nature of laser scanning mechanism.Ten conventional local classifiers are tested, including popular generative and discriminative classifiers, and experimental results validate that the line-based method can achieve satisfying classification performance. However, local classifiers implement labeling task on individual line independently of its neighborhood, the inference of which often suffers from similar local appearance across different object classes. The second contribution is to propose a multi-range asymmetric Conditional Random Field (maCRF) model, which uses object context as post-classification to improve the performance of a local generative classifier. The maCRF incorporates appearance, local smoothness constraint, and global scene layout regularity together into a probabilistic graphical model. The local smoothness enforces that lines in a local area to have the same class label, while scene layout favours an asymmetric regularity of spatial arrangement between different object classes within long-range, which is considered both in vertical (above-bellow relation) and horizontal (front-behind) directions. The asymmetric regularity allows capturing directional spatial arrangement between pairwise objects (e.g. it allows ground is lower than building, not vice-versa). The third contribution is to extend the maCRF model by adding across scan profile context, which is called Across scan profile Multi-range Asymmetric Conditional Random Field (amaCRF) model. Due to the sweeping nature of laser scanning, the sequentially acquired TLS data has strong spatial dependency, and the across scan profile context can provide more contextual information. The final contribution is to propose a sequential classification strategy. Along the sweeping direction of laser scanning, amaCRF models were sequentially constructed. By dynamically updating posterior probability of common scan profiles, contextual information propagates through adjacent scan profiles

Machine Learning Techniques for Stellar Light Curve Classification

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (~ 2 - 4%) and good accuracy (~ 75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger datasets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.Comment: Accepted to The Astronomical Journa

Audio-coupled video content understanding of unconstrained video sequences

Unconstrained video understanding is a difficult task. The main aim of this thesis is to recognise the nature of objects, activities and environment in a given video clip using both audio and video information. Traditionally, audio and video information has not been applied together for solving such complex task, and for the first time we propose, develop, implement and test a new framework of multi-modal (audio and video) data analysis for context understanding and labelling of unconstrained videos. The framework relies on feature selection techniques and introduces a novel algorithm (PCFS) that is faster than the well-established SFFS algorithm. We use the framework for studying the benefits of combining audio and video information in a number of different problems. We begin by developing two independent content recognition modules. The first one is based on image sequence analysis alone, and uses a range of colour, shape, texture and statistical features from image regions with a trained classifier to recognise the identity of objects, activities and environment present. The second module uses audio information only, and recognises activities and environment. Both of these approaches are preceded by detailed pre-processing to ensure that correct video segments containing both audio and video content are present, and that the developed system can be made robust to changes in camera movement, illumination, random object behaviour etc. For both audio and video analysis, we use a hierarchical approach of multi-stage classification such that difficult classification tasks can be decomposed into simpler and smaller tasks. When combining both modalities, we compare fusion techniques at different levels of integration and propose a novel algorithm that combines advantages of both feature and decision-level fusion. The analysis is evaluated on a large amount of test data comprising unconstrained videos collected for this work. We finally, propose a decision correction algorithm which shows that further steps towards combining multi-modal classification information effectively with semantic knowledge generates the best possible results

Analyzing Predictive Features of Epileptic Seizures in Human Intracranial EEG Recordings

Epilepsiahooge on üritatud ennustada mitmeid aastakümneid, kasutades tipptasemel tunnuseid ja masinõppemeetodeid. Kui õnnestuks välja töötada süsteem, mis reaalajas hoiatab patsiente eelseisvate hoogude eest, parandaks see oluliselt patsientide elukvaliteeti. Epilepsiahoogude ennustamine koosneb kahest etapist: tunnuste ekstraheerimine ning näidiste klassifitseerimine hoogudevaheliseks (tavaline ajaperiood) või hooeelseks. Enamasti kasutatakse EEG andmeid, sest EEG on odav, transporditav ning väljendab muutusi ajudünaamikas kõige täpsemini. Kui enamik uuringuid keskendub uudsete tunnuste ekstraheerimisele või uute klassifitseerimisalgoritmide rakendamisele, siis antud bakalaureusetöö eesmärk oli välja selgitada, missugused kasutatavad tunnused on kõige olulisemad. Kui on teada, missugused tunnused kõige rohkem mõjutavad ennustamistulemusi, aitab see paremini aru saada nii klassifitseerimisalgoritmide tööprotsessist kui ka ajudünaamikast ning vähendada tunnuste hulka, mida masinõppes kasutada, muutes seega klassifitseerimisprotsessi efektiivsemaks. Bakalaureusetöös kasutati kahe patsiendi intrakraniaalseid EEG andmeid ning kolme algoritmi scikit-learn teegist, mida kombineeriti meetoditega, mis hindavad tunnuste mõju. Saadud ennustustäpsused olid mõõdukalt head kuni suurepärased ning võimaldasid seega analüüsida tunnuste mõju usaldusväärselt iga klassifitseerimisalgoritmi kohta.Epilepsy seizure prediction is a challenge that scientists have tried to overcome throughout many decades, using different state-of-the-art features and machine learning methods. If a forecasting system could predict and warn epilepsy patients of impeding seizures in real time, it would greatly improve their quality of life. Seizure prediction consists of two stages: feature extraction from the data and sample classification to interictal (non-seizure) or preictal (preseizure) state. EEG data is commonly used, as it is inexpensive, portable and it most clearly reflects the changes in the brain’s dynamics. While most studies focus on extracting novel features or using new classifiers, this Thesis focuses on ascertaining the most significant features among some that are commonly used in seizure prediction. Knowing which features influence the prediction results the most, helps to understand the inner workings of both the classifiers and the brain activity and to reduce the feature set in future research, making the classification process more effective. Intracranial EEG data of two patients was used in this Thesis with three classifiers from the scikit-learn library, which were combined with methods for evaluating feature importance. Moderately good to excellent prediction accuracies were achieved with these methods, which allowed to reliably analyze the feature importance results of the different classifiers

OBJECT-BASED CLASSIFICATION OF EARTHQUAKE DAMAGE FROM HIGH-RESOLUTION OPTICAL IMAGERY USING MACHINE LEARNING

Object-based approaches to the segmentation and supervised classification of remotely-sensed images yield more promising results compared to traditional pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods and trial and error are often used, but time consuming and yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time sensitive applications such as earthquake induced damage assessment. Our research takes a systematic approach to evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely-sensed imagery using Trimble’s eCognition software. We tested a variety of algorithms and parameters on post-event aerial imagery of the 2011 earthquake in Christchurch, New Zealand. Parameters and methods are adjusted and results compared against manually selected test cases representing different classifications used. In doing so, we can evaluate the effectiveness of the segmentation and classification of buildings, earthquake damage, vegetation, vehicles and paved areas, and compare different levels of multi-step image segmentations. Specific methods and parameters explored include classification hierarchies, object selection strategies, and multilevel segmentation strategies. This systematic approach to object-based image classification is used to develop a classifier that is then compared against current pixel-based classification methods for post-event imagery of earthquake damage. Our results show a measurable improvement against established pixel-based methods as well as object-based methods for classifying earthquake damage in high resolution, post-event imagery

SENTIMENT ANALYSIS OF CHINESE MICROBLOG MESSAGE USING NEURAL NETWORK-BASED VECTOR REPRESENTATION FOR MEASURING REGIONAL PREJUDICE

Regional prejudice is prevalent in Chinese cities in which native residents and migrants lack a basic level of trust in the other group. Like Twitter, Sina Weibo is a social media platform where people actively engage in discussions on various social issues. Thus, it provides a good data source for measuring individuals’ regional prejudice on a large scale. We find that a resentful tone dominates in Weibo messages related to migrants. In this paper, we propose a novel approach, named DKV, for recognizing polarity and direction of sentiment for Weibo messages using distributed real-valued vector representation of keywords learned from neural networks. Such a representation can project rich context information (or embedding) into the vector space, and subsequently be used to infer similarity measures among words, sentences, and even documents. We provide a comprehensive performance evaluation to demonstrate that by exploiting the keyword embeddings, DKV paired with support vector machines can effectively recognize a Weibo message into the predefined sentiment and its direction. Results demonstrate that our method can achieve the best performances compared to other approaches