Modelling atmospheric ozone concentration using machine learning algorithms

Air quality monitoring is one of several important tasks carried out in the area of environmental science and engineering. Accordingly, the development of air quality predictive models can be very useful as such models can provide early warnings of pollution levels increasing to unsatisfactory levels. The literature review conducted within the research context of this thesis revealed that only a limited number of widely used machine learning algorithms have been employed for the modelling of the concentrations of atmospheric gases such as ozone, nitrogen oxides etc. Despite this observation the research and technology area of machine learning has recently advanced significantly with the introduction of ensemble learning techniques, convolutional and deep neural networks etc. Given these observations the research presented in this thesis aims to investigate the effective use of ensemble learning algorithms with optimised algorithmic settings and the appropriate choice of base layer algorithms to create effective and efficient models for the prediction and forecasting of specifically, ground level ozone (O3). Three main research contributions have been made by this thesis in the application area of modelling O3 concentrations. As the first contribution, the performance of several ensemble learning (Homogeneous and Heterogonous) algorithms were investigated and compared with all popular and widely used single base learning algorithms. The results have showed impressive prediction performance improvement obtainable by using meta learning (Bagging, Stacking, and Voting) algorithms. The performances of the three investigated meta learning algorithms were similar in nature giving an average 0.91 correlation coefficient, in prediction accuracy. Thus as a second contribution, the effective use of feature selection and parameter based optimisation was carried out in conjunction with the application of Multilayer Perceptron, Support Vector Machines, Random Forest and Bagging based learning techniques providing significant improvements in prediction accuracy. The third contribution of research presented in this thesis includes the univariate and multivariate forecasting of ozone concentrations based of optimised Ensemble Learning algorithms. The results reported supersedes the accuracy levels reported in forecasting Ozone concentration variations based on widely used, single base learning algorithms. In summary the research conducted within this thesis bridges an existing research gap in big data analytics related to environment pollution modelling, prediction and forecasting where present research is largely limited to using standard learning algorithms such as Artificial Neural Networks and Support Vector Machines often available within popular commercial software packages

Classification of underwater pipeline events using deep convolutional neural networks

Automatic inspection of underwater pipelines has been a task of growing importance for the detection of four different types of events: inner coating exposure, presence of algae, flanges and concrete blankets. Such inspections might benefit of machine learning techniques in order to accurately classify such occurrences. In this work, we present a deep convolutional neural network algorithm for the classification of underwater pipeline events. The neural network architecture and parameters that result in optimal classifier performance are selected. The convolutional neural network technique outperforms the perceptron algorithm preceded by wavelet feature extraction for different event classes, reaching on average 93.2% classification accuracy, while the accuracy achieved by the perceptron is 91.2%. Besides the results obtained in the test set, accuracy and cross entropy curves obtained in the validation set during training are analyzed, so that the performances of each method and for each event class are compared. Visualizations of the convolutional neural network intermediate layer outputs are also provided. These visualizations are interpreted and associated to the results obtained.A inspeção automática de dutos submarinos tem sido uma tarefa de crescente importância para a detecção de diferentes tipos de eventos, dos quais destacam-se armadura exposta, presença de algas, flanges e manta. Tais inspeções podem se beneficiar de técnicas de aprendizado de máquinas para classificar acuradamente essas ocorrências. Neste trabalho, apresenta-se um algoritmo de redes neurais convolucionais para classificação de eventos em dutos submarinos. A arquitetura e os parâmetros da rede neural que resultam em desempenho de classificação ótimo são selecionados. A técnica de rede neural convolucional, em comparação ao algoritmo do perceptron precedido por extração de features wavelet, apresenta desempenho superior para diferentes classes de eventos, alcançando em média acurácia de classificação de 93.2%, enquanto o desempenho alcançado pelo perceptron é de 91.2%. Além dos resultados obtidos no conjunto de teste, são analisadas as curvas de acurácia e de entropia cruzada obtidas para o conjunto de validação ao longo do treinamento, de modo a comparar os desempenhos de cada método e para cada classe de eventos. São também fornecidas visualizações das saídas das camadas intermediárias da rede convolucional. Essas visualizações são interpretadas e associadas aos resultados obtidos

Autonomous Wireless Radar Sensor Mote for Target Material Classification

An autonomous wireless sensor network consisting of different types of sensor modalities is a topic of intense research due to its versatility and portability.These types of autonomous sensor networks commonly include passive sensor nodes such as infrared,acoustic,seismic and magnetic.However,fusion of another active sensor such as Doppler radar in the integrated sensor network may offer powerful capabilities for many different sensing and classification tasks.In this work,we demonstrate the design and implementation of an autonomous wireless sensor network integrating a Doppler sensor into wireless sensor node with commercial off the shelf components.We also investigate the effect of different types of target materials on return radar signal as one of the applications of the newly designed radar-mote network.Usually type of materials can affect the amount of energy reflected back to the source of an electromagnetic wave.We obtain mathematical and simulation models for the reflectivity of different homogeneous non-conducting materials and study the effect of such reflectivity on different types of targets.We validate our simulation results on effect of reflectivity on different types of targets using real toy experiment data collected through our autonomous radar-mote sensor network

A Machine Learning Approach for Driver Identification Based on CAN-BUS Sensor Data

Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our aim is to identify the driver through supervised learning algorithms based on driving behavior analysis. To determine the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from the CAN-BUS sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II, drive identification is possible. However, we have gained two types of accuracy on a complete data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to the higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorith

Textile Fingerprinting for Dismount Analysis in the Visible, Near, and Shortwave Infrared Domain

The ability to accurately and quickly locate an individual, or a dismount, is useful in a variety of situations and environments. A dismount\u27s characteristics such as their gender, height, weight, build, and ethnicity could be used as discriminating factors. Hyperspectral imaging (HSI) is widely used in efforts to identify materials based on their spectral signatures. More specifically, HSI has been used for skin and clothing classification and detection. The ability to detect textiles (clothing) provides a discriminating factor that can aid in a more comprehensive detection of dismounts. This thesis demonstrates the application of several feature selection methods (i.e., support vector machines with recursive feature reduction, fast correlation based filter) in highly dimensional data collected from a spectroradiometer. The classification of the data is accomplished with the selected features and artificial neural networks. A model for uniquely identifying (fingerprinting) textiles are designed, where color and composition are determined in order to fingerprint a specific textile. An artificial neural network is created based on the knowledge of the textile\u27s color and composition, providing a uniquely identifying fingerprinting of a textile. Results show 100% accuracy for color and composition classification, and 98% accuracy for the overall textile fingerprinting process

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Color and morphological features extraction and nuclei classification in tissue samples of colorectal cancer

Cancer is an important public health problem and the third most leading cause of death in North America. Among the highest impact types of cancer are colorectal, breast, lung, and prostate. This thesis addresses the features extraction by using different artificial intelligence algorithms that provide distinct solutions for the purpose of Computer-AidedDiagnosis (CAD). For example, classification algorithms are employed in identifying histological structures, such as lymphocytes, cancer-cells nuclei and glands, from features like existence, extension or shape. The morphological aspect of these structures indicates the degree of severity of the related disease. In this paper, we use a large dataset of 5000 images to classify eight different tissue types in the case of colorectal cancer. We compare results with another dataset. We perform image segmentation and extract statistical information about the area, perimeter, circularity, eccentricity and solidity of the interest points in the image. Finally, we use and compare four popular machine learning techniques, i.e., Naive Bayes, Random Forest, Support Vector Machine and Multilayer Perceptron to classify and to improve the precision of category assignation. The performance of each algorithm was measured using 3 types of metrics: Precision, recall and F1-Score representing a huge contribution to the existing literature complementing it in a quantitative way. The large number of images has helped us to circumvent the overfitting and reproducibility problems. The main contribution is the use of new characteristics different from those already studied, this work researches about the color and morphological characteristics in the images that may be useful for performing tissue classification in colorectal cancer histology

Deep learning for land cover and land use classification

Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications