Feature Extraction and Selection in Automatic Sleep Stage Classification

Sleep stage classification is vital for diagnosing many sleep related disorders and Polysomnography (PSG) is an important tool in this regard. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. The automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. In this research work, we focused on feature extraction and selection steps. The main goal of this thesis was identifying a robust and reliable feature set that can lead to efficient classification of sleep stages. For achieving this goal, three types of contributions were introduced in feature selection, feature extraction and feature vector quality enhancement. Several feature ranking and rank aggregation methods were evaluated and compared for finding the best feature set. Evaluation results indicated that the decision on the precise feature selection method depends on the system design requirements such as low computational complexity, high stability or high classification accuracy. In addition to conventional feature ranking methods, in this thesis, novel methods such as Stacked Sparse AutoEncoder (SSAE) was used for dimensionality reduction. In feature extration area, new and effective features such as distancebased features were utilized for the first time in sleep stage classification. The results showed that these features contribute positively to the classification performance. For signal quality enhancement, a loss-less EEG artefact removal algorithm was proposed. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy

Hybrid dragonfly algorithm with neighbourhood component analysis and gradient tree boosting for crime rates modelling

In crime studies, crime rates time series prediction helps in strategic crime prevention formulation and decision making. Statistical models are commonly applied in predicting time series crime rates. However, the time series crime rates data are limited and mostly nonlinear. One limitation in the statistical models is that they are mainly linear and are only able to model linear relationships. Thus, this study proposed a time series crime prediction model that can handle nonlinear components as well as limited historical crime rates data. Recently, Artificial Intelligence (AI) models have been favoured as they are able to handle nonlinear and robust to small sample data components in crime rates. Hence, the proposed crime model implemented an artificial intelligence model namely Gradient Tree Boosting (GTB) in modelling the crime rates. The crime rates are modelled using the United States (US) annual crime rates of eight crime types with nine factors that influence the crime rates. Since GTB has no feature selection, this study proposed hybridisation of Neighbourhood Component Analysis (NCA) and GTB (NCA-GTB) in identifying significant factors that influence the crime rates. Also, it was found that both NCA and GTB are sensitive to input parameter. Thus, DA2-NCA-eGTB model was proposed to improve the NCA-GTB model. The DA2-NCA-eGTB model hybridised a metaheuristic optimisation algorithm namely Dragonfly Algorithm (DA) with NCA-GTB model to optimise NCA and GTB parameters. In addition, DA2-NCA-eGTB model also improved the accuracy of the NCA-GTB model by using Least Absolute Deviation (LAD) as the GTB loss function. The experimental result showed that DA2-NCA-eGTB model outperformed existing AI models in all eight modelled crime types. This was proven by the smaller values of Mean Absolute Percentage Error (MAPE), which was between 2.9195 and 18.7471. As a conclusion, the study showed that DA2-NCA-eGTB model is statistically significant in representing all crime types and it is able to handle the nonlinear component in limited crime rate data well

ANÁLISE DAS EVIDÊNCIAS DA CRISE FINANCEIRA DE 2008 NOS PRODUTOS HORTÍCOLAS CELEBRADOS NAS CEASAS DO BRASIL

Na literatura econômica, a crise mundial de 2008 desencadeou recessão nos mercados e provocou desaceleração da economia, impactando as transações comerciais. Os resultados empíricos, para a maioria dos setores econômicos, têm apresentado resultados convergentes. Nesse sentido, este trabalho objetiva analisar evidências dessa crise e suas implicações no volume de produção e nos preços dos hortigranjeiros celebrados nas Centrais de Abastecimento no Brasil com vistas a dissecar os reflexos da crise financeira mundial de 2008 no comportamento dos hortigranjeiros. Para tanto, aplicou-se uma pesquisa descritiva com o uso do teste-t de amostras pareadas em uma amostra longitudinal com dados secundários obtidos do IBGE, FAO e Ceasa Santa Catarina. O período de estudo compreendeu os anos de 2005 a 2012. Os resultados mostram que o preço e a produção do agronegócio não podem ser compreendidos pelo setor, mas sim por produto hortícola específico. Dessa forma, a crise mundial muito pouco interferiu na produção e no preço, porém as oscilações do preço e do volume estão altamente relacionadas a flutuações do mercado local

Pengaruh E-Wom (Electronic Word of Mouth) pada Situs social Commerce terhadap Niat Beli Generasi X,Y dan Z

Pesatnya pertumbuhan internet di Indonesia memacu pertumbuhan pasar elektonik (e-commerce). Seiring dengan pertumbuhan tersebut berkembang pula social commerce yang merupakan perkembangan dari e-commerce. Teknologi dari social commerce memungkinkan terjadinya e-WOM (Electronic Word of Mouth), yang memungkinkan konsumen mencari informasi sebelum membeli produk dengan melihat ulasan dari konsumen lain. Informasi e-WOM memberi pengaruh yang sangat penting terhadap niat pembelian sebuah produk. Setiap Generasi usia mempunyai pengalaman dan penerimaan teknologi yang berbeda. Banyak perusahaan yang berinvestasi besar untuk meneliti perbedaan sikap dan perilaku setiap generasi untuk kepentingan pemasaran. Hal tersebut juga berpengaruh terhadap perilaku belanja online dan keterlibatan dalam E-Wom. Fakor Usia juga menjadi faktor penentu dalam belanja online dan niat konsumen untuk berbelanja Sebagian besar penelitian-penelitian sebelumnya membahas bagaimana pengaruh e-WOM terhadap niat beli, namum tidak menyertakan pengaruh e-WOM terhadap niat beli berdasarkan generasi konsumen. Dalam penelitian ini akan teliti pengaruh E-Wom yang dapat mempengaruhi niat beli di Social commerce dengan memperhatikan faktor generasi X,Y dan Z. Berdasarkan tujuan tersebut diusulkan sebuah konseptual model yang dibangun berdasarkan TRA (Theory of Reasoned Action) dan TAM (Technology Acceptance Model). Model yang dibuat divalidasi dengan SEM (Structural Equation Modelling). Dengan menggunakan pendekatan component based dengan alat bantu Generalized Structured Component Analysis (GSCA), penelitian ini mendapatkan hasil bahwa kuliatas informasi, kredibilitas sumber informasi, persepsi tentang kuantitas informasi berpengaruh terhadap kegunaan informasi, kegunaan informasi berpengaruh terhadap sikap terhadap informasi, dan sikap terhadap informasi terbukti berpengaruh terhadap niat beli generasi X,Y dan Z. ================================================================= The growth of internet in Indonesia caused the growth of electronic market (e-commerce). The growth of the internet also led to the growth of social commerce which is the development of e-commerce. Technology from social commerce enables e-WOM (Electronic Word of Mouth), which allows consumers to search for information before buying a product by looking at reviews from other consumers. The e-WOM information has a very important effect on the purchase intent of a product Each generation of age has different experience and acceptance of technology. Many companies are investing heavily to examine the differences in attitudes and behavior of each generation for marketing purposes. It also affects online shopping behavior and involvement in E-Wom. Age factors are also a determining factor in online shopping and consumer intentions for shopping. Most of the previous studies discussed how e-WOM influences purchasing intentions, but does not include e-WOM effects on purchasing intentions based on consumer generation. In this research will examine the influence of E-Wom that can affect the buying intention in Social commerce by considering the factors of generation X, Y and Z. Based on the objective is proposed a conceptual model built on TRA (Theory of Reasoned Action) and TAM (Technology Acceptance Model). The model created is validated by SEM (Structural Equation Modeling). By using component based approach with Generalized Structured Component Analysis (GSCA) tool, this research get result that quality of information, credibility of information source, perception about quantity of information influence to usefulness of information, usage of information influence to attitude to information, and attitude to information proven Affect the intention to buy generation X, Y and Z