Speaker and Speech Recognition Using Hierarchy Support Vector Machine and Backpropagation

Voice signal processing has been proposed to improve effectiveness and facilitate the public, such as Smart Home. This study aims a smart home simulation model to move doors, TVs, and lights from voice instructions. Sound signals are processed using Mel-frequency Cepstrum Coefficients (MFCC) to perform feature extraction. Then, the voice is recognized by the speaker using a hierarchy Support Vector Machine (SVM). So that unregistered speakers are not processed or are declared not having access rights. For the process of recognizing spoken words such as "Open the Door”,"Close the Door","Turn on the TV","Turn off the TV","Turn on the Lights" and "Turn Offthe Lights" are done using Backpropagation. The results showed that hierarchy SVM provided an accuracy of 71% compared to the single SVM of 45%

Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking

This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications

Can You Really Predict Markets With Twitter?

In this paper, I attempt to apply an emotional proxy derived by applying the Affective Norms for English Words (ANEW) to messages posted to the Twitter social networking service in order to forecast the movement two stock market indices: the Dow Jones Industrial Average (DJIA) and the CBOE Volatility Index (VIX). In contrast to previous works, I have compared the results of various forecast models employing different sentiment variables, as well as comparing the neural network approach to more standard logistic re- gression. Additionally, several of the models used employ an as-yet unique sentiment proxy, focusing on the average of expressed emotion rather than the volume of expressed emotion. The results indicate that while there is a distinct possibility that sentiment variables can assist in accurately forecasting market movement, the differences in choice of sentiment proxy and forecast method are less important than anticipated

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)

PREDICTION OF RECURRENCE AND MORTALITY OF ORAL TONGUE CANCER USING ARTIFICIAL NEURAL NETWORK (A case study of 5 hospitals in Finland and 1 hospital from Sao Paulo, Brazil)

Cancer is a dreadful disease that had caused the death of millions of people. It is characterized by an uncontrollable growth of cell to form lumps or masses of tissue that are known as tumour. Therefore, it is a concern to all and sundry as these tumours mostly release hormones which have negative impact on the body system. Data mining approaches, statistical methods and machine learning algorithms have been proposed for effective cancer data classification. Artificial Neural Networks (ANN) have been used in this thesis for the prediction of recurrence and mortality of oral tongue cancer in patients. Similarly, ANN was also used to examine the diagnostic and prognostic factors. This was aimed at determining which of these diagnostic and prognostics factors had influence on the prediction of recurrence and mortality of oral tongue cancer in patients. Three different ANN have been applied for the learning and testing phases. The aim was to find the most effective technique. They are Elman, Feedforward, and Layer Recurrent neural networks techniques. Elman neural network was not able to make acceptable prediction of the recurrence or the mortality of tongue cancer based on the data. In contrast, Feedforward neural network captured the relationship between the prognostic factors and correctly predicted recurrence. However, it failed to predict the mortality based on the patient's data. Layer Recurrence neural network has been very effective and successfully predicted the recurrence and the mortality of oral tongue cancer in patients. The constructed layered recurrence neural network has been used to investigate the correlation between the prognostic factors. It was found that out of 11 prognostic factors in the data sheet, it was only 5 of them that had considerable impact on the recurrence and mortality. These are grade, depth, budding, modified stage, and gender. Time in months and disease free months were also used to train the network.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format