本研究定義運用技術指標建構投資決策之步驟，明確描述各步驟細節，投資人根據此流程定義，可利用技術指標逐步運算出投資標的之投資價值，作為最終投資決策之依據。同時，本研究建立技術指標、偵測機制等分類架構，讓投資人主觀的投資需求對應（map）到技術指標，建立個人化的投資決策。 / This paper defines the stages that how to build an investment decision with technical indicators and describes the details of each stage definitely. According to the process definition, investors can calculate the investment value of the investment target with technical indicators step by step. The investment value can be the foundation of the final investment decision. This paper also establishs both classificaton models of technical indicators and detect mechanisms. It makes investors map their subjective demand for investment information to technical indicators, personlize their investment descions.

A Lagoa dos Patos (LP) é um dos ambientes oticamente complexos mais bem estudados no Brasil, e sua grande abrangência espacial, permite a união de diferentes medidas in situ com produtos de sensoriamento remoto, sendo possível entender melhor como os componentes óticos da água influenciam na sua cor. Florações de fitoplâncton possuem grande relevância ecológica e econômica, sendo o desenvolvimento de metologias simples para o seu monitoramento de vital importância. O presente trabalho utilizou produtos de coloração do oceano de imagens diárias dos sensores MODIS/Aqua e SeaWiFS durante os anos de 2002-2005, dados de modelos meteorológicos de reanálise para precipitação e velocidade do vento e dados de clorofila-a e salinidade obtidos in situ. Foi identificado que o espectro de reflectância de sensoriamento remoto é controlado pelo regime de El Niño e La Niña, variação intra anual e espacial (p<0.05), sendo a cor da água da LP em geral, característica de ambientes com alta concentração de CDOM/sedimentos ou de domínio misto. Partindo do pressuposto que o fitoplâncton domina o coeficiente de absorção da luz durante florações de fitoplâncton, e que as possíveis florações na LP se restringem a diatomáceas e cianobactérias, foi desenvolvido um modelo de classificação para discriminar a ocorrência desses eventos. O modelo proposto permite a classificação de águas oticamente complexas de acordo com o componente ótico predominante, e é pioneiro na exploração de dados do sensor MODIS/Aqua para detecção de florações de fitoplâncton em um ambiente lagunar / Patos Lagoon (PL) is one of the optical complex environment best studied in Brazil, and it large spatial extent, allows the union of different in situ and remote sensing measures, enabling a better understandment of how the optical components in water can influence its color. Phytoplankton blooms have great ecological and economic relevance, and the development of simple methodologies for your monitoring of vital importance. The present work used ocean color products from daily MODIS/Aqua and SeaWiFS images during the years 2002-2005, meteorological model data for precipitation and wind speed and chlorophyll-a and sailinity data obtained in situ. It was detected that remote sensing reflectance spectra is controlled by the regime of El Niño and La Niña, intra annual and spatial changes (p<0.05), ande the water color from PL in general, characteristic of environments with high CDOM/sediments concentration or with mixed domain. Assuming that the phytoplankton dominate light absorption coefficient during phytoplankton blooms, and that PL possible blooms are restricted to diatom and cyanobacteria, a classification model was developed to discriminate the occurance of those events. The proposed model allows for the classification of optically complex waters according to the predominant optical component, and it is pioneer in the exploration of data from MODIS/Aqua sensor to detect phytoplankton blooms in lagunar environment

In recent years, various researches are under progress to enhance the quality of the travel survey. These researches were mainly performed with the aid of GPS technology. Initially the researches were mainly focused on the vehicle travel mode due to the availability of GPS technology in vehicle. But, nowadays due to the accessible of GPS devices for personal uses, researchers have diverted their focus on personal mobility in all travel modes. This master’s thesis aimed at developing a mechanism to extract one type of travel survey information particularly travel mode from collected GPS dataset. The available GPS dataset is collected for travel modes of walk, bike, car, and public transport travel modes such as bus, train and subway. The developed procedure consists of two stages where the first is the dividing the track trips into trips and further the trips into segments by means of a segmentation process. The segmentation process is based on an assumption that a traveler switches from one transportation mode to the other. Thus, the trips are divided into walking and non walking segments. The second phase comprises a procedure to develop a classification model to infer the separated segments with travel modes of walk, bike, bus, car, train and subway. In order to develop the classification model, a supervised classification method has been used where decision tree algorithm is adopted. The highest obtained prediction accuracy of the classification system is walk travel mode with 75.86%. In addition, the travel modes of bike and bus have shown the lowest prediction accuracy. Moreover, the developed system has showed remarkable results that could be used as baseline for further similar researches.

Σκοπός της διπλωματικής εργασίας είναι η λεπτομερής ανάλυση κυβερνητικών επενδύσεων για έργα ΤΠΕ. Ο συνδυασμός της στατιστικής ανάλυσης, της συσχέτισης (correlation) και της ανάλυσης με τεχνικές εξόρυξης δεδομένων δημιούργησε χρήσιμα συμπεράσματα για τα έργα ΤΠΕ. Επίσης, περιγράφεται ένα μοντέλο αξιολόγησης με βάση τις αποκλίσεις από τους αρχικούς στόχους και την εκτίμηση των διαχειριστών των έργων (Project managers). Σημαντικό τμήμα αυτού του μοντέλου αποτελεί η πρόβλεψη της ολίσθησης του κόστους με την χρήση κατηγοριοποίησης. Τέλος η παρουσίαση της απόδοσης των ελληνικών έργων ΤΠΕ γίνεται με το σχεδιασμό ενός βελτιωμένου ταμπλό (dashboard) για την παρακολούθηση και τον έλεγχο για τις ελληνικές επενδύσεις στις ΤΠΕ. / The goal of this master thesis is the detailed analysis of governmental ICT projects. The combination of statistical, correlation and mining analysis extracts useful conclusions for ICT projects. Also a detailed description of an evaluation model is presented for evaluating the performance of ICT project and we introduce an improved ICT dashboard for monitoring and controlling for the Greek ICT investments as well as a classification model for predicting the performance’s slippage.

A Lagoa dos Patos (LP) é um dos ambientes oticamente complexos mais bem estudados no Brasil, e sua grande abrangência espacial, permite a união de diferentes medidas in situ com produtos de sensoriamento remoto, sendo possível entender melhor como os componentes óticos da água influenciam na sua cor. Florações de fitoplâncton possuem grande relevância ecológica e econômica, sendo o desenvolvimento de metologias simples para o seu monitoramento de vital importância. O presente trabalho utilizou produtos de coloração do oceano de imagens diárias dos sensores MODIS/Aqua e SeaWiFS durante os anos de 2002-2005, dados de modelos meteorológicos de reanálise para precipitação e velocidade do vento e dados de clorofila-a e salinidade obtidos in situ. Foi identificado que o espectro de reflectância de sensoriamento remoto é controlado pelo regime de El Niño e La Niña, variação intra anual e espacial (p<0.05), sendo a cor da água da LP em geral, característica de ambientes com alta concentração de CDOM/sedimentos ou de domínio misto. Partindo do pressuposto que o fitoplâncton domina o coeficiente de absorção da luz durante florações de fitoplâncton, e que as possíveis florações na LP se restringem a diatomáceas e cianobactérias, foi desenvolvido um modelo de classificação para discriminar a ocorrência desses eventos. O modelo proposto permite a classificação de águas oticamente complexas de acordo com o componente ótico predominante, e é pioneiro na exploração de dados do sensor MODIS/Aqua para detecção de florações de fitoplâncton em um ambiente lagunar / Patos Lagoon (PL) is one of the optical complex environment best studied in Brazil, and it large spatial extent, allows the union of different in situ and remote sensing measures, enabling a better understandment of how the optical components in water can influence its color. Phytoplankton blooms have great ecological and economic relevance, and the development of simple methodologies for your monitoring of vital importance. The present work used ocean color products from daily MODIS/Aqua and SeaWiFS images during the years 2002-2005, meteorological model data for precipitation and wind speed and chlorophyll-a and sailinity data obtained in situ. It was detected that remote sensing reflectance spectra is controlled by the regime of El Niño and La Niña, intra annual and spatial changes (p<0.05), ande the water color from PL in general, characteristic of environments with high CDOM/sediments concentration or with mixed domain. Assuming that the phytoplankton dominate light absorption coefficient during phytoplankton blooms, and that PL possible blooms are restricted to diatom and cyanobacteria, a classification model was developed to discriminate the occurance of those events. The proposed model allows for the classification of optically complex waters according to the predominant optical component, and it is pioneer in the exploration of data from MODIS/Aqua sensor to detect phytoplankton blooms in lagunar environment

Brain tumors present the second highest cause of death among pediatric cancers. About 60% are located in the posterior fossa region of the brain; among the most frequent types the ones considered for this project were astrocytomas, medulloblastomas, and ependymomas. Diagnosis can be done either through invasive histopathology exams or by non-invasive magnetic resonance (MR) scans. The tumors listed can be difficult to diagnose, even for trained radiologists, so machine learning methods, in particular deep learning, can be useful in helping to assess a diagnosis. Deep learning has been investigated only in a few other studies.The dataset used included 115 different subjects, some with multiple scan sessions, for which there were 142 T2-w, 119 T1Gd-w, and 89 volumes that presented both MR modalities. 2D slices have been manually extracted from the registered and skull-stripped volumes in the transversal, sagittal, and frontal anatomical plane and have been preprocessed by normalizing them and selecting the slices containing the tumor. The scans employed are T2-w, T1Gd-w, and a combination of the two referred to as multimodal images. The images were divided session-wise into training, validation, and testing, using stratified cross-validation and have also been augmented. The convolutional neural networks (CNN) investigated were ResNet50, VGG16, and MobileNetV2. The model performances were evaluated for two-class and three-class classification tasks by computing the confusion matrix, accuracy, receiver operating characteristic curve (ROC), the area under the curve (AUROC), and F1-score. Moreover,  explanations for the behavior of networks were investigated using GradCAMs and occlusion maps. Preliminary investigations showed that the best plane and modality were the transversal one and T2-w images. Overall the best model was VGG16, for the two-class tasks the best classification was between astrocytomas and medulloblastomas which reached an F1-score of 0.86 for both classes on multimodal images, followed by astrocytomas and ependymomas with an F1-score of 0.76 for astrocytomas and 0.74 for ependymomas on T2-w, and last F1-score of 0.30 for ependymomas and 0.65 for medulloblastomas on multimodal images. The three-class classification reached F1-score values of 0.59 for astrocytomas, 0.46 for ependymomas, and 0.64 for medulloblastomas on T2-w images. GradCAMs and occlusion maps showed that VGG16 was able to focus mostly on the tumor region but that there also seemed to be other information in the background of the images that contributed to the final classification.To conclude, the classification of infratentorial pediatric brain tumors can be achieved with acceptable results by means of deep learning and using a single MR modality, though one might have to account for the dataset size, number of classes and class imbalance. GradCAMs and occlusion maps offer important insights into the decision process of the networks

Diagnostic classification models used in conjunction with diagnostic assessments are to classify individual respondents into masters and nonmasters at the level of attributes. Previous researchers (Madison & Bradshaw, 2015) recommended items on the assessment should measure all patterns of attribute combinations to ensure classification accuracy, but in practice, certain attributes may not be measured by themselves. Moreover, the model estimation requires large sample size, but in reality, there could be unanswered items in the data. Therefore, the current study sought to provide suggestions on selecting between two alternative Q-matrix designs when an attribute cannot be measured in isolation and when using maximum likelihood estimation in the presence of missing responses. The factorial ANOVA results of this simulation study indicate that adding items measuring some attributes instead of all attributes is more optimal and that other missing data treatments should be sought if the percent of missing responses is greater than 5%.

Research philosophies and paradigms (RPPs) reveal researchers’ assumptions and provide a systematic way in which research can be carried out effectively and appropriately. Different studies highlight cognitive and comprehension challenges of RPPs concepts at the postgraduate level. This study develops a natural language processing (NLP) supervised classification application that guides students in identifying RPPs applicable to their study. By using algorithms rooted in a quantitative research approach, this study builds a corpus represented using the Bag of Words model to train the naïve Bayes, Logistic Regression, and Support Vector Machine algorithms. Computer experiments conducted to evaluate the performance of the algorithms reveal that the Naïve Bayes algorithm presents the highest accuracy and precision levels. In practice, user testing results show the varying impact of knowledge, performance, and effort expectancy. The findings contribute to the minimization of issues postgraduates encounter in identifying research philosophies and the underlying paradigms for their studies. / Science and Technology Education / MTech. (Information Technology)

A huge amount of data is generated daily by digital technologies such as social media, web logs, traffic sensors, on-line transactions, tracking data, videos, and so on. This has led to the archiving and storage of larger and larger datasets, many of which are multi-modal, or contain different types of data which contribute to the problem that is now known as “Big Data”. In the area of Big Data, volume, variety and velocity problems remain difficult to solve. The work presented in this thesis focuses on the variety aspect of Big Data. For example, data can come in various and mixed formats for the same feature(attribute) or different features and can be identified mainly by one of the following data types: real-valued, crisp and linguistic values. The increasing variety and ambiguity of such data are particularly challenging to process and to build accurate machine learning models. Therefore, data heterogeneity requires new methods of analysis and modelling techniques to enable useful information extraction and the modelling of achievable tasks. In this thesis, new approaches are proposed for handling heterogeneous Big Data. these include two techniques for filtering heterogeneous data objects are proposed. The two techniques called Two-Dimensional Similarity Space(2DSS) for data described by numeric and categorical features, and Three-Dimensional Similarity Space(3DSS) for real-valued, crisp and linguistic data are proposed for filtering such data. Both filtering techniques are used in this research to reduce the noise from the initial dataset and make the dataset more homogeneous. Furthermore, a new similarity measure based on intuitionistic fuzzy set theory is proposed. The proposed measure is used to handle the heterogeneity and ambiguity within crisp and linguistic data. In addition, new combine similarity models are proposed which allow for a comparison between the heterogeneous data objects represented by a combination of crisp and linguistic values. Diverse examples are used to illustrate and discuss the efficiency of the proposed similarity models. The thesis also presents modification of the k-Nearest Neighbour classifier, called k-Nearest Neighbour Weighted Average (k-NNWA), to classify the heterogeneous dataset described by real-valued, crisp and linguistic data. Finally, the thesis also introduces a novel classification model, called FCCM (Filter Combined Classification Model), for heterogeneous data classification. The proposed model combines the advantages of the 3DSS and k-NNWA classifier and outperforms the latter algorithm. All the proposed models and techniques have been applied to weather datasets and evaluated using accuracy, Fscore and ROC area measures. The experiments revealed that the proposed filtering techniques are an efficient approach for removing noise from heterogeneous data and improving the performance of classification models. Moreover, the experiments showed that the proposed similarity measure for intuitionistic fuzzy data is capable of handling the fuzziness of heterogeneous data and the intuitionistic fuzzy set theory offers some promise in solving some Big Data problems by handling the uncertainties, and the heterogeneity of the data.

<p dir="ltr">Within Electronic Health Record (EHR) Systems, physicians face extensive documentation, leading to alarming mental burnout. The disproportionate focus on data entry over direct patient care underscores a critical concern. Integration of Natural Language Processing (NLP) powered EHR systems offers relief by reducing time and effort in record maintenance.</p><p dir="ltr">Our research introduces the Automated Electronic Health Record System, which not only transcribes dialogues but also employs advanced clinical text classification. With an accuracy exceeding 98.97%, it saves over 90% of time compared to manual entry, as validated on MIMIC III and MIMIC IV datasets.</p><p dir="ltr">In addition to our system's advancements, we explore integration of Diagnostic Decision Support System (DDSS) leveraging Large Language Models (LLMs) and transformers, aiming to refine healthcare documentation and improve clinical decision-making. We explore the advantages, like enhanced accuracy and contextual understanding, as well as the challenges, including computational demands and biases, of using various LLMs.</p>