ANALYZING STRUCTURED TEXT ENTITIES USING A CITIZEN ORIENTED APPLICATION

The concept of citizen oriented informatics application is presented in the context of the knowledge society. The differences between these applications and the traditional applications are highlighted. The diversity of problems the citizens has lead to a high diversity of application structures that is described in the paper. Usual applications are taken into discussion and comments are made on their citizen orientation. Quality standards for informatics applications are described. An application for the analysis of the structured entities is presented. The methods that were used to orientate it towards the citizens are described. The procedure for the score computing is described. The performance of the application measured by automatic means is analyzed. Performance improvements are discussed. Future work directions and improvements are discussed.citizen oriented applications, knowledge, analysis, structured text entities

Frequency shifting approach towards textual transcription of heartbeat sounds

Auscultation is an approach for diagnosing many cardiovascular problems. Automatic analysis of heartbeat sounds and extraction of its audio features can assist physicians towards diagnosing diseases. Textual transcription allows recording a continuous heart sound stream using a text format which can be stored in very small memory in comparison with other audio formats. In addition, a text-based data allows applying indexing and searching techniques to access to the critical events. Hence, the transcribed heartbeat sounds provides useful information to monitor the behavior of a patient for the long duration of time. This paper proposes a frequency shifting method in order to improve the performance of the transcription. The main objective of this study is to transfer the heartbeat sounds to the music domain. The proposed technique is tested with 100 samples which were recorded from different heart diseases categories. The observed results show that, the proposed shifting method significantly improves the performance of the transcription

An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area which we feel deserves much further attention. Towards this aim, this paper proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains -- a feature which both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance is studied in several applications. Through a Bayesian variable selection example, the authors demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm's adaptive proposal to induce mode-jumping is illustrated through a trimodal density and a Bayesian mixture modeling application. Lastly, through a 2D Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models.Comment: 33 pages, 20 figures (the supplementary materials are included as appendices

Deep Learning Framework for Covid-19 Detection and Severity Classification towards Clinical Decision Support System

Chest CT scans are widely used for COVID-19 diagnosis. Existing methods focused more on the detection of the disease. However, there is need for detection of severity towards making decisions for suitable course of action. Towards this end, we proposed a deep learning framework for automatic COVID-19 diagnosis and severity detection. Our framework is based on enhanced Convolutional Neural Network (CNN) model which is found efficient for medical image analysis. We proposed two algorithms to realize the framework. The first algorithm is known as Deep Learning based Automatic COVID-19 Diagnosis (DL-ACD). This algorithm is meant for diagnosis of COVID-19 with learning based phenomena. The second algorithm is known as Automatic COVID-19 Severity Detection (ACSD). It is designed to know severity of the disease which helps in making treatment appropriate. Our framework is evaluated against existing deep learning models and found to have superior performance over the existing models

How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms

This paper reviews and compares twenty-one different model selection algorithms (MSAs) representing a diversity of approaches, including (i) information criteria such as AIC and SIC; (ii) selection of a “portfolio” or best subset of models; (iii) general-to-specific algorithms, (iv) forward-stepwise regression approaches; (v) Bayesian Model Averaging; and (vi) inclusion of all variables. We use coefficient unconditional mean-squared error (UMSE) as the basis for our measure of MSA performance. Our main goal is to identify the factors that determine MSA performance. Towards this end, we conduct Monte Carlo experiments across a variety of data environments. Our experiments show that MSAs differ substantially with respect to their performance on relevant and irrelevant variables. We relate this to their associated penalty functions, and a bias-variance tradeoff in coefficient estimates. It follows that no MSA will dominate under all conditions. However, when we restrict our analysis to conditions where automatic variable selection is likely to be of greatest value, we find that two general-to-specific MSAs, Autometrics, do as well or better than all others in over 90% of the experiments.Model selection algorithms; Information Criteria; General-to-Specific modeling; Bayesian Model Averaging; Portfolio Models; AIC; SIC; AICc; SICc; Monte Carlo Analysis; Autometrics