Primjena regresijskog modela u analizi ključnih čimbenika koji pridonose težini nesreća u građevinskoj industriji u Iranu

Construction industry involves the highest risk of occupational accidents and bodily injuries, which range from mild to very severe. The aim of this cross-sectional study was to identify the factors associated with accident severity rate (ASR) in the largest Iranian construction companies based on data about 500 occupational accidents recorded from 2009 to 2013. We also gathered data on safety and health risk management and training systems. Data were analysed using Pearson’s chi-squared coefficient and multiple regression analysis. Median ASR (and the interquartile range) was 107.50 (57.24-381.25). Fourteen of the 24 studied factors stood out as most affecting construction accident severity (p<0.05). These findings can be applied in the design and implementation of a comprehensive safety and health risk management system to reduce ASR.Građevinska se industrija povezuje s najvišim rizikom od nesreća na radu i tjelesnih ozljeda u rasponu od blagih do vrlo teških. Cilj ovoga presječnog istraživanja bio je utvrditi čimbenike povezane s indeksom težine nesreća među najvećim građevinskim tvrtkama u Iranu na temelju podataka iz 500 izvještaja o nesrećama na radu prikupljanih od 2009. do 2013. Usto smo prikupili podatke o upravljanju rizikom za sigurnost i zdravlje radnika te o njihovu obrazovanju u tom pogledu. Podaci su analizirani Pearsonovim hi-kvadratnim testom i modelom višestruke regresije. Medijan indeksa težine nesreća (i interkvartilni raspon) iznosio je 107,50 (57,24-381,25). Na težinu nesreća najviše je utjecalo četrnaest od 24 ispitana čimbenika (p<0,05). Ovi rezultati mogu biti korisni u osmišljavanju i uspostavi obuhvatnih sustava upravljanja rizikom za sigurnost i zdravlje radnika kako bi se smanjio indeks težine nesreća na radu

A Feature Ranking Algorithm in Pragmatic Quality Factor Model for Software Quality Assessment

Software quality is an important research area and has gain considerable attention from software engineering community in identification of priority quality attributes in software development process. This thesis describes original research in the field of software quality model by presenting a Feature Ranking Algorithm (FRA) for Pragmatic Quality Factor (PQF) model. The proposed algorithm is able to improve the weaknesses in PQF model in updating and learning the important attributes for software quality assessment. The existing assessment techniques lack of the capability to rank the quality attributes and data learning which can enhance the quality assessment process. The aim of the study is to identify and propose the application of Artificial Intelligence (AI) technique for improving quality assessment technique in PQF model. Therefore, FRA using FRT was constructed and the performance of the FRA was evaluated. The methodology used consists of theoretical study, design of formal framework on intelligent software quality, identification of Feature Ranking Technique (FRT), construction and evaluation of FRA algorithm. The assessment of quality attributes has been improved using FRA algorithm enriched with a formula to calculate the priority of attributes and followed by learning adaptation through Java Library for Multi Label Learning (MULAN) application. The result shows that the performance of FRA correlates strongly to PQF model with 98% correlation compared to the Kolmogorov-Smirnov Correlation Based Filter (KSCBF) algorithm with 83% correlation. Statistical significance test was also performed with score of 0.052 compared to the KSCBF algorithm with score of 0.048. The result shows that the FRA was more significant than KSCBF algorithm. The main contribution of this research is on the implementation of FRT with proposed Most Priority of Features (MPF) calculation in FRA for attributes assessment. Overall, the findings and contributions can be regarded as a novel effort in software quality for attributes selection

Feature subset selection in large dimensionality domains

Searching for an optimal feature subset from a high dimensional feature space is known to be an NP-complete problem. We present a hybrid algorithm, SAGA, for this task. SAGA combines the ability to avoid being trapped in a local minimum of Simulated Annealing with the very high rate of convergence of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of Generalized Regression Neural Networks. We compare the performance over time of SAGA and well-known algorithms on synthetic and real datasets. The results show that SAGA outperforms existing algorithms

A new method to improve feature selection with meta-heuristic algorithm and chaos theory

Finding a subset of features from a large data set is a problem that arises in many fields of study. It is important to have an effective subset of features that is selected for the system to provide acceptable performance. This will lead us in a direction that to use meta-heuristic algorithms to find the optimal subset of features. The performance of evolutionary algorithms is dependent on many parameters which have significant impact on its performance, and these algorithms usually use a random process to set parameters. The nature of chaos is apparently random and unpredictable; however it also deterministic, it can suitable alternative instead of random process in meta-heuristic algorithm

Recognition of emotions in Czech texts

Díky rozvoji informačních a komunikačních technologií v posledních letech došlo k velkému nárůstu množství informací, které denně vznikají ve formě elektronických dokumentů. Třídění a zpracování informací se stalo pro člověka velmi obtížné, a proto vzrůstá obliba systémů automatického dolování znalostí z textu. Zajímavou podoblastí jsou systémy pro analýzu sentimentu a automatického rozpoznání emocí v textech, které mají potencionálně široké uplatnění. V rámci této práce byl navržen a implementován systém využívající technik dolování znalostí z textu za účelem rozpoznávání emocí v česky psaných textech a bylo provedeno zhodnocení jeho úspěšnosti. Protože je systém postaven převážně na metodě strojového učení, byla navrhnuta a vytvořena trénovací množina, která byla posléze použita k vytvoření modelu klasifikátoru pomocí algoritmu podpůrných vektorů (SVM). Pro potřeby zpřesnění výsledků klasifikace textových dokumentů do předem definovaných emočních tříd, jsou do systému integrovány další prvky, jako např.: lexikální databáze, lemmatizátor a odvozený slovník klíčových slov. Součástí práce je také zhodnocení několika přístupů ke klasifikaci s různými modifikacemi navrženého systému.With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.

DEVELOPMENT OF A REAL-TIME SMARTWATCH ALGORITHM FOR THE DETECTION OF GENERALIZED TONIC-CLONIC SEIZURES

Generalized Tonic Clonic Seizure (GTCS) detection has been an ongoing problem in the healthcare industry. Algorithms and devices for this problem do exist on the market, but they either have poor False Positive Rates, are expensive, or cannot be used as anything other than a seizure detector. There is currently a need to provide a portable seizure detection algorithm that can meets patient demands. In this thesis, we develop a two-stage end-to-end seizure detection algorithm that is implemented on an Apple Watch, and validated on Epilepsy Monitoring Unit (EMU) patients. 124 features are extracted from the collected dataset, after which 9 are empirically selected. We have provided mutual information based feature selection methods that cannot yet be implemented on the watch due to computational restrictions. In stage one we compare common anomaly detection methods of One Class SVM, SVDD, Isolation Forest and Extended Isolation Forest over a thorough cross-validation to determine which is ideal to use as an anomaly detector. Isolation Forest (Sensitivity: 0.9, FPR: 3.4/day, Latency: 69s) was chosen despite the good sensitivity and latency of SVDD (Sensitivity: 1.0, FPR: 17.28/day, Latency: 8.9s) due to better implementation characteristics. During in-vivo testing, we record a sensitivity of 100% over 24 recorded tonic seizures with FPR: 1.29/day. To further limit false positive detections, a second stage is incorporated to separate between true and false positives using deep learning methods. We compare a Deep-LSTM, CNN-LSTM and TCN network. CNN-LSTM (Sensitivity: 0.93, FPR: 0.047/day) was finally used on the watch due to its tractable implementation, though TCN (Sensitivity: 1.0, FPR: 0/day) performed significantly better during cross-validation. During in-vivo testing, the 2-stage algorithm showed sensitivity: 100%, FPR: 0.05/day over 2004 tracked hours and 12 seizures. The mean latency was 62 seconds, which is on the threshold of clinical acceptability for this task

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection