Feature selection using Fuzzy Entropy measures with Yu's Similarity measure

In this study, feature selection in classification based problems is highlighted. The role of feature selection methods is to select important features by discarding redundant and irrelevant features in the data set, we investigated this case by using fuzzy entropy measures. We developed fuzzy entropy based feature selection method using Yu's similarity and test this using similarity classifier. As the similarity classifier we used Yu's similarity, we tested our similarity on the real world data set which is dermatological data set. By performing feature selection based on fuzzy entropy measures before classification on our data set the empirical results were very promising, the highest classification accuracy of 98.83% was achieved when testing our similarity measure to the data set. The achieved results were then compared with some other results previously obtained using different similarity classifiers, the obtained results show better accuracy than the one achieved before. The used methods helped to reduce the dimensionality of the used data set, to speed up the computation time of a learning algorithm and therefore have simplified the classification task

The Effects of Anesthetic Induced Loss of Consciousness on Quantitative Electroen Cephalogram, and Bispectral and Spectral Entropy Indices. Studies on Healthy Male

Changes in the electroencephalography (EEG) signal have been used to study the effects of anesthetic agents on the brain function. Several commercial EEG based anesthesia depth monitors have been developed to measure the level of the hypnotic component of anesthesia. Specific anesthetic related changes can be seen in the EEG, but still it remains difficult to determine whether the subject is consciousness or not during anesthesia. EEG reactivity to external stimuli may be seen in unconsciousness subjects, in anesthesia or even in coma. Changes in regional cerebral blood flow, which can be measured with positron emission tomography (PET), can be used as a surrogate for changes in neuronal activity. The aim of this study was to investigate the effects of dexmedetomidine, propofol, sevoflurane and xenon on the EEG and the behavior of two commercial anesthesia depth monitors, Bispectral Index (BIS) and Entropy. Slowly escalating drug concentrations were used with dexmedetomidine, propofol and sevoflurane. EEG reactivity at clinically determined similar level of consciousness was studied and the performance of BIS and Entropy in differentiating consciousness form unconsciousness was evaluated. Changes in brain activity during emergence from dexmedetomidine and propofol induced unconsciousness were studied using PET imaging. Additionally, the effects of normobaric hyperoxia, induced during denitrogenation prior to xenon anesthesia induction, on the EEG were studied. Dexmedetomidine and propofol caused increases in the low frequency, high amplitude (delta 0.5-4 Hz and theta 4.1-8 Hz) EEG activity during stepwise increased drug concentrations from the awake state to unconsciousness. With sevoflurane, an increase in delta activity was also seen, and an increase in alpha- slow beta (8.1-15 Hz) band power was seen in both propofol and sevoflurane. EEG reactivity to a verbal command in the unconsciousness state was best retained with propofol, and almost disappeared with sevoflurane. The ability of BIS and Entropy to differentiate consciousness from unconsciousness was poor. At the emergence from dexmedetomidine and propofol induced unconsciousness, activation was detected in deep brain structures, but not within the cortex. In xenon anesthesia, EEG band powers increased in delta, theta and alpha (8-12Hz) frequencies. In steady state xenon anesthesia, BIS and Entropy indices were low and these monitors seemed to work well in xenon anesthesia. Normobaric hyperoxia alone did not cause changes in the EEG. All of these results are based on studies in healthy volunteers and their application to clinical practice should be considered carefully.

Intelligence acquisition methods in cyber domain : examining the circumstantial applicability of cyber intelligence acquisition methods using a hierarchical model

Phenomena in cyber domain, especially threats to security and privacy, have proven an increasingly heated topic addressed by different writers and scholars at an increasing pace – both nationally and internationally. However little public research has been done on the subject of cyber intelligence. The main research question of the thesis was: To what extent is the applicability of cyber intelligence acquisition methods circumstantial? The study was conducted in sequential a manner, starting with defining the concept of intelligence in cyber domain and identifying its key attributes, followed by identifying the range of intelligence methods in cyber domain, criteria influencing their applicability, and types of operatives utilizing cyber intelligence. The methods and criteria were refined into a hierarchical model. The existing conceptions of cyber intelligence were mapped through an extensive literature study on a wide variety of sources. The established understanding was further developed through 15 semi-structured interviews with experts of different backgrounds, whose wide range of points of view proved to substantially enhance the perspective on the subject. Four of the interviewed experts participated in a relatively extensive survey based on the constructed hierarchical model on cyber intelligence that was formulated in to an AHP hierarchy and executed in the Expert Choice Comparion online application. It was concluded that Intelligence in cyber domain is an endorsing, cross-cutting intelligence discipline that adds value to all aspects of conventional intelligence and furthermore that it bears a substantial amount of characteristic traits – both advantageous and disadvantageous – and furthermore that the applicability of cyber intelligence methods is partly circumstantially limited.