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16512. Using artificial neural networks to examine event-related potentials of face memory
- Creator:
- Graham , Reiko and Dawson , Michael R. W.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Artificial neural networks, event-related potentials, face recognition, repetition effects, and memory
- Language:
- English
- Description:
- The N250r is a face-sensitive event-related potential (ERP) deflection whose long-term memory sensitivity remains uncertain. We investigated the possibility that long-term memory-related voltage changes are represented in the early ERP's to faces but methodological considerations could affect how these changes appear to be manifested. We examined the effects of two peak analysis procedures in the assessment of the memory-sensitivity of the N250r elicited in an old/new recognition paradigm using analysis of variance (ANOVA) and artificial neural networks (ANN's). When latency was kept constant within subjects, ANOVA was unable to detect differences between ERP's to remembered and new faces; however, an ANN was. Network interpretation suggested that the ANN was detecting amplitude differences at occipitotemporal and frontocentral sites corresponding to the N250r. When peak latency was taken into account, ANOVA detected a significant decrease in onset latency of the N250r to remembered faces and amplitude differences were not detectable, even with an ANN. Results suggest that the N250r is sensitive to long-term memory. This effect may be a priming phenomenon that is attenuated at long lags between faces. Choice of peak analysis procedures is critical to the interpretation of phasic memory effects in ERP data.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16513. Using detection dogs to reveal illegal pesticide poisoning of raptors in Hungary
- Creator:
- Deák, Gábor, Árvay, Márton, and Horváth, Márton
- Format:
- počítač and online zdroj
- Type:
- model:article and TEXT
- Subject:
- avian poisoning, canine, carbofuran, insecticide, phorate, raptor, and rodenticide
- Language:
- English
- Description:
- In Hungary, during the 2000s, pesticide poisoning became the most important threat for raptors, especially for the globally threatened Eastern imperial eagle (Aquila heliaca). In September 2013, with a focus on carbofuran and phorate, the first poison and carcass detection dog (PCDD) unit was formed in Hungary with a specifically trained detection dog and handler. Two more dogs were subsequently trained and joined the unit in 2017 and 2020 respectively. Between its inception until August 2020, the PCDD unit conducted 1,083 searches in five countries, which revealed 329 poisoned animals of 15 bird and nine mammal species, 120 poisoned baits and five pesticide products. Globally threatened species, including eight Eastern imperial eagles and four saker falcons (Falco cherrug), were among the detected victims. Present at 66.45% of wildlife poisoning events, the unit revealed 37.87% of the victims and 79.70% of the poisoned baits known in Hungary during the period 2013-2020. Compared to human surveys, the PCDD unit demonstrated a significantly higher find rate for poisoned baits. At 22 poisoning events (14.38% of all cases) only the PCDD unit revealed victims or poisoned baits; cases that would probably have gone undetected without the PCCD unit. Of the two focal pesticides, carbofuran was more frequently detected – in 88.56% of the positive samples. The unit played a significant role in detecting and combating wildlife poisoning incidents by deterring potential offenders and facilitating police investigations through retrieval of evidence otherwise difficult to obtain.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16514. Using fuzzy logic operators for construction of data mining quantifiers
- Creator:
- Ivánek, Jiří
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- fuzzy logic, data mining, and four-fold table quantifiers
- Language:
- English
- Description:
- Relations between two Boolean attributes derived from data can be quantified by truth functions defined on four-fold tables corresponding to pairs of the attributes. Several classes of such quantifiers (implicational, double implicational, equivalence ones) with truth values in the unit interval were investigated in the frame of the theory of data mining methods. In the fuzzy logic theory, there are well-defined classes of fuzzy operators, namely t-norms representing various types of evaluations of fuzzy conjunction (and t-conorms representing fuzzy disjunction), and operators of fuzzy implications. In the contribution, several types of constructions of quantifiers using fuzzy operators are described. Definitions and theorems presented by the author in previous contributions to WUPES workshops are summarized and illustrated by examples of well-known quantifiers and operators.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16515. Using genetic programming to select the informative EEG-based features to distinguish schizophrenic patients
- Creator:
- Sabeti , Malihe, Boostani, Reza, and Zoughi, Toktam
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Features selection, GP, Adaboost, LDA, MLDA, schizophrenic, and EEG
- Language:
- English
- Description:
- There is growing interest to analyze electroencephalogram (EEG) signals with the objective of classifying schizophrenic patients from the control subjects. In this study, EEG signals of 15 schizophrenic patients and 19 age-matched control subjects are recorded using twenty surface electrodes. After the preprocessing phase, several features including autoregressive (AR) model coefficients, band power and fractal dimension were extracted from their recorded signals. Three classifiers including Linear Discriminant Analysis (LDA), Multi-LDA (MLDA) and Adaptive Boosting (Adaboost) were implemented to classify the EEG features of schizophrenic and normal subjects. Leave-one (participant)-out cross validation is performed in the training phase and finally in the test phase; the results of applying the LDA, MLDA and Adaboost respectively provided 78%, 81% and 82% classification accuracies between the two groups. For further improvement, Genetic Programming (GP) is employed to select more informative features and remove the redundant ones. After applying GP on the feature vectors, the results are remarkably improved so that the classification rate of the two groups with LDA, MLDA and Adaboost classifiers yielded 82%, 84% and 93% accuracies, respectively.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16516. Using internally transcribed spacer 2 sequences to re-examine the taxonomic status of several cryptic species of Trichogramma (Hymenoptera: Trichogrammatidae)
- Creator:
- Li, Zheng-Xi, Zheng, Li, and Shen, Zou-Rui
- Type:
- article, model:article, and TEXT
- Subject:
- Internally transcribed spacer 2, ITS2, cryptic species, molecular taxonomy, Trichogramma chilonis, T. confusum, T. brassicae, T. maidis, and T. evanescens
- Language:
- English
- Description:
- Mass releases of Trichogramma confusum Viggiani and T. maidis Pintureau & Voegele are widely used to control lepidopterous pests. They have long been considered to be the subspecies of T. chilonis Ishii and T. brassicae Bezdenko, respectively. To re-examine the taxonomic status of these closely related Trichogramma species, the internally transcribed spacer 2 (ITS2) of ribosomal DNA was used as a molecular marker to detect between-species differences. The ITS2 regions of 7 different Trichogramma species collected from China, Germany and France were sequenced and the inter-species distances were calculated. To quantify within-species sequence variation, the ITS2 regions of 6 geographical populations of T. dendrolimi Matsumura collected from across China were sequenced and compared. The results show that the ITS2 sequences of T. confusum and T. maidis are sufficiently different from those of T. chilonis and T. brassicae, respectively, that it is difficult to group them as cryptic species, whereas there are only minor differences between the T. dendrolimi populations. The ITS2 sequences identified in this study, coupled with 67 ITS2 sequences from a wide geographical distribution retrieved from GenBank, were then used for phylogenetic analyses. The results support previous records of minor within-species ITS2 sequence divergence and distinct interspecies differences. The cladograms show the T. maidis sequence clustered within T. evanescens Westwood, while the ITS2 sequences of T. confusum and T. chilonis are clustered in different branches. Taken together, these data suggest that T. maidis is not T. brassicae, but a cryptic or sibling species of T. evanescens; T. confusum and T. chilonis are not cryptic species but two closely related sister species.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16517. Using Kullbak-Leibler divergence to predict on artificial neural network
- Creator:
- Ismail, I. A. and Nabil, Tamer
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- neural network, time series forecasting, Kullback-Leibler divergence, and maximum entropy
- Language:
- English
- Description:
- Several algorithms have been developed for time series forecasting. In this paper, we develop a type of algorithm that makes use of the numerical methods for optimizing on objective function that is the Kullbak-Leibler divergence between the joint probability density function of a time series xi, X2, Xn and the product of their marginal distributions. The Grani-charlier expansion is ušed for estimating these distributions. Using the weights that have been obtained by the neural network, and adding to them the Kullback-Leibler divergence of these weights, we obtain new weights that are ušed for forecasting the new value of Xn+k.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16518. Using MF-ARTMAP neural network for financial data analysis
- Creator:
- Sinčák, Peter, Hric, Marcel, Vaľo, Richard, Horanský, Pavol, and Karel, Pavel
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Neural Networks, ART neural networks, fuzzy logic, feature space, classification procedures, fuzzy cluster, fuzzy class, and classification accuracy assessment
- Language:
- English
- Description:
- The paper deals with application of MF-ARTMAP neural network on financial fraud data. The focus was on classification of data into 5 types of fraud based on expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized by 22 features and the verbal features were encoded into numerical values to be able to use them in the classification proceduře. The results show that in the čase of sufficient data (fraud) representation neural networks could be used with success; in case there are rather small examples, expert generated rules are preferred.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16519. Using path dependence theory to explain housing regime change: the traps of super-homeownership
- Creator:
- Lux, Martin and Sunega, Petr
- Format:
- počítač and online zdroj
- Type:
- model:article and TEXT
- Subject:
- housing regime, path-dependence, home-ownership, and post-socialist
- Language:
- English
- Description:
- The goal of this paper is to demonstrate the usefulness of path dependence theory to explain the convergence of housing regimes among post-socialist countries, both at the beginning and in the later phases of housing-regime transformation. We especially seek to show the selected common traps that were recently created by the legacy of giveaway privatisation and the super-homeownership regime, traps that increase intergenerational inequality, which to now has been effectively mitigated by within-family financial transfers.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
16520. Using text and visual mining to analyze clinical diagnosis records
- Creator:
- Chen, Chien-Hsing and Hsu, Chung-Chian
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Clinical diagnosis record, ICD-9 code, keyword extraction, ViSOM, and SOM
- Language:
- English
- Description:
- Hospitals must index each case of inpatient medical care with codes from the International Classification of Diseases, 9th Revision (ICD-9), under regulations from the Bureau of National Health Insurance. This paper aims to investigate the analysis of free-textual clinical medical diagnosis documents with ICD-9 codes using state-of-the-art techniques from text and visual mining fields. In this paper, ViSOM and SOM approaches inspire several analyses of clinical diagnosis records with ICD-9 codes. ViSOM and SOM are also used to obtain interesting patterns that have not been discovered with traditional, nonvisual approaches. Furthermore, we addressed three principles that can be used to help clinical doctors analyze diagnosis records effectively using the ViSOM and SOM approaches. The experiments were conducted using real diagnosis records and show that ViSOM and SOM are helpful for organizational decision-making activities.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public