Search results for: multi-level Fourier features

Authors: Umberto Platini

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This paper analyses the role of government fragmentation as predictor of the use of emergency decrees in parliamentary democracies. In particular, it focuses on the relationship between ideological divisiveness within cabinets and the choice by executives to issue emergency decrees rather initiating ordinary legislative procedures. A Bayesian multilevel analysis conducted on the population of government-initiated legislation in Italy between 1996 and 2018 finds significant evidence that those legislative proposals which are further away from the ideological centre of gravity of the executive are around three times more likely to be issued as emergency decrees. Likewise, legislative projects regulating more contentious policy areas are significantly more likely to be issued by decree. However, for more contentious issues the importance of ideological distance as a predictor diminishes. This evidence suggests that cabinets prefer decrees to ordinary legislative procedures when they expect that the bargaining environment in Parliament is more hostile. These results persist regardless of the fluctuations of the political-economic cycle. Their robustness is also tested against a battery of controls and against fixed effects both at the government level and at the legislature level.

Keywords: Bayesian multilevel logit models, executive action, executive decrees, ideology, legislative studies, polarization

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

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Authors: Sulaiman D. Al Sabei, Amy M. Ross, Christopher S. Lee

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Nurse leaders play a fundamental role in transforming healthcare system and improving quality of patient care. Several healthcare organizations have called to increase the number of nurse leaders across all levels and in every practice setting. Identification of factors influencing nurses’ willingness to lead can inform healthcare leaders and policy makers of potentially illuminating strategies for establishing favorable work environments that motivate nurses to engage in leadership roles. The aim of this study was to investigate determinants of nurses’ willingness to engage in future leadership roles. The study was conducted at a public hospital in the Sultanate of Oman. A total of 171 registered nurses participated. A multilevel modeling was conducted. Findings revealed that 80% of nurses were likely to seek out opportunities to engage in leadership roles. The quality of the nurse-physician collegial relationships was a significant predictor of nurses’ willingness to lead. Establishing a work environment’s culture of positive nurse-physician relationships is critical to enhance nurses’ work attitude and engage them in leadership roles.

Keywords: interprofessional relationship, leadership, motivation, nurses

Procedia PDF Downloads 155

Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

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In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

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Authors: Bashayer Almalki, Mayar Bajrai, Dana Mirah, Kholood Alghamdi, Hala Sanyour

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This research aims to investigate the features of an AI chess app that are most preferred by users. A questionnaire was used as the methodology to gather responses from a varied group of participants. The questionnaire consisted of several questions related to the features of the AI chess app. The responses were analyzed using descriptive statistics and factor analysis. The findings indicate that the most preferred features of an AI chess app are the ability to play against the computer, the option to adjust the difficulty level, and the availability of tutorials and puzzles. The results of this research could be useful for developers of AI chess apps to enhance the user experience and satisfaction.

Keywords: chess, game, application, computics

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Authors: Yuanxiang Miao

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This paper aims to examine the perceptual features of couchsurfers on a new hospitality tourism platform, the free homestay website couchsurfing. As a local host, the author has accepted 61 couchsurfers in Kyoto, Japan, and attempted to figure out couchsurfers' characteristics on perception by hosting them. Moreover, the methodology of this research is mainly based on in-depth interviews, by talking with couchsurfers, observing their behaviors, doing questionnaires, etc. Five dominant perceptual features of couchsurfers were identified: (1) Trusting; (2) Meeting; (3) Sharing; (4) Reciprocity; (5) Worries. The value of this research lies in figuring out a deeper understanding of the perceptual features of couchsurfers, and the author indeed hosted and stayed with 61 couchsurfers from 30 countries and areas over one year. Lastly, the author offers practical suggestions for future research.

Keywords: couchsurfing, depth interview, hospitality tourism, perceptual features

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Authors: Jiyoung Bae, Gyoomi Kim

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This study explores a range of linguistic features used in Korean college students’ argumentative writings for the purpose of developing a model that identifies variables which predict writing proficiencies. This study investigated the latent variable structure of L2 linguistic features, including syntactic complexity, the lexical complexity, and fluency. One hundred forty-six university students in Korea participated in this study. The results of the study’s confirmatory factor analysis (CFA) showed that indicators of linguistic features from this study-provided a foundation for re-categorizing indicators found in extant research on L2 Korean writers depending on each latent variable of linguistic features. The CFA models indicated one measurement model of L2 syntactic complexity and L2 learners’ writing proficiency; these two latent factors were correlated with each other. Based on the overall findings of the study, integrated linguistic features of L2 writings suggested some pedagogical implications in L2 writing instructions.

Keywords: linguistic features, syntactic complexity, lexical complexity, fluency

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Authors: Essam Al Daoud

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Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

Keywords: gradient boosting, XGBoost, LightGBM, CatBoost, home credit

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Authors: Jaqueline Nichi, Leila Da Costa Ferreira, Fabiana Barbi Seleguim, Gabriela Marques Di Giulio, Mariana Barbieri

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The transport sector is responsible for approximately 55% of global greenhouse gas (GHG) emissions, in addition to pollution and other negative externalities, such as road accidents and congestion, that impact the routine of those who live in large cities. The objective of this article is to discuss the application and use of distinct mobility technologies such as climate adaptation and mitigation measures in the context of smart cities in the Global South. The documentary analysis is associated with 22 semi structured interviews with managers who work with mobility technologies in the public and private sectors and in civil society organizations to explore solutions in multilevel governance for smart and low-carbon mobility based on the case study from the city of São Paulo, Brazil. The hypothesis that innovation and technology to mitigate and adapt to climate impacts are not yet sufficient to make mobility more sustainable has been confirmed. The results indicate four relevant aspects for advancing a climate agenda in smart cities: integrated planning, coproduction of knowledge, experiments in governance, and new means of financing to guarantee the sustainable sociotechnical transition of the sector.

Keywords: urban mobility, climate change, smart cities, multilevel governance

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: Yasmeen Bassas, Sandra Kuebler, Allen Riddell

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Native language identification is one of the growing subfields in natural language processing (NLP). The task of native language identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features, when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL), and then the trained models are evaluated on different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and logistic regression. Results show that content-based features are more accurate and robust than content independent ones when tested within the corpus and across corpus.

Keywords: NLI, NLP, content-based features, content independent features, social media corpus, ML

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Authors: Han Longxiang

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In the process of treating industrial oil wastewater with complex components, the traditional treatment methods (flotation, coagulation, microwave heating, etc.) often produce high operating costs, secondary pollution, and other problems. In order to solve these problems, the materials with high flux and stability applied to surfactant-stabilized emulsions separation have gained huge attention in the treatment of oily wastewater. Nevertheless, four stable oil-in-water emulsions can be formed due to different surfactants (surfactant-free, anionic surfactant, cationic surfactant, and non-ionic surfactant), and the previous advanced materials can only separate one or several of them, cannot effectively separate in one step. Herein, a facile synthesis method of graphene-based multilevel filter materials (GMFM) can efficiently separate the oil-in-water emulsions stabilized with different surfactants only through its gravity. The prepared materials with high stability of 20 cycles show a high flux of ~ 5000 L m-2 h-1 with a high separation efficiency of > 99.9 %. GMFM can effectively separate the emulsion stabilized by mixed surfactants and oily wastewater from factories. The results indicate that the GMFM has a wide range of applications in oil-in-water emulsions separation in industry and environmental science.

Keywords: emulsion, filtration, graphene, one-step

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Authors: Brouri Adil, Giri Fouad

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The problem of identifying Wiener-Hammerstein systems is addressed in the presence of two linear subsystems of structure totally unknown. Presently, the nonlinear element is allowed to be noninvertible. The system identification problem is dealt by developing a two-stage frequency identification method such a set of points of the nonlinearity are estimated first. Then, the frequency gains of the two linear subsystems are determined at a number of frequencies. The method involves Fourier series decomposition and only requires periodic excitation signals. All involved estimators are shown to be consistent.

Keywords: Wiener-Hammerstein systems, Fourier series expansions, frequency identification, automation science

Procedia PDF Downloads 502

Authors: Han Longxiang

Abstract:

In the process of treating industrial oily wastewater with complex components, the traditional treatment methods (flotation, coagulation, microwave heating, etc.) often produce high operating costs, secondary pollution, and other problems. In order to solve these problems, the materials with high flux and stability applied to surfactant-stabilized emulsions separation have gained huge attention in the treatment of oily wastewater. Nevertheless, four stable oil-in-water emulsions can be formed due to different surfactants (surfactant-free, anionic surfactant, cationic surfactant, and non-ionic surfactant), and the previous advanced materials can only separate one or several of them, cannot effectively separate in one step. Herein, a facile synthesis method of graphene-based multilevel filter materials (GMFM) which can efficiently separate the oil-in-water emulsions stabilized with different surfactants only through its gravity. The prepared materials with high stability of 20 cycles show a high flux of ~ 5000 L m-2 h-1 with a high separation efficiency of > 99.9 %. GMFM can effectively separate the emulsion stabilized by mixed surfactants and oily wastewater from factories. The results indicate that the GMFM have a wide range of applications in oil-in-water emulsions separation in industry and environmental science.

Keywords: emulsion, filtration, graphene, one-step

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Authors: Osamede Asowata, Christo Pienaar, Johan Bekker

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Photovoltaic (PV) modules may become a trend for future PV systems because of their greater flexibility in distributed system expansion, easier installation due to their nature, and higher system-level energy harnessing capabilities under shaded or PV manufacturing mismatch conditions. This is as compared to the single or multi-string inverters. Novel residential scale PV arrays are commonly connected to the grid by a single DC–AC inverter connected to a series, parallel or series-parallel string of PV panels, or many small DC–AC inverters which connect one or two panels directly to the AC grid. With an increasing worldwide interest in sustainable energy production and use, there is renewed focus on the power electronic converter interface for DC energy sources. Three specific examples of such DC energy sources that will have a role in distributed generation and sustainable energy systems are the photovoltaic (PV) panel, the fuel cell stack, and batteries of various chemistries. A high-efficiency inverter using Metal Oxide Semiconductor Field-Effect Transistors (MOSFETs) for all active switches is presented for a non-isolated photovoltaic and AC-module applications. The proposed configuration features a high efficiency over a wide load range, low ground leakage current and low-output AC-current distortion with no need for split capacitors. The detailed power stage operating principles, pulse width modulation scheme, multilevel bootstrap power supply, and integrated gate drivers for the proposed inverter is described. Experimental results of a hardware prototype, show that not only are MOSFET efficient in the system, it also shows that the ground leakage current issues are alleviated in the proposed inverter and also a 98 % maximum associated driver circuit is achieved. This, in turn, provides the need for a possible photovoltaic panel switching technique. This will help to reduce the effect of cloud movements as well as improve the overall efficiency of the system.

Keywords: grid connected photovoltaic (PV), Matlab efficiency simulation, maximum power point tracking (MPPT), module integrated converters (MICs), multilevel converter, series connected converter

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: decision tree, feature selection, intrusion detection system, support vector machine

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Authors: Mohammed S. Al-Rawi, J. Bastos, J. Rodriguez

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Object detection and object recognition are essential components of every computer vision system. Despite the high computational complexity and other problems related to numerical stability and accuracy, Zernike moments of 2D images (ZMs) have shown resilience when used in object recognition and have been used in various image analysis applications. In this work, we propose a novel method for computing ZMs via Fast Fourier Transform (FFT). Notably, this is the first algorithm that can generate ZMs up to extremely high orders accurately, e.g., it can be used to generate ZMs for orders up to 1000 or even higher. Furthermore, the proposed method is also simpler and faster than the other methods due to the availability of FFT software and/or hardware. The accuracies and numerical stability of ZMs computed via FFT have been confirmed using the orthogonality property. We also introduce normalizing ZMs with Neumann factor when the image is embedded in a larger grid, and color image reconstruction based on RGB normalization of the reconstructed images. Astonishingly, higher-order image reconstruction experiments show that the proposed methods are superior, both quantitatively and subjectively, compared to the q-recursive method.

Keywords: Chebyshev polynomial, fourier transform, fast algorithms, image recognition, pseudo Zernike moments, Zernike moments

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Authors: Evan W. Krieger, Vijayan K. Asari, Saibabu Arigela

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Video imagery captured for real-time security and surveillance applications is typically captured in complex lighting conditions. These less than ideal conditions can result in imagery that can have underexposed or overexposed regions. It is also typical that the video is too low in resolution for certain applications. The purpose of security and surveillance video is that we should be able to make accurate conclusions based on the images seen in the video. Therefore, if poor lighting and low resolution conditions occur in the captured video, the ability to make accurate conclusions based on the received information will be reduced. We propose a solution to this problem by using image preprocessing to improve these images before use in a particular application. The proposed algorithm will integrate an intensity enhancement algorithm with a super resolution technique. The intensity enhancement portion consists of a nonlinear inverse sign transformation and an adaptive contrast enhancement. The super resolution section is a single image super resolution technique is a Fourier phase feature based method that uses a machine learning approach with kernel regression. The proposed technique intelligently integrates these algorithms to be able to produce a high quality output while also being more efficient than the sequential use of these algorithms. This integration is accomplished by performing the proposed algorithm on the intensity image produced from the original color image. After enhancement and super resolution, a color restoration technique is employed to obtain an improved visibility color image.

Keywords: dynamic range compression, multi-level Fourier features, nonlinear enhancement, super resolution

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Authors: Huangmei Liu, Si Liu, Jia’nan Liu

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The present experiment investigated and compared the effectiveness of two kinds of methods of attention control: Task distraction and visual enhancement. In the study, the effectiveness of task distractions to explicit features and of visual enhancement to implicit features of the same group of Chinese characters were compared based on their effect on the participants’ reaction time, subjective confidence rating, and verbal report. We found support that the visual enhancement on implicit features did overcome the contrary effect of training distraction and led to awareness of those implicit features, at least to some extent.

Keywords: task distraction, visual enhancement, attention, awareness, learning

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Authors: Tamil Chelvi Vadivelu, Nurazean Maarop, Rasimah Che Yusoff, Farhana Aini Saludin

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Implementing a remote healthcare system needs to consider many security features. Therefore, before any deployment of the remote healthcare system, a feasibility study from the security perspective is crucial. Remote healthcare system using WBAN technology has been used in other countries for medical purposes but in Malaysia, such projects are still not yet implemented. This study was conducted qualitatively. The interview results involving five healthcare practitioners are further elaborated. The study has addressed four important security features in order to incorporate remote healthcare system using WBAN in Malaysian government hospitals.

Keywords: remote healthcare, IT security, security features, wireless sensor application

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Authors: Voodi Kalandhar, Veera Reddy, Yuva Tejasree

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Researchers have become interested in multilevel inverters (MLI) because of their potential for medium- and high-power applications. MLIs are becoming more popular as a result of their ability to generate higher voltage levels, minimal power losses, small size, and low price. These inverters used in high voltage and high-power applications because the stress on the switch is low. Even though many traditional topologies, such as the cascaded H-bridge MLI, the flying capacitor MLI, and the diode clamped MLI, exist, they all have some drawbacks. A complicated control system is needed for the flying capacitor MLI to balance the voltage across the capacitor and diode clamped MLI requires more no of diodes when no of levels increases. Even though the cascaded H-Bridge MLI is popular in terms of modularity and simple control, it requires more no of isolated DC source. Therefore, a topology with fewer devices has always been necessary for greater efficiency and reliability. A new single-phase MLI topology has been introduced to minimize the required switch count in the circuit and increase output levels. With 3 dc voltage sources, 8 switches, and 13 levels at the output, this new single- phase MLI topology was developed. To demonstrate the proposed converter's superiority over the other MLI topologies currently in use, a thorough analysis of the proposed topology will be conducted.

Keywords: DC-AC converter, multi-level inverter (MLI), diodes, H-bridge inverter, switches

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Authors: Hamdy M. Youssef

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In this work, the usual Euler–Bernoulli nanobeam has been modeled in the context of Lord-Shulman thermoelastic theorem, which contains non-Fourier heat conduction law. The nanobeam has been subjected to a constant magnetic field and ramp-type thermal loading. The Laplace transform definition has been applied to the governing equations, and the solutions have been obtained by using a direct approach. The inversions of the Laplace transform have been calculated numerically by using Tzou approximation method. The solutions have been applied to a nanobeam made of silicon nitride. The distributions of the temperature increment, lateral deflection, strain, stress, and strain-energy density have been represented in figures with different values of the magnetic field intensity and ramp-time heat parameter. The value of the magnetic field intensity and ramp-time heat parameter have significant effects on all the studied functions, and they could be used as tuners to control the energy which has been generated through the nanobeam.

Keywords: nanobeam, vibration, constant magnetic field, ramp-type thermal loading, non-Fourier heat conduction law

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Authors: Ni'mal Baroya, Andrei Ramani, Irma Prasetyowati

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The access to reproduction health services, including with safe and effective contraception were human rights regardless of social stratum and residence. In addition to individual factors, family and contextual factors were also believed to be the cause in the use of contraceptive methods. This study aimed to assess the determinants of long-term contraceptive methods (LTCM) by considering all the factors at either the individual level or contextual level. Thereby, this study could provide basic information for program development of prevalence enhancement of MKJP in East Java. The research, which used cross-sectional design, utilized Riskesdas 2013 data, particularly in East Java Province for further analysis about multilevel modeling of MKJP application. The sample of this study consisted of 20.601 married women who were not in pregnant that were drawn by using probability sampling following the sampling technique of Riskesdas 2013. Variables in this study were including the independent variables at the individual level that consisted of education, age, occupation, access to family planning services (KB), economic status and residence. As independent variables in district level were the Human Development Index (HDI, henceforth as IPM) in each districts of East Java Province, the ratio of field officers, the ratio of midwives, the ratio of community health centers and the ratio of doctors. As for the dependent variable was the use of Long-Term Contraceptive Method (LTCM or MKJP). The data were analyzed by using chi-square test and Pearson product moment correlation. The multivariable analysis was using multilevel logistic regression with 95% of Confidence Interval (CI) at the significance level of p < 0.05 and 80% of strength test. The results showed a low CPR LTCM was concentrated in districts in Madura Island and the north coast. The women which were 25 to 35 or more than 35 years old, at least high school education, working, and middle-class social status were more likely to use LTCM or MKJP. The IPM and low PLKB ratio had implications for poor CPR LTCM / MKJP.

Keywords: multilevel, long-term contraceptive methods, east java, contextual factor

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Authors: Xudong Guan, Ainong Li, Gaohuan Liu, Chong Huang, Wei Zhao

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Due to the low spatial resolution of MODIS data, the accuracy of small-area plaque extraction with a high degree of landscape fragmentation is greatly limited. To this end, the study combines Landsat data with higher spatial resolution and MODIS data with higher temporal resolution for decision-level fusion. Considering the importance of the land heterogeneity factor in the fusion process, it is superimposed with the weighting factor, which is to linearly weight the Landsat classification result and the MOIDS classification result. Three levels were used to complete the process of data fusion, that is the pixel of MODIS data, the pixel of Landsat data, and objects level that connect between these two levels. The multilevel decision fusion scheme was tested in two sites of the lower Mekong basin. We put forth a comparison test, and it was proved that the classification accuracy was improved compared with the single data source classification results in terms of the overall accuracy. The method was also compared with the two-level combination results and a weighted sum decision rule-based approach. The decision fusion scheme is extensible to other multi-resolution data decision fusion applications.

Keywords: image classification, decision fusion, multi-temporal, remote sensing

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Authors: Jatinderdeep Kaur

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In this paper, the results of Kano from one-dimensional cosine and sine series are extended to two-dimensional cosine and sine series. To extend these results, some classes of coefficient sequences such as the class of semi convexity and class R are extended from one dimension to two dimensions. Under these extended classes, I have checked the function f(x,y) is two dimensional Fourier Cosine and Sine series or equivalently it represents an integrable function. Further, some results are obtained which are the generalization of Moricz's results.

Keywords: conjugate dirichlet kernel, conjugate fejer kernel, fourier series, semi-convexity

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Atefeh Bagherianziarat, Dana Hamplova

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The inverse social gradient in life satisfaction (LS) is a well-established research finding. To estimate the influence of inequality on LS, most of the studies have explored the effect of the objective aspects of inequality or individuals’ socioeconomic status (SES). However, relatively fewer studies have confirmed recently the significant effect of the subjective aspect of inequality or subjective socioeconomic status (SSS) on life satisfaction over and above SES. In other words, it is confirmed by some studies that individuals’ perception of their unequal status in society or SSS can moderate the impact of their absolute unequal status on their life satisfaction. Nevertheless, this newly confirmed moderating link has not been affirmed to work likewise in societies with different levels of social inequality and also for people who believe in the value of equality, at different levels. In this study, we compared the moderative influence of subjective inequality on the link between objective inequality and life satisfaction. In particular, we focus on differences across welfare state regimes based on Esping-Andersen's theory. Also, we explored the moderative role of believing in the value of equality on the link between objective and subjective inequality on LS in the given societies. Since our studied variables were measured at both individual and country levels, we applied a multilevel analysis to the European Social Survey data (round 9). The results showed that people in deferent regimes reported statistically meaningful different levels of life satisfaction that is explained to different extends by their household income and their perception of their income inequality. The findings of the study supported the previous findings of the moderator influence of perceived inequality on the link between objective inequality and LS. However, this link is different in various welfare state regimes. The results of the multilevel modeling showed that country-level subjective equality is a positive predictor for individuals’ life satisfaction, while the GINI coefficient that was considered as the indicator of absolute inequality has a smaller effect on life satisfaction. Also, country-level subjective equality moderates the confirmed link between individuals’ income and their life satisfaction. It can be concluded that both individual and country-level subjective inequality slightly moderate the effect of individuals’ income on their life satisfaction.

Keywords: individual values, life satisfaction, multilevel analysis, objective inequality, subjective inequality, welfare regimes status

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Authors: Christine F. Boos, Fernando M. Azevedo

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Electroencephalogram (EEG) is a record of the electrical activity of the brain that has many applications, such as monitoring alertness, coma and brain death; locating damaged areas of the brain after head injury, stroke and tumor; monitoring anesthesia depth; researching physiology and sleep disorders; researching epilepsy and localizing the seizure focus. Epilepsy is a chronic condition, or a group of diseases of high prevalence, still poorly explained by science and whose diagnosis is still predominantly clinical. The EEG recording is considered an important test for epilepsy investigation and its visual analysis is very often applied for clinical confirmation of epilepsy diagnosis. Moreover, this EEG analysis can also be used to help define the types of epileptic syndrome, determine epileptiform zone, assist in the planning of drug treatment and provide additional information about the feasibility of surgical intervention. In the context of diagnosis confirmation the analysis is made using long term EEG recordings with at least 24 hours long and acquired by a minimum of 24 electrodes in which the neurophysiologists perform a thorough visual evaluation of EEG screens in search of specific electrographic patterns called epileptiform discharges. Considering that the EEG screens usually display 10 seconds of the recording, the neurophysiologist has to evaluate 360 screens per hour of EEG or a minimum of 8,640 screens per long term EEG recording. Analyzing thousands of EEG screens in search patterns that have a maximum duration of 200 ms is a very time consuming, complex and exhaustive task. Because of this, over the years several studies have proposed automated methodologies that could facilitate the neurophysiologists’ task of identifying epileptiform discharges and a large number of methodologies used neural networks for the pattern classification. One of the differences between all of these methodologies is the type of input stimuli presented to the networks, i.e., how the EEG signal is introduced in the network. Five types of input stimuli have been commonly found in literature: raw EEG signal, morphological descriptors (i.e. parameters related to the signal’s morphology), Fast Fourier Transform (FFT) spectrum, Short-Time Fourier Transform (STFT) spectrograms and Wavelet Transform features. This study evaluates the application of these five types of input stimuli and compares the classification results of neural networks that were implemented using each of these inputs. The performance of using raw signal varied between 43 and 84% efficiency. The results of FFT spectrum and STFT spectrograms were quite similar with average efficiency being 73 and 77%, respectively. The efficiency of Wavelet Transform features varied between 57 and 81% while the descriptors presented efficiency values between 62 and 93%. After simulations we could observe that the best results were achieved when either morphological descriptors or Wavelet features were used as input stimuli.

Keywords: Artificial neural network, electroencephalogram signal, pattern recognition, signal processing

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Authors: Joshi Manisha, Shivaram, Anand Vinekar, Tanya Susan Mathews, Yeshaswini Nagaraj

Abstract:

Retinopathy of prematurity (ROP) is abnormal blood vessel development in the retina of the eye in a premature infant. The principal object of the invention is to provide a technique for detecting demarcation line and ridge detection for a given ROP image that facilitates early detection of ROP in stage 1 and stage 2. The demarcation line is an indicator of Stage 1 of the ROP and the ridge is the hallmark of typically Stage 2 ROP. Thirty Retcam images of Asian Indian infants obtained during routine ROP screening have been used for the analysis. A graphical user interface has been developed to detect demarcation line/ridge and to extract ground truth. This novel algorithm uses multilevel vessel enhancement to enhance tubular structures in the digital ROP images. It has been observed that the orientation of the demarcation line/ridge is normal to the direction of the blood vessels, which is used for the identification of the ridge/ demarcation line. Quantitative analysis has been presented based on gold standard images marked by expert ophthalmologist. Image based analysis has been based on the length and the position of the detected ridge. In image based evaluation, average sensitivity and positive predictive value was found to be 92.30% and 85.71% respectively. In pixel based evaluation, average sensitivity, specificity, positive predictive value and negative predictive value achieved were 60.38%, 99.66%, 52.77% and 99.75% respectively.

Keywords: ROP, ridge, multilevel vessel enhancement, biomedical

Procedia PDF Downloads 364

Authors: Ola A. Younis, Said Ghoul

Abstract:

Systems running these days are huge, complex and exist in many versions. Controlling these versions and tracking their changes became a very hard process as some versions are created using meaningless names or specifications. Many versions of a system are created with no clear difference between them. This leads to mismatching between a user’s request and the version he gets. In this paper, we present a system versions meta-modeling approach that produces versions based on system’s features. This model reduced the number of steps needed to configure a release and gave each version its unique specifications. This approach is applicable for systems that use features in its specification.

Keywords: features, meta-modeling, semantic modeling, SPL, VCS, versioning

Procedia PDF Downloads 413