One of the first causes of human deaths in recent years in our world is heart diseases or cardiovascular diseases. Phonocardiograms (PCG) and electrocardiograms (ECG) are usually used for the detection of heart diseases. Studies on cardiac signals focus especially on the classification of heart sounds. Naturally, researches generally try to increase accuracy of classification. For this purpose, many studies use for the segmentation of heart sounds into S1 and S2 segments by methods such as Shannon energy, discreet wavelet transform and Hilbert transform. In this study, two different heart sounds data in the PhysioNet Atraining data set such as normal, and abnormal are classified with convolutional neural networks. For this purpose, the S1 and S2 parts of the heart sounds were segmented by the resampled energy method. The images of Phonocardiograms which were obtained from S1 and S2 parts in the heart sounds were used for classification. The resized small images of phonocardiogram were classified by convolutional neural networks. The obtained results were compared with the results from previous studies. The classification with CNN has performance as classification accuracy of 97.21%, sensitivity of 94.78%, and specificity of 99.65%. According to this, CNN classification with segmented S1-S2 sounds showed better results than the results of previous studies. In studies carried out, it has been seen that segmentation and convolutional neural networks increases the accuracy of classification and contributes to the classification studies efficiently.

Quick Response (QR) codes are getting widely popular as the demand for mobile computing is increasing, too. However, a QR code has apparently a data limit problem when we consider designing color QR codes. Despite having color QR codes decoding from printed material, new problems appear due to different printer's color depth, paper quality, environmental effect, dust, light glare and other environmental effects. In our model, we proposed a new algorithm to find an area consisting of several bit positions. We have applied an area-based bit detection method based on the percentage of the color level instead of the general binary color bit detection system. This is independent of the printer and the printed substances effect. There is no need for the color correction palette. Based on our experimental results, our findings demonstrate that we can produce a better result without having any image filtering first.

We introduce now for the first time the neutrosophic modal logic. The Neutrosophic Modal Logic includes the neutrosophic operators that express the modalities. It is an extension of neutrosophic predicate logic and of neutrosophic propositional logic.

This study aims to investigate possible differences in the formation and development of personality traits for children with various language disorders and children who do not have language disorders from a neuropsychological perspective. The child's personality is a psychological and social construct with strong implications on how to relate it to the surrounding individuals and brain function. During childhood, the individual manifests a variety of typologies of behavior and attitudes depending on the context in which they are, so personality traits are formed as a result of existing interactions and the family and educational context. In the case of children with different language disorders, the personality must be structured in a more secure and stable emotional environment, leading to the development of adaptation capacities to the external environment and an increase in stress resistance. Emotions and moods play a significant role in the development of personality traits. In this sense, we have started the research that aims to analyze the personality traits of children with speech difficulties and those who do not have these difficulties. The participants in this study are 60 children aged 7-12 years old from urban areas. The method used is represented by the HiPIC - Hierarchical Personality Inventory for Children (I Mervielde and Filip de Fruyt), a psychological tool for assessing the personality of children aged 6 to 13 and is based on the established Big Five model. The inventory is a personality HiPIC contains 144 items and allows the evaluation of the emotional, interpersonal, motivational and behavioral style of children based on the five dimensions of personality. Parametric statistical methods were used to identify possible differences between the two groups of participants in research. Also factor loadings and correlations between factors as results of exploratory factor analyses were evaluated. The study outputs confirms existing differences in the development of certain personality traits, the specificity being given by the situations experienced in the educational environment, the family environment and in the context of the process of recovering and improving the language disorders.

Abstract

This study is focusing on the badminton top players vs. regular adult population. In our previous study on adolescents, variances showed the impact of puberty on timing skills (Hromčík & Zvonař, 2018). The timing in training is topical these days (Forner-Cordero, Quadrado, Tsagbey, & Smits-Engelsman, 2018), and also brain specifics and learning anticipation skills (Wang, Dong, Wang, Zheng, & Potenza, 2018). We added some new insight in this theme and tried to determine the dynamics in the accuracy of sensorimotor skills, which plays an essential role in ball games. Only boys from one club were tested. Subjects have undergone a special PC test with a length of about 45 minutes to test their response and timing of movement with number of tasks in which they tried to hit a moving target, which appeared on the screen at 3 different angles (0°, 15° and 30°) and at different speeds (accelerating, decelerating, constant). Everything happened at unpredictable intervals in 45 minutes rotation. We compared these outcomes with our measurements from last year through specific timing hits and missed shots, and in the terms of sport season. Predictive motor timing suggests that the cerebellum training plays the relevant role in integrating incoming visual information with the motor output reaction.

Background and Aim: So far, several strategies have been used to reduce and minimize the stress and anxiety of patients admitted in open heart surgery. Writing about the most important and traumatic experiences of life can improve the physical and emotional health state of patients. The purpose of this study was to investigate the effect of written emotional disclosure on depression, anxiety, and stress in hospitalized patients after open heart surgery.

Methods: This study was an experimental study with two groups and two control groups before and after the intervention. A total of 100 patients admitted in the open heart surgery department of Amiralmomenin-e-Kordkuy Hospital (2018) were assigned in the form of random numbers to four groups of emotional disclosure of negative, positive, neutral and non-interventional. The groups exhibited 10-15 minutes of emotional disclosure written in four periods for one week. Data were collected by the DASS21 questionnaire before, one week and one month after the intervention by the groups. Data were analyzed by SPSS software version 22 and descriptive and inferential tests (repeated variance analysis and one way ANOVA). The significance level was considered as P<0.05.

Results: The severity of stress, anxiety and depression in the two groups of test and control before and after the intervention using  ANOVA repeated measure showed significant difference (P=0.0001), (P=0.0001), (P=0.0001) (P=0.0001), respectively. But there was no significant difference between the two groups before and after the intervention (P<0.05).

Conclusion: Written emotional disclosure reduces depression, anxiety and stress in patients after open heart surgery.

ROC analysis is a visual and numerical method used to evaluate the performance of classification algorithms, such as those used to predict the structure and functions from string data. The main objective of the paper is to use the ROC analysis to evaluate the accuracy of the Random Forest algorithm to classify road surface defects on three different sets of data collected from Portugal, Irkutsk city - Russia Federation and Thai Nguyen city - Vietnam. This article summarizes the basics of ROC analysis and interprets the results analysis with other thresholds to build ROC curve. In addition, we present the steps to build a system to automatically classify road surface defects based on visual techniques and machine learning methods.

In this paper we present an algorithm for rule based systems that uses Bidirectional Associative Memories (BAMs) to memorize the inputs and their corresponding outputs, and outputs and their corresponding inputs of the system. Adaptive rule based system are becoming very important because rule based systems are now used in many applications. One drawback of current adaptive rule based systems is that these systems care only about forward chaining mechanism which diminish their performance. Because most of the applications that use rule based systems follow the forward chaining, adaption attempts which is concerned with predicting the inputs given the output is almost null, but this does not eliminate the importance of backward chaining since it is used in too many applications. To tackle this problem we propose a new algorithm that utilizes the good theoretical ground of BAMs to memorize and adapt rules in rule based systems. The main difference between the proposed algorithm and other algorithms is that the proposed algorithm is simple to code the rules and adapt them. In addition, because BAMs supports bi-directions, the proposed algorithm is the only algorithm with adaptation that is able to expect what conditions should be true if we provide the system with outputs. The proposed algorithm considers "and" and "or" rules in the used rules, whereas in all the previous systems, only "and" relation is considered.   The proposed solution will be useful in adapting any rule based system whether it uses forward chaining or backward chaining, which is considered to be a significant contribution. The proposed algorithm has six parts; cod table creation, input and output vectors construction, weight matrix calculation, procedure to test the system, procedure to run the proposed algorithm and finally rule extraction or what we call rule code decoding. To illustrate parts of the proposed algorithm, a simple example is provided.

The article analyzes penitentiary work and treats it as a possible model of prevention with regard to prisoners. Emphasis is placed on the existing legal norms that allow for the realization of this universal human right, as well as some psychosocial aspects in its application.

The aim is to present the problem of the existence of a unified, realistic legal model for penitentiary work in terms of its psychosocial aspect, protected by law.

The subject of analysis is the framework of the existing legal system, which has adopted the international legal norms for the implementation of penitentiary work and has become an essential part of the social life of the prisoners.

The possibilities for its positive impact on this category of citizens are outlined.

Individuals who are restricted in some movements, senses or functions for physical or mental reasons constitute a group of the society. In North Cyprus, these individuals and/or their familes are directly or indirectly confronted with various problems in society. Today this phenomenea can be perceived in areas like education, health, transportation accomodation, technology, informatics, social security, etc. The developmental level of a country is directly related to the efforts to solve the mentioned problems. The main aim of this study is to examine a conceptual framework for suggestions to increase the quality of services for the disabled in this context as well as putting forward action plans for the process of development and necessary changes. In this respect, all the studies done in the workshops after 2013 in North Cyprus to improve the services provided for the disabled with life-sustaining limitations are summarized and reported. In addition, the guidance in designing a detailed framework for the protection of the rights of the disabled and the suggestion of action plans to carry out their responsibilities are provided. Consequently, this study suggests the framework for participant planning process to support necesarry developments.

Main purpose of the present study is to develop an appropriate computer program to size and count particles in digital images focusing on eliminating undesirable objects from the image. In this study, the user friendly Graphical User interface (GUI) tool is developed using MATLAB to measure the size of every particle of the digital image. The step by step process optimizes synthesis process of micro particles. It also facilitates generation of the required statistical analysis for medical science, material science and other applications. The described image processing system reduces total measuring time, eliminates subjective observer error in sizing and determines fine particles, considerably. This method can be used as a simple pattern recognition technique to size and count a relatively large number of particles in a short time. It is applicable to filter out images of undesirable objects such as redundant spots or noises in the image and clearly, by taking high resolution photos, the result of the processed images would be more reliable.    

This study aims to investigate the emotions and stressful events experienced by secondary education teachers. A number of 132 teachers in lower and upper secondary education were sampled following a convenience procedure. A mixed and transversal design blending both qualitative and quantitative research was preferred for the present study. During the last month of the school year, seven focus groups were conducted with teachers in secondary education. The focus groups aimed to identify a corpus of events and experiences considered to be stressful. A number of 43 events were identified. The second stage of the study consisted of applying the instruments to assess job-related stress, burnout, and teaching emotions. To measure teaching emotions, the TES questionnaire (Frenzel, Pekrun, Goetz, Daniels, Durksen, Becker-Kurz, and Klassen, 2016) was applied. To measure burnout among teachers in secondary education, the Maslach Burnout Inventory - Educators Survey (Maslach & Jackson, 1981) was administered. The paper reveals the presence of negative emotions associated with teaching. In addition, the level of distress and burnout are described and discussed. To conclude, the article reflects on the effects of teachers' emotions on their performance, students' motivation, and learning outcomes.

Approximation of Non-central Chi-square distribution as an empirical distribution of log-likelihood ratio test statistics (-2logλ; abbreviated as LRT) has been a concern in the field of structural equation modeling. Under extremely severe misspecification (Chun & Shapiro, 2009) reported that non-central Chi-square is not a good choice. In this paper, we have used a bootstrap sampling procedure to investigate the empirical null distribution of LRT specifically in the context of a latent class model (LCM) via frequentist framework (that is, EM algorithm). We used two types of data sets. The first type includes those sets of data on which LCM had been carried out (published results; named as "training data"). The other type is that of those data sets which are not published earlier (i.e. "real" collected data; named as "test data"). Non-central χ² distribution with degrees of freedom equals to the expected value of bootstrap LRT and non-centrality parameter equals to inverse of the variance of bootstrapped LRT is found to be very well fitted empirical null distribution of LRT in case of LCM. These results will help in obtaining the significance value of LRT for deciding on the number of classes present in a latent variable.

The use of fuzzy entropy for image segmentation is one of the most popular methods, which is used today. In a classical fuzzy entropy, using a fuzzy complement with an equilibrium point of 0.5 is a limitation, which reduces the chances of obtaining an optimal result. We use generalized fuzzy entropy phrases in this paper, which uses fuzzy complements of Sugeno and Yager, and corresponds the equilibrium point to the m parameter (0<m<1), and increases the chance of finding the optimal threshold. So, we will have many pictures depending on the points of balance, and by the genetic algorithm, we choose the best decision among them. The effect of this method have considered in medical images to find the brain tumors. Results have shown that the use of generalized fuzzy entropy and the genetic algorithm can greatly be used to find the optimal threshold. Presented method is very effective for reducing the number of intensity levels. Problems may cause images with height amount of unwanted information which is saved to the expanse of subjective more important information.

The purpose of this study is to define the international students' use of flipped classroom and its effect on their academic performance, participation levels, learning attitudes and academic success during their foreign language learning. The participants were 2 lecturers and 17 international students from the Preparatory School of Near East University in the academic year of 2017-2018 fall semester. This study is based on qualitative and descriptive research.

As a result, it has been introduced that flip lessons were more useful instructional designs than non-flip lessons. The findings indicate that the students who use Edmodo to follow the flip class achieve better learning outcomes and promote better attitudes toward their learning experiences.

Clustering algorithms are applied to numerous problems in multiple domains including historic data analysis, financial markets analysis for portfolio optimization and image processing. Recent years have witnessed a surge in use of nature inspired computing (NIC) techniques for data clustering to solve various real world optimization problems. Granular Computing (GC) is an emerging technique to handle pieces of information, known as information granules. In this paper, an ensemble of fuzzy clustering using Particle Swarm Optimization and Granular computing for stock market portfolio optimization. The model is then tested on stocks listed in Hong Kong Stock Exchange. Experimental results suggested that clusters formed through Fuzzy Particle Swarm Optimization (FPSO) with Granular computing are well suited and efficient for portfolio optimization. For comparison, we have used a benchmark index of Hong Kong Stock Exchange called as Hang Sang Composite Index (HSCI). Results proved that results of proposed approach are better in comparison to benchmark results of HSCI.