B R A I N -  Broad Research in Artificial Intelligence and Neuroscience
Volume 9, Issue 2
May, 2018

ISSN 2068 — 0473
E-ISSN 2067 - 3957

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Vehicle routing problems (VRP) are complicated problems, which can be encountered in a variety of different fields and are not possible to solve using conventional methods. Thanks to the technological advancements in areas such as the global positioning system (GPS), geographical information systems (GIS), mobile communication networks, and traffic sensors, it is now possible to solve the VRP in a dynamic and real-time manner. In this study, the school bus routing problem (SBRP), which is a sub-branch of VRPs, was optimized by means of an application developed using Genetic Algorithms(GA), which is one of the heuristic methods. By using the application developed based on mobile communication and GPS, the instantaneous locations of stops and the school bus were determined and the shortest and most convenient route for the vehicle to follow was dynamically identified. The experimental results obtained at the end of this study showed that the existing school bus routes can be optimized using GA and therefore the costs associated with the routing can be reduced.

The diagnosis of heart diseases from heart sounds is a matter of many years. This is the effect of having too many people with heart diseases in the world. Studies on heart sounds are usually based on classification for helping doctors. In other words, these studies are a substructure of clinical decision support systems. In this study, three different heart sound data in the PASCAL Btraining data set such as normal, murmur, and extrasystole are classified. Phonocardiograms which were obtained from heart sounds in the data set were used for classification. Both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used for classification to compare obtained results. In these studies, the obtained results show that the CNN classification gives the better result with 97.9% classification accuracy according to the results of ANN. Thus, CNN emerges as the ideal classification tool for the classification of heart sounds with variable characteristics.

Small scaled image lost some important bits of information which cannot be recovered when scaled back. Using multi-objective genetic algorithm, we can recover these lost bits. In this paper, we described a genetic algorithm approach to recover lost bits while image resized to the smaller version using the original image data bit counts which are stored while the image is scaled. This method is very scalable to apply in a distributed system. Also, the same method can be applied to recover error bits in any types of data blocks. In this paper, we showed proof of concept by providing the implementation and results.

Currently, Hadoop MapReduce framework has been applied to many productive fields to analyze big data. MapReduce applications based on the MapReduce programming model are used to generate and process such huge data. Due to various computational purpose, MapReduce applications have different resource requirements. For specific applications, the resource bottleneck of the cloud computing platform must inevitably impact its executive performance. Therefore, identification of the bottleneck about the allocated resource for MapReduce applications is crucially needed from the viewpoint of either cloud operators or program developers. In this paper, we model the relationship of resource usage parameters of MapReduce applications using multiple linear regression methods and investigate the minimum sampling time for stable modeling. Based on the analysis, we propose the approach which can be used to build stable performance model to expose the bottleneck resource of Hadoop platform and give the effective optimization suggestion.

Given the importance of phonological awareness in development of literacy skills, it is essential to consider the possible differences of phonological awareness (PA) among pre-school children to plan for training programs in these courses. For this purpose, two groups of pre-school children at the ages of 5- to 6- years old who were Kurdish-Persian bilinguals and Persian monolinguals were selected in order to investigate the possible differences among pre-school bilinguals and the monolinguals in terms of phonological awareness. Soleymani and Dastjerdi's (2002) Phonological Awareness Test was used as the instrument. Furthermore, application of the independent samples t-tests indicated a higher ability of Kurdish-Persian compared to the Persian monolingual pre-school children regarding some aspects of phonological awareness. Findings of the
study could have implications for children second learning language, second language teachers and teacher trainers, task designers and curriculum developers to be more familiar with the factors influencing the phonological achievement.

During stair walking, there is an increased risk of falling among pregnant women. A substantial contribution for the foot placement and balance control during stair walking is provided by vision. The purpose of this study was to determine whether there are any pregnancy-related changes in gaze behavior during the stair ascending and stair descending. Six women participated in this study during their pregnancy, at the 14, 27, 31and 38 gestational weeks. Each data collection consisted of descending and ascending a 22-treads staircase, one tread at a time. To monitor the gaze location, a SensoMotoric Instruments eye-tracking glasses system (SMI, Inc.) was used. To compare the differences in the stair descent and stair ascent between the first, second, third and fourth data collection sessions effect size obtained by Cohen's d was used. Results of the gaze vector analysis revealed the turn of the gaze toward the handrail placement (gaze vector x) and toward the stair treads (gaze vector y), suggesting a wider awareness of the safety facility and a stronger need of the foot placement control during advanced phases of pregnancy.

Many difficult problems, from the philosophy of computation point of view, could require computing systems that have some kind of intelligence in order to be solved. Recently, we have seen a large number of artificial intelligent systems used in a number of scientific, technical and social domains. Usage of such an approach often has a focus on healthcare. These systems can provide solutions to a very large set of problems such as, but not limited to: elder patient care; medical diagnosis; medical decision support; out-of-hospital emergency care; drug classification among others. A recent key focus is that most of these developed intelligent systems are agent-based approaches, or in other words, they can be considered as agent-based intelligent systems (ABISs). ABISs are formally based on a set of interacting intelligent agents (IAs) in addition to the use of intelligent cooperative approaches namely forming intelligent cooperative multiagent systems (ICMASs). The main direction of study consists in the possibility to measure the artificial systems intelligence, frequently called machine intelligence quotient (MIQ). Recently, we performed some research related to the measuring of the machine intelligence. There is presented a comprehensive review of the scientific literature related to the measuring of the MIQ. We consider that the measuring of the machine intelligence is very actual and important, which could allow the differentiation of ABISs based on their intelligence, choosing of the agent-based systems able to solve the most intelligently specific problems. As the main conclusion of the performed study, we mention that cannot be given a unanimous definition of the ABISs intelligence. Even if the machine intelligence cannot be defined, it could be measured. We discuss this affirmation more in-depth in the paper. This is similar to the human intelligence that is not understood very well but can be measured using human intelligence tests.

There is a controversial concept among many studies whether neonatal seizures are risk factors for neonatal death and/or neurodevelopment impairments (in case of newborn survivors). Multiple factors have been analyzed in literature, including perinatal factors, etiology factors, seizures characteristics factors, investigations findings factors, therapy-related factors. This paper aims to review the characteristics and the application context of different computational models developed for identifying both the risk factor of morbidity (epilepsy, cerebral palsy, development disability or their combination) and the mortality outcome after neonatal seizures. Consequently, we determined the groups of main risk factors using factor analysis. The vast majority of identified models are logistic regression models, but also decision tree models. In the literature, there is a large variation in establishing the risk factors determining poor or favorable outcome after a neonatal seizure, with similarities and inconsistencies. These findings could be a consequence of different approaches regarding inclusion criteria, methodologies used to identify seizure, seizures definition or description, analysis using computational models.

Even though at the beginning of the 1980s the results of the first neuroscience experiments made some researchers label free will as an "illusion", researches of recent years have unexpectedly changed this perspective. It is raised the issue of post-classical, post-dualistic views in which free will can no longer be regarded as an "all-or-nothing" phenomenon, and in which freedom itself is paradoxically redefined as an unconscious predetermination out of an infinite number of options. The present paper summarizes the perspectives on free will generated by the first experiments in neuroscience and aims to find an answer regarding the freedom of choice, taking into account the latest scientific communications on this subject.

Smart cities integrate a wide variety of technologies and support those innovations capable of delivering sustainable socio-economic development of cities. They are complex environments that are shaped by their innovation capacity, information and communication technologies (ICTs) development and adoption, living standards, residents' readiness, and, last but not least, the willingness to invest. Higher urbanization rates and "mega-cities" with 10 million inhabitants or more make difficult to create a sustainable and cost-effective environment and a high quality of life for the citizens. To overcome this shortcoming, the latest Artificial Intelligence (AI) techniques are needed to increase ICTs solutions and implicitly, to augment the cities competitiveness. Our paper objective is to analyse the public attitude regarding the influence of AI on smart cities characteristics and to identify if there are significant differences in their perspective by gender and by age group. The statistical data analysis was performed using two-way measure analysis of variance (ANOVA). The differences between groups were analysed using inferential statistics. This paper contributes to the understanding of the importance of AI techniques in improving urban living.

Attention deficit hyperactivity disorder is one of the developmental-behavioral disorders whose prevalence is between three to five years of age. There are several ways to cope with this problem. Two common methods for treating this disorder are verbal self-instruction and neurofeedback. This study intended to compare the effect of neurofeedback and verbal selfinstruction on children afflicted with attention-deficit hyperactivity disorder using a cognitivebehavioral pproach. To this end, 84 children afflicted with attention deficit hyperactivity disorder (ADHD) were selected. They were selected using purposeful sampling, and then they were randomly assigned to three groups of 28. The first two groups were selected as the experimental groups and the third group was selected as the control group. The children in the first group
received verbal self-instruction for 16 sessions for sixteen weeks and the children in the second group received neurofeedback training for 32 sessions for sixteen weeks (twice a week). The third group, however, received no treatment whatsoever. Three instruments were used in this study, namely, Child Symptom Inventory-4 (CSI-4), Strengths and Difficulties Questionnaire (SDQ), and the Wechsler Intelligence Scale for Children (WISC-IV). Having analyzed the data, the researchers found that by controlling for the effect of pretest, there was a significant difference in the posttest scores of the groups. According to the post-hoc analysis, there was a significant decrease in the ADHD symptoms of the two experimental groups. However, the effectiveness of neurofeedback was higher than that of verbal self-instruction.

Analysis of live and dynamic movements by computer is one of the areas that draws a great deal of interest to itself. One of the important parts of this area is the motion capture process that can be based on the appearance and facial mode estimation. The aim of this study is to represent 3D facial movements from estimated facial expressions using video image sequences and applying them to computer-generated 3D faces. We propose an algorithm which can classify the given image sequences into one of the motion frames. The contributions of this work lie mainly in two aspects. Firstly, an optical flow algorithm is used for feature extraction, that instead of using two subsequent images (or two subsequent frames in a video), the distinction between images and the normal state is used. Secondly, we realize a multilayer perceptron network that their inputs are matrices obtained from optical flow algorithm to model a mapping between person movements and database movement categories. A three-dimensional avatar, which is made by means of Kinect data, is used to represent the face movements in a graphical environment. In order to evaluate the proposed method, several videos are recorded in order to compare the available modes and discovered modes. The results indicate that the proposed method is effective.

This paper continues the research that considers a new cognitive model based strongly on the human brain. In particular, it considers the neural binding structure of an earlier paper. It also describes some new methods in the areas of image processing and behaviour simulation. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with "˜full linking'. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared to. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.

Political interpreting, as a significant means for foreign language speakers to access a government's official policies, has been regarded as an intensive and stressful task. Any single misinterpretation or misuse of strategy can lead to regional and even international disputes. It will therefore be interesting to study the pragmatic strategies applied by interpreters working in political settings, especially when they render propositions that may sound unfavorable or contrastive to people's presuppositions. In this regard, the use of contrastive markers, an important type of pragmatic markers, serves as an important linguistic indicator of the application of such strategies. Nevertheless, not much has been explored in this aspect. This paper, therefore, studies the use of contrastive markers in the interpreting of policy addresses from Cantonese to English. A parallel corpus, consisting of policy addresses delivered by Chief Executives in Hong Kong (about 0.22 million words) and their English interpretations (about 0.29 million words), was used in the study. The Cantonese contrastive markers "” bat gwo and daan (hai) (comparable in meaning to however and but respectively in English), and their renditions in English "” were compared and analyzed. The two Cantonese contrastive markers were found to correspond to a variety of renditions in English. These findings show how interpreters apply pragmatic strategies when dealing with the extreme situations in political interpreting. They shed light on the development of e-learning for pragmatic competence training of interpreters working in political settings, as well as natural language processing applications for handling such a high-level linguistic feature.

In this article we formulate and analyze a class of diffusion PDE models for oscillation systems of coupled—elasticity in 2-D bounded domains. Approximate method in Green-Lindsay formulation with thermal and diffusion relaxation times has been developed. Basic Boundarycontact problems for isotropic inhomogeneous finite and infinite media with the inclusion of piecewise elastic material in assumptions that surface is sufficiently smooth have been investigated. The tools applied in this development are based on singular integral equations, Laplace transform, the potential method, Green's Tensors and generalized Fourier series analysis.

Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots — machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, Artificial Intelligence Safety and the associated research topics like Future of Artificial Intelligence and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of "˜Are we safe enough in the future of Artificial Intelligence?'. In detail, the discussion includes a comprehensive focus on "˜dystopic' scenarios, enables interested researchers to think about some "˜moral dilemmas' and finally have some ethical outputs that are considerable for developing good intelligent systems. From a general perspective, the discussion taken here is a good opportunity to improve awareness on the mentioned, remarkable research topics associated with not only Artificial Intelligence but also many other natural and social sciences taking role in the humankind.

In this research, the ARIMA model of the time series has been applied for the prediction of the rate of the dropout of the primary schools for the male and female students during the period (2007-2015) by estimating the autocorrelation and partial coefficients. It shows that the time series is unstable. After estimating autocorrelation and partial coefficients, it manifests that the appropriate ARIMA models (1,1,0) for males and ARIMA (1,1,0) for females and ARIMA (1,1,0) for males and females together. Also, it has been assured that these models are good and give accurate predictions and close to the reality through statistical calculation Q. It turns out that the selected models are appropriate and good. Finally, the prediction of the rate of the dropout of the primary schools for the male and female students during the period (2007-2015) and required results were obtained by using the Genetic Algorithm through the applications (MATLAB R2013a Version: 8.1) and (GRETL 2016).

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