Unconstrained Arabic Online Handwritten Words Segmentation using New HMM State Design

In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentation-recognition using HMMs of unique design trained using online features most of which are novel. The HMM output characters boundaries represent the proposed segmentation points (PSP) which are then validated by rules-based post stage without any contextual information help to solve different segmentation errors. The HMM has been designed and tested using a self collected dataset (OHASD) [1]. Most errors cases are cured and remarkable segmentation enhancement is achieved. Very promising word and character segmentation rates are obtained regarding the unconstrained Arabic handwriting difficulty and not using context help.

Use of Multiple Linear Regressions to Evaluate the Influence of O3 and PM10 on Biological Pollutants

Exposure to ambient air pollution has been linked to a number of health outcomes, starting from modest transient changes in the respiratory tract and impaired pulmonary function, continuing to restrict activity/reduce performance and to the increase emergency rooms visits, hospital admissions or mortality. The increase of allergenic symptoms has been associated with air contaminants such as ozone, particulate matter, fungal spores and pollen. Considering the potential relevance of crossed effects of nonbiological pollutants and airborne pollens and fungal spores on allergy worsening, the aim of this work was to evaluate the influence of non-biological pollutants (O3 and PM10) and meteorological parameters on the concentrations of pollen and fungal spores using multiple linear regressions. The data considered in this study were collected in Oporto which is the second largest Portuguese city, located in the North. Daily mean of O3, PM10, pollen and fungal spore concentrations, temperature, relative humidity, precipitation, wind velocity, pollen and fungal spore concentrations, for 2003, 2004 and 2005 were considered. Results showed that the 90th percentile of the adjusted coefficient of determination, P90 (R2aj), of the multiple regressions varied from 0.613 to 0.916 for pollen and from 0.275 to 0.512 for fungal spores. O3 and PM10 showed to have some influence on the biological pollutants. Among the meteorological parameters analysed, temperature was the one that most influenced the pollen and fungal spores airborne concentrations. Relative humidity also showed to have some influence on the fungal spore dispersion. Nevertheless, the models for each pollen and fungal spore were different depending on the analysed period, which means that the correlations identified as statistically significant can not be, even so, consistent enough.

Cluster Analysis for the Statistical Modeling of Aesthetic Judgment Data Related to Comics Artists

We compare three categorical data clustering algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art-s evolution. To establish this, we use a categorical data set and we study it by employing three categorical data clustering algorithms. The performances of these algorithms are compared each other, while interpretations of the clustering results are also given.

Genetic Programming Based Data Projections for Classification Tasks

In this paper we present a GP-based method for automatically evolve projections, so that data can be more easily classified in the projected spaces. At the same time, our approach can reduce dimensionality by constructing more relevant attributes. Fitness of each projection measures how easy is to classify the dataset after applying the projection. This is quickly computed by a Simple Linear Perceptron. We have tested our approach in three domains. The experiments show that it obtains good results, compared to other Machine Learning approaches, while reducing dimensionality in many cases.

A Study of Social and Cultural Context for Tourism Management by Community Kamchanoad District, Amphoe Ban Dung, Udon Thani Province

This research was to study on background and social and cultural context of Kamchanoad community for sustainable tourism management. All data was collected through in-depth interview with village headmen, community committees, teacher, monks, Kamchanoad forest field officers and respected senior citizen above 60 years old in the community who have lived there for more than 40 years. Altogether there were 30 participants for this research. After analyzing the data, content from interview and discussion, Kamchanoad has both high land and low land in the region as well as swamps that are very capable of freshwater animals’ conservation. Kamchanoad is also good for agriculture and animal farming. 80% of Kamchanoad’s land are forest, freshwater and rice farms. Kamchanoad was officially set up as community in 1994 as “Baan Nonmuang”. Inhabitants in Kamchanoad make a living by farming based on sufficiency economy. They have rice farm, eucalyptus farm, cassava farm and rubber tree farm. Local people in Kamchanoad still believe in the myth of Srisutto Naga. They are still religious and love to preserve their traditional way of life. In order to understand how to create successful tourism business in Kamchanoad, we have to study closely on local culture and traditions. Outstanding event in Kamchanoad is the worship of Grand Srisutto, which is on the fullmoon day of 6th month or Visakhabucha Day. Other big events are also celebration at the end of Buddhist lent, Naga firework, New Year celebration, Boon Mahachart, Songkran, Buddhist Lent, Boon Katin and Loy Kratong. Buddhism is the main religion in Kamchanoad. The promotion of tourism in Kamchanoad is expected to help spreading more income for this region. More infrastructures will be provided for local people as well as funding for youth support and people activities.

Flexible Sensor Array with Programmable Measurement System

This study is concerned with pH solution detection using 2 × 4 flexible sensor array based on a plastic polyethylene terephthalate (PET) substrate that is coated a conductive layer and a ruthenium dioxide (RuO2) sensitive membrane with the technologies of screen-printing and RF sputtering. For data analysis, we also prepared a dynamic measurement system for acquiring the response voltage and analyzing the characteristics of the working electrodes (WEs), such as sensitivity and linearity. In this condition, an array measurement system was designed to acquire the original signal from sensor array, and it is based on the method of digital signal processing (DSP). The DSP modifies the unstable acquisition data to a direct current (DC) output using the technique of digital filter. Hence, this sensor array can obtain a satisfactory yield, 62.5%, through the design measurement and analysis system in our laboratory.

A Novel Adaptive E-Learning Model Based on Developed Learner's Styles

Adaptive e-learning today gives the student a central role in his own learning process. It allows learners to try things out, participate in courses like never before, and get more out of learning than before. In this paper, an adaptive e-learning model for logic design, simplification of Boolean functions and related fields is presented. Such model presents suitable courses for each student in a dynamic and adaptive manner using existing database and workflow technologies. The main objective of this research work is to provide an adaptive e-learning model based learners' personality using explicit and implicit feedback. To recognize the learner-s, we develop dimensions to decide each individual learning style in order to accommodate different abilities of the users and to develop vital skills. Thus, the proposed model becomes more powerful, user friendly and easy to use and interpret. Finally, it suggests a learning strategy and appropriate electronic media that match the learner-s preference.

CFD Simulations of a Co-current Spray Dryer

This paper presents the prediction of air flow, humidity and temperature patterns in a co-current pilot plant spray dryer fitted with a pressure nozzle using a three dimensional model. The modelling was done with a Computational Fluid Dynamic package (Fluent 6.3), in which the gas phase is modelled as continuum using the Euler approach and the droplet/ particle phase is modelled by the Discrete Phase model (Lagrange approach).Good agreement was obtained with published experimental data where the CFD simulation correctly predicts a fast downward central flowing core and slow recirculation zones near the walls. In this work, the effects of the air flow pattern on droplets trajectories, residence time distribution of droplets and deposition of the droplets on the wall also were investigated where atomizing of maltodextrin solution was used.

Bandwidth Efficient Diversity Scheme Using STTC Concatenated With STBC: MIMO Systems

Multiple-input multiple-output (MIMO) systems are widely in use to improve quality, reliability of wireless transmission and increase the spectral efficiency. However in MIMO systems, multiple copies of data are received after experiencing various channel effects. The limitations on account of complexity due to number of antennas in case of conventional decoding techniques have been looked into. Accordingly we propose a modified sphere decoder (MSD-1) algorithm with lower complexity and give rise to system with high spectral efficiency. With the aim to increase signal diversity we apply rotated quadrature amplitude modulation (QAM) constellation in multi dimensional space. Finally, we propose a new architecture involving space time trellis code (STTC) concatenated with space time block code (STBC) using MSD-1 at the receiver for improving system performance. The system gains have been verified with channel state information (CSI) errors.

A Novel Approach to Iris Localization for Iris Biometric Processing

Iris-based biometric system is gaining its importance in several applications. However, processing of iris biometric is a challenging and time consuming task. Detection of iris part in an eye image poses a number of challenges such as, inferior image quality, occlusion of eyelids and eyelashes etc. Due to these problems it is not possible to achieve 100% accuracy rate in any iris-based biometric authentication systems. Further, iris detection is a computationally intensive task in the overall iris biometric processing. In this paper, we address these two problems and propose a technique to localize iris part efficiently and accurately. We propose scaling and color level transform followed by thresholding, finding pupil boundary points for pupil boundary detection and dilation, thresholding, vertical edge detection and removal of unnecessary edges present in the eye images for iris boundary detection. Scaling reduces the search space significantly and intensity level transform is helpful for image thresholding. Experimental results show that our approach is comparable with the existing approaches. Following our approach it is possible to detect iris part with 95-99% accuracy as substantiated by our experiments on CASIA Ver-3.0, ICE 2005, UBIRIS, Bath and MMU iris image databases.

Are Adolescent Girls More Depressive than Adolescent Boys in Turkey?

Depression is a serious mental health problem that affects people of all ages, including children and adolescents. Studies showed that female gender is one of the risk factors may influence the development of depression in adolescents. However, some of the studies from Turkey suggested that gender does not lead to any significant difference in the youth depression level. Therefore, the presented study investigated whether girls differ from boys in respect of depression. The association between genders and test scores for the adolescents in a population of primary and secondary school students was also evaluated. The study was consisting of 254 adolescents (122 boys and 132 girls) with a mean age of 13.86±1.43 (Mean±SD) ranging from 12-16 years. Psychological assessment was performed using Children-s Depression Inventory (CDI). Chi-square and Student-s t-test statistics were employed to analyze the data. The mean of the CDI scores of the girls were higher than boys- CDI scores (t = -4.580, p = 0.001). Higher ratio appeared for the girls when they compared with boy group-s depression levels using a CDI cut-off point of 19 (p = 0.001, Odds Ratio = 2,603). The findings of the present study suggested that adolescent girls have high level of depression than adolescent boys aged between 12-16 years in Turkey. Although some studies reported that there is no any differences depression level between adolescent boys and girls in Turkey, result of the present study showed that adolescent girls have high level of depression than adolescent boys in Turkey.

Sentiment Analysis: Popularity of Candidates for the President of the United States

This article deals with the popularity of candidates for the president of the United States of America. The popularity is assessed according to public comments on the Web 2.0. Social networking, blogging and online forums (collectively Web 2.0) are for common Internet users the easiest way to share their personal opinions, thoughts, and ideas with the entire world. However, the web content diversity, variety of technologies and website structure differences, all of these make the Web 2.0 a network of heterogeneous data, where things are difficult to find for common users. The introductory part of the article describes methodology for gathering and processing data from Web 2.0. The next part of the article is focused on the evaluation and content analysis of obtained information, which write about presidential candidates.

A Programming Solution for Moving Mobile Transaction

In this paper, our concern is the management of mobile transactions in the shared area among many servers, when the mobile user moves from one cell to another in online partiallyreplicated distributed mobile database environment. We defined the concept of transaction and classified the different types of transactions. Based on this analysis, we propose an algorithm that handles the disconnection due to moving among sites.

A Numerical Framework to Investigate Intake Aerodynamics Behavior in Icing Conditions

One of the major parts of a jet engine is air intake, which provides proper and required amount of air for the engine to operate. There are several aerodynamic parameters which should be considered in design, such as distortion, pressure recovery, etc. In this research, the effects of lip ice accretion on pitot intake performance are investigated. For ice accretion phenomenon, two supervised multilayer neural networks (ANN) are designed, one for ice shape prediction and another one for ice roughness estimation based on experimental data. The Fourier coefficients of transformed ice shape and parameters include velocity, liquid water content (LWC), median volumetric diameter (MVD), spray time and temperature are used in neural network training. Then, the subsonic intake flow field is simulated numerically using 2D Navier-Stokes equations and Finite Volume approach with Hybrid mesh includes structured and unstructured meshes. The results are obtained in different angles of attack and the variations of intake aerodynamic parameters due to icing phenomenon are discussed. The results show noticeable effects of ice accretion phenomenon on intake behavior.

Rapid Finite-Element Based Airport Pavement Moduli Solutions using Neural Networks

This paper describes the use of artificial neural networks (ANN) for predicting non-linear layer moduli of flexible airfield pavements subjected to new generation aircraft (NGA) loading, based on the deflection profiles obtained from Heavy Weight Deflectometer (HWD) test data. The HWD test is one of the most widely used tests for routinely assessing the structural integrity of airport pavements in a non-destructive manner. The elastic moduli of the individual pavement layers backcalculated from the HWD deflection profiles are effective indicators of layer condition and are used for estimating the pavement remaining life. HWD tests were periodically conducted at the Federal Aviation Administration-s (FAA-s) National Airport Pavement Test Facility (NAPTF) to monitor the effect of Boeing 777 (B777) and Beoing 747 (B747) test gear trafficking on the structural condition of flexible pavement sections. In this study, a multi-layer, feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD backcalculation function. The synthetic database generated using an advanced non-linear pavement finite-element program was used to train the ANN to overcome the limitations associated with conventional pavement moduli backcalculation. The changes in ANN-based backcalculated pavement moduli with trafficking were used to compare the relative severity effects of the aircraft landing gears on the NAPTF test pavements.

MONPAR - A Page Replacement Algorithm for a Spatiotemporal Database

For a spatiotemporal database management system, I/O cost of queries and other operations is an important performance criterion. In order to optimize this cost, an intense research on designing robust index structures has been done in the past decade. With these major considerations, there are still other design issues that deserve addressing due to their direct impact on the I/O cost. Having said this, an efficient buffer management strategy plays a key role on reducing redundant disk access. In this paper, we proposed an efficient buffer strategy for a spatiotemporal database index structure, specifically indexing objects moving over a network of roads. The proposed strategy, namely MONPAR, is based on the data type (i.e. spatiotemporal data) and the structure of the index structure. For the purpose of an experimental evaluation, we set up a simulation environment that counts the number of disk accesses while executing a number of spatiotemporal range-queries over the index. We reiterated simulations with query sets with different distributions, such as uniform query distribution and skewed query distribution. Based on the comparison of our strategy with wellknown page-replacement techniques, like LRU-based and Prioritybased buffers, we conclude that MONPAR behaves better than its competitors for small and medium size buffers under all used query-distributions.

Implementing Adaptive Steganography by Exploring the Ycbcr Color Model Characteristics

Stegnography is a new way of secret communication the most widely used mechanism on account of its simplicity is the use of the least significant bit. We have used the least significant bit (2 LSB and 4 LSB) substitution method. Depending upon the characteristics of the individual portions of cover image we decide whether to use 2 LSB or 4 LSB thus it is an adaptive stegnography technique. We used one of the three channels to behave as indicator to indicate the presence of hidden data in other two channels. The module showed impressive results in terms of capacity to hide the data. In proposed method, instead of using RGB color space directly, YCbCr color space is used to make use of human visual system characteristic.

In Silico Analysis of Quinoxaline Ligand Conformations on 1ZIP: Adenylate Kinase

Adenylate kinase (AK) catalyse the phosphotransferase reaction plays an important role in cellular energy homeostasis. The inhibitors of bacterial AK are useful in the treatment of several bacterial infections. To the novel inhibitors of AK, docking studies performed by using the 3D structure of Bacillus stearothermophilus adenylate kinase from protein data bank (IZIP). 46 Quinoxaline analogues were docked in 1ZIP and selected the highly interacting compounds based on their binding energies, for further studies

Application of a Novel Audio Compression Scheme in Automatic Music Recommendation, Digital Rights Management and Audio Fingerprinting

Rapid progress in audio compression technology has contributed to the explosive growth of music available in digital form today. In a reversal of ideas, this work makes use of a recently proposed efficient audio compression scheme to develop three important applications in the context of Music Information Retrieval (MIR) for the effective manipulation of large music databases, namely automatic music recommendation (AMR), digital rights management (DRM) and audio finger-printing for song identification. The performance of these three applications has been evaluated with respect to a database of songs collected from a diverse set of genres.

Fuzzy Processing of Uncertain Data

In practice, we often come across situations where it is necessary to make decisions based on incomplete or uncertain data. In control systems it may be due to the unknown exact mathematical model, or its excessive complexity (e.g. nonlinearity) when it is necessary to simplify it, respectively, to solve it using a rule base. In the case of databases, searching data we compare a similarity measure with of the requirements of the selection with stored data, where both the select query and the data itself may contain vague terms, for example in the form of linguistic qualifiers. In this paper, we focus on the processing of uncertain data in databases and demonstrate it on the example multi-criteria decision making in the selection of variants, specified by higher number of technical parameters.