Search results for: Pattern classification.

Authors: Kasthurirangan Gopalakrishnan, Siddhartha Khaitan, Anshu Manik

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Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.

Keywords: Artificial neural networks, cluster analysis, Kohonen maps, wine recognition.

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Authors: Adel Sabry Eesa, Adnan Mohsin Abdulazeez Brifcani, Zeynep Orman

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Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a new design of distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 dataset is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

Keywords: Distributed intrusion detection system, mobile agent, feature selection, Bees Algorithm, decision tree.

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Authors: Khaled Ramadan Elbeydi

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This study is carried out to provide an insight into the analysis of the impact of selected macro-economic variables on gross fixed capital formation in Libya using annual data over the period (1970-2010). The importance of this study comes from the ability to show the relative important factors that impact the Libyan gross fixed capital formation. This understanding would give indications to decision makers on which policy they must focus to stimulate the economy. An Autoregressive Distributed Lag (ARDL) modeling process is employed to investigate the impact of the Gross Domestic Product, Monetary Base and Trade Openness on Gross Fixed Capital Formation in Libya. The results of this study reveal that there is an equilibrium relationship between capital formation and its determinants. The results also indicate that GDP and trade openness largely explain the pattern of capital formation in Libya. The findings and recommendations provide vital information relevant for policy formulation and implementation aimed to improve capital formation in Libya.

Keywords: ARDL, Bounds test, capital formation, Cointegration, Libya.

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Authors: Vishal Awasthi, Atul Kumar Agnihotri

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Face is a non-intrusive strong biometrics for identification of original and dummy facial by different artificial means. Face recognition is extremely important in the contexts of computer vision, psychology, surveillance, pattern recognition, neural network, content based video processing. The availability of a widespread face database is crucial to test the performance of these face recognition algorithms. The openly available face databases include face images with a wide range of poses, illumination, gestures and face occlusions but there is no dummy face database accessible in public domain. This paper presents a face detection algorithm based on the image segmentation in terms of distance from a fixed point and template matching methods. This proposed work is having the most appropriate number of nodal points resulting in most appropriate outcomes in terms of face recognition and detection. The time taken to identify and extract distinctive facial features is improved in the range of 90 to 110 sec. with the increment of efficiency by 3%.

Keywords: Face recognition, Principal Component Analysis, PCA, Linear Discriminant Analysis, LDA, Improved Support Vector Machine, iSVM, elastic bunch mapping technique.

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Authors: Anastasis Kounoudes, Stephanos Mavromoustakos

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Electronic commerce is growing rapidly with on-line sales already heading for hundreds of billion dollars per year. Due to the huge amount of money transferred everyday, an increased security level is required. In this work we present the architecture of an intelligent speaker verification system, which is able to accurately verify the registered users of an e-commerce service using only their voices as an input. According to the proposed architecture, a transaction-based e-commerce application should be complemented by a biometric server where customer-s unique set of speech models (voiceprint) is stored. The verification procedure requests from the user to pronounce a personalized sequence of digits and after capturing speech and extracting voice features at the client side are sent back to the biometric server. The biometric server uses pattern recognition to decide whether the received features match the stored voiceprint of the customer who claims to be, and accordingly grants verification. The proposed architecture can provide e-commerce applications with a higher degree of certainty regarding the identity of a customer, and prevent impostors to execute fraudulent transactions.

Keywords: Speaker Recognition, Biometrics, E-commercesecurity.

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Authors: Mahmoud Elmezain, Ayoub Al-Hamadi, Jörg Appenrodt, Bernd Michaelis

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Gesture recognition is a challenging task for extracting meaningful gesture from continuous hand motion. In this paper, we propose an automatic system that recognizes isolated gesture, in addition meaningful gesture from continuous hand motion for Arabic numbers from 0 to 9 in real-time based on Hidden Markov Models (HMM). In order to handle isolated gesture, HMM using Ergodic, Left-Right (LR) and Left-Right Banded (LRB) topologies is applied over the discrete vector feature that is extracted from stereo color image sequences. These topologies are considered to different number of states ranging from 3 to 10. A new system is developed to recognize the meaningful gesture based on zero-codeword detection with static velocity motion for continuous gesture. Therefore, the LRB topology in conjunction with Baum-Welch (BW) algorithm for training and forward algorithm with Viterbi path for testing presents the best performance. Experimental results show that the proposed system can successfully recognize isolated and meaningful gesture and achieve average rate recognition 98.6% and 94.29% respectively.

Keywords: Computer Vision & Image Processing, Gesture Recognition, Pattern Recognition, Application

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Authors: Ihor Trots, Yuriy Tasinkevych, Andrzej Nowicki, Marcin Lewandowski

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The paper presents the study of synthetic transmit aperture method applying the Golay coded transmission for medical ultrasound imaging. Longer coded excitation allows to increase the total energy of the transmitted signal without increasing the peak pressure. Signal-to-noise ratio and penetration depth are improved maintaining high ultrasound image resolution. In the work the 128-element linear transducer array with 0.3 mm inter-element spacing excited by one cycle and the 8 and 16-bit Golay coded sequences at nominal frequencies 4 MHz was used. Single element transmission aperture was used to generate a spherical wave covering the full image region and all the elements received the echo signals. The comparison of 2D ultrasound images of the wire phantom as well as of the tissue mimicking phantom is presented to demonstrate the benefits of the coded transmission. The results were obtained using the synthetic aperture algorithm with transmit and receive signals correction based on a single element directivity function.

Keywords: Golay coded sequences, radiation pattern, synthetic aperture, ultrasound imaging.

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Authors: M. A. S. Fahim, J. Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realization often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: Air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter.

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Authors: M. Afshin, A. Sohankar, M. Dehghan Manshadi, M. R. Daneshgar, G. R. Dehghan Kamaragi

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In this paper, the influence of upstream structures on the flow patternaround and inside the wind-catcher is experimentally investigated by smoke flow visualization techniques. Wind-catchers are an important part of natural ventilation in residential buildings or public places such as shopping centers, libraries, etc. Wind-catchers might be also used in places of high urban densities; hence their potential to provide natural ventilation is dependent on the presence of upstream structures. In this study, the two-sided wind-catcher model was based on a real wind-catcher observed in the city of Yazd, Iran. The present study focuses on the flow patterns around and inside the isolated two-sided wind-catcher, and on a two-sided wind-catcher in the presence of an upstream structure. The results show that the presence of an upstream structure influences the airflow pattern force and direction. Placing a high upstream structure reverses the airflow direction inside the wind-catcher.

Keywords: Natural Ventilation, Smoke Flow Visualization, Two-Sided Wind-Catcher.

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Authors: Kamarul Hawari Ghazali, Mohd. Marzuki Mustafa, Aini Hussain

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Machine vision is an application of computer vision to automate conventional work in industry, manufacturing or any other field. Nowadays, people in agriculture industry have embarked into research on implementation of engineering technology in their farming activities. One of the precision farming activities that involve machine vision system is automatic weeding strategy. Automatic weeding strategy in oil palm plantation could minimize the volume of herbicides that is sprayed to the fields. This paper discusses an automatic weeding strategy in oil palm plantation using machine vision system for the detection and differential spraying of weeds. The implementation of vision system involved the used of image processing technique to analyze weed images in order to recognized and distinguished its types. Image filtering technique has been used to process the images as well as a feature extraction method to classify the type of weed images. As a result, the image processing technique contributes a promising result of classification to be implemented in machine vision system for automated weeding strategy.

Keywords: Machine vision, Automatic Weeding Strategy, filter, feature extraction

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Authors: C. Valderrama-Ulloa, C. Schmitt, J.-P. Marchetti, V. Bucarey

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Access to adequate housing is a fundamental human right and a crucial factor for health. Housing should be inclusive, accessible, and able to meet the needs of all its inhabitants at every stage of their lives without hindering their health, autonomy, or independence. This article addresses the importance of designing housing for people with disabilities, which varies depending on individual abilities, preferences, and cultural considerations. Based on the components of the International Classification of Functioning, Disability and Health, wheelchair users, little people (achondroplasia), children with autism spectrum disorder and Down syndrome were characterized, and six domains of activities related to daily life inside homes were defined. The article describes the main barriers homes present for this group of people. It proposes a list of architectural and design aspects to reduce barriers to housing use. The aspects are divided into three main groups: space management, building services, and supporting facilities. The article emphasizes the importance of consulting professionals and users with experience designing for diverse needs to create inclusive, safe, and supportive housing for people with disabilities.

Keywords: Achondroplasia, autism spectrum disorder, disability, down syndrome, wheelchair user.

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Authors: Su-Ling Li, Qun-Yu Gao

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Mung bean starches were subjected to heat-moisture treatment (HMT) by different moisture contents (15%, 20%, 25%, 30% and 35%) at 120Ôäâ for 12h. The impact on the yields of resistant starch (RS), microstructure, physicochemical and functional properties was investigated. Compared to native starch, the RS content of heat-moisture treated starches increased significantly. The RS level of HMT-20 was the highest of all the starches. Birefringence was displayed clear at the center of native starch. For HMT starches, pronounced birefringence was exhibited on the periphery of starch granules; however, birefringence disappeared at the centre of some starch granules. The shape of HMT starches hadn-t been changed and the integrity of starch granules was preserved for all the conditions. Concavity could be observed on HMT starches under scanning electronic microscopy. After HMT, apparent amylose contents were increased and starch macromolecule was degraded in comparison with those of native starch. There was a reduction in swelling power on HMT starches, but the solubility of HMT starches was higher than that of native starch. Both of native and HMT starches showed A-type X-ray diffraction pattern. Furthermore, there is a higher intensity at the peak of 15.0 and 22.9 Å than those of native starch.

Keywords: Resistant starch, mung bean (Phaseolus radiatus) starch, heat-moisture treatment, physicochemical properties.

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Authors: Hadi G. Altabatabaei, Nguyen L. L. Anh

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There are positive significant relationships between the Need for Cognitive Closure (NFC) and Religious Fundamentalism (RF) among students. The preliminary assumption of the current study was: There would be a stronger pattern of association between these constructs, if the participants of the study are more exposed to the study's main concept which is religiosity. In other words, close-mindedness would be more related to homogeneous samples of practicing devotees of monotheistic religions compared to student samples. The main hypothesis was that concerning the Muslim sample, there will be a significant and positive correlation between the need for closure (and all facets of it, except decisiveness) and RF. Both the student sample (n=88), and the Muslim practicing mosque attending sample (n=40), were administrated three scales of Need for Closure (NFCS), Religious Fundamentalism (RFS), and Four Basic Dimensions of Religiousness (FBDRS). The results of the study moderately confirmed the hypothesis and showed a positive correlation between NFCS and RFS with the Muslim sample. Specifically, preference for order, preference for predictability and discomfort with ambiguity facets of the NFCS positively correlated with RFS. However, with regards to the student sample such relationships between the constructs were not found.

Keywords: Religiosity, close-mindedness, religious fundamentalism, need for closure, monotheistic religions.

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Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-Doppler features.

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Authors: Henry Foulds, Magda Huisman, Gunther R. Drevin

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Privacy is regarded as a fundamental human right and it is clear that the study of digital privacy is an important field. Digital privacy is influenced by new and constantly evolving technologies and this continuous change makes it hard to create legislation to protect people’s privacy from being exploited by misuse of these technologies.

This study aims to benefit digital privacy legislation efforts by evaluating the awareness and perceived importance of digital privacy legislation among computer science students. The chosen fixed variables for the population are study year and gamer classification.

The use of location based services in mobile applications and games are a concern for digital privacy. For this reason the study focused on computer science students as they have a high likelihood to use and develop this type of software. Surveys were used to evaluate awareness and perceived importance of digital privacy legislation.

The results of the study show that privacy legislation and awareness of privacy legislation are important to people. The perception of the importance of privacy legislation increases with academic experience. Awareness of privacy legislation increases from non-gamers to pro gamers. 

Keywords: Digital privacy, Legislation awareness, Gaming.

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Authors: Hyun-Woo Cho

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Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: Process Monitoring, kernel methods, multivariate filtering, data-driven techniques, quality improvement.

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Authors: Marcus C. Garcia, Giselle C. Guimarães, Luciana H. Yamane, Renato R. Siman

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The environmental indicators and the classification of beach waste are essential tools to diagnose the current situation and to indicate ways to improve the quality of this environment. The purpose of this paper was to perform a quali-quantitative analysis of the beach waste on the Curva da Jurema Beach (Espírito Santo - Brazil). Three transects were used with equidistant positioning over the total length of the beach for the solid waste collection. Solid wastes were later classified according to their use and primary raw material from the low and high summer season. During the low season, average values of 7.10 items.m^-1, 18.22 g.m^-1 and 0.91 g.m^-2 were found for the whole beach, and transect 3 contributed the most waste, with the total sum of items equal to 999 (49%), a total mass of 5.62 kg and a total volume of 21.31 L. During the high summer season, average values of 8.22 items.m^-1, 54.40 g.m^-1 and 2.72 g.m^-2 were found, with transect 2 contributing the most to the total sum with 1,212 items (53%), a total mass of 10.76 kg and a total volume of 51.99 L. Of the total collected, plastic materials represented 51.4% of the total number of items, 35.9% of the total mass and 68% of the total volume. The implementation of reactive and proactive measures is necessary so that the management of the solid wastes on Curva da Jurema Beach is in accordance with principles of sustainability.

Keywords: Beach solid waste, environmental indicators, quali-quantitative analysis, waste management.

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Authors: Wael M Albadri, Hassnen M Jafer, Ehab H Sfoog

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Relationship between undrained shear strength (Su) and over consolidation ratio (OCR) of clay soil (marine clay) is very important in the field of geotechnical engineering to estimate the settlement behaviour of clay and to prepare a small scale physical modelling test. In this study, a relationship between shear strength and OCR parameters was determined using the laboratory vane shear apparatus and the fully automatic consolidated apparatus. The main objective was to establish non-linear correlation formula between shear strength and OCR and comparing it with previous studies. Therefore, in order to achieve this objective, three points were chosen to obtain 18 undisturbed samples which were collected with an increasing depth of 1.0 m to 3.5 m each 0.5 m. Clay samples were prepared under undrained condition for both tests. It was found that the OCR and shear strength are inversely proportional at similar depth and at same undrained conditions. However, a good correlation was obtained from the relationships where the R2 values were very close to 1.0 using polynomial equations. The comparison between the experimental result and previous equation from other researchers produced a non-linear correlation which has a similar pattern with this study.

Keywords: Shear strength, over-consolidation ratio, vane shear test, clayey soil.

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125 Hz to 8000 Hz. The data contain patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R2 values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R2 values for the best models for KNN ranges from .89 to .95. The best imputation models received R2 between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our imputation models versus constant imputations by a two percent increase.

Keywords: Machine Learning, audiograms, data imputations, single imputations.

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Authors: M. Tehranizadeh, E. Shoushtari Rezvani

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Seismic performance of steel moment-resisting frame structures is investigated considering nonlinear soil-structure interaction (SSI) effects. 10-, 15-, and 20-story planar building frames with aspect ratio of 3 are designed in accordance with current building codes. Inelastic seismic demands of the superstructure are considered using concentrated plasticity model. The raft foundation system is designed for different soil types. Beam-on-nonlinear Winkler foundation (BNWF) is used to represent dynamic impedance of the underlying soil. Two sets of pulse-like as well as no-pulse near-fault earthquakes are used as input ground motions. The results show that the reduction in drift demands due to nonlinear SSI is characterized by a more uniform distribution pattern along the height when compared to the fixed-base and linear SSI condition. It is also concluded that beneficial effects of nonlinear SSI on displacement demands is more significant in case of pulse-like ground motions and performance level of the steel moment-resisting frames can be enhanced.

Keywords: Soil-structure interaction, uplifting, soil plasticity, near-fault earthquake, tall building.

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Authors: Rosa Nurtazina, Yerkebulan Zhumashov, Maral Tomanova

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Humanity is entering an era when "virtual reality" as the image of the world created by the media with the help of the Internet does not match the reality in many respects, when new communication technologies create a fundamentally different and previously unknown "global space". According to these technologies, the state begins to change the basic technology of political communication of the state and society, the state and the state. Nowadays image of the state becomes the most important tool and technology.

Image is a purposefully created image granting political object (person, organization, country, etc.) certain social and political values and promoting more emotional perception.

Political image of the state plays an important role in international relations. The success of the country's foreign policy, development of trade and economic relations with other countries depends on whether it is positive or negative. Foreign policy image has an impact on political processes taking place in the state: the negative image of the country's can be used by opposition forces as one of the arguments to criticize the government and its policies.

Keywords: Image of the country, country's image classification, function of the country image, country's image components.

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Authors: Rezaul Azim, A. Toaha Mobashsher, M. Tariqul Islam

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In this paper a novel ultra-wideband (UWB) slot antenna with band notch characteristics for world interoperability for microwave access (WiMAX) is proposed. The designed antenna consists of a rectangular radiating patch and a ground plane with tapered shape slot. To realize a notch band, a curved parasitic element has been etched out along with the radiating patch. It is observed that by adjusting the length, thickness and position of the parasitic element, the proposed antenna can achieved an impedance bandwidth of 8.01GHz (2.84 to 10.85GHz) with a notched band of 3.28-3.85GHz. Compared to the recently reported band notch antennas, the proposed antenna has a simple configuration to realize band notch characteristics in order to mitigate the potential interference between WiMAX and UWB system. Furthermore, a stable radiation pattern and moderate gain except at the notched band makes the proposed antenna suitable for various UWB applications. 

Keywords: Band notch, Filter element, Ultra-wideband (UWB), WiMAX.

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Authors: Muhammad Sollehhuddin A. Jalil, Mohd Ibrahim Shapiai, Rubiyah Yusof

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Nowadays, ontology is common in many areas like artificial intelligence, bioinformatics, e-commerce, education and many more. Ontology is one of the focus areas in the field of Information Retrieval. The purpose of an ontology is to describe a conceptual representation of concepts and their relationships within a particular domain. In other words, ontology provides a common vocabulary for anyone who needs to share information in the domain. There are several ontology domains in various fields including engineering and non-engineering knowledge. However, there are only a few available ontology for engineering knowledge. Fuzzy logic as engineering knowledge is still not available as ontology domain. In general, fuzzy logic requires step-by-step guidelines and instructions of lab experiments. In this study, we presented domain ontology for Fuzzy Logic Control (FLC) knowledge. We give Table of Content (ToC) with middle strategy based on the Uschold and King method to develop FLC ontology. The proposed framework is developed using Protégé as the ontology tool. The Protégé’s ontology reasoner, known as the Pellet reasoner is then used to validate the presented framework. The presented framework offers better performance based on consistency and classification parameter index. In general, this ontology can provide a platform to anyone who needs to understand FLC knowledge.

Keywords: Engineering knowledge, fuzzy logic control ontology, ontology development, table of contents.

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Authors: Rao M. V., Joshi S. C., Balaji M.

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Coastal zone combines terrestrial, marine and atmospheric factors and gives rise to unique landforms that play an important role in long-term sustainability of the hinterland and economy of maritime nations. World over, efforts have been put forth to understand plants of the seacoasts. In India also, plants of several geographical entities have been well documented, but works devoted to plant communities of the vast tropical coast of India and its States are still insufficient. Therefore, an inventory of plants flourishing in a stretch of ~450km of the Coastal Regulatory Zone I encompassing a total of 84 villages in 6 revenue Districts of northern Andhra Pradesh (15o42’06”N, 80^o51’03”E to 19o05’51”N, 84^o47’44”E) along Bay of Bengal was carried out. The study revealed presence of a total of 364 species belonging to 225 genera under 71 families. In addition to inventory, zonation pattern, ethnobotany, and certain interesting ecological facts are included.

Keywords: Ecology, Ethnobotany, Inventory, Tropical coast, Zonation.

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Authors: Eric P. Jiang

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As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.

Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.

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Authors: Thomas Kerstein, Martin Laurowski, Philipp Klein, Michael Weyrich, Hubert Roth, Jürgen Wahrburg

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Optical 3D measurement of objects is meaningful in numerous industrial applications. In various cases shape acquisition of weak textured objects is essential. Examples are repetition parts made of plastic or ceramic such as housing parts or ceramic bottles as well as agricultural products like tubers. These parts are often conveyed in a wobbling way during the automated optical inspection. Thus, conventional 3D shape acquisition methods like laser scanning might fail. In this paper, a novel approach for acquiring 3D shape of weak textured and moving objects is presented. To facilitate such measurements an active stereo vision system with structured light is proposed. The system consists of multiple camera pairs and auxiliary laser pattern generators. It performs the shape acquisition within one shot and is beneficial for rapid inspection tasks. An experimental setup including hardware and software has been developed and implemented.

Keywords: automated optical inspection, depth from structured light, stereo vision, surface reconstruction

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Authors: Rong Zhou, Shun’ich Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu

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In this paper, we propose a robust disease detection method, called adaptive orientation code matching (Adaptive OCM), which is developed from a robust image registration algorithm: orientation code matching (OCM), to achieve continuous and site-specific detection of changes in plant disease. We use two-stage framework for realizing our research purpose; in the first stage, adaptive OCM was employed which could not only realize the continuous and site-specific observation of disease development, but also shows its excellent robustness for non-rigid plant object searching in scene illumination, translation, small rotation and occlusion changes and then in the second stage, a machine learning method of support vector machine (SVM) based on a feature of two dimensional (2D) xy-color histogram is further utilized for pixel-wise disease classification and quantification. The indoor experiment results demonstrate the feasibility and potential of our proposed algorithm, which could be implemented in real field situation for better observation of plant disease development.

Keywords: Cercospora Leaf Spot (CLS), Disease detection, Image processing, Orientation Code Matching (OCM), Support Vector Machine (SVM).

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Authors: Carlos Vera, Tetyana Baydyk, Ernst Kussul, Graciela Velasco, Miguel Aparicio

Abstract:

This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed.

Keywords: Contour orientation histogram, meteors, night sky, RSC neural classifier, stars.

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Authors: A. I. A. Rahman, Sh-Hussain Salleh, K. Ahmad, K. Anuar

Abstract:

Analysis of vocal fold vibration is essential for understanding the mechanism of voice production and for improving clinical assessment of voice disorders. This paper presents a Dynamic Time Warping (DTW) based approach to analyze and objectively classify vocal fold vibration patterns. The proposed technique was designed and implemented on a Glottal Area Waveform (GAW) extracted from high-speed laryngeal images by delineating the glottal edges for each image frame. Feature extraction from the GAW was performed using Linear Predictive Coding (LPC). Several types of voice reference templates from simulations of clear, breathy, fry, pressed and hyperfunctional voice productions were used. The patterns of the reference templates were first verified using the analytical signal generated through Hilbert transformation of the GAW. Samples from normal speakers’ voice recordings were then used to evaluate and test the effectiveness of this approach. The classification of the voice patterns using the technique of LPC and DTW gave the accuracy of 81%.

Keywords: Dynamic Time Warping, Glottal Area Waveform, Linear Predictive Coding, High-Speed Laryngeal Images, Hilbert Transform.

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

Abstract:

Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: Pollen recognition, logistic model tree, expectation-maximization, local binary pattern.

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