Search results for: Extended Park´s vector approach

Authors: Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-Nearest Neighbors, Neural Network, Radial Basis Function, Red blood cells, Support vector machine.

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Authors: Wan-Yu Liu, Yen-Hsiang Liu, Shing-Yi Huang, Hao-Zhi Wen

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The private theme parks are gradually surpassing public-owned scenic areas after many years of development and have become a mainstream choice for domestic tourists. Previous studies show that visitors from different backgrounds differ in consumer behavior and satisfaction factors. An understanding of visitor satisfaction is therefore of extreme importance to operators of privately-owned theme parks. Importance-Performance Analysis (IPA) is used to measure consumer's potential satisfaction with services and has become a widely used management tool for strength and weakness analysis for brands, products, services and point of sales. As IPA has so far not been used to evaluate the visitor satisfaction with privately-owned theme parks, in this study the IPA method is used to analyze visitor satisfaction with Janfusun Fancyworld (one of the most popular private theme parks in Taiwan) and to rank visitor focus and satisfaction on/in theme park facilities and services. Results of the analysis provide private theme park operators with an understanding of user or consumer demands as well as an assessment of the quality of services currently offered.

Keywords: Satisfaction, Importance-Performance Analysis, Theme Parks, Janfusun Fancyworld.

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Authors: Parvinder S. Sandhu, Hardeep Singh

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Automatic reusability appraisal could be helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this paper, we have mentioned two-tier approach by studying the structural attributes as well as usability or relevancy of the component to a particular domain. Latent semantic analysis is used for the feature vector representation of various software domains. It exploits the fact that FeatureVector codes can be seen as documents containing terms -the idenifiers present in the components- and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. Further, we devised Neuro- Fuzzy hybrid Inference System, which takes structural metric values as input and calculates the reusability of the software component. Decision tree algorithm is used to decide initial set of fuzzy rules for the Neuro-fuzzy system. The results obtained are convincing enough to propose the system for economical identification and retrieval of reusable software components.

Keywords: Clustering, ID3, LSA, Neuro-fuzzy System, SVD

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Authors: Un-Hwan Park, Jun-Hyeok Heo, In-Sung Lee, Tae-Hyeon Oh, Dae-Gyu Park

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Automotive interior material consisting of several material layers has the sound-absorbing function. It is difficult to predict sound absorbing coefficient because of several material layers. So, many experimental tunings are required to achieve the target of sound absorption. Therefore, while the car interior materials are developed, so much time and money is spent. In this study, we present a method to predict the sound absorbing performance of the material with multi-layer using physical properties of each material. The properties are predicted by Foam-X software using the sound absorption coefficient data measured by impedance tube. Then, we will compare and analyze the predicted sound absorption coefficient with the data measured by scaled reverberation chamber and impedance tubes for a prototype. If the method is used instead of experimental tuning in the development of car interior material, the time and money can be saved, and then, the development effort can be reduced because it can be optimized by simulation.

Keywords: Multi-layer nonwoven, sound absorption coefficient, scaled reverberation chamber, impedance tubes.

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Authors: Zhibo Xiao, Tharini Nayanika de Silva, Kezhi Mao

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In this paper, we present an evolving knowledge extraction system named AKEOS (Automatic Knowledge Extraction from Online Sources). AKEOS consists of two modules, including a one-time learning module and an evolving learning module. The one-time learning module takes in user input query, and automatically harvests knowledge from online unstructured resources in an unsupervised way. The output of the one-time learning is a structured vector representing the harvested knowledge. The evolving learning module automatically schedules and performs repeated one-time learning to extract the newest information and track the development of an event. In addition, the evolving learning module summarizes the knowledge learned at different time points to produce a final knowledge vector about the event. With the evolving learning, we are able to visualize the key information of the event, discover the trends, and track the development of an event.

Keywords: Evolving learning, knowledge extraction, knowledge graph, text mining.

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Authors: N. A. Ibrahim, A. Kudus, I. Daud, M. R. Abu Bakar

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Competing risks survival data that comprises of more than one type of event has been used in many applications, and one of these is in clinical study (e.g. in breast cancer study). The decision tree method can be extended to competing risks survival data by modifying the split function so as to accommodate two or more risks which might be dependent on each other. Recently, researchers have constructed some decision trees for recurrent survival time data using frailty and marginal modelling. We further extended the method for the case of competing risks. In this paper, we developed the decision tree method for competing risks survival time data based on proportional hazards for subdistribution of competing risks. In particular, we grow a tree by using deviance statistic. The application of breast cancer data is presented. Finally, to investigate the performance of the proposed method, simulation studies on identification of true group of observations were executed.

Keywords: Competing risks, Decision tree, Simulation, Subdistribution Proportional Hazard.

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Authors: Olofintoye Lawrence Kayode, Olorunniyi Omojola Felix

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Schistosomiasis is a vector-borne water-based disease transmitted by Bulinus globosus, causing haematuria in the urine of man, while fascioliasis is a trematode zoonosis infectious transmitted by Lymnaea natalensis causing liver disease in man and animals. Adult Bulinus globosus and Lymnaea natalensis were used for the experiment. Aqueous leaf extract of Ageratum conyzoides and Datura stramonium were prepared into 25, 50, 75, 100, 200 and 400 ppm concentrations. Ten snails of each species were exposed to different concentrations in triplicates, and dechlorinated water was used as control at 24h, 48h, and 72h exposure. The results revealed that 100 ppm of both plants leaves extracts indicated mortality rates between 76.7% and 100% at 24h, 48h, and 72h for both snail species. (P < 0.05). In conclusion, the extract exercised molluscicidal activity to control the snail vector at lethal doses LC50 (66.611-72.021 ppm), CI = 63.083-77.90 ppm and LC90 (92.623-102.350), CI = 87.715-110.12 ppm.

Keywords: Snail, plant leaf, aqueous extract, mortality.

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Authors: Surinder Deswal, D. V. S. Verma

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Aeration by a plunging water jet is an energetically attractive way to effect oxygen-transfer than conventional oxygenation systems. In the present study, a new type of conical shaped plunging aeration device is fabricated to generate hollow inclined ined plunging jets (jet plunge angle of π/3 ) to investigate its oxygen transfer capacity. The results suggest that the volumetric oxygen-transfer coefficient and oxygen-transfer efficiency of the conical plunging jet aerator are competitive with other types of aeration systems. Relationships of volumetric oxygen-transfer coefficient with jet power per unit volume and jet parameters are also proposed. The suggested relationships predict the volumetric oxygentransfer coefficient within a scatter of ± 15% . Further, the application of Support Vector Machines on the experimental data revealed its utility in the prediction of volumetric oxygen-transfer coefficient and development of conical plunging jet aerators.

Keywords: Conical plunging jet, oxygen-transfer efficiency, support vector machines, volumetric oxygen-transfer coefficient.

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Authors: G. Haupt

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Existing literature ondesign reasoning seems to give either one sided accounts on expert design behaviour based on internal processing. In the same way ecological theoriesseem to focus one sidedly on external elementsthat result in a lack of unifying design cognition theory. Although current extended design cognition studies acknowledge the intellectual interaction between internal and external resources, there still seems to be insufficient understanding of the complexities involved in such interactive processes. As such,this paper proposes a novelmulti-directional model for design researchers tomap the complex and dynamic conduct controlling behaviour in which both the computational and ecological perspectives are integrated in a vertical manner. A clear distinction between identified intentional and emerging physical drivers, and relationships between them during the early phases of experts- design process, is demonstrated by presenting a case study in which the model was employed.

Keywords: External representation, early phases, extended design cognition, internal processes and external drivers, conduct controlling behaviour.

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Authors: Zhang Wenbo, Tian Ye, Sun Peigen, Xu Haifeng

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According to the mobility of the satellite network nodes and the characteristic of management domain dynamic partition in the satellite network, the login and logout mechanism of the satellite network dynamic management domain partition was proposed in the paper. In the mechanism, a ground branch-station sends the packets of login broadcasting to satellites in view. After received the packets, the SNMP agents on the satellites adopt link-delay test to respond. According to the mechanism, the SNMP primitives were extended, and the new added primitives were as follows: broadcasting, login, login confirmation,delay_testing, test responses, and logout. The definition of primitives, which followed RFC1157 criterion, could be encoded by the BER coding. The policy of the dynamic management domain partition on the basis of the login and logout mechanism, which was supported by the SNMP protocol, was realized by the design of the extended primitives.

Keywords: Satellites Network, network management, communication primitive, SNMP

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Authors: Jongho Park, Taekyun Kim, Jinwoong Choi, Sungnam Hong, Sun-Kyu Park

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Recently, the climate change is the one of the main problems. This abnormal phenomenon is consisted of the scorching heat, heavy rain and snowfall, and cold wave that will be enlarged abnormal climate change repeatedly. Accordingly, the width of temperature change is increased more and more by abnormal climate, and it is the main factor of cracking in the reinforced concrete. The crack of the reinforced concrete will affect corrosion of steel re-bar which can decrease durability of the structure easily. Hence, the elimination of the durability weakening factor (steel re-bar) is needed. Textile which weaves the carbon, AR-glass and aramid fiber has been studied actively for exchanging the steel re-bar in the Europe for about 15 years because of its good durability. To apply textile as the concrete reinforcement, the bond strength between concrete and textile will be investigated closely. Therefore, in this paper, pull-out test was performed with change of development length of textile. Significant load and stress was increasing at D80. But, bond stress decreased by increasing development length.

Keywords: Bond strength, climate change, pull-out test, replacement of reinforcement material, textile.

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Authors: Norah Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD: Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by SVM, achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: Autism Spectrum Disorder, ASD, Machine Learning, ML, Feature Selection, Support Vector Machine, SVM.

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Authors: M. D. Kokate, T. R. Sontakke, P. W. Wani

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This paper deals with an adaptive multiuser detector for direct sequence code division multiple-access (DS-CDMA) systems. A modified receiver, precombinig LMMSE is considered under time varying channel environment. Detector updating is performed with two criterions, mean square estimation (MSE) and MOE optimization technique. The adaptive implementation issues of these two schemes are quite different. MSE criterion updates the filter weights by minimizing error between data vector and adaptive vector. MOE criterion together with canonical representation of the detector results in a constrained optimization problem. Even though the canonical representation is very complicated under time varying channels, it is analyzed with assumption of average power profile of multipath replicas of user of interest. The performance of both schemes is studied for practical SNR conditions. Results show that for poor SNR, MSE precombining LMMSE is better than the blind precombining LMMSE but for greater SNR, MOE scheme outperforms with better result.

Keywords: LMMSE, MOE, MUD.

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Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: Face recognition, Binary vector quantization (BVQ), Local Binary Patterns (LBP), DCT coefficients.

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: Data mining, hybrid storage system, recurrent neural network, support vector machine.

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Authors: Kyoungwoo Park, Byeong Sam Kim, Hyo-Jae Lim, Ji Won Han, Park Kyoun Oh, Juhee Lee, Keun-Yeol Yu

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Predictions of flow and heat transfer characteristics and shape optimization in internally finned circular tubes have been performed on three-dimensional periodically fully developed turbulent flow and thermal fields. For a trapezoidal fin profile, the effects of fin height h, upper fin widths d1, lower fin widths d2, and helix angle of fin ? on transport phenomena are investigated for the condition of fin number of N = 30. The CFD and mathematical optimization technique are coupled in order to optimize the shape of internally finned tube. The optimal solutions of the design variables (i.e., upper and lower fin widths, fin height and helix angle) are numerically obtained by minimizing the pressure loss and maximizing the heat transfer rate, simultaneously, for the limiting conditions of d1 = 0.5~1.5 mm, d2 = 0.5~1.5 mm, h= 0.5~1.5mm, ? = 10~30 degrees. The fully developed flow and thermal fields are predicted using the finite volume method and the optimization is carried out by means of the multi-objective genetic algorithm that is widely used in the constrained nonlinear optimization problem.

Keywords: Computational fluid dynamics, Genetic algorithm, Internally finned tube with helix angle, Optimization.

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Authors: Veruska C. Dutra, Afonso R. Aquino

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Since the 1980s, global tourism activity has consolidated worldwide to become an important economic contributor, and consequently, the sociocultural and environmental impacts are starting to become evidenced. This raises the need of discussing about actions for sustainable tourism that should be linked not only to the economy, but also to the environment and social aspects. The work that is going to be presented is part of a doctoral research project in Sciences undertaken at the Sao Paulo University, Brazil. It aims to analyze whether the monitoring of the tourism sector with a focus on sustainability is applicable or not, through those indicators, put in a case study in the Jalapão State Park (JSP) conservation unit, in the state of Tocantins, Brazil. This is a study of an interdisciplinary nature that had the deductive method as its guide. We concluded that the key points of the sustainable tourism, when analyzed with the focal point in environmental indicators, are an important evaluation and quantification tool of that activity in the study locus. It displayed itself as an adequate tool for monitoring, thus decoding, the main environmental impacts that occur in tourism regions and their intensity, which is made possible through analysis, and has the objective to trace ways to prevent and correct the presented impacts.

Keywords: Economic indicators, tourism, sustainability, Jalapão.

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Authors: Hamza A. Ali, Waleed A. J. Rasheed

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The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values according to the input pattern variations. The new approach is based on splitting the whole network into two subnets; main traditional net and a supportive net. The first deals with the required output of trained patterns with predefined settings, while the second tolerates output generation dynamically with tuning capability for any newly applied input. This tuning comes in the form of an adjustment to the threshold values. Two levels of supportive net were studied; one implements an extended additional layer with adjustable neuronal threshold setting mechanism, while the second implements an auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net that is constructed in dual-layer architecture. Experiment results and analysis of the proposed designs have given quite satisfactory conducts. The supportive layer approach achieved over 90% recognition rate, while the multiple network technique shows more effective and acceptable level of recognition. However, this is achieved at the price of network complexity and computation time. Recognition generalization may be also improved by accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities with the needs of further advanced learning phases.

Keywords: Classification, Recognition, Neural Networks, Pattern Recognition, Generalization.

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Authors: Jin-Sung Kim, Jin-Hwan Kim, Ji-Mo Park, Sung-Man Park, Won-Yong Choe, Hoon Heo

Abstract:

An optimal control of Reverse Osmosis (RO) plant is studied in this paper utilizing the auto tuning concept in conjunction with PID controller. A control scheme composing an auto tuning stochastic technique based on an improved Genetic Algorithm (GA) is proposed. For better evaluation of the process in GA, objective function defined newly in sense of root mean square error has been used. Also in order to achieve better performance of GA, more pureness and longer period of random number generation in operation are sought. The main improvement is made by replacing the uniform distribution random number generator in conventional GA technique to newly designed hybrid random generator composed of Cauchy distribution and linear congruential generator, which provides independent and different random numbers at each individual steps in Genetic operation. The performance of newly proposed GA tuned controller is compared with those of conventional ones via simulation.

Keywords: Genetic Algorithm, Auto tuning, Hybrid random number generator, Reverse Osmosis, PID controller

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Authors: Seungkyum Kim, Yongtae Park

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Recently, business environment and customer needs have become rapidly changing, hence it is very difficult to fulfill sophisticated customer needs by product or service innovation only. In practice, to cope with this problem, various manufacturing companies have developed services to combine with their products. Along with this, many academic studies on PSS (Product Service System) which is the integrated system of products and services have been conducted from the viewpoint of manufacturers. On the other hand, service providers are also attempting to develop service-supporting products to increase their service competitiveness and provide differentiated value. However, there is a lack of research based on the service-centric point of view. Accordingly, this paper proposes a concept generation method for service-supporting product development from the service-centric point of view. This method is designed to be executed in five consecutive steps: situation analysis, problem definition, problem resolution, solution evaluation, and concept generation. In the proposed approach, some tools of TRIZ (Theory of Solving Inventive Problem) such as ISQ (Innovative Situation Questionnaire) and 40 inventive principles are employed in order to define problems of the current services and solve them by generating service-supporting product concepts. This research contributes to the development of service-supporting products and service-centric PSSs.

Keywords: TRIZ, PSS (Product Service System), service-supporting product, concept generation

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Authors: Anupam Kumar, Abdul Hamid Bhat, Pramod Agarwal

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Bidirectional energy transfer capability with high efficiency and reduced cost is fast gaining prominence in the central part of a lot of power conversion systems in Direct Current (DC) microgrid. Preferably, under the economics constraints, these systems utilise a single high efficiency power electronics conversion system and a dual active bridge converter. In this paper, modeling and performance of Dual Active Bridge (DAB) converter with Extended Phase Shift (EPS) is evaluated with two batteries on both sides of DC bus and bidirectional energy transfer is facilitated and this is further compared with the Single Phase Shift (SPS) mode of operation. Optimum operating zone is identified through exhaustive simulations using MATLAB/Simulink and SimPowerSystem software. Reduced rules based fuzzy logic controller is implemented for closed loop control of DAB converter. The control logic enables the bidirectional energy transfer within the batteries even at lower duty ratios. Charging and discharging of batteries is supervised by the fuzzy logic controller. State of charge, current and voltage for both the batteries are plotted in the battery characteristics. Power characteristics of batteries are also obtained using MATLAB simulations.

Keywords: Fuzzy logic controller, rule base, membership functions, dual active bridge converter, bidirectional power flow, duty ratio, extended phase shift, state of charge.

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Authors: Ioannis Manikas

Abstract:

The increased consumer demand for environmentally friendly production and distribution practices and the stricter environmental regulations turned environmental aspects into important criteria in business decision-making. On the other hand, Food and Nutrition Security (FNS) has evolved dramatically during the last decades in theory and practice serving as a reference point for exchanging experiences among all agents involved in programs and projects to fostering policy and strategy development. Global pressures make it more important than ever to gain a better understanding of the contribution that agrifood businesses make to FNS and to examine ways to make them more resilient in an increasingly globalized and uncertain world. This study extends the standard three-dimensional model of sustainability to include two more dimensions: A technological dimension and a policy/political dimension. Apart from the economic, environmental and social dimensions regularly used in sustainability literature, the extended model will accurately represent the measures and policies addressing food and nutrition security.

Keywords: Food and nutrition security, sustainability, food safety, resilience.

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Authors: Iman A. Saad, Loay E. George

Abstract:

Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.

Keywords: Iris recognition, contrast stretching, gradient features, texture features, Euclidean metric.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: V. Zangeneh, S. Mohammadi

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Today, node-disjoint routing becomes inessential technique in communication of packets among various nodes in networks. Meanwhile AODV (Ad Hoc On-demand Multipath Distance Vector) creates single-path route between a pair of source and destination nodes. Some researches has done so far to make multipath node-disjoint routing based on AODV protocol. But however their overhead and end-to-end delay are relatively high, while the detail of their code is not available too. This paper proposes a new approach of multipath node-disjoint routing based on AODV protocol. Then the algorithm of analytical model is presented. The extensive results of this algorithm will be presented in the next paper.

Keywords: AODV; MANET; Multipath Routing; Node-disjoint;transmission delay.

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Authors: Ashwag O. Maghraby, Nida N. Khan, Hosnia A. Ahmed, Ghufran N. Brohi, Hind F. Assouli, Jawaher S. Melibari

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The Arabic language is one of the most important languages. Learning it is so important for many people around the world because of its religious and economic importance and the real challenge lies in practicing it without grammatical or syntactical mistakes. This research focused on detecting and correcting the syntactic mistakes of Arabic syntax according to their position in the sentence and focused on two of the main syntactical rules in Arabic: Dual and Plural. It analyzes each sentence in the text, using Stanford CoreNLP morphological analyzer and machine-learning approach in order to detect the syntactical mistakes and then correct it. A prototype of the proposed system was implemented and evaluated. It uses support vector machine (SVM) algorithm to detect Arabic grammatical errors and correct them using the rule-based approach. The prototype system has a far accuracy 81%. In general, it shows a set of useful grammatical suggestions that the user may forget about while writing due to lack of familiarity with grammar or as a result of the speed of writing such as alerting the user when using a plural term to indicate one person.

Keywords: Arabic Language acquisition and learning, natural language processing, morphological analyzer, part-of-speech.

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Authors: Daniel I. Morariu, Radu G. Cretulescu, Lucian N. Vintan

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In text categorization problem the most used method for documents representation is based on words frequency vectors called VSM (Vector Space Model). This representation is based only on words from documents and in this case loses any “word context" information found in the document. In this article we make a comparison between the classical method of document representation and a method called Suffix Tree Document Model (STDM) that is based on representing documents in the Suffix Tree format. For the STDM model we proposed a new approach for documents representation and a new formula for computing the similarity between two documents. Thus we propose to build the suffix tree only for any two documents at a time. This approach is faster, it has lower memory consumption and use entire document representation without using methods for disposing nodes. Also for this method is proposed a formula for computing the similarity between documents, which improves substantially the clustering quality. This representation method was validated using HAC - Hierarchical Agglomerative Clustering. In this context we experiment also the stemming influence in the document preprocessing step and highlight the difference between similarity or dissimilarity measures to find “closer" documents.

Keywords: Text Clustering, Suffix tree documentrepresentation, Hierarchical Agglomerative Clustering

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Authors: Sunee Pongpinigpinyo, Wanchai Rivepiboon

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Word sense disambiguation is one of the most important open problems in natural language processing applications such as information retrieval and machine translation. Many approach strategies can be employed to resolve word ambiguity with a reasonable degree of accuracy. These strategies are: knowledgebased, corpus-based, and hybrid-based. This paper pays attention to the corpus-based strategy that employs an unsupervised learning method for disambiguation. We report our investigation of Latent Semantic Indexing (LSI), an information retrieval technique and unsupervised learning, to the task of Thai noun and verbal word sense disambiguation. The Latent Semantic Indexing has been shown to be efficient and effective for Information Retrieval. For the purposes of this research, we report experiments on two Thai polysemous words, namely  /hua4/ and /kep1/ that are used as a representative of Thai nouns and verbs respectively. The results of these experiments demonstrate the effectiveness and indicate the potential of applying vector-based distributional information measures to semantic disambiguation.

Keywords: Distributional semantics, Latent Semantic Indexing, natural language processing, Polysemous words, unsupervisedlearning, Word Sense Disambiguation.

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Authors: Sang Wook Park, Se Woon Choi, Yousok Kim, Byung Kwan Oh, Hyo Seon Park

Abstract:

Various retrofit techniques for reinforced concrete frame with infill wall have been steadily developed. Among those techniques, strengthening methodology based on diagonal FRP strips (FRP bracings) has numerous advantages such as feasibility of implementing without interrupting the building under operation, reduction of cost and time, and easy application. Considering the safety of structure and retrofit cost, the most appropriate retrofit solution is needed. Thus, the objective of this study is to suggest pareto-optimal solution for existing building using FRP bracings. To find pareto-optimal solution analysis, NSGA-II is applied. Moreover, the seismic performance of retrofit building is evaluated. The example building is 5-storey, 3-bay RC frames with infill wall. Nonlinear static pushover analyses are performed with FEMA 356. The criterion of performance evaluation is inter-story drift ratio at the performance level IO, LS, CP. Optimal retrofit solutions is obtained for 32 individuals and 200 generations. Through the proposed optimal solutions, we confirm the improvement of seismic performance of the example building.

Keywords: Retrofit, FRP bracings, reinforced concrete frame with infill wall, seismic performance evaluation, NSGA-II.

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Authors: Rajendra M Sonar

Abstract:

The future of business intelligence (BI) is to integrate intelligence into operational systems that works in real-time analyzing small chunks of data based on requirements on continuous basis. This is moving away from traditional approach of doing analysis on ad-hoc basis or sporadically in passive and off-line mode analyzing huge amount data. Various AI techniques such as expert systems, case-based reasoning, neural-networks play important role in building business intelligent systems. Since BI involves various tasks and models various types of problems, hybrid intelligent techniques can be better choice. Intelligent systems accessible through web services make it easier to integrate them into existing operational systems to add intelligence in every business processes. These can be built to be invoked in modular and distributed way to work in real time. Functionality of such systems can be extended to get external inputs compatible with formats like RSS. In this paper, we describe a framework that use effective combinations of these techniques, accessible through web services and work in real-time. We have successfully developed various prototype systems and done few commercial deployments in the area of personalization and recommendation on mobile and websites.

Keywords: Business Intelligence, Customer Relationship Management, Hybrid Intelligent Systems, Personalization and Recommendation (P&R), Recommender Systems.

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