World Academy of Science, Engineering and Technology

Authors: R. Poonkuzhali, M. Y. Sanavullah, A. Sabari, T. Dhivyaa

Abstract:

Opportunistic Routing (OR) increases the transmission reliability and network throughput. Traditional routing protocols preselects one or more predetermined nodes before transmission starts and uses a predetermined neighbor to forward a packet in each hop. The opportunistic routing overcomes the drawback of unreliable wireless transmission by broadcasting one transmission can be overheard by manifold neighbors. The first cooperation-optimal protocol for Multirate OR (COMO) used to achieve social efficiency and prevent the selfish behavior of the nodes. The novel link-correlation-aware OR improves the performance by exploiting the miscellaneous low correlated forward links. Context aware Adaptive OR (CAOR) uses active suppression mechanism to reduce packet duplication. The Context-aware OR (COR) can provide efficient routing in mobile networks. By using Cooperative Opportunistic Routing in Mobile Ad hoc Networks (CORMAN), the problem of opportunistic data transfer can be tackled. While comparing to all the protocols, COMO is the best as it achieves social efficiency and prevents the selfish behavior of the nodes.

Keywords: CAOR, COMO, COR, CORMAN, MANET, Opportunistic Routing, Reliability, Throughput.

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Authors: Thossaporn Thossansin

Abstract:

This paper presents the development of a mobile application for students at the Faculty of Information Technology, Rangsit University (RSU), Thailand. RSU upgrades an enrollment process by improving its information systems. Students can download the RSU APP easily in order to access the RSU substantial information. The reason of having a mobile application is to help students to access the system regardless of time and place. The objectives of this paper include: 1. To develop an application on iOS platform for those students at the Faculty of Information Technology, Rangsit University, Thailand. 2. To obtain the students’ perception towards the new mobile app. The target group is those from the freshman year till the senior year of the faculty of Information Technology, Rangsit University. The new mobile application, called as RSU APP, is developed by the department of Information Technology, Rangsit University. It contains useful features and various functionalities particularly on those that can give support to students. The core contents of the app consist of RSU’s announcement, calendar, events, activities, and ebook. The mobile app is developed on the iOS platform. The user satisfaction is analyzed from the interview data from 81 interviewees as well as a Google application like a Google form which 122 interviewees are involved. The result shows that users are satisfied with the application as they score it the most satisfaction level at 4.67 SD 0.52. The score for the question if users can learn and use the application quickly is high which is 4.82 SD 0.71. On the other hand, the lowest satisfaction rating is in the app’s form, apps lists, with the satisfaction level as 4.01 SD 0.45.

Keywords: Mobile application, development of mobile application, framework of mobile development, software development for mobile devices.

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Authors: P. Sengottuvelan, T. Gopalakrishnan

Abstract:

Web Usage Mining is the application of data mining techniques to find usage patterns from web log data, so as to grasp required patterns and serve the requirements of Web-based applications. User’s expertise on the internet may be improved by minimizing user’s web access latency. This may be done by predicting the future search page earlier and the same may be prefetched and cached. Therefore, to enhance the standard of web services, it is needed topic to research the user web navigation behavior. Analysis of user’s web navigation behavior is achieved through modeling web navigation history. We propose this technique which cluster’s the user sessions, based on the K-medoids technique.

Keywords: Clustering, K-medoids, Recommendation, User Session, Web Usage Mining.

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Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

Abstract:

Background modeling and subtraction in video analysis has been widely used as an effective method for moving objects detection in many computer vision applications. Recently, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are the most frequently occurred problems in the practical situation. This paper presents a favorable two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean value of each RGB color channel. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the output of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate very competitive performance compared to previous models.

Keywords: Background subtraction, codebook model, local binary pattern, dynamic background, illumination changes.

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Authors: M. Mohemmed Sha, T. Manesh, A. Mohamed Mustaq Ahmed

Abstract:

In this corporate world, the technology of Web services has grown rapidly and its significance for the development of web based applications gradually rises over time. The success of Business to Business integration rely on finding novel partners and their services in a global business environment. However, the selection of the most suitable Web service from the list of services with the identical functionality is more vital. The satisfaction level of the customer and the provider’s reputation of the Web service are primarily depending on the range it reaches the customer’s requirements. In most cases, the customer of the Web service feels that he is spending for the service which is undelivered. This is because the customer always thinks that the real functionality of the web service is not reached. This will lead to change of the service frequently. In this paper, a framework is proposed to evaluate the Quality of Service (QoS) and its cost that makes the optimal correlation between each other. In addition, this research work proposes some management decision against the functional deviancy of the web service that is guaranteed at time of selection.

Keywords: Web service, service level agreement, quality of a service, cost of a service, QoS, CoS, SOA, WSLA, WsRF.

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Authors: N. Ghatwary, A. Ahmed, H. Jalab

Abstract:

In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.

Keywords: Fractional differential (FD), Computed Tomography (CT), fusion.

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Authors: Radek Malinský, Ivan Jelínek

Abstract:

The current web has become a modern encyclopedia, where people share their thoughts and ideas on various topics around them. This kind of encyclopedia is very useful for other people who are looking for answers to their questions. However, with the growing popularity of social networking and blogging and ever expanding network services, there has also been a growing diversity of technologies along with a different structure of individual web sites. It is therefore difficult to directly find a relevant answer for a common Internet user. This paper presents a web application for the real-time end-to-end analysis of selected Internet trends where the trend can be whatever the people post online. The application integrates fully configurable tools for data collection and analysis using selected webometric algorithms, and for its chronological visualization to user. It can be assumed that the application facilitates the users to evaluate the quality of various products that are mentioned online.

Keywords: Trend, visualizer, web analysis, web 2.0.

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Authors: Shimal Das, Dibyendu Ghoshal

Abstract:

Fractal based digital image compression is a specific technique in the field of color image. The method is best suited for irregular shape of image like snow bobs, clouds, flame of fire; tree leaves images, depending on the fact that parts of an image often resemble with other parts of the same image. This technique has drawn much attention in recent years because of very high compression ratio that can be achieved. Hybrid scheme incorporating fractal compression and speedup techniques have achieved high compression ratio compared to pure fractal compression. Fractal image compression is a lossy compression method in which selfsimilarity nature of an image is used. This technique provides high compression ratio, less encoding time and fart decoding process. In this paper, fractal compression with quad tree and DCT is proposed to compress the color image. The proposed hybrid schemes require four phases to compress the color image. First: the image is segmented and Discrete Cosine Transform is applied to each block of the segmented image. Second: the block values are scanned in a zigzag manner to prevent zero co-efficient. Third: the resulting image is partitioned as fractals by quadtree approach. Fourth: the image is compressed using Run length encoding technique.

Keywords: Fractal coding, Discrete Cosine Transform, Iterated Function System (IFS), Affine Transformation, Run length encoding.

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Authors: Li Feng

Abstract:

In IEEE 802.11 networks, it is well known that the traditional time-domain contention often leads to low channel utilization. The first frequency-domain contention scheme, the time to frequency (T2F), has recently been proposed to improve the channel utilization and has attracted a great deal of attention. In this paper, we present the latest research progress on the weighed frequency-domain contention. We compare the basic ideas, work principles of these related schemes and point out their differences. This paper is very useful for further study on frequency-domain contention.

Keywords: 802.11, wireless LANs, frequency-domain contention, T2F.

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Authors: Chang-Uk Kwak, Sun-Joong Kim, Seong-Bae Park, Sang-Jo Lee

Abstract:

This paper proposes a method of learning topics for broadcasting contents. There are two kinds of texts related to broadcasting contents. One is a broadcasting script, which is a series of texts including directions and dialogues. The other is blogposts, which possesses relatively abstracted contents, stories, and diverse information of broadcasting contents. Although two texts range over similar broadcasting contents, words in blogposts and broadcasting script are different. When unseen words appear, it needs a method to reflect to existing topic. In this paper, we introduce a semantic vocabulary expansion method to reflect unseen words. We expand topics of the broadcasting script by incorporating the words in blogposts. Each word in blogposts is added to the most semantically correlated topics. We use word2vec to get the semantic correlation between words in blogposts and topics of scripts. The vocabularies of topics are updated and then posterior inference is performed to rearrange the topics. In experiments, we verified that the proposed method can discover more salient topics for broadcasting contents.

Keywords: Broadcasting script analysis, topic expansion, semantic correlation analysis, word2vec.

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Authors: Kriddikorn Kaewwongsri, Nikom Suvonvorn

Abstract:

In the deep south of Thailand, checkpoints for people verification are necessary for the security management of risk zones, such as official buildings in the conflict area. In this paper, we propose an automatic checkpoint system that verifies persons using information from ID cards and facial features. The methods for a person’s information abstraction and verification are introduced based on useful information such as ID number and name, extracted from official cards, and facial images from videos. The proposed system shows promising results and has a real impact on the local society.

Keywords: Face comparison, card recognition, OCR, checkpoint system, authentication.

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: Active Contour, Bayesian, Echocardiographic image, Feature vector.

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

Abstract:

One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm, to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: Rough Set Theory, Attribute Reduction, Fuzzy Logic, Memetic Algorithms, Record to Record Algorithm, Great Deluge Algorithm.

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Authors: Aedah A. Rahman, Nurdatillah Hasim

Abstract:

Software quality issues require special attention especially in view of the demands of quality software product to meet customer satisfaction. Software development projects in most organisations need proper defect management process in order to produce high quality software product and reduce the number of defects. The research question of this study is how to produce high quality software and reducing the number of defects. Therefore, the objective of this paper is to provide a framework for managing software defects by following defined life cycle processes. The methodology starts by reviewing defects, defect models, best practices, and standards. A framework for defect management life cycle is proposed. The major contribution of this study is to define a defect management roadmap in software development. The adoption of an effective defect management process helps to achieve the ultimate goal of producing high quality software products and contributes towards continuous software process improvement.

Keywords: Defects, defect management, life cycle process, software quality.

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Authors: Ivan Stanev, Maria Koleva

Abstract:

Common Platform for Automated Programming (CPAP) is defined in details. Two versions of CPAP are described: Cloud based (including set of components for classic programming, and set of components for combined programming); and Knowledge Based Automated Software Engineering (KBASE) based (including set of components for automated programming, and set of components for ontology programming). Four KBASE products (Module for Automated Programming of Robots, Intelligent Product Manual, Intelligent Document Display, and Intelligent Form Generator) are analyzed and CPAP contributions to automated programming are presented.

Keywords: Automated Programming, Cloud Computing, Knowledge Based Software Engineering, Service Oriented Architecture.

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Authors: Si-Gwan Kim

Abstract:

Small-size and low-power sensors with sensing, signal processing and wireless communication capabilities is suitable for the wireless sensor networks. Due to the limited resources and battery constraints, complex routing algorithms used for the ad-hoc networks cannot be employed in sensor networks. In this paper, we propose node-disjoint multi-path hexagon-based routing algorithms in wireless sensor networks. We suggest the details of the algorithm and compare it with other works. Simulation results show that the proposed scheme achieves better performance in terms of efficiency and message delivery ratio.

Keywords: Clustering, multi-path, routing protocol, sensor network.

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Authors: G. Kalpanadevi, S. Ravimaran, M. Shanmugapriya

Abstract:

A Reconfigurable Wilkinson power divider is proposed in this paper. In existing system only a limited number of bandwidth is used at the output ports, in the proposed Wilkinson power divider different band of frequencies are obtained by using PIN diode. By tuning the PIN diode, different frequencies are achieved. The size of the power divider is reduced for the operating frequency and increases the fractional bandwidth.

Keywords: Isolation loss, PIN diode, Reconfigurable Wilkinson power divider and WLAN applications.

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Authors: G. Rajesh, B. Vinayaga Sundaram, C. Aarthi

Abstract:

In wireless sensor network, sensor node transmits the sensed data to the sink node in multi-hop communication periodically. This high traffic induces congestion at the node which is present one-hop distance to the sink node. The packet transmission and reception rate of these nodes should be very high, when compared to other sensor nodes in the network. Therefore, the energy consumption of that node is very high and this effect is known as the “funneling effect”. The tree based-data aggregation technique (TBDA) is used to reduce the energy consumption of the node. The throughput of the overall performance shows a considerable decrease in the number of packet transmissions to the sink node. The proposed scheme, TBDA, avoids the funneling effect and extends the lifetime of the wireless sensor network. The average case time complexity for inserting the node in the tree is O(n log n) and for the worst case time complexity is O(n2).

Keywords: Data Aggregation, Funneling Effect, Traffic Congestion, Wireless Sensor Network.

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Authors: Navdeep Goel, Salvador Gabarda

Abstract:

Digital images are widely used in computer applications. To store or transmit the uncompressed images requires considerable storage capacity and transmission bandwidth. Image compression is a means to perform transmission or storage of visual data in the most economical way. This paper explains about how images can be encoded to be transmitted in a multiplexing time-frequency domain channel. Multiplexing involves packing signals together whose representations are compact in the working domain. In order to optimize transmission resources each 4 × 4 pixel block of the image is transformed by a suitable polynomial approximation, into a minimal number of coefficients. Less than 4 × 4 coefficients in one block spares a significant amount of transmitted information, but some information is lost. Different approximations for image transformation have been evaluated as polynomial representation (Vandermonde matrix), least squares + gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev polynomials or singular value decomposition (SVD). Results have been compared in terms of nominal compression rate (NCR), compression ratio (CR) and peak signal-to-noise ratio (PSNR) in order to minimize the error function defined as the difference between the original pixel gray levels and the approximated polynomial output. Polynomial coefficients have been later encoded and handled for generating chirps in a target rate of about two chirps per 4 × 4 pixel block and then submitted to a transmission multiplexing operation in the time-frequency domain.

Keywords: Chirp signals, Image multiplexing, Image transformation, Linear canonical transform, Polynomial approximation.

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Authors: Peifei Zhu, Zisheng Li, Yasuki Kakishita, Mayumi Suzuki, Tomoaki Chono

Abstract:

Segmentation of left ventricle (LV) from cardiac ultrasound images provides a quantitative functional analysis of the heart to diagnose disease. Active Shape Model (ASM) is widely used for LV segmentation, but it suffers from the drawback that initialization of the shape model is not sufficiently close to the target, especially when dealing with abnormal shapes in disease. In this work, a two-step framework is improved to achieve a fast and efficient LV segmentation. First, a robust and efficient detection based on Hough forest localizes cardiac feature points. Such feature points are used to predict the initial fitting of the LV shape model. Second, ASM is applied to further fit the LV shape model to the cardiac ultrasound image. With the robust initialization, ASM is able to achieve more accurate segmentation. The performance of the proposed method is evaluated on a dataset of 810 cardiac ultrasound images that are mostly abnormal shapes. This proposed method is compared with several combinations of ASM and existing initialization methods. Our experiment results demonstrate that accuracy of the proposed method for feature point detection for initialization was 40% higher than the existing methods. Moreover, the proposed method significantly reduces the number of necessary ASM fitting loops and thus speeds up the whole segmentation process. Therefore, the proposed method is able to achieve more accurate and efficient segmentation results and is applicable to unusual shapes of heart with cardiac diseases, such as left atrial enlargement.

Keywords: Hough forest, active shape model, segmentation, cardiac left ventricle.

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Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

Abstract:

Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars, and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: Remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction.

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Authors: A. Ben Yaghlane, M. N. Azaiez, M. Mrad

Abstract:

We investigate the large scale of networks in the context of network survivability under attack. We use appropriate techniques to evaluate and the attacker-based- and the defenderbased- network survivability. The attacker is unaware of the operated links by the defender. Each attacked link has some pre-specified probability to be disconnected. The defender choice is so that to maximize the chance of successfully sending the flow to the destination node. The attacker however will select the cut-set with the highest chance to be disabled in order to partition the network. Moreover, we extend the problem to the case of selecting the best p paths to operate by the defender and the best k cut-sets to target by the attacker, for arbitrary integers p,k>1. We investigate some variations of the problem and suggest polynomial-time solutions.

Keywords: Defense/attack strategies, large scale, networks, partitioning a network.

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Authors: Ekrem Aksoy, Nihat Adar, Selçuk Canbek

Abstract:

Ambient Computing or Ambient Intelligence (AmI) is emerging area in computer science aiming to create intelligently connected environments and Internet of Things. In this paper, we propose communication middleware architecture for AmI. This middleware architecture addresses problems of communication, networking, and abstraction of applications, although there are other aspects (e.g. HCI and Security) within general AmI framework. Within this middleware architecture, any application developer might address HCI and Security issues with extensibility features of this platform.

Keywords: AmI, ambient computing, middleware, distributedsystems, software-defined networking.

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Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

Abstract:

With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers, but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a highquality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: Fake reviews, Feature collection, Opinion spam, Spam detection.

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

Abstract:

The practical efficient approach is suggested for estimation of the seismoacoustic sources energy in C-OTDR monitoring systems. This approach is represents the sequential plan for confidence estimation both the seismoacoustic sources energy, as well the absorption coefficient of the soil. The sequential plan delivers the non-asymptotic guaranteed accuracy of obtained estimates in the form of non-asymptotic confidence regions with prescribed sizes. These confidence regions are valid for a finite sample size when the distributions of the observations are unknown. Thus, suggested estimates are non-asymptotic and nonparametric, and also these estimates guarantee the prescribed estimation accuracy in form of prior prescribed size of confidence regions, and prescribed confidence coefficient value.

Keywords: C-OTDR-system, guaranteed estimates, nonparametric estimation, sequential confidence estimation, multichannel monitoring systems.

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

Abstract:

In this paper, we propose the variational EM inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multiclass. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: Bayesian rule, Gaussian process classification model with multiclass, Gaussian process prior, human action classification, laplace approximation, variational EM algorithm.

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Authors: T. Aydin, M. F. Alaeddinoglu

Abstract:

People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatiotemporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newlyformed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: Apriori algorithm, association rules, data mining, spatio-temporal data.

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Authors: Loke Mun Sei

Abstract:

Software testing has become a mandatory process in assuring the software product quality. Hence, test management is needed in order to manage the test activities conducted in the software test life cycle. This paper discusses on the challenges faced in the software test life cycle, and how the test processes and test activities, mainly on test cases creation, test execution, and test reporting is being managed and automated using several test automation tools, i.e. Jira, Robot Framework, and Jenkins.

Keywords: Test automation tools, test case, test execution, test reporting.

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Authors: K. S. Neelukumari, K. B. Jayanthi

Abstract:

In this paper genetic based test data compression is targeted for improving the compression ratio and for reducing the computation time. The genetic algorithm is based on extended pattern run-length coding. The test set contains a large number of X value that can be effectively exploited to improve the test data compression. In this coding method, a reference pattern is set and its compatibility is checked. For this process, a genetic algorithm is proposed to reduce the computation time of encoding algorithm. This coding technique encodes the 2n compatible pattern or the inversely compatible pattern into a single test data segment or multiple test data segment. The experimental result shows that the compression ratio and computation time is reduced.

Keywords: Backtracking, test data compression (TDC), x-filling, x-propagating and genetic algorithm.

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

Abstract:

In the context of the handwriting recognition, we propose an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods. The Distribution parameters, the centered moments of the different projections of the different segments, the centered moments of the word image coding according to the directions of Freeman, and the Barr features applied binary image of the word and on its different segments. The classification is achieved by a multi layers perceptron. A detailed experiment is carried and satisfactory recognition results are reported.

Keywords: Handwritten word recognition, neural networks, image processing, pattern recognition, features extraction.

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