Data Split and its Effect on Optimizing the Database Access Time in Mobile Networks

Mehdi Samie, Hassan Moradi, and Nafiseh Saberi

Iran Telecommunication Research Center, Tehran, Iran

Abstract: In this paper, we present a method that reduces access time to subscriber database in mobile networks. At first, we assume a model to study the mathematical analysis about database and then we will show that this method can optimize the access time to database. So, we analyze related concepts and present an analytical model to formulate service time for a database. Also, we have studied and analyzed the effect of environment on the access time to database which is reviewed here. The environment is mobile network and the database is a mobile system database. Mobile network has some of special parameters which define both the rate and type of queries to database. We have analyzed environment parameters and formulated the access time to database based on them. Our method is data split. Compared to flatly storing of data, this method is influenced by the new environment parameters. These parameters include rate of access to split parts, split ratio of data and etc. We formulated our method and showed reduction of access time. Furthermore, we present some diagrams which are showing the access time versus split ratio. These diagrams also are showing conditions and limitations which split ratio optimizes access time. Finally, we have simulated our idea and observed that simulation results confirm theoretical results.

 Keywords: Mobile database system, mobile network, subscriber database, data split, access rate, access time, service time.

Adaptive Optimizing of Hello Messages in Wireless Ad-Hoc Networks

Essam Natsheh, Adznan Jantan, Sabira Khatun, and Shamala Subramaniam

 Department of Computer and Communication Systems, University Putra Malaysia, Malaysia

 Abstract: Routing is an important functional aspect in wireless ad-hoc networks that handles discovering and maintaining the paths between nodes within a network. Due to nodes mobility, the efficiency of a dynamic ad-hoc routing protocol depends highly on updating speed of network topology changes. To achieve continuous updated routing tables, the nodes periodically broadcast short hello messages to their neighbors. Although benefits of these messages have been proven, many studies show some drawbacks for these messages. In this paper, we adaptively optimize the frequent needs of those messages using a fuzzy logic system. The proposed fuzzy algorithm used to model the uncertainty measurements for updating local connectivity successfully in time. Extensive performance analysis via simulation proves the effectiveness of the proposed method to improve the accuracy of neighborhood information and hence the overall network performance.  

Keywords: Ad-hoc networks, AODV, beacon messages, fuzzy systems, intelligent networks.

Quantum Computing for Solving a System of Nonlinear Equations over GF(q)

 Essam Al Daoud

Computer Science Department, Zarqa Private University, Jordan

Abstract: Grover’s quantum search algorithm is one of the most widely studied and has produced results in some search applications faster than their classical counterpart by a square-root. This paper modifies Grover’s algorithm to solve nonlinear equations over Galois Finite field GF(q) in O() iteration, while the best classical general solution takes O(2nm) iteration. The modification is done by using a register for each variable and represent it by n qubits. The paper also introduces the implementation of the suggested algorithm by using the simulator QCL 5.1.

Keywords: Quantum computing, quantum operations, nonlinear equations, quantum simulator.

Tracking Morphophonemic Transformation in Arabic Word Generation and Root Extraction

Sane Yagi¹ and Jim Yaghi²

¹Department of Linguistics & Phonetics, University of Jordan, Amman, Jordan

²Computer Science Department, MacQuire University, Sydney, Australia

Abstract: Performing root-based searching, concor­dancing, and grammar checking in Arabic re­quires an efficient method for matching stems with roots and vice versa. Such mapping is com­plicated by the hundreds of manifestations of the same root; the radicals often undergo re­placement, fusion, inversion, and/or deletion. It is a challenge, therefore, to keep track of original radicals. An algorithm based on methods used by native speakers is proposed here to track root radicals in the generation process and the subsequent reversal process of root extraction. Verb roots are classified by the types of their radicals and the stems they generate. Roots are molded with morphosemantic and morphosyntactic patterns to generate stems modified for tense, voice, and mode, affixed for different subject number, gender, and person. The surface forms of applicable morphophonemic transformation are then derived using finite state machines. This paper defines what is meant by `stem', describes a stem generation engine that the authors developed, and outlines how a generated stem database is compiled for all Arabic verbs.

 Keywords: Arabic, morphology, generation, extraction, root, finite state.

Morpheme Based Language Model for Tamil Speech Recognition System

Selvarajan Saraswathi and Thekkumpurath Geetha

Department of Computer Science and Engineering, Anna University, India

 Abstract: This paper describes the design of a morpheme based language model for Tamil language. It aims to alleviate the main problems encountered in processing the Tamil language, like enormous vocabulary growth caused by large number of different forms derived for one word. The size of the vocabulary is reduced by decomposing the words into stems and endings and storing these sub word units (morphemes) for training the language model The modified morpheme based language model was applied to avoid the ambiguities in the recognized Tamil words. The perplexity, Out Of Vocabulary (OOV) rate and Word Error Rate (WER) parameters were obtained to check the efficiency of the model for Tamil speech recognition system. The results were compared with the traditional word based statistical bigram and trigram language models.  From the results, it was analyzed that the modified morpheme based trigram model with Katz back off smoothing effect improved the performance of the Tamil speech recognition system when compared to the word based N-Gram language models.

 Keywords: Language model, morphemes, perplexity, out of vocabulary rate, word error rate.

Software Reuse for Mobile Robot Applications Through Analysis Patterns

Dayang Jawawi¹, Safaai Deris¹, and Rosbi Mamat²

¹Department of Software Engineering, Universiti Teknologi Malaysia, Malaysia

²Department of Mechatronics and Robotics Engineering, Universiti Teknologi Malaysia, Malaysia

 Abstract: Software analysis pattern is an approach of software reuse which provides a way to reuse expertise that can be used across domains at early level of development. Developing software for a mobile robot system involves multi-disciplines expert knowledge which includes embedded systems, real-time software issues, control theories and artificial intelligence aspects. This paper focuses on analysis patterns as a means to facilitate mobile robot software knowledge reuse by capturing conceptual models in those domains in order to allow reuse across applications. The use of software analysis patterns as a means to facilitate Autonomous Mobile Robots (AMR) software knowledge reuse through component-based software engineering is proposed. The software analysis patterns for AMR were obtained through a pattern mining process, and documented using a standard catalogue template. These analysis patterns are categorized according to hybrid deliberate layered architecture of robot software: Reactive layer, supervisor layer and deliberative layer. Particularly, the analysis patterns in the reactive layer are highlighted and presented. The deployment of the analysis patterns are illustrated and discussed using an AMR software case study. To verify the existence of the pattern in AMR systems, pattern-based reverse engineering was performed on two existing AMR systems. The reuse potential of these patterns is evaluated by measuring the reusability of components in the analysis patterns.

 Keywords: Analysis pattern, software reuse, component-based development, pattern-based reverse engineering.

Distributed Network Management with Secured Mobile Agent Support

Mohammed Ibrahim

Faculty of Engineering and Information Technology, Taiz University, Yemen

 Abstract: Network computing is changing rapidly these days. The mobile agent technology invented to overcome the complexity resulting due to the increasing size of network components that rises new network management schemes. Many prototype applications providing mobile agent capability have been proposed for being used in network management. E-commerce and information retrieval are some of them. The motive behind the agent mobility is that, it addresses some limitations faced by traditional centralized client-server architecture, which are mainly, minimizing bandwidth consumption, supporting network load balancing, enhancing scalability as well as flexibility, increase fault-tolerance and solve problems caused by unreliable network connections. However, despite its benefits, mobile agent systems still pose security threats.  In this paper, we propose a mobile agent architecture that supports flexible and reliable interaction of autonomous components in a distributive network environment. We present a management scheme in a hierarchical level that provides to a user with a reliable and flexible global access to internet/network information services. We further describe a protection mechanism to both agents and their hosting sites of execution called agent servers.

 Keywords: Mobile agent, domain manager, manager of managers, agent server, agent transfer protocol.

Genetic Programming Approach for Multi-Category Pattern Classification Applied to

Network Intrusions Detection

Kamel Faraoun¹ and Aoued Boukelif²

¹Evolutionary Engineering and Distributed IS Laboratory, University of Sidi Bel Abbès, Algeria

²Communication Networks, Architectures, and Multimedia Lab, University of Sidi Bel Abbès, Algeria

Abstract: This paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically coevolving a population of non-linear transformations on the input data to be classified, and map them to a new space with a reduced dimension, in order to get a maximum inter-classes discrimination. The classification of new samples is then performed on the transformed data, and so become much easier. Contrary to the existing GP-classification techniques, the proposed one use a dynamic repartition of the transformed data in separated intervals, the efficacy of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were first performed using the Fisher’s Iris dataset, and then, the KDD’99 Cup dataset was then used to study the intrusion detection and classification problem. Obtained results demonstrate that the proposed genetic approach outperform the existing GP-classification methods, and give a very accepted results compared to other existing techniques.

 Keywords: Genetic programming, patterns classification, intrusion detection.

A Rough-Fuzzy Hybrid Algorithm for Computer Intrusion Detection

Witcha Chimphlee¹, Abdul Hanan Abdullah², Mohd Noor Md Sap², Siriporn Chimphlee¹, and Surat Srinoy¹

¹Faculty of Science and Technology, Suan Dusit Rajabhat University, Thailand

²Faculty of Computer Science and Information Systems, University Technology of Malaysia, Malaysia

 Abstract: In this paper, we propose an intrusion detection method that combines rough sets theory and fuzzy c-means for anomaly detection. The first step consists of attribute selection which is based on rough set theory for each of the 5 classes of intrusions in the Defense Advanced Research Projects Agency (DARPA) data is identified. The next phase is clustering by using fuzzy c-means; we are using rough sets for cleaning and to filtering out redundant, spurious information. Fuzzy c-means allow objects to belong to several clusters simultaneously, with different degrees of membership. Our method is an accurate model for handling complex attack patterns in large networks. We used data set from 1999 Knowledge Discovery and Data mining (KDD) intrusion detection contest. The main goal of this paper is to apply this method to increase the efficiency of a given intrusion detection model and to be able to reduce the data set by looking for overlapping categories and also to filter in the desired ones.

Keywords: Network security, intrusion detection system, anomaly detection, rough sets, fuzzy c-means.

GeoW@re: A Multipurpose Geo-Based Groupware Platform Using Integrated Approach

Mohamed Dbouk

Faculty of Sciences, Lebanese University, Lebanon

 Abstract: This paper describes an integrated groupware platform, called “GeoW@re”. GeoW@re approach tends to coordinate competitive works launched by various independent organizations; the main issue is to increase organizations productivity by sharing knowledge. It deals with multipurpose geo-referenced entities (i. e., urban, construction geographic-related projects). GeoW@re can be seen as multipartite inter-organizations coordinator (coordination-board). It incites organizations to adhere to some devoted business rules (a specific coordination protocol). GeoW@re is urban-planning stereotyped tool; most of the concerned organizations apply GIS facilities. GeoW@re consists of an open component-based system, built on top of Linux/Apache technologies. It provides an easy internet support integrating groupware and data warehousing facilities. It was prototyped and validated with satisfaction. Future works consists to find out an ad-equation between GeoW@re and e-learning discipline.

 Keywords: Groupware, computer support cooperative work, GIS, urban planning, web, data warehousing.

A Learning-Classification Based Approach for Word Prediction

Hisham Al-Mubaid

Computer Science Department, University of Houston-Clear Lake, USA

 Abstract: Word prediction is an important NLP problem in which we want to predict the correct word in a given context. Word completion utilities, predictive text entry systems, writing aids, and language translation are some of common word prediction applications. This paper presents a new word prediction approach based on context features and machine learning.  The proposed method casts the problem as a learning-classification task by training word predictors with highly discriminating features selected by various feature selection techniques. The contribution of this work lies in the new way of presenting this problem, and the unique combination of a top performer in machine learning, svm, with various feature selection techniques MI, X2, and more.  The method is implemented and evaluated using several datasets. The experimental results show clearly that the method is effective in predicting the correct words by utilizing small contexts. The system achieved impressive results, compared with similar work; the accuracy in some experiments approaches 91% correct predictions.

 Keywords: Word prediction, word completion, machine learning, natural language processing.

Equi-Join Table Optimization Technique for Temporal Data

Mohd Shafry Mohd Rahim, Norazrin binti Kurmin, Mohd Taib Wahid, and Daut Daman

Faculty of Computer Science and Information Systems, University Technology Malaysia, Malaysia

Abstract: Temporal data management is significant to applications such as environmental management systems. It is to ensure that the process of data storing, retrieving and manipulation can be conducted in an efficient manner. The main focus of this research is on the retrieval of temporal data. Evidently, in the area of temporal data retrieval, the issue that is given most attention by researchers is how to speed up data retrieval time. In our research, we attempt to tackle this issue in an information system which stores hydrological data using a database that utilizes the cube method. As an end result, we managed to establish a technique called the equi-join table optimization technique that was implemented to an existing database system. This technique will also analyze a query statement with several possible query executions to determine the most possible optimum implementation. The outcome indicated that there is indeed an improvement concerning the data retrieval response time.

 Keywords: Database, information retrieval, optimization, temporal data management, hydrological data.

Segmentation of Magnetic Resonance Brain Images Using Edge and Region Cooperation

Characterization of Stroke Lesions

Yazid Cherfa,⁽¹⁾ Assia Cherfa,⁽¹⁾  Yacine Kabir,⁽¹⁾ Sammyh Kassous,⁽¹⁾

Assia Jaillard,⁽⁴⁾ Michel Dojat,⁽³⁾ and Catherine Garbay⁽²⁾

⁽¹⁾ Laboratoire LARIM, Dpt d’électronique, Université de Blida, Algérie

⁽²⁾ Laboratoire CLIPS, Université Joseph Fourier, France

⁽³⁾ Unité mixte INSERM/UJF 594, CHU, France

⁽⁴⁾ CHU Michalon de Grenoble, Département Neurologie, France

Abstract: This paper describes an MR brain image segmentation system based on an edge and region cooperation. After an appropriate preprocessing using an improvement of image quality and brain insulation, we apply the cooperative segmentation to the preprocessed image. This segmentation is evaluated thereafter on reference image. The procedure is applied to several MR images from different subjects having undergone a cerebral vascular attack (stroke), this in order to extract the lesion and characterise them. Furthermore a comparison of our results with these obtained from manual measurements carried out by neurologists is presented.

 Keywords: Magnetic resonance image, anisotropic filtering, brain segmentation, stroke, characterisation.