Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
A New Approach to Develop Collaborative Ontology based BPMN Case Study: Generation of Fiscal Code
Статья научная
Collaborative platforms are becoming a necessity for enterprises and organizations since they have become extended and have to work with other organizations in joint projects. Knowledge management is considered one of the critical successes of collaborative tools and platforms, especially collaborative ontologies. This paper aims to propose a new approach for developing a collaborative ontology that can be used to support a collaborative platform. Our approach begins with the idea of using a business process model of the collaborative situation, represented in BPMN notation, and to transform it into a collaborative ontology. Our collaborative process will be modeled collaboratively, and its transformation to an ontology will be done through a first transformation to Subject oriented BPM (S-BPM). We also proposed to validate our collaborative ontology using competency questions that will be formulated at the beginning by domain experts and the ontologist and verified at the end by the ontologist. Fiscal code generation of a newborn was adopted as a case study to prove the importance of this approach. Compared to other approaches cited in the literature, our approach allows the construction of a collaborative ontology, that does not need enrichment perspectives, and that involves all collaborative partners during its construction process.
Бесплатно
A New Architecture for Making Moral Agents Based on C4.5 Decision Tree Algorithm
Статья научная
Regarding to the influence of robots in the various fields of life, the issue of trusting to them is important, especially when a robot deals with people directly. One of the possible ways to get this confidence is adding a moral dimension to the robots. Therefore, we present a new architecture in order to build moral agents that learn from demonstrations. This agent is based on Beauchamp and Childress’s principles of biomedical ethics (a type of deontological theory) and uses decision tree algorithm to abstract relationships between ethical principles and morality of actions. We apply this architecture to build an agent that provides guidance to health care workers faced with ethical dilemmas. Our results show that the agent is able to learn ethic well.
Бесплатно
A New Centrality Measure for Tracking Online Community in Social Network
Статья научная
This paper presents a centrality measurement and analysis of the social networks for tracking online community. The tracking of single community in social networks is commonly done using some of the centrality measures employed in social network community tracking. The ability that centrality measures have to determine the relative position of a node within a network has been used in previous research work to track communities in social networks using betweenness, closeness and degree centrality measures. It introduces a new metric K-path centrality, and a randomized algorithm for estimating it, and shows empirically that nodes with high K-path centrality have high node betweenness centrality.
Бесплатно
A New Clustering Algorithm for Face Classification
Статья научная
In This paper, we proposed new clustering algorithm depend on other clustering algorithm ideas. The proposed algorithm idea is based on getting distance matrix, then the exclusion of the matrix points which will be clustered by saving the location (row, column) of these points and determine the minimum distance of these points which will be belongs the group (class) and keep the other points which are not clustering yet. The propose algorithm is applied to image data base of the human face with different environment (direction, angles... etc.). These data are collected from different resource (ORL site and real images collected from random sample of Thi_Qar city population in lraq). Our algorithm has been implemented on three types of distance to calculate the minimum distance between points (Euclidean, Correlation and Minkowski distance) .The efficiency ratio of proposed algorithm has varied according to the data base and threshold, the efficiency of our algorithm is exceeded (96%). Matlab (2014) has been used in this work.
Бесплатно
A New Dynamic Data Cleaning Technique for Improving Incomplete Dataset Consistency
Статья научная
This paper presents a new approach named Dynamic Data Cleaning (DDC) aims to improve incomplete dataset consistency by identifying, reconstructing and removing inconsistent data objects for future data analysis process. The proposed DDC approach consists of three methods: Identify Normal Object (INO), Reconstruct Normal Object (RNO) and Dataset Quality Measure (DQM). The first method INO divides the incomplete dataset into normal objects and abnormal objects (outliers) based on degree of missing attributes values in each individual object. Second, the (RNO) method reconstructs missed attributes values in the normal objects by the closest object based on a distance metric and removes inconsistent data objects (outliers) with higher missed data. Finally, the DQM method measures the consistency and inconsistency among the objects in improved dataset with and without outlier. Experimental results show that the proposed DDC approach is suitable to identify and reconstruct the incomplete data objects for improving dataset consistency from lower to higher level without user knowledge.
Бесплатно
A New Fault Detection Method Using End-to-End Data and Sequential Testing for Computer Networks
Статья научная
Fault localization, a central part of network fault management, is a process of deducing the exact source of a failure from a set of observed failure indications. in the network, end systems and hosts communicate through routers and links connecting them. When a link or a router faces with a fault, the information sent through these components will be damaged. Hence, faulty components in a network need to be detected and repaired to sustain the health of the network. In this paper we introduce an end to end method that detect and repair the faulty components in the network. The proposed method is a heuristic algorithm that uses the embedded information retrieved from disseminated data over the network to detect and repair faulty components. Simulation results show that our heuristic scheme only requires testing a very small set of network components to detect and repair all faults in the network.
Бесплатно
Статья научная
The advantages and disadvantages of BP neural network and grey Verhulst model for time series prediction are analyzed respectively, this article proposes a new time series forecasting model for the time series growth in S-type or growth being saturated. From the data fitting's viewpoint, the new model named grey Verhulst neural network is established based on grey Verhulst model and BP neural network. Firstly, the Verhulst model is mapped to a BP neural network, the corresponding relationships between grey Verhulst model parameters and BP network weights is established. Then, the BP neural network is trained by means of BP algorithm, when the BP network convergences, the optimized weights can be extracted, and the optimized grey Verhulst neural network model can be obtained. The experiment results show that the new model is effective with the advantages of high precision, less samples required and simple calculation, which makes full use of the similarities and complementarities between grey system model and BP neural network to settle the disadvantage of applying grey model and neural network separately. It is concluded that grey Verhulst neural network is a feasible and effective modeling method for the time series increasing in the curve with S-type.
Бесплатно
A New Measure of the Calculation of Semantic Distance between Ontology Concepts
Статья научная
Semantic similarity calculation models are found in many applications, with the aim to give additional knowledge to reason about their data. The choice of a similarity measure is quite crucial for a successful implementation of reasoning. In this work, we present an update of similarity calculation presented by Wu and Palmer which is considered the fastest in time generation of similarity. The results obtained show that the measure produced provides a significant improvement in the relevance of the values produced for the similarity of two concepts in ontology.
Бесплатно
Статья научная
One of the most important concepts in multi programming Operating Systems is scheduling. It helps in choosing the processes for execution. Round robin method is one of the most important algorithms in scheduling. It is the most popular algorithm due to its fairness and starvation free nature towards the processes, which is achieved by using proper quantum time. The main challenge in this algorithm is selection of quantum time. This parameter affects on average Waiting Time and average Turnaround Time in execution queue. As the quantum time is static, it causes less context switching in case of high quantum time and high context switching in case of less quantum time. Increasing context switch leads to high average waiting time, high average turnaround time which is an overhead and degrades the system performance. With respect to these points, the algorithms should calculate proper value for the quantum time. Two main classes of algorithms that are proposed to calculate the quantum time include static and dynamic methods. In static methods quantum time is fixed during the scheduling. Dynamic algorithms are one of these methods that change the value of quantum time in each cycle. For example in one method the value of quantum time in each cycle is equal to the median of burst times of processes in ready queue and for another method this value is equal to arithmetic mean of burst times of ready processes. In this paper we proposed a new method to obtaining quantum time in each cycle based on arithmetic-harmonic mean (HARM). Harmonic mean is calculated by dividing the number of observations by the reciprocal of each number in the series. With examples we show that in some cases it can provides better scheduling criteria and improves the average Turnaround Time and average Waiting Time.
Бесплатно
A New OWL2 Based Approach for Relational Database Description
Статья научная
Nowadays, the scientific community is more and more interested by the mediation problem within Peer-to-Peer (P2P) systems and by data sources migration within the semantic web. Data integration and interoperability become a necessity to meet the need for information exchange between heterogeneous information systems. They reflects the ability of an information system to collaborate with other systems sometimes of a very different nature and aims at developing architectures and tools for sharing, exchanging and controlling data. In this context we have proposed a new heterogeneous and distributed data management system in a P2P environment called MedPeer. Among this system functions, we have focused in this article on relational databases description through the use of ontologies. We thus propose Relational.OWL2E, a new approach that, starting from the relational schema, generates an ontology based on the OWL2 language. Our main contribution lies in the semantics we have added to relational databases concepts in representing attributes by rich XML schema datatypes, primary keys, unique keys, foreign keys and by associating to each class a set of synonyms in order to guide the process of discovering semantic correspondences.
Бесплатно
A New Platform NIDS Based On WEMA
Статья научная
The increasing speed of today's computer networks directly affects the performance of Network Intrusion Detection Systems (NIDS) in terms of speed of detection of threads. Therefore, the performance of the existing algorithms needs to be improved to enhance the speed of detection engine used in NIDS applications. Hence, this paper defines a new platform NIDS to enhance the speed of detection engine based on Weighted Exact Matching Algorithm (WEMA). Furthermore, this platform can be run in sequential and in parallel mode, using the pthread techniques, in order to increase the total performance of NIDS applications.
Бесплатно
A New Query Expansion Approach for Improving Web Search Ranking
Статья научная
Information systems have come a long way in the 21st century, with search engines emerging as the most popular and well-known retrieval systems. Several techniques have been used by researchers to improve the retrieval of relevant results from search engines. One of the approaches employed for improving relevant feedback of a retrieval system is Query Expansion (QE). The challenge associated with this technique is how to select the most relevant terms for the expansion. In this research work, we propose a query expansion technique based on Azak & Deepak's WWQE model. Our extended WWQE technique adopts Candidate Expansion Terms selection with the use of in-links and out-links. The top two relevant Wikipedia articles from the user's initial search were found using a custom search engine over Wikipedia. Following that, we ranked further Wikipedia articles that are semantically connected to the top two Wikipedia articles based on cosine similarity using TF-IDF Vectorizer. The expansion terms were then taken from the top 5 document titles. The results of the evaluation of our methodology utilizing TREC query topics (126-175) revealed that the system with extended features gave ranked results that were 11% better than those from the system with unexpanded queries.
Бесплатно
A New Secure Multicast Key Distribution Scheme Using Tabulation Method
Статья научная
In the present paper, we propose a new scheme for a scalable multicast key distribution scheme. The present scheme is based on the Key Management using Tabulation method of Boolean Function Simplification technique. It explores the use of batching of group membership changes to reduce the frequency, and hence the cost, of key re-distribution operations. It focuses explicitly on the issue of snowballing member removal and presents an algorithm that minimizes the number of messages required to distribute new keys to the remaining group members. The algorithm is used in conjunction with a new scalable multicast key distribution scheme which uses a set of auxiliary keys in order to improve scalability. In contrast to previous schemes which generate a fixed hierarchy of keys, the proposed scheme dynamically generates the most suitable key hierarchy by composing different keys. Our snowballing member removal uses one of the Boolean function simplification techniques called tabulation method, and outperforms all other schemes known to us in terms of message complexity. Most importantly, our technique is superior in minimizing the number of messages when multiple members leave the session in the same round.
Бесплатно
A New Technique for Segmentation of Handwritten Numerical Strings of Bangla Language
Статья научная
Segmentation of handwritten input into individual characters is a crucial step in connected handwriting recognition systems. In this paper we propose a robust scheme to segment handwritten Bangla numbers (numerical strings) against the variability involved in the writing style of different individuals. The segmentation of digits from a number is usually very tricky, as the digits in a Bangla number are seldom vertically separable. We have introduced the concept of Degenerated Lower Chain (DLC) for this purpose. The DLC method was proved efficient in case of segmenting handwriting digits in our experiments. Ten pages of handwritten Bangla numerical strings containing 2000 individual digits that construct 700 numbers written by five different writers of variable ages were segmented by the developed system. The system achieves more than 90% segmentation accuracy on average.
Бесплатно
A Novel Approach for Association Rule Mining using Pattern Generation
Статья научная
Data mining has become a process of significant interest in recent years due to explosive rate of the accumulation of data. It is used to discover potentially valuable implicit knowledge from the large transactional databases. Association rule mining is one of the well known techniques of data mining. It typically aims at discovering associations between attributes in the large databases. The first and the most influential traditional algorithm for association rule discovery is Apriori. Multiple scans of database, generation of large number of candidates item set and discovery of interesting rules are the main challenging issues for the improvement of Apriori algorithm. Therefore in order to decrease the multiple scanning of database, a new method of association rule mining using pattern generation is proposed in this paper. This method involves three steps. First, patterns are generated using items from the transaction database. Second, frequent item set is obtained using these patterns. Finally association rules are derived. The performance of this method is evaluated with the traditional Apriori algorithm. It shows that behavior of the proposed method is much more similar to Apriori algorithm with less memory space and reduction in multiple times scanning of database. Thus it is more efficient than the traditional Apriori algorithm.
Бесплатно
A Novel Approach for Identification of Hadoop Cloud Temporal Patterns Using Map Reduce
Статья научная
Due to the latest developments in the area of science and Technology resulted in the developments of efficient data transfer, capability of handling huge data and the retrieval of data efficiently. Since the data that is stored is increasing voluminously, methods to retrieve relative information and security related concerns are to be addressed efficiently to secure this bulk data. Also with emerging concepts of big data, these security issues are a challenging task. This paper addresses the issue of secure data transfer using the concepts of data mining in cloud environment using hadoop mapreduce. Based on the experimentation done results are analyzed and represented with respect to time and space complexity when compared hadoop with non hadoop approach.
Бесплатно
A Novel Approach for Reduce Energy Consumption in Mobile Cloud Computing
Статья научная
In recent years, using mobile devices has a special place in human life and applicability of these devices leads to increased number of users. Business companies have integrated them with cloud computing technology and have provided mobile cloud in order to improve using mobile devices and overcome the energy consumption of mobile devices. In mobile cloud computing, computations and storages of mobile devices applications are transferred to cloud data centers and mobile devices are used merely as user interface to access services. Therefore, cloud computing will help to reduce energy consumption of mobile devices. In this paper, a new approach is given to reduce energy consumption of based on Learning Automata in mobile cloud computing. Simulation results show that our proposed approach dramatically saves energy consumption through determining the appropriate location for application.
Бесплатно
A Novel Approach for Spectrum Access Using Fuzzy Logic in Cognitive Radio
Статья научная
In this paper, we have proposed a novel approach using Fuzzy Logic System (FLS) for the potential management of the spectrum access. We have used four descriptors such as spectrum utilization efficiency of the secondary user, mobility, distance of the primary user, and signal strengths of the secondary users. On the basis of these descriptors, there are 81 fuzzy rules have been setup, which are based on the linguistic knowledge. The output of the FLS provide the possibility of accessing spectrum band for secondary users and the user with the greatest possibility has to assigned the available spectrum band.
Бесплатно
A Novel Big Data Approach to Classify Bank Customers - Solution by Combining PIG, R and Hadoop
Статья научная
Large amount of data that is characterized by its volume, velocity, veracity, value and variety is termed Big Data. Extracting hidden patterns, customer preferences, market trends, unknown correlations, or any other useful business information from large collection of structured or unstructured data set is called Big Data analysis. This article explores the scope of analyzing bank transaction data to categorize customers which could help the bank in efficient marketing, improved customer service, better operational efficiency, increased profit and many other hidden benefits. Instead of relying on a single technology to process large scale data, we make use of a combination of strategies like Hadoop, PIG, R etc for efficient analysis. RHadoop is an upcoming research trend for Big Data analysis, as R is a very efficient and easy to code, data analysis and visualization tool compared to traditional MapReduce program. K-Means is chosen as the clustering algorithm for classification.
Бесплатно
A Novel Cat Swarm Optimization Algorithm for Unconstrained Optimization Problems
Статья научная
Cat Swarm Optimization (CSO) is one of the new swarm intelligence algorithms for finding the best global solution. Because of complexity, sometimes the pure CSO takes a long time to converge and cannot achieve the accurate solution. For solving this problem and improving the convergence accuracy level, we propose a new improved CSO namely ‘Adaptive Dynamic Cat Swarm Optimization’. First, we add a new adaptive inertia weight to velocity equation and then use an adaptive acceleration coefficient. Second, by using the information of two previous/next dimensions and applying a new factor, we reach to a new position update equation composing the average of position and velocity information. Experimental results for six test functions show that in comparison with the pure CSO, the proposed CSO can takes a less time to converge and can find the best solution in less iteration.
Бесплатно
