@article {803, title = {Ontological Approach Based on Multi-Agent System for Indexing and Filtering Arabic Documents-}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {06/2019}, pages = {145-163}, type = {Research}, chapter = {145}, abstract = {

: In recent years, Automatic Natural Language Processing (ANLP) for Arabic language has received a great amount of attention for the development of several applications such as question answering, information retrieval and translation, etc. However, there are a few automated applications using Semantic Web technologies for retrieving Arabic-language documents despite the high demand and need for this content. In addition, the Arabic language presents serious challenges to researchers and developers of NLP applications. These challenges are due to the complexity of the morphological, syntactic and semantic characteristics specific to the Arabic text, which requires the use of semantic resources such as ontology. In our work, we propose a new approach based on ontology and multi-agent systems to index and filter Arabic documents. Our proposal is composed of five layers, each layer contains several agents: (1) Lexical Layer; (2) Syntactic Layer; (3) Semantic Layer; (4) Indexing Layer; and GUI/Interface Layer. Our Arabic ontology is manually constructed on the basis of schemes\  and their semantics meanings. We use also combination of Arabic WordNet contents and Arabic VerbNet in the process of constructing the ontology. We use the semantic similarity to find the relevant documents according to the user{\textquoteright}s queries. The aim of this paper is to study the effect of patterns in solving the problem of the semantic indexing system (SIS). The main objective is to improve the quality of the indexing process to ensure the accuracy of the information search of relevant documents based on us ers{\textquoteright} multiword queries, and also to reduce indexing and search time. Indeed, our experiments are conducted on the basis of the combination of two Arab corpus: OSAC\  and SemEval. We compared our results

}, doi = {https://doi.org/10.6025/jdim/2019/17/3/145-163}, url = {http://dline.info/fpaper/jdim/v17i3/jdimv17i3_4.pdf}, author = {Samia Zouaoui and Khaled Rezeg} } @article {717, title = {Ontological Approach Based on Multi-Agent System for Indexing and Filtering Arabic Documents}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {06/2019}, pages = {145-163}, type = {Research}, chapter = {145}, doi = {https://doi.org/10.6025/jdim/2019/17/3/145-163}, url = {http://dline.info/fpaper/jdim/v17i3/jdimv17i3_4.pdf}, author = {Samia Zouaoui and Khaled Rezeg} } @article {792, title = {Optimization of Topic Recognition Model for News Texts Based on LDA-}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {10/2019}, pages = {257-269}, type = {Research}, chapter = {257}, abstract = {

\ Latent Dirichlet Allocation (LDA) is the technique most commonly used in topic modeling methods, but it requires the number of topics generated by LDA to be specified for topic recognition modeling. Except the main iterative methods based on perplexity and nonparametric methods, recent research has no simple way to select the optimal number of topics in the model. Aiming at appropriately determining the number of topics and then optimizing the LDA topic model, this paper proposes a non-iterative method for automatically determining the number of topics. The clustering method is based on fast seeking and locating density peaks. This method transforms the traditional topic cluster number selection problem into clustering problem and thus can be used to optimize the topic recognition model for news texts. It does not need iterative optimization and can simplify model development. This method uses Word2Vec for word embedding on corpus text to explore the superior performance of word-related relationships and to express the implicit semantic relationship between topic corpora. Then, using a clustering algorithm that quickly searches for and finds the cluster peaks; the word vectors after word embedding are clustered to obtain the number of word vector clusters after word embedding. The number of clusters is used as the number of topics in the text. Finally, the experimental results show that the proposed method enjoys better precision and F1 value than the perplexitybased method, and is suitable for the identification of the number of topics in corpora in different sizes. This method can effectively find the appropriate number of topics from the news text dataset and improve the accuracy of the LDA theme model.

}, doi = {https://doi.org/10.6025/jdim/2019/17/5/257-269}, url = {http://dline.info/fpaper/jdim/v17i5/jdimv17i5_1.pdf}, author = {Hongbin Wang and Jianxiong Wang and Yafei Zhang and Meng Wang and Cunli Mao} } @article {683, title = {One-layer Neural Network for Solving Least Absolute Deviation Problems with Box and Equality Constraints}, journal = {Journal of Electronic Systems}, volume = {8}, year = {2018}, month = {06/2018}, pages = {57-71}, type = {Research}, author = {Cuiping Li} } @article {836, title = {Ontology Matching as Regression Problem-}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {02/2018}, pages = {33-42}, type = {Research}, chapter = {33}, abstract = {

\ Ontology matching is one of the most important works to achieve the goal of the semantic web. To fulfill this task, element-level matching is an indispensable step to obtain the fundamental alignment. In element-level matching process, previous work generally utilizes multiple measures to compute the similarities among elements, and then combine these similarities using a weighted sum formula to determine later the semantic correspondences. The proper selection and combination of these similarities strongly influence the final quality of the matching system.

}, url = {http://dline.info/fpaper/jdim/v16i1/jdimv16i1_4.pdf}, author = {Nadia Alboukaey and Ammar Joukhadar} } @article {617, title = {Ontology Matching as Regression Problem}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {02/2018}, pages = {42}, type = {Research}, chapter = {33}, url = {http://dline.info/fpaper/jdim/v16i1/jdimv16i1_4.pdf}, author = {Nadia Alboukaey and Ammar Joukhadar} } @article {821, title = {Optimal Web Service Selection Model using Fuzzy Extended AHP and Weighted Sum Method-}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {08/2018}, pages = {157-168}, type = {Research}, chapter = {157}, abstract = {

\ Different services have different values for their different properties (functional and non-functional). Moreover, consumers have different preferences for these properties. Since these preferences may be incomplete/ vague/imprecise, this inevitably can in turn cause selection of a non-optimal service for the consumer which may not have the desired value for these properties for the consumer. Therefore optimal web service selection based on the consumer{\textquoteright}s preferences is a challenging task. In this paper, a new service selection model is proposed which considers consumer preferences for different service properties as well as the actual values of its properties. Services are evaluated and priority weights are computed separately for functional and non-functional properties using modified Fuzzy Extended Analytic Hierarchy Process (FEAHP) and Weighted Sum Method (WSM) respectively. Real time data is used to conduct experiments.

}, doi = {https://doi.org/10.6025/jdim/2018/16/4/157-168}, url = {http://dline.info/fpaper/jdim/v16i4/jdimv16i4_1.pdf}, author = {Rachna Kohar} } @article {685, title = {Optimal Web Service Selection Model using Fuzzy Extended AHP and Weighted Sum Method}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {08/2018}, pages = {157-168}, type = {Research}, author = {Rachna Kohar} } @article {478, title = {OCC-mix approach for concurrent mixture of fixed and mobile transactions}, journal = {Journal of Digital Information Management}, volume = {15}, year = {2017}, note = {Export Date: 22 February 2018}, month = {2017}, pages = {272 - 288}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036609704\&partnerID=40\&md5=302cae68771bb14fc6e5fdb2bfea7cbb}, author = {Al-Qerem, A. and Hamarsheh, A. and Nashwan, S.} } @article {591, title = {Optimization of reconstruction of 2D medical images based on computer 3D reconstruction technology}, journal = {Journal of Digital Information Management}, volume = {13}, year = {2015}, note = {Cited By :2Export Date: 22 February 2018}, month = {2015}, pages = {142 - 146}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938604272\&partnerID=40\&md5=1f0030c870b50621048bce878bc27d66}, author = {Liu, S. and Peng, J.} } @article {384, title = {Optimization and application of support vector machine based on SVM algorithm parameters}, journal = {Journal of Digital Information Management}, volume = {11}, year = {2013}, note = {Export Date: 10 July 2014}, month = {2013}, pages = {165 - 169}, abstract = {The hospital customer classification is very important for the integration and allocation of hospital resources, can greatly enhance the market competitiveness of the hospital. Support Vector Machine (SVM) is an approach to solve classification problem by using optimization method. Selecting different kernel parameters can construct different classifiers, meanwhile parameters decide their learning and generalization ability. In order to solve the limitation of selecting parameters by experience, so the particle swarm (PSO) pattern search algorithm is proposed to search optimal parameters and take them into the practice of hospital customer classification; The PSO mode search algorithm is to combine the advantages of PSO algorithm and pattern search algorithm, PSO mode search algorithm has strong global search capability and good advantage of local convergence. The result of experiment shows that this method is not only efficient, but also to search the optimal parameters achieving a high accuracy, which is an effective method of SVM parameter optimization.}, keywords = {Customer data classification, Kernel parameters selection, Particle swarm pattern search, Support vector machine}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879098093\&partnerID=40\&md5=aa26078b417c2386e1244d36d3bacb09}, author = {Yan, H.-F. and Wang, W.-F. and Liu, J.} } @article {418, title = {Orchestrating the natural language processing software in the cloud computing environment}, journal = {Journal of Digital Information Management}, volume = {11}, year = {2013}, note = {Export Date: 10 July 2014}, month = {2013}, pages = {396 - 399}, abstract = {The most of natural language processing problems are data-intensive. An important step in the distributed orchestration of natural language processing software is a rational choice of the specific middleware. The middleware should solve the presented problem with minimal deployment, support and usage costs. It is necessary to run and use that software in the distributed cloud computing environment to achieve such advantages such as consolidation, isolation, and efficient use of the existent infrastructure. It is often impossible to modify the existent natural language processing software to integrate it into the cloud computing environment because of licensing or organizational issues. This paper studies various popular distributed data processing tools and evaluates the selected natural language processing tools on a relatively large document collection in distributed way using the Gearman framework. The document collection is a 10{\textquoteright}000 sentences from the Russian news subcorpus of the Leipzig corpora. The benchmarks are presented and discussed.}, keywords = {Cloud computing, Data-intensive computing, Distributed computing, Natural Language Processing, Service orchestration}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890443570\&partnerID=40\&md5=80168707d25eb1e8a3acabfb35fdf93f}, author = {Ustalov, D. and Goldshtein, M.} } @article {263, title = {An ontology-based approach for detecting knowledge intensive tasks}, journal = {Journal of Digital Information Management}, volume = {9}, year = {2011}, note = {Cited By (since 1996):2Export Date: 10 July 2014}, month = {2011}, pages = {9 - 18}, abstract = {In the context detection field, an important challenge is automatically detecting the user{\textquoteright}s task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task detection approach, and evaluate it against previous work. We show that knowledge intensive tasks cannot be accurately classified using only the window title. We argue that our approach allows classifying such tasks better, by providing feature combinations that can adapt to the domain and the degree of freedom in task execution.}, keywords = {Classification, Context-awareness, Task detection, User context detection, User context ontology}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960684788\&partnerID=40\&md5=2680764b7de9a96a3bfc50e670a65ce8}, author = {Rath, A.S. and Devaurs, D. and Lindstaedt, S.N.} } @article {204, title = {Optimizing Heating system management using an activity-based pervasive application}, journal = {Journal of Digital Information Management}, volume = {7}, year = {2009}, note = {Export Date: 10 July 2014}, month = {2009}, pages = {327 - 335}, abstract = {The greenhouse gas effect has become a major preoccupation worldwide. Heating of buildings and houses is one of the major sources of this effect. We propose to optimize heating control systems by directly integrating the recognition of people{\textquoteright}s activities and context in the process of temperature regulation. We introduce the concept of kinetic-awareness as a way of making a heating control system aware of a user{\textquoteright}s motion in physical space in order to adjust temperature according to their current activities.}, keywords = {Context awareness, Heat control systems, Heating system management}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960692759\&partnerID=40\&md5=2401f279d8595a2a81a6c410b1bb3ae4}, author = {Bruegger, P. and Pallotta, V. and Hirsbrunner, B.} } @article {124, title = {The OntoNL semantic relatedness measure for OWL ontologies}, journal = {Journal of Digital Information Management}, volume = {6}, year = {2008}, note = {Export Date: 10 July 2014}, month = {2008}, pages = {366 - 374}, abstract = {An effect of the growing importance of the Semantic Web used for sharing knowledge over the Internet was the development and publishing of many ontologies in different domains. This led to the need of developing mechanisms for capturing the semantics of the ontologies. In this paper, we introduce the OntoNL Semantic Relatedness Measure, a fully automated way of measuring, in an asymmetric way, semantic relatedness between concepts of domain ontologies. We have developed metrics to guide the automation of the procedure by using feedback from an extensive evaluation with human subjects. We conclude with an application based evaluation using the OntoNL Framework as the application platform, a Natural Language Interface Generator for Knowledge Repositories.}, keywords = {Domain ontology, Knowledge Repositories, OntoNL Framework}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84865464416\&partnerID=40\&md5=b36024693390715e4aab217ee0f62b12}, author = {Karanastasi, A. and Christodoulakis, S.} } @article {131, title = {Optimal two-pass frame-layer bit allocation for H.264 Video}, journal = {Journal of Digital Information Management}, volume = {6}, year = {2008}, note = {Cited By (since 1996):1Export Date: 10 July 2014}, month = {2008}, pages = {174 - 179}, abstract = {In this work, the frame-layer bit allocation problem is studied for H.264 video. We first impose a GOP (Group of Pictures) structure on the underlying H.264 video, and then develop a two-pass rate control algorithm based on the Lagrange optimization framework. The proposed algorithm estimates the rates and the distortions of all frames in a GOP by preencoding them using a constant quantization parameter (QP) in the first pass. The target number of bits of each frame is determined by searching the Lagrange multiplier that meets the bit budget constraint for the GOP. The proposed algorithm is useful for nonconversational applications such as video streaming where video sources can be encoded off-line. Experimental results demonstrate that the proposed algorithm achieves substantially higher picture quality than the rate control algorithm in the H.264 reference encoder.}, keywords = {Bit allocation, H.264, Rate control, Rate-distortion optimization}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-72449209699\&partnerID=40\&md5=c41f97ca2d0ee3770b908360518c3556}, author = {Kwon, D.-K. and Shen, M.-Y. and Kuo, C.-C.J.} } @article {58, title = {Optimal placement of Web proxies in the Internet with link capacity constraints}, journal = {Journal of Digital Information Management}, volume = {4}, year = {2006}, note = {Cited By (since 1996):1Export Date: 10 July 2014}, month = {2006}, pages = {238 - 242}, abstract = {This paper considers the problem of placing a fixed number of proxies in the Web modelled as a tree network. The study assumes that each link in the network has a fixed capacity, and that the total load passing through a link cannot exceed this capacity. We are interested in finding a minimum cost replication set that optimises the cost of servicing access requests in a read-only environment taking into account the capacity constraints of the links.}, keywords = {Dynamic programming, Link capacity, Replication, Tree network, Web server}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33845933875\&partnerID=40\&md5=9e8af6a6d40a5eb4fb54dbc61e62f0cc}, author = {Tenzakhti, F.} } @article {65, title = {Optimization of Boolean queries in information retrieval systems using genetic algorithms - Genetic programming and fuzzy logic}, journal = {Journal of Digital Information Management}, volume = {4}, year = {2006}, note = {Export Date: 10 July 2014}, month = {2006}, pages = {249 - 254}, abstract = {This paper proposes to use two information retrieval system models (Boolean information retrieval model and extended Boolean (fuzzy) information retrieval model). These models differ by using Boolean queries or fuzzy weighted queries. It also proposes a way for optimizing user query for the two models by using genetic programming and fuzzy logic. And proposes to use more number of Boolean operators (AND, OR, XOR, OF, and NOT) instead of the standard Boolean operators (AND, OR, and NOT), and use weights for Boolean operators and for terms in fuzzy models.}, keywords = {Boolean operator weights, Boolean query, Fuzzy logic, Genetic algorithms, Genetic programming, Information retrieval, Term weights}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33845945357\&partnerID=40\&md5=bb2fe8678839544324f8ce90e9dca876}, author = {Owais, S.S.J.} }