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Journal articles on the topic 'ICONDA (Information retrieval system)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Kumar, Ram, and S. C. Sharma. "Information Retrieval System." International Journal of Technology Diffusion 9, no. 1 (January 2018): 1–10. http://dx.doi.org/10.4018/ijtd.2018010101.

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Information Retrieval Systems (IRS) have dramatically changed the ways how people acquire information for their need. Information Retrieval (IR) enables user to find relevant document from collection of countless resources. This article presents an overview of IRS. Objectives of this article is to answer all the basic and specific questions related to IRS. In contrast to other review papers, the authors provide a complete understanding of IR in single paper. Starting from definition and importance it covers retrieval process, performance issues, and comparison among various approaches. This article also includes description of different models along with analysis of their merits and demerits. This article proposes a list of challenges, still unanswered by existing systems. Before offering a conclusion, the major applications of IR are also listed.

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2

Sung, Sam Y., and Tianming Hu. "Iconic pictorial retrieval using multiple attributes and spatial relationships." Knowledge-Based Systems 19, no. 8 (December 2006): 687–95. http://dx.doi.org/10.1016/j.knosys.2006.05.013.

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3

Tewari, Ashish Kumar, Rashi, Gulshan Wadhwa, Sanjeev Kumar Sharma, and Chakresh Kumar Jain. "BIRS – Bioterrorism Information Retrieval System." Bioinformation 9, no. 2 (January 18, 2013): 112–15. http://dx.doi.org/10.6026/97320630009112.

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4

Md. Kowsher, Imran Hossen, and SkShohorab Ahmed. "Bengali Information Retrieval System(BIRS)." International Journal on Natural Language Computing 8, no. 5 (October 31, 2019): 1–12. http://dx.doi.org/10.5121/ijnlc.2019.8501.

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5

Choi, Okkyung, Kangseok Kim, Duksang Wang, Hongjin Yeh, and Manpyo Hong. "Personalized Mobile Information Retrieval System." International Journal of Advanced Robotic Systems 9, no. 1 (January 1, 2012): 11. http://dx.doi.org/10.5772/50910.

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Building a global Network Relations with the internet has made huge changes in personal information system and even comments left on a webpage of SNS(Social Network Services) are appreciated as important elements that would provide valuable information for someone. Social Network is a relation between individuals or groups, represented in a graph model, which converts the concept of psychological and social relations into a logical structure by using node and link. But, most of the current personalized systems on the basis of Social Network are built and constructed mainly in the PC environment, and the systems are neither designed nor implemented in mobile environment. Hence, the objective of this study is to propose methods of providing Personalized Mobile Information Retrieval System using NFC (Near Field Communication) Smartphone, which will be then used for Smartphone users. Besides, this study aims to verify its efficiency through a comparative analysis of existing studies.

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6

Hong, Seok-Joo, and Young-Bae Park. "Information Retrieval System for R2SS." Journal of the Korea Contents Association 9, no. 12 (December 28, 2009): 39–51. http://dx.doi.org/10.5392/jkca.2009.9.12.039.

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7

SHIRAHASHI, Akihiro. "Information retrieval system through Internet." Journal of Information Processing and Management 37, no. 1 (1994): 19–26. http://dx.doi.org/10.1241/johokanri.37.19.

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8

Chkiwa, Mounira, Anis Jedidi, and Faiez Gargouri. "Semantic / Fuzzy Information Retrieval System." International Journal of Information Technology and Web Engineering 12, no. 1 (January 2017): 37–56. http://dx.doi.org/10.4018/ijitwe.2017010103.

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In this paper, the authors present an overall description of their information retrieval system which makes a practical collaboration between Semantic Web and Fuzzy logic in order to have profit from their advantages in the information retrieval domain. Their system is dedicated for kids, for this reason the semantic/fuzzy collaboration materialized must be in the background of the information retrieval process because such category of users cannot certainly control semantic web technologies neither fuzzy logic commands. In this paper, the authors present the different services proposed by their system and how they use Semantic Web and Fuzzy logic to develop it. Evaluation tests of the system using universal measures show clearly its efficiency.

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9

Gudi, Anirudh. "Hypertext Based Information Retrieval System." IETE Technical Review 12, no. 4 (July 1995): 261–67. http://dx.doi.org/10.1080/02564602.1995.11416538.

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10

Lucarella, D., and R. Morara. "FIRST: Fuzzy Information Retrieval SysTem." Journal of Information Science 17, no. 2 (April 1991): 81–91. http://dx.doi.org/10.1177/016555159101700202.

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11

Hiromichi, F. "Knowledge based information retrieval system." Expert Systems with Applications 9, no. 3 (1995): IV—V. http://dx.doi.org/10.1016/0957-4174(95)98747-7.

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12

Davcev, Danco, Dusan Cakmakov, and Vanco Cabukovski. "Distributed multimedia information retrieval system." Computer Communications 15, no. 3 (April 1992): 177–84. http://dx.doi.org/10.1016/0140-3664(92)90078-s.

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13

Escobedo, Richard, Scott D. G. Smith, and Thomas P. Caudell. "A neural information retrieval system." International Journal of Advanced Manufacturing Technology 8, no. 4 (July 1993): 269–73. http://dx.doi.org/10.1007/bf01748637.

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14

Zelitchenko, Alexander I. "Information retrieval expert system “matchmaker”." Computers in Human Behavior 8, no. 4 (December 1992): 281–96. http://dx.doi.org/10.1016/0747-5632(92)90025-a.

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15

YOKOYAMA, Kiyoshi, Hidenori MURAKAMI, and Nobuyuki FUKASAWA. "Toward intelligent information system. (4). Expert system for information retrieval." Journal of Information Processing and Management 32, no. 3 (1989): 223–34. http://dx.doi.org/10.1241/johokanri.32.223.

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16

Bedi, P., and S. Chawla. "Agent Based Information Retrieval System Using Information Scent." Journal of Artificial Intelligence 3, no. 4 (September 15, 2010): 220–38. http://dx.doi.org/10.3923/jai.2010.220.238.

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17

Bovey, J. D. "A graphical retrieval system." Journal of Information Science 19, no. 3 (June 1993): 179–87. http://dx.doi.org/10.1177/016555159301900302.

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18

ZAKI, U., M. MEMON, D. N. HAKRO, K. U. R. KHOUMBATI, M. HAMEED,, M. A. ZAKI, and G. NABI. "Implementation Challenges in Information Retrieval System." SINDH UNIVERSITY RESEARCH JOURNAL -SCIENCE SERIES 51, no. 02 (June 22, 2019): 339–44. http://dx.doi.org/10.26692/sujo/2019.6.55.

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19

Lata, Suman, and Ajmer Kundu. "Wheat Production Information Retrieval System (WPIRS)." International Journal of Computer Applications 58, no. 2 (November 15, 2012): 16–19. http://dx.doi.org/10.5120/9253-3422.

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20

Kim, Jae-Hoon, and Hyung-Chul Kim. "Information Retrieval System for Mobile Devices." Journal of the Korean Society of Marine Engineering 33, no. 4 (May 31, 2009): 569–77. http://dx.doi.org/10.5916/jkosme.2009.33.4.569.

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21

Niki, Kazuhisa. "Self-organized Information Retrieval System: SIR." Brain & Neural Networks 1, no. 1 (1994): 27–34. http://dx.doi.org/10.3902/jnns.1.27.

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22

Ertwine, Robyn, and Beatrice Jakubowski. "CHID: An Automated Information Retrieval System." Diabetes Educator 12, no. 1 (January 1986): 55–60. http://dx.doi.org/10.1177/014572178601200114.

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The Combined Health Information Database (CHID) is an automated information system designed for health care educators and other health professionals. It contains 20,000 references to jour nal articles, fact sheets, brochures, audiovisual materials, health program descriptions, and other health-related information. Topics include diabetes, arthritis, digestive diseas es, high blood pressure, and health promotion and health education. Access to this information is available nationwide through a subscription service. The information included in CHID can be searched and retrieved quickly by computer, and the results can be orga nized and prepared according to the specific needs of the user.

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23

Kulkarni, D. R., and R. R. Bharucha. "READFAST — an online information retrieval system." Program 20, no. 3 (March 1986): 314–19. http://dx.doi.org/10.1108/eb046945.

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24

Shu-dong, Shi, and Li Zhi-tang. "A high-speed information retrieval system." Wuhan University Journal of Natural Sciences 9, no. 4 (July 2004): 425–28. http://dx.doi.org/10.1007/bf02830436.

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25

Angdresey, Apriandy, Miguel Angelo Lamongi, and Rinaldi Munir. "Information Retrieval System in the Bible." CogITo Smart Journal 7, no. 1 (June 30, 2021): 111. http://dx.doi.org/10.31154/cogito.v7i1.300.111-120.

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Information retrieval is used to search for relevant documents so that they can be obtained quickly and precisely. There are many Christians who want to study the gospel. However, often experience problems in finding the Gospel verse and topics dealing with the need to search by the user. Therefore, have to search individually, each verse in the four Gospels to find the topic or verse that the user wants to find out. In this study, the authors used the Bible verses in the Gospels as documents, so that these verses could be searched for the level of relevance or similarity to the entered keywords. Furthermore, to determine the level of relevance between documents and keywords is calculated using the Vector Space Model. Based on the application that has been successfully built, the application can be show 10 documents related to the keywords that are searched and sorted from the most relevant, with the highest similarity value, namely 78.65%.Keywords - Information Retrieval, Vector Space Model, Bible.

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26

Gao, Ruoyuan. "Toward a fairer information retrieval system." ACM SIGIR Forum 55, no. 1 (June 2021): 1–2. http://dx.doi.org/10.1145/3476415.3476429.

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With the increasing popularity and social influence of information retrieval (IR) systems, various studies have raised concerns on the presence of bias in IR and the social responsibilities of IR systems. Techniques for addressing these issues can be classified into pre-processing , in-processing and post-processing. Pre-processing reduces bias in the data that is fed into machine learning models. In-processing encodes fairness constraints as a part of the objective function or learning process. Post-processing operates as a top layer over the trained model to reduce the presentation bias exposed to users. This dissertation explored ways to bring the pre-processing and post-processing approaches, together with the fairness-aware evaluation metrics, into a unified framework as an attempt to break the vicious cycle of bias and improve fairness in IR. We first investigated the existing bias presented in search engine results. Specifically, we focused on the top-k fairness ranking in terms of statistical parity fairness and disparate impact fairness definitions. With Google search and a general purposed text cluster as a lens, we explored several topical diversity fairness ranking strategies to understand the relationship between relevance and fairness in search results. Our experimental results showed that different fairness ranking strategies resulted in distinct utility scores and performed differently with distinct datasets. Second, to further investigate the relationship of data and fairness algorithms, we developed a statistical framework that was able to facilitate various analysis and decision making. Our framework could effectively and efficiently estimate the domain of data and solution space. We derived theoretical expressions to identify the fairness and relevance bounds for data of different distributions, and applied them to both synthetic datasets and real world datasets. We presented a series of use cases to demonstrate how our framework was applied to associate data and provide insights to fairness optimization problems. Third, we proposed an evaluation metric FAIR for the ranking results that encoded fairness, diversity, novelty and relevance. This metric offered a new perspective of evaluating fairness-aware ranking results. Based on this metric, we developed an effective ranking algorithm that jointly optimized for fairness and utility. Our experiments showed that our new metric was able to highlight results that achieved good user utility and fair information exposure at the same time. We showed how FAIR metric related to existing metrics through correlation analysis and case studies, and demonstrated the effectiveness of our FAIR-based algorithm.

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27

McWhirr, Alan. "GRS (General Retrieval System): a retrieval system for use in the humanities." Program 20, no. 3 (March 1986): 275–88. http://dx.doi.org/10.1108/eb046942.

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28

Yin, Peng-Yeng, and Chin-Wen Liu. "A new relevance feedback technique for iconic image retrieval based on spatial relationships." Journal of Systems and Software 82, no. 4 (April 2009): 685–96. http://dx.doi.org/10.1016/j.jss.2008.09.033.

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29

Li, Li, Wei Jiang Li, and Yuan Yuan Fan. "Topic-Oriented Information Retrieval." Advanced Materials Research 989-994 (July 2014): 4845–50. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4845.

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With the rapid development of Internet, more and more information has been displayed to people. More and more researcher pay more attention to how to find useful information from this huge information ocean. We design the topic-oriented information retrieval system in order to overcome the drawback of generic crawler. The system retrieves topic information efficiently and helps user get useful information rapidly and exactly.

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30

Kovalcik, Justin, and Mike Villalobos. "Automated Storage & Retrieval System." Information Technology and Libraries 38, no. 4 (December 16, 2019): 114–24. http://dx.doi.org/10.6017/ital.v38i4.11273.

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The California State University, Northridge (CSUN) Oviatt Library was the first library in the world to integrate an automated storage and retrieval system (AS/RS) into its operations. The AS/RS continues to provide efficient space management for the library. However, added value has been identified in materials security and inventory as well as customer service. The concept of library as space, paired with improved services and efficiencies, has resulted in the AS/RS becoming a critical component of library operations and future strategy. Staffing, service, and security opportunities paired with support and maintenance challenges, enable the library to provide a unique critique and assessment of an AS/RS.

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31

Jones, Kevin P., and Colin L. M. Bell. "MORPHS—an intelligent retrieval system." Aslib Proceedings 38, no. 3 (March 1986): 71–79. http://dx.doi.org/10.1108/eb051000.

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32

Swathi, J., and G. Seethalakshmi. "Content and Location based Information Retrieval System." International Journal of Computer Applications 107, no. 11 (December 18, 2014): 1–4. http://dx.doi.org/10.5120/18792-0115.

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33

Davis, G. L., Edward F. Gilman, and Howard W. Beck. "An Electronically Based Horticultural Information Retrieval System." HortTechnology 6, no. 4 (October 1996): 332–36. http://dx.doi.org/10.21273/horttech.6.4.322.

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A large horticultural database and an electronic retrieval system for extension education programs were developed using compact disk-read only memory (CD-ROM) and World Wide Web (WWW) as the medium for information delivery. Object-oriented database techniques were used to organize the information. Conventional retrieval techniques including hypertext, full text searching, and expert systems were integrated into a complete package for accessing information stored in the database. A multimedia user interface was developed to provide a variety of capabilities including computer graphics and high resolution digitized images. Information for the CD-ROM was gathered from extension publications that were tagged using the standard generalized markup language (SGML)-based document markup language (International Standards Organization, 1986). Combining funds from the state legislator with grants from the USDA and other institutions, the CD-ROM system has been implemented in all 67 county extension offices in Florida and is available to the public as a for-sale CD-ROM. Public access is also available to most of the database through the WWW.

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34

KumarKatiyar, Vijay, and Dalip Kamboj. "TIRS: SMS based Transportation Information Retrieval System." International Journal of Computer Applications 76, no. 12 (August 23, 2013): 15–19. http://dx.doi.org/10.5120/13299-0757.

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35

Deo, Arpit. "A SURVEY PAPER ON INFORMATION RETRIEVAL SYSTEM." International Journal of Advanced Research in Computer Science 9, no. 1 (February 20, 2018): 778–81. http://dx.doi.org/10.26483/ijarcs.v9i1.5505.

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36

T, Jyothi. "Efficient Information Retrieval System for Cloud Environments." IJARCCE 6, no. 4 (April 30, 2017): 312–15. http://dx.doi.org/10.17148/ijarcce.2017.6459.

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37

Liu, Na, and Kun Liu. "Mobile Agents Build Web Information Retrieval System." Applied Mechanics and Materials 543-547 (March 2014): 3373–76. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3373.

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In recent years, mobile agent has always been the hot spot of the academic research, this paper introduces the concept of mobile agent, mobile agent system architecture and key technologies, combined with the mobile technology and web information retrieval technology, design a model of information retrieval system based on mobile agent, and expounds the key technology to realize the model needs to solve.

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38

Lokhande, Kalyani, and Dhanashree Tayade. "English-Marathi Cross Language Information Retrieval System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 112. http://dx.doi.org/10.23956/ijarcsse.v7i8.34.

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Nowadays, diﬀerent types of content in diﬀerent languages are available on World Wide Web and their usage is increasing rapidly. Cross Language Information Retrieval (CLIR) deals with retrieval of documents in another language than the language of the requested query. Various researchers worked on Cross Language Information Retrieval systems for Indian languages using diﬀerent translation approaches. There is still CLIR system to be developed which allow user to retrieve Marathi documents when English query is given. In the proposed English to Marathi Cross Language Information Retrieval system, translation is based on query translation approach. The proposed system retrieves Marathi documents depending on matching terms in query. The performance of the proposed system is improved by query pre-processing and query expansion using WordNet.

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39

Radhouani, Saïd, Claire-Lise Mottaz Jiang, and Gilles Falquet. "FlexIR: a Domain-Specific Information Retrieval System." Polibits 39 (June 30, 2009): 27–31. http://dx.doi.org/10.17562/pb-39-4.

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40

Wiesman, F., Arie Hasman, and H. J. van den Herik. "Information retrieval: an overview of system characteristics." International Journal of Medical Informatics 47, no. 1-2 (November 1997): 5–26. http://dx.doi.org/10.1016/s1386-5056(97)00094-4.

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41

Wrba, Th, G. Duftschmid, P. Sachs, W. Gall, Ch Rehnelt, G. Boldt, W. Premauer, and W. Dorda. "ArchiMed: A Medical Information and Retrieval System." Methods of Information in Medicine 38, no. 01 (1999): 16–24. http://dx.doi.org/10.1055/s-0038-1634148.

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Abstract:ArchiMed is a highly flexible medical data storage and retrieval system which adds sophisticated clinical research support to a standard hospital information system (HIS).Currently, the HIS of Vienna General Hospital-University Hospital (2000 beds) stores the clinical data of over 2 million patients. While this system supports patient care (e.g., ADT, clinical chemistry, diagnosis, procedures), it has no features to facilitate research, such as the management of clinical studies.ArchiMed is designed to support clinical research. It includes an independent database, which mirrors virtually all the information held in the HIS while also allowing new data to be collected independently and to be added to the database. Flexible retrieval and analysis of data contained in the database are then possible. Thus, existing patient data can be smoothly incorporated into a study together with data collected specifically for research purposes. The system has already been successfully installed in the departments of surgery and soon in other departments as well.

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42

Wood, Murray, and Ian Sommerville. "An information retrieval system for software components." Software Engineering Journal 3, no. 5 (1988): 198. http://dx.doi.org/10.1049/sej.1988.0025.

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43

Myna, A. N., K. Deepthi, and Samvrudhi V. Shankar. "Hybrid Recommender System for Music Information Retrieval." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4145–49. http://dx.doi.org/10.1166/jctn.2020.9035.

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Music plays an integral role in our lives as the most popular type of recreation. With the advent of new technologies such as Internet and portable media players, large amount of music data is available online which can be distributed and easily made available to people. Enormous amount of music data is released every year by several artists with songs varying in features, genre and so on. Because of this, a need for reliable and easy access of songs based on user preferences is necessary. The recommender system focuses on generating playlists based on the physical, perceptual and acoustical properties of the song (content based filtering approach), or on commonalities between users on a particular basis like ratings or user data history (collaborative filtering). The system thus developed is a hybrid music recommender tool which creates a user centric suggestion system accompanied by feature extraction which in turn enhances the accuracy of music recommendations.

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44

Bordogna, G., P. Carrara, I. Gagliardi, D. Merelli, F. Naldi, and M. Padula. "A system architecture for multimedia information retrieval." Journal of Studies in International Education 16, no. 4 (January 1, 1990): 229–38. http://dx.doi.org/10.1177/102831539001600403.

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45

BAGGIA, P., L. FISSORE, E. GIACHIN, G. MICCA, C. RULLENT, and P. LAFACE. "A SPEECH UNDERSTANDING SYSTEM FOR INFORMATION RETRIEVAL." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (February 1994): 71–97. http://dx.doi.org/10.1142/s0218001494000048.

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This paper describes a Continuous Speech Understanding System that allows information services to be accessed through the telephone line. It accepts queries within a restricted semantic domain, expressed in free but syntactically correct natural language, with a lexicon of the order of 800 words. In the implementation here described, a user can access an electronic mailbox or a train information service through a PABX telephone line. The architecture of the system is based on two main modules that represent and use different knowledge sources. A speaker independent recognition module generates, for each utterance, a lattice of word hypotheses which is the interface to an understanding module that performs the syntactic and semantic analysis. The recognition module is based on Hidden Markov Models of subword units, and performs the acoustic decoding process according to a beam search strategy. The understanding module finds the most likely sequence of words and represents its meaning in a format which facilitates the access to a database. It makes use of a modified caseframe analysis guided by the word hypotheses scores. Experiments were performed with 600 sentences from 10 speakers on the E-Mail application task. Using 15 Gaussian mixtures per state, a word accuracy of 75.7 was obtained with a test vocabulary of 787 words and no linguistic constraints. Linguistic processing of the corresponding lattices achieved a sentence understanding rate of 82%.

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46

Velloso-Ferreira, C., and R. Paviotti-Corcuera. "Information retrieval system for ENDF format libraries." Annals of Nuclear Energy 24, no. 18 (December 1997): 1515–24. http://dx.doi.org/10.1016/s0306-4549(97)00013-3.

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Bender, Avi. "An Optical Disk‐Based Information Retrieval System." Library Hi Tech 6, no. 3 (March 1988): 81–85. http://dx.doi.org/10.1108/eb047734.

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48

Oshaiba, Mohamed Marouf Z., Enas M. F. El Houby, and Akram Salah. "Semantic Annotation for Biological Information Retrieval System." Advances in Bioinformatics 2015 (February 9, 2015): 1–11. http://dx.doi.org/10.1155/2015/597170.

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Online literatures are increasing in a tremendous rate. Biological domain is one of the fast growing domains. Biological researchers face a problem finding what they are searching for effectively and efficiently. The aim of this research is to find documents that contain any combination of biological process and/or molecular function and/or cellular component. This research proposes a framework that helps researchers to retrieve meaningful documents related to their asserted terms based on gene ontology (GO). The system utilizes GO by semantically decomposing it into three subontologies (cellular component, biological process, and molecular function). Researcher has the flexibility to choose searching terms from any combination of the three subontologies. Document annotation is taking a place in this research to create an index of biological terms in documents to speed the searching process. Query expansion is used to infer semantically related terms to asserted terms. It increases the search meaningful results using the term synonyms and term relationships. The system uses a ranking method to order the retrieved documents based on the ranking weights. The proposed system achieves researchers’ needs to find documents that fit the asserted terms semantically.

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Kim, Young-Geil. "The Information Retrieval System for Software Reuse." Journal of Korea Institute of Information, Electronics, and Communication Technology 9, no. 1 (February 29, 2016): 1–8. http://dx.doi.org/10.17661/jkiiect.2016.9.1.001.

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Lal, Niranjan, Samimul Qamar, and Savita Shiwani. "Information Retrieval System and challenges with Dataspace." International Journal of Computer Applications 147, no. 8 (August 16, 2016): 23–28. http://dx.doi.org/10.5120/ijca2016911128.

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