Relevant bibliographies by topics / Retrieval systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Retrieval systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Retrieval systems"

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Tokumi, Yuta, Junki Hakamata, and Masataka Tokumaru. "Development of a Nutritional Management System for a Healthy Eating Habits Support System." Journal of Advanced Computational Intelligence and Intelligent Informatics 17, no. 2 (March 20, 2013): 324–34. http://dx.doi.org/10.20965/jaciii.2013.p0324.

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In this study, we propose a Nutritional Management System (NMS) that optimizes nutritional balance using a tabu search. Contemporary recipe retrieval systems generally retrieve a recipe either by using a keyword or by recommending a popular recipe. However, these recipe retrieval systems yield the same retrieval results for different users, and thus, the results do not necessarily reflect an individual user’s tastes. In addition, the search results delivered by many recipe retrieval systems do not always describe the nutritional details of the recipes. Therefore, we developed a Healthy Eating Habits Support System for resolving these issues. This system is composed of an NMS and a Kansei Retrieval System (KRS). The NMS retrieves nutritionally balanced menus using a tabu search. The KRS learns a user’s taste preferences, and selects menus suitable for a user’s tastes from among the menus retrieved by the NMS. The KRS needs multiple nutritionally balanced menus to learn a user’s taste preferences. Thus, in this study, we simulated scenarios to examine whether the NMS can retrieve multiple nutritionally balanced menus in the long-term without duplication, using quasi recipe data and real recipe data. When we used quasi recipe data in a simulation with a very large number of recipes, the NMS could retrieve a sustained quantity of menus in the long-term. In addition, when we used real recipe data, the NMS could quickly retrieve menus over the long-term. Therefore, the effectiveness of the NMS was confirmed.
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Crook, D. A., B. J. Adair, and L. J. Hetherington. "Low-cost device for retrieval of moorings deployed with underwater remote release systems." Marine and Freshwater Research 69, no. 3 (2018): 473. http://dx.doi.org/10.1071/mf17189.

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Underwater acoustic and timed-release systems are commonly deployed with sensors and other equipment for ecological and oceanographic research in aquatic environments. These systems allow equipment deployed at depth to be remotely released from their moorings for retrieval. However, a current limitation is that most systems do not incorporate mechanisms for retrieval of the mooring anchor and attachments (e.g. shackles, cable). Herein we provide a detailed description and evaluation of a ‘retrieval pod’ that can be attached directly to remotely released underwater devices to facilitate mooring retrieval. The system was tested using acoustic release telemetry receivers under field conditions in a large estuary in the Northern Territory, Australia. Six receivers fitted with pods were retrieved from the field after two consecutive 6-month deployments (12 retrievals in total) with no failed mooring retrievals. Although our tests were confined to shallow depths (<8m), it should be straightforward to modify the system for use in deeper water by increasing the spool capacity. The retrieval pods are reusable, require minimal construction skills and can be assembled from basic equipment available at most hardware or boating stores, making them a convenient and low-cost option for the retrieval of moorings deployed with remote release systems.
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Orscheschek, Franziska, Tilo Strobach, Torsten Schubert, and Timothy Rickard. "Two retrievals from a single cue: A bottleneck persists across episodic and semantic memory." Quarterly Journal of Experimental Psychology 72, no. 5 (May 28, 2018): 1005–28. http://dx.doi.org/10.1177/1747021818776818.

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There is evidence in the literature that two retrievals from long-term memory cannot occur in parallel. To date, however, that work has explored only the case of two retrievals from newly acquired episodic memory. These studies demonstrated a retrieval bottleneck even after dual-retrieval practice. That retrieval bottleneck may be a global property of long-term memory retrieval, or it may apply only to the case of two retrievals from episodic memory. In the current experiments, we explored whether that apparent dual-retrieval bottleneck applies to the case of one retrieval from episodic memory and one retrieval from highly overlearned semantic memory. Across three experiments, subjects learned to retrieve a left or right keypress response form a set of 14 unique word cues (e.g., black—right keypress). In addition, they learned a verbal response which involved retrieving the antonym of the presented cue (e.g., black—“white”). In the dual-retrieval condition, subjects had to retrieve both the keypress response and the antonym word. The results suggest that the retrieval bottleneck is superordinate to specific long-term memory systems and holds across different memory components. In addition, the results support the assumption of a cue-level response chunking account of learned retrieval parallelism.
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Abubacker, K. A. Shaheer, J. Sutha, and K. A. Shahul Hameed. "A simple multi-feature based stereoscopic medical image retrieval system." Polish Journal of Medical Physics and Engineering 25, no. 2 (June 1, 2019): 127–30. http://dx.doi.org/10.2478/pjmpe-2019-0017.

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Abstract This paper describes a method of retrieving stereoscopic medical images from the database that consists of feature extraction, similarity measure, and re-ranking of retrieved images. This method retrieves similar images of the query image from the database and re-ranks them according to the disparity map. The performance is evaluated using the metrics namely average retrieval precision (APR) and average retrieval rate (ARR). According to the performance outcomes, the multi-feature based image retrieval using Mahalanobis distance measure has produced better result compared to other distance measures namely Euclidean, Minkowski, the sum of absolute difference (SAD) and the sum of squared absolute difference (SSAD). Therefore, the stereo image retrieval systems presented has high potential in biomedical image storage and retrieval systems.
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Luo, Youmeng, Wei Li, Xiaoyu Ma, and Kaiqiang Zhang. "Image Retrieval Algorithm Based on Locality-Sensitive Hash Using Convolutional Neural Network and Attention Mechanism." Information 13, no. 10 (September 24, 2022): 446. http://dx.doi.org/10.3390/info13100446.

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With the continuous progress of image retrieval technology, in the field of image retrieval, the speed of a search for a desired image from a great deal of image data becomes a hot issue. Convolutional Neural Networks(CNN) have been used in the field of image retrieval. However, many image retrieval systems based on CNN have a poor ability to express image features, resulting in a series of problems such as low retrieval accuracy and robustness. When the target image is retrieved from a large amount of image data, the vector dimension after image coding is high and the retrieval efficiency is low. Locality-sensitive hash is a method to find similar data from massive high latitude data. It reduces the data dimension of the original spatial data through hash coding and conversion, and can also maintain the similarity between the data. The retrieval time and space complexity are low. Therefore, this paper proposes a locality-sensitive hash image retrieval method based on CNN and the attention mechanism. The steps of the method are as follows: using the ResNet50 network as the feature extractor of the image, adding the attention module after the convolution layer of the model, and using the output of the network full connection layer to retrieve the features of the image database, then using the local-sensitive hash algorithm to hash code the image features of the database to reduce the dimension and establish the index, and finally measuring the features of the image to be retrieved and the image database to get the most similar image, completing the content-based image retrieval task. The method in this paper is compared with other image retrieval methods on corel1k and corel5k datasets. The experimental results show that this method can effectively improve the accuracy of image retrieval, and the retrieval efficiency is significantly improved. It also has higher robustness in different scenarios.
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Tseng, Chien-Hao, Chia-Chien Hsieh, Dah-Jing Jwo, Jyh-Horng Wu, Ruey-Kai Sheu, and Lun-Chi Chen. "Person Retrieval in Video Surveillance Using Deep Learning–Based Instance Segmentation." Journal of Sensors 2021 (August 21, 2021): 1–12. http://dx.doi.org/10.1155/2021/9566628.

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Video surveillance systems are deployed at many places such as airports, train stations, and malls for security and monitoring purposes. However, it is laborious to search for and retrieve persons in multicamera surveillance systems, especially with cluttered backgrounds and appearance variations among multiple cameras. To solve these problems, this paper proposes a person retrieval method that extracts the attributes of a masked image using an instance segmentation module for each object of interest. It uses attributes such as color and type of clothes to describe a person. The proposed person retrieval system involves four steps: (1) using the YOLACT++ model to perform pixelwise person segmentation, (2) conducting appearance-based attribute feature extraction using a multiple convolutional neural network classifier, (3) employing a search engine with a fundamental attribute matching approach, and (4) implementing a video summarization technique to produce a temporal abstraction of retrieved objects. Experimental results show that the proposed retrieval system can achieve effective retrieval performance and provide a quick overview of retrieved content for multicamera surveillance systems.
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Dr. V. Suma. "A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm." September 2020 02, no. 03 (August 28, 2020): 151–60. http://dx.doi.org/10.36548/jitdw.2020.3.003.

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The recent technology development fascinates the people towards information and its services. Managing the personal and pubic data is a perennial research topic among researchers. In particular retrieval of information gains more attention as it is important similar to data storing. Clustering based, similarity based, graph based information retrieval systems are evolved to reduce the issues in conventional information retrieval systems. Learning based information retrieval is the present trend and in particular deep neural network is widely adopted due to its retrieval performance. However, the similarity between the information has uncertainties due to its measuring procedures. Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work. Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to retrieve the necessary information from distributed cloud. Experimental results prove that the proposed model attains better retrieval accuracy and accuracy compared to conventional models such as support vector machine and deep neural network.
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Anandh, A., K. Mala, and R. Suresh Babu. "Combined global and local semantic feature–based image retrieval analysis with interactive feedback." Measurement and Control 53, no. 1-2 (December 12, 2019): 3–17. http://dx.doi.org/10.1177/0020294018824122.

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Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.
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Mawhesaran, T. "Document Vectorization for Large-Scale Information Retrieval Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (February 7, 2025): 1–9. https://doi.org/10.55041/ijsrem41430.

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The invention presents an entirely new paradigm of document-search framework to make the retrieval and access of specific information from several thousands of documents, especially PDF files, as simple and easy as possible. The document content and metadata are retrieved for search using a vector-based approach with state-of-the-art vectorization techniques and vector databases such as MilvusDB. Document content is prepared and vectorized using preprocessing methods such as stop word removal, tokenization, normalization, etc. The vector data are stored in the database and indexed by such techniques that embrace similarity cosines to make fast and accurate retrievals of relevant results. It is an offline system that works in one's locality, enhancing privacy and dependability in the absence of a network. The solution is scalable, and flexible and supports every possible document format; thus, it is one stop solution for students, research scholars, and job-workers. Spending less time searching through documents increases productivity and has taken working with an incredibly user-friendly approach enriched with metadata. Keywords: Document Search, Vector Store, Information Retrieval, PDF Search, MilvusDB, Vectorization, Tokenization, Metadata, Similarity Search, Offline Search, Scalable Solution, AI-powered Search, Research Tool, Productivity Enhancement.
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Arunarani, Ar, and D. Manjula Perkinian. "Intelligent Techniques for Providing Effective Security to Cloud Databases." International Journal of Intelligent Information Technologies 14, no. 1 (January 2018): 1–16. http://dx.doi.org/10.4018/ijiit.2018010101.

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Cloud databases have been used in a spate of web-based applications in recent years owing to their capacity to store big data efficiently. In such a scenario, access control techniques implemented in relational databases are so modified as to suit cloud databases. The querying features of cloud databases are designed with facilities to retrieve encrypted data. The performance with respect to retrieval and security needs further improvements to ensure a secured retrieval process. In order to provide an efficient secured retrieval mechanism, a rule- and agent-based intelligent secured retrieval model has been proposed in this paper that analyzes the user, query and contents to be retrieved so as to effect rapid retrieval with decryption from the cloud databases. The major advantage of this retrieval model is in terms of its improved query response time and enhanced security of the storage and retrieval system. From the experiments conducted in this work, proposed model increased storage and access time and, in addition, intensified the security of the data stored in cloud databases.
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Dissertations / Theses on the topic "Retrieval systems"

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Mills, Timothy J. "Content modelling in multimedia information retrieval systems : the Cobra retrieval system." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442405.

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Al-Maskari, Azzah. "Evaluation of interactive information retrieval systems." Thesis, University of Sheffield, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538079.

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Zhang, Dengsheng 1963. "Image retrieval based on shape." Monash University, School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8688.

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Romano, Nicholas C., Dmitri G. Roussinov, Jay F. Nunamaker, and Hsinchun Chen. "Collaborative Information Retrieval Environment: Integration of Information Retrieval with Group Support Systems." HICSS, 1999. http://hdl.handle.net/10150/105688.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>Observations of Information Retrieval (IR) system user experiences reveal a strong desire for collaborative search while at the same time suggesting that collaborative capabilities are rarely, and then only in a limited fashion, supported by current searching and visualization tools. Equally interesting is the fact that observations of user experiences with Group Support Systems (GSS) reveal that although access to external information and the ability to search for relevant material is often vital to the progress of GSS sessions, integrated support for collaborative searching and visualization of results is lacking in GSS systems. After reviewing both user experiences described in IR and GSS literature and observing and interviewing users of existing IR and GSS commercial and prototype systems, the authors conclude that there is an obvious demand for systems supporting multi-user IR.. It is surprising to the authors that very little attention has been given to the common ground shared by these two important research domains. With this in mind, our paper describes how user experiences with IR and GSS systems has shed light on a promising new area of collaborative research and led to the development of a prototype that merges the two paradigms into a Collaborative Information Retrieval Environment (CIRE). Finally the paper presents theory developed from initial user experiences with our prototype and describes plans to test the efficacy of this new paradigm empirically through controlled experimentation.
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Xie, Jinsheng. "On the analysis of selected automated storage and retrieval systems." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/25020.

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Watkins, Alfred R. "Continuous media object modeling, storage, and retrieval." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/9184.

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Portabella, Arnús Marcos. "Wind Field Retrieval from Satellite Radar Systems." Doctoral thesis, Universitat de Barcelona, 2002. http://hdl.handle.net/10803/734.

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Wind observations are essential for determining the atmospheric flow. In particular, sea-surface wind observations are very useful for many meteorological and oceanographic applications. In this respect, most of the satellite remote-sensing radar systems can provide sea-surface wind information. This thesis reviews the current wind retrieval procedures for such systems, identifies the most significant unresolved problems, and proposes new methods to overcome such problems.<br/>In order to invert the geophysical model function (GMF), which relates the radar backscatter measurement with the wind speed and direction (unknowns), two independent measurements over the same scene (wind cell) are at least needed. The degree of independence of such measurements is given by the azimuth (view) angle separation among them. This thesis is focused on improving the wind retrieval for determined systems (two or more measurements) with poor azimuth diversity and for underdetermined systems (one single measurement). For such purpose, observations from two different radar systems, i.e., SeaWinds and SAR (Synthetic Aperture Radar), are used.<br/>The wind retrieval methods proposed in this book for determined (Multiple Solution Scheme, denoted MSS) and underdetermined (SAR Wind Retrieval Algorithm, denoted SWRA) systems are based on Bayesian methodology, that is, on maximizing the probability of obtaining the "true" wind given the radar measurements and the a priori wind information (often provided by numerical weather prediction models), assuming that all wind information sources contain errors. In contrast with the standard procedure for determined systems, the MSS fully uses the information obtained from inversion, which turns out to positively impact the wind retrieval when poor azimuth diversity. On the other hand, in contrast with the various algorithms used nowadays to resolve the wind vector for underdetermined systems, the SWRA assumes not only that the system can not be solved without additional information (underdetermination assumption) but also that both the algorithms and the additional information (which are combined to retrieved the wind vector) contain errors and these should be well characterized. The MSS and the SWRA give promising results, improving the wind retrieval quality as compared to the methods used up to now.<br/>Finally, a generic quality control is proposed for determined systems. In general, high-quality retrieved wind fields can be obtained from scatterometer (determined systems) measurements. However, geophysical conditions other than wind (e.g., rain, confused sea state or sea ice) can distort the radar signal and, in turn, substantially decrease the wind retrieval quality. The quality control method uses the inversion residual (which is sensitive to inconsistencies between observations and the geophysical model function that are mainly produced when conditions other than wind dominate the radar backscatter signal) to detect and reject the poor-quality retrievals. The method gives good results, minimizing the rejection of good-quality data and maximizing the rejection of poor-quality data, including rain contamination.
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Engström, Felix, and Eli Pleaner. "Learning Methods for Improving News Retrieval Systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187492.

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Content providers require an efficient and accurate way of retrieving relevant content with minimal human aid. News retrieval, for instance, often requires human intervention to recognize which text documents are news articles and which are not. The differences between a factual news article and an opinionated blog piece may be subtle, yet are critical for providing informative and relevant content to users. This thesis explores the problem of format classification: the task of classifying text documents based on the format in which they are written, such as a news article, blog entry or forum text. More explicitly, the goal of the thesis is to examine how well state-of-the-art supervised text classifica- tion techniques work for format classification. We select a number of classifiers that have been shown to perform well in other text classification tasks and evaluate their perfor- mance in this unexplored task. Experimental evaluation, performed on a novel dataset created from multiple existing datasets, explores both binary and multi-class classification in a bag-of-words feature space. Based on our experimental results, we have found that state-of-the-art supervised text classification techniques perform acceptably well at format classification. Furthermore, we propose a Gradient Boost model as a candidate classifier for the task of format clas- sification, and provide a discussion of future work.<br>Företag som tillhandahåller innehållshanteringstjänster be- höver effektiva och precisa metoder för att med minsta möjliga mänskliga arbetskraft utvinna relevant innehåll ur stora mängder data. Ett exempel på detta är tjänster för insamlande av nyheter, där nyheter skall utvinnas från olika källor. Som en del av den processen krävs att de kan avgöra om en text är en nyhetsartikel eller någon annan form av text. Skillnaden mellan en nyhetsartikel och en text skriven för en blogg kan vara subtil, men är avgörande för dessa företag. Denna rapport utforskar formatklassifi- cering: uppgiften att klassificera texter baserat på vilket format de är skrivna för. Exempel på format är: nyhet- sartikel, bloggtext eller forumtext. Mer specifikt tar den sig an uppgiften att undersöka hur väl de metoder som idag används i den väl utforskade uppgiften att klassificera texter baserat på ämne fungerar applicerade på formatk- lassificering. Det utforskas med experimentell evaluering på ett nytt dataset som konstruerats genom att kombin- era ett flertal existerande dataset. Detta görs både som en binär- och multiklassificeringsuppgift i en bag-of-word vektorrymd. Ett antal ämnesklassificeringsmetoder väljs baserat på resultat från tidigare forskning, och hur dessa presterar på formatklassificering undersöks. Vi drar slut- satsen att våra resultat visar att de textklassificeringsme- toder vi testat fungerar acceptabelt väl på formatklassifi- cering. Vi föreslår vidare gradient-boost eller multinomial naive bayes för att lösa uppgiften, beroende på om fokus ligger på kvaliteten av klassificeringen eller prestanda. Slut- ligen diskuteras resultaten , de sätts i relation till de begrän- sningar som förelegat och förslag till framtida forskning ges.
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Liu, Chi-Cheng. "Implementation of parallel intelligent information retrieval systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq30509.pdf.

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Tang, Wai-hung, and 鄧偉雄. "An anonymity scheme for file retrieval systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B40887972.

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Books on the topic "Retrieval systems"

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Gallery, Art Alive, ed. Retrieval systems. New Delhi: Art Alive Gallery, 2009.

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1953-, Willett Peter, ed. Document retrieval systems. London: Taylor Graham, 1988.

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Heikkila, Ralph C. Election signature retrieval systems. Washington, D.C: National Clearinghouse on Election Administration, Federal Election Commission, 1992.

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T, Meadow Charles, Boyce Bert R, and Kraft Donald H, eds. Text information retrieval systems. 3rd ed. Amsterdam: Academic, 2007.

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R, Boyce Bert, and Kraft Donald H, eds. Text information retrieval systems. 2nd ed. San Diego: Academic Press, 2000.

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Heikkila, Ralph C. Election signature retrieval systems. Washington, D.C: National Clearinghouse on Election Administration, Federal Election Commission, 1992.

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Kowalski, Gerald J., and Mark T. Maybury. Information Storage and Retrieval Systems. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/b116174.

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Ribeiro, Berthier de Araújo Neto, 1960-, ed. Modern information retrieval. New York: ACM Press, 1999.

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J, Warner Amy, and Lancaster F. Wilfrid 1933-, eds. Information retrieval today. Arlington, Va: Information Resources Press, 1993.

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Frédéric, Precioso, Cord Matthieu, and SpringerLink (Online service), eds. Visual Indexing and Retrieval. New York, NY: Springer New York, 2012.

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Book chapters on the topic "Retrieval systems"

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Castells, Pablo, and Dietmar Jannach. "Recommender Systems." In Information Retrieval, 49–109. New York, NY, USA: ACM, 2024. https://doi.org/10.1145/3674127.3674131.

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Baeza-Yates, Ricardo, Leena Murgai, and Shiran Dudy. "Bias in Retrieval Systems." In Information Retrieval, 389–444. New York, NY, USA: ACM, 2024. https://doi.org/10.1145/3674127.3674138.

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Ferro, Nicola, and Maria Maistro. "Evaluation of IR Systems." In Information Retrieval, 111–91. New York, NY, USA: ACM, 2024. https://doi.org/10.1145/3674127.3674132.

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Hiemstra, Djoerd, and Wessel Kraaij. "Evaluation of Multimedia Retrieval Systems." In Multimedia Retrieval, 347–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72895-5_13.

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Hersh, William R., William M. Detmer, and Mark E. Frisse. "Information-Retrieval Systems." In Medical Informatics, 539–72. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-0-387-21721-5_15.

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Lalmas, Mounia, and Andrew Trotman. "XML Retrieval." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_474-2.

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Balog, Krisztian. "Entity Retrieval." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80724-1.

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Amati, Giambattista. "Information Retrieval." In Encyclopedia of Database Systems, 1–6. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_915-2.

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Lalmas, Mounia, and Andrew Trotman. "XML Retrieval." In Encyclopedia of Database Systems, 3616–21. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_474.

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Amati, Giambattista. "Information Retrieval." In Encyclopedia of Database Systems, 1519–23. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_915.

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Conference papers on the topic "Retrieval systems"

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Rani, Maneeha, Bhupesh Kumar Mishra, Dhavalkumar Thakker, and Mohammad Nouman Khan. "To Enhance Graph-Based Retrieval-Augmented Generation (RAG) with Robust Retrieval Techniques." In 2024 18th International Conference on Open Source Systems and Technologies (ICOSST), 1–6. IEEE, 2024. https://doi.org/10.1109/icosst64562.2024.10871140.

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Gabburo, Matteo, Nicolaas Jedema, Siddhant Garg, Leonardo Ribeiro, and Alessandro Moschitti. "Measuring Retrieval Complexity in Question Answering Systems." In Findings of the Association for Computational Linguistics ACL 2024, 14636–50. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.872.

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Song, Yiping, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao, and Rui Yan. "An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/609.

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Human-computer conversation systems have attracted much attention in Natural Language Processing. Conversation systems can be roughly divided into two categories: retrieval-based and generation-based systems. Retrieval systems search a user-issued utterance (namely a query ) in a large conversational repository and return a reply that best matches the query. Generative approaches synthesize new replies. Both ways have certain advantages but suffer from their own disadvantages. We propose a novel ensemble of retrieval-based and generation-based conversation system. The retrieved candidates, in addition to the original query, are fed to a reply generator via a neural network, so that the model is aware of more information. The generated reply together with the retrieved ones then participates in a re-ranking process to find the final reply to output. Experimental results show that such an ensemble system outperforms each single module by a large margin.
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Liang, Yan, Wen Feng Lu, Ying Liu, and Soon Chong Johnson Lim. "Interactive Interface Design for Design Rationale Search and Retrieval." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28392.

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In the rapid product design and development, design rationale (DR) information is critical for designers to understand the design technology and for better knowledge reuse. The visualization of DR capture, search and retrieval systems is important in assisting designers to gain insights and benefits from the increasing DR repository, e.g. major design issues of an artifact and design alternatives to the issues. In literatures, we have observed that the current DR systems provide limited interactive guidance in helping designers to DR navigate and search. In our previous work, we have proposed an ISAL (Issue, Solution and Artifact Layer) model for DR discovery in which the DR information is mined from a large amount of archival documents using text mining techniques. In this paper, we focus on interactive user interface (UI) design for our DR retrieval system. With functions like view, search and analysis, designers are visually guided during DR searching. Using patents as our research data, the view functions enable engineering designers to intuitively navigate DR repository. Search functions support designers to retrieve relevant DR from multiple aspects and visualize the structure of relevant DRs from a holistic view. By applying analysis functions on the retrieved DR, visualization is able to suggest some useful DR insights, e.g. key components of certain issues, possible solutions of relevant issues, major issues of an artifact and technology development trend analysis, etc. Together with the function examples, we illustrate the merits of our interface design for DR search and retrieval.
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Chan, Yi Meng, Richard T. Harvey, and Dan Smith. "Building systems to block pornography." In Challenge of Image Retrieval. BCS Learning & Development, 1999. http://dx.doi.org/10.14236/ewic/cir1999.8.

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Mulhem, P., and L. Nigay. "Interactive information retrieval systems." In the 19th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/243199.243280.

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Zhang, Guoxian, and Devendra P. Garg. "Mobile Multi-Robot Control in Target Search and Retrieval." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2196.

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In this paper, the design of a controller is proposed for a multi-robot target search and retrieval system. Inspired by research in insect foraging and swarm robotics, we developed a transition mechanism for the multi-robot system. Environmental information and task performance obtained by the robot system are used to adjust individual robot’s parameters and guide environment exploration. The proposed control system is applicable in the solution of multi-target problem also where several robots may be needed to cooperate together to retrieve a large target. Simulations show that the task performance improves significantly with the proposed method by sharing information in parameter learning and environment exploration.
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Mi Hee, Yoon Yong Ik, and Kio Chung Kim. "Unified video retrieval system supporting similarity retrieval." In Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99. IEEE, 1999. http://dx.doi.org/10.1109/dexa.1999.795298.

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Morris, Elissa, and Daniel A. McAdams. "Bioinspired Origami: Case Studies Using a Keyword Search Algorithm." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22228.

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Abstract Numerous folding patterns, structures, and behaviors exist in nature that may provide design solutions to engineering problems. While applying biological solutions to engineering design is evidently valuable, the retrieval of useful design inspiration remains a primary challenge preventing the transfer of knowledge from biology to the engineering domain. In prior research, information retrieval techniques are employed to retrieve useful biological design solutions and a text-based search algorithm is developed to return passages where folding in nature is observed. The search algorithm, called FoldSearch, integrates tailored biological keywords and filtering methods to retrieve passages from an extensive biological corpus. The objective of this paper is two-fold — 1) to demonstrate the functionality of FoldSearch, and 2) to create abstract models of the retrieved biological systems from FoldSearch which can be used for the development of novel origami crease patterns and foldable structures. In this paper, the utility of FoldSearch is demonstrated through two case studies where the retrieved biological examples undergo a design abstraction process that leads to the development of bioinspired origami crease patterns and novel foldable structures. The abstraction process is presented as a systematic design methodology for bioinspired origami for the growing research field of origami engineering.
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Palaniappan, Somasundaram, Rohit Mali, Luca Cuomo, Mauro Vitale, Ali Youssef, Athira Puthanveetil Madathil, Malathi Murugesan, Alessandro Bettini, Giovanni De Magistris, and Giacomo Veneri. "Enhancing Enterprise-Wide Information Retrieval through RAG Systems Techniques, Evaluation, and Scalable Deployment." In ADIPEC. SPE, 2024. http://dx.doi.org/10.2118/222032-ms.

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Abstract Organizations in the Oil and Gas Industry deal with a vast repository of knowledge dispersed across various structured and unstructured data sources. The process of retrieving information for a particular task can be time-consuming and necessitates the involvement of multiple subject matter experts. Large Language Models (LLMs) supported by Retrieval Augmented Generation (RAG) systems can help expedite information retrieval and processing. However, Enterprise-scale LLM implementation faces challenges, that include confidentiality, scalability, handling hallucinations, handling domain data, feedback, and model improvement. In this technical paper, we describe an optimized RAG system using LLMs for accelerated information retrieval from vast, highly confidential sources. We present the software architecture, the data flow that facilitates the application's ability to gather feedback for model fine-tuning and the techniques used to optimize our solution for accuracy, scalability, throughput. In the proposed architecture, the LLM-as-a-service component generated an average throughput of 989 tokens per second for summarization tasks involving up to 2500 tokens per request when run on A100 GPUs (40 GB). In the Oil &amp; Gas Industry, the application allows Field Service Engineers to directly retrieve technical details about machinery, details from maintenance manuals etc and helps design teams to speed up information retrieval. This has resulted in an estimated improved productivity in the range of 20% and a positive impact on Customer Relationship, Time to Serve and Cost of Quality.
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Reports on the topic "Retrieval systems"

1

RIECK, C. A. Project W-211, initial tank retrieval systems, retrieval control system software configuration management plan. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/781535.

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Baylor, Robert. Automated information retrieval systems for legal research. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.648.

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Rieck, C. A. Engineer/constructor description of work for Tank 241-SY-102 retrieval system, project W-211, initial tank retrieval systems. Office of Scientific and Technical Information (OSTI), February 1996. http://dx.doi.org/10.2172/483376.

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Bitz, D. A., D. L. Berry, and L. J. Jardine. Decision and systems analysis for underground storage tank waste retrieval systems and tank waste remediation system. Office of Scientific and Technical Information (OSTI), March 1994. http://dx.doi.org/10.2172/10196816.

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Berry, D. L., and L. J. Jardine. Decision and systems analysis for underground storage tank waste retrieval systems and tank waste remediation system. Office of Scientific and Technical Information (OSTI), October 1993. http://dx.doi.org/10.2172/10133252.

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RIECK, C. A. System Engineering Management and implementation plan for Project W-211, ''Initial tank retrieval systems''. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/781523.

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Huckfeldt, R. A. Preliminary fire hazards analysis for W-211, Initial Tank Retrieval Systems. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/34338.

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LEONARD, M. W., and G. A. HOFFERBER. Systems Engineering Implementation Plan for Single Shell Tanks (SST) Retrieval Projects. Office of Scientific and Technical Information (OSTI), November 2000. http://dx.doi.org/10.2172/805455.

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VAN BEEK, J. E. System Engineering Management and Implementation Plan for Project W-211 Initial Tank Retrieval Systems (ITRS). Office of Scientific and Technical Information (OSTI), May 2000. http://dx.doi.org/10.2172/803633.

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VAN BEEK, J. E. Project Execution Plan for Project W-211 Initial Tank Retrieval Systems (ITRS). Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/798001.

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