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Academic literature on the topic 'Techniques of Filtering Information'

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Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Techniques of Filtering Information"

Lu, Jie, Jun Ma, and Guangquan Zhang. "Warning message generation by information filtering techniques." International Journal of Nuclear Knowledge Management 2, no. 4 (2007): 435. http://dx.doi.org/10.1504/ijnkm.2007.014039.

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Bodard, Katia. "Free access to information challenged by filtering techniques." Information & Communications Technology Law 12, no. 3 (October 2003): 263–79. http://dx.doi.org/10.1080/1360083032000198772.

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Khozooii, Narges Sadat, Saman Haratizadeh, and Mohammad Reza Keyvanpour. "An Analytical Framework for Web Information Filtering Techniques." International Journal of Hybrid Information Technology 6, no. 6 (November 30, 2013): 345–58. http://dx.doi.org/10.14257/ijhit.2013.6.6.31.

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Mu, Zhen Hai. "A Multiagent-Based Network Information Filtering System." Advanced Materials Research 532-533 (June 2012): 772–76. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.772.

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Many issues such as low accuracy rate, poor filtration efficiency, and too much network resource occupancy exist in most information filtering systems in the current market. Therefore, there is a great demand to develop a new network filtering system. In this paper, agent techniques are first reviewed, and then the architecture and the working mechanism of a multiagent-based information filtering system is presented. Finally, the design method for core components of the system is given.

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Rafeh, Reza, and Arash Bahrehmand. "An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems." Journal of Information Science 38, no. 3 (March 15, 2012): 205–21. http://dx.doi.org/10.1177/0165551512437517.

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Recommendation systems manage information overload in order to present personalized content to users based on their interests. One of the most efficient recommendation approaches is collaborative filtering, through which recommendation is based on previously rated data. Collaborative filtering techniques feature impressive solutions for suggesting favourite items to certain users. However, recommendation methods fail to reflect fluctuations in users’ behaviour over time. In this article, we propose an adaptive collaborative filtering algorithm which takes time into account when predicting users’ behaviour. The transitive relationship from one user to another is considered when computing the similarity of two different users. We predict variations of users’ preferences using their profiles. Our experimental results show that the proposed algorithm is more accurate than the classical collaborative filtering technique.

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Margaris, Dionisis, Costas Vassilakis, and Panagiotis Georgiadis. "Query personalization using social network information and collaborative filtering techniques." Future Generation Computer Systems 78 (January 2018): 440–50. http://dx.doi.org/10.1016/j.future.2017.03.015.

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Loepp, Benedikt, Katja Herrmanny, and Jürgen Ziegler. "Merging Interactive Information Filtering and Recommender Algorithms – Model and Concept Demonstrator." icom 14, no. 1 (April 15, 2015): 5–17. http://dx.doi.org/10.1515/icom-2015-0006.

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AbstractTo increase controllability and transparency in recommender systems, recent research has been putting more focus on integrating interactive techniques with recommender algorithms. In this paper, we propose a model of interactive recommending that structures the different interactions users can have with recommender systems. Furthermore, as a novel approach to interactive recommending, we describe a technique that combines faceted information filtering with different algorithmic recommender techniques. We refer to this approach as blended recommending. We also present an interactive movie recommender based on this approach and report on its user-centered design process, in particular an evaluation study in which we compared our system with a standard faceted filtering system. The results indicate a higher level of perceived user control, more detailed preference settings, and better suitability when the search goal is vague.

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Lei, Jian Lan, Jin Wang, Guo Dong Lu, and Shao Mei Fei. "Applying Collaborative Filtering Techniques for Individual Fashion Recommendation." Advanced Materials Research 102-104 (March 2010): 31–35. http://dx.doi.org/10.4028/www.scientific.net/amr.102-104.31.

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Collaborative filtering (CF) technique is the most successful method for recommendation system. In this article, we developed a fashion recommendation system by using CF technique. In order to improve on data sparseness problems in CF technique, firstly we built users’ similarities based on users’ background information which is related with fashion, then the neighbors’ predicting ratings were filled into the U-I rating matrix in advance before the traditional collaborative filtering. While computing the background information similarities, we develop a hybrid similarity model which can deal with different types of properties. The method can solve the data sparseness of U-I rating matrix effectively.

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Khare, Saurabh, and Praveen Kaushik. "Gradient nuclear norm minimization-based image filter." Modern Physics Letters B 33, no. 19 (July 8, 2019): 1950214. http://dx.doi.org/10.1142/s0217984919502142.

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Designing an efficient filtering technique is an ill-posed problem especially for image affected from high density of noise. The majority of existing techniques suffer from edge degradation and texture distortion issues. Therefore, in this paper, an efficient weighted nuclear norm minimization (NNM)-based filtering technique to preserve the edges and texture information of filtered images is proposed. The proposed technique significantly improves the quantitative improvements on the low rank approximation of nonlocal self-similarity matrices to deal with the overshrink problem. Extensive experiments reveal that the proposed technique preserves edges and texture details of filtered image with lesser number of visual artifacts on visual quality. The proposed technique outperforms the existing techniques over the competitive filtering techniques in terms of structural similarity index metric (SSIM), peak signal-to-noise ratio (PSNR) and edge preservation index (EPI).

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Sharma, Ambuj, Sandeep Kumar, and Amit Tyagi. "Noise filtering techniques for Lamb waves in structural health monitoring." Multidiscipline Modeling in Materials and Structures 14, no. 4 (December 3, 2018): 676–94. http://dx.doi.org/10.1108/mmms-08-2017-0089.

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Purpose The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities. Design/methodology/approach Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal. Findings Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application. Practical implications The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage. Originality/value Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

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Dissertations / Theses on the topic "Techniques of Filtering Information"

Elling, Eva, and Hannes Fornander. "A Study of Recommender Techniques Within the Field of Collaborative Filtering." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214728.

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Recommender systems can be seen everywhere today,having endless possibilities of implementation. However,operating in the background, they can easily be passed withoutnotice. Essentially, recommender systems are algorithms thatgenerate predictions by operating on a certain data set. Eachcase of recommendation is environment sensitive and dependenton the condition of the data at hand. Consequently, it is difficultto foresee which method, or combination of methods, to apply in aparticular situation for obtaining desired results. The area of recommendersystems that this thesis is delimited to is Collaborativefiltering (CF) and can be split up into three different categories,namely memory based, model based and hybrid algorithms. Thisthesis implements a CF algorithm for each of these categoriesand sets focus on comparing their prediction accuracy and theirdependency on the amount of available training data (i.e. asa function of sparsity). The results show that the model basedalgorithm clearly performs better than the memory based, bothin terms of overall accuracy and sparsity dependency. With anincreasing sparsity level, the problem of having users without anyratings is encountered, which greatly impacts the accuracy forthe memory based algorithm. A hybrid between these algorithmsresulted in a better accuracy than the model based algorithmitself but with an insignificant improvement.

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Jarrell, Jason A. "Employ sensor fusion techniques for determining aircraft attitude and position information." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5894.

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Thesis (M.S.)--West Virginia University, 2008.
Title from document title page. Document formatted into pages; contains xii, 108, [9] p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 104-108).

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Grönberg, David, and Otto Denesfay. "Comparison and improvement of time aware collaborative filtering techniques : Recommender systems." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160360.

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Recommender systems emerged in the mid '90s with the objective of helping users select items or products most suited for them. Whether it is Facebook recommending people you might know, Spotify recommending songs you might like or Youtube recommending videos you might want to watch, recommender systems can now be found in every corner of the internet. In order to handle the immense increase of data online, the development of sophisticated recommender systems is crucial for filtering out information, enhancing web services by tailoring them according to the preferences of the user. This thesis aims to improve the accuracy of recommendations produced by a classical collaborative filtering recommender system by utilizing temporal properties, more precisely the date on which an item was rated by a user. Three different time-weighted implementations are presented and evaluated: time-weighted prediction approach, time-weighted similarity approach and our proposed approach, weighting the mean rating of a user on time. The different approaches are evaluated using the well known MovieLens 100k dataset. Results show that it is possible to slightly increase the accuracy of recommendations by utilizing temporal properties.

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Cabir, Hassane Natu Hassane. "A Comparison Of Different Recommendation Techniques For A Hybrid Mobile Game Recommender System." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615173/index.pdf.

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As information continues to grow at a very fast pace, our ability to access this information effectively does not, and we are often realize how harder is getting to locate an object quickly and easily. The so-called personalization technology is one of the best solutions to this information overload problem: by automatically learning the user profile, personalized information services have the potential to offer users a more proactive and intelligent form of information access that is designed to assist us in finding interesting objects. Recommender systems, which have emerged as a solution to minimize the problem of information overload, provide us with recommendations of content suited to our needs. In order to provide recommendations as close as possible to a user&rsquo
s taste, personalized recommender systems require accurate user models of characteristics, preferences and needs. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users, But it suffers from several issues like data sparsity and cold start. In one-class collaborative filtering, a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples, the challenge is even greater, since these datasets are even sparser. In this thesis, we present a series of experiments conducted on a real-life customer purchase database from a major Turkish E-Commerce site. The sparsity problem is handled by the use of content-based technique combined with TFIDF weights, memory based collaborative filtering combined with different similarity measures and also hybrids approaches, and also model based collaborative filtering with the use of Singular Value Decomposition (SVD). Our study showed that the binary similarity measure and SVD outperform conventional measures in this OCCF dataset.

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Parameswaran, Rupa. "A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11459.

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Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters and #146; lists, pose a threat to personal privacy. The concern over individual privacy has led to the development of legal codes for safeguarding privacy in several countries. However, the ignorance of individuals as well as loopholes in the systems, have led to information breaches even in the presence of such rules and regulations. Protection against data privacy requires modification of the data itself. The term {em data obfuscation} is used to refer to the class of algorithms that modify the values of the data items without distorting the usefulness of the data. The main goal of this thesis is the development of a data obfuscation technique that provides robust privacy protection with minimal loss in usability of the data. Although medical and financial services are two of the major areas where information privacy is a concern, privacy breaches are not restricted to these domains. One of the areas where the concern over data privacy is of growing interest is collaborative filtering. Collaborative filtering systems are being widely used in E-commerce applications to provide recommendations to users regarding products that might be of interest to them. The prediction accuracy of these systems is dependent on the size and accuracy of the data provided by users. However, the lack of sufficient guidelines governing the use and distribution of user data raises concerns over individual privacy. Users often provide the minimal information that is required for accessing these E-commerce services. The lack of rules governing the use and distribution of data disallows sharing of data among different communities for collaborative filtering. The goals of this thesis are (a) the definition of a standard for classifying DO techniques, (b) the development of a robust cluster preserving data obfuscation algorithm, and (c) the design and implementation of a privacy-preserving shared collaborative filtering framework using the data obfuscation algorithm.

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Schwenk, Karsten Verfasser], Dieter W. [Akademischer Betreuer] [Fellner, and Carsten [Akademischer Betreuer] Dachsbacher. "Filtering Techniques for Low-Noise Previews of Interactive Stochastic Ray Tracing / Karsten Schwenk. Betreuer: Dieter W. Fellner ; Carsten Dachsbacher." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2013. http://d-nb.info/1107771080/34.

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Schwenk, Karsten [Verfasser], Dieter W. [Akademischer Betreuer] Fellner, and Carsten [Akademischer Betreuer] Dachsbacher. "Filtering Techniques for Low-Noise Previews of Interactive Stochastic Ray Tracing / Karsten Schwenk. Betreuer: Dieter W. Fellner ; Carsten Dachsbacher." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2013. http://nbn-resolving.de/urn:nbn:de:tuda-tuprints-35906.

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Dahal, Ashok. "Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc822759/.

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There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and colonic polyps. Automatic detection of these diseases would inform the endoscopist of possible sub-optimal inspection during the colonoscopy procedure as well as save time during post-procedure evaluation. But existing systems only detects few of those disorders like colonic polyps. In this dissertation, we address the automatic detection of another important disorder called ulcerative colitis. We propose a novel texture feature extraction technique to detect the severity of ulcerative colitis in block, image, and video levels. We also enhance the current informative frame filtering methods by detecting water and bubble frames using our proposed technique. Our feature extraction algorithm based on accumulation of pixel value difference provides better accuracy at faster speed than the existing methods making it highly suitable for real-time systems. We also propose a hybrid approach in which our feature method is combined with existing feature method(s) to provide even better accuracy. We extend the block and image level detection method to video level severity score calculation and shot segmentation. Also, the proposed novel feature extraction method can detect water and bubble frames in colonoscopy videos with very high accuracy in significantly less processing time even when clustering is used to reduce the training size by 10 times.

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Zéboudj, Rachid. "Filtrage, seuillage automatique, contraste et contours : du pré-traitement à l'analyse d'image." Saint-Etienne, 1988. http://www.theses.fr/1988STET4001.

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Etude de quelques aspects du traitement et de l'analyse d'image : présentation d'un lissage adaptatif mettant en évidence les régions qui composent une image; introduction de la notion de contraste utile en seuillage d'image; segmentation d'image; techniques d'extraction d'information par seuillage d'image et détection de contours; classification de formes utilisant la courbure

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SANTOS, André Luis Silva dos. "UM MODELO DE SISTEMA DE FILTRAGEM HÍBRIDA PARA UM AMBIENTE COLABORATIVO DE ENSINO APRENDIZAGEM." Universidade Federal do Maranhão, 2008. http://tedebc.ufma.br:8080/jspui/handle/tede/293.

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Made available in DSpace on 2016-08-17T14:52:38Z (GMT). No. of bitstreams: 1 Andre Luis Silva dos Santos.pdf: 7753143 bytes, checksum: 538ea307ce9dad0b071cd12c49ac05f0 (MD5) Previous issue date: 2008-02-15
Nowadays, the World Wide Web (WWW) is an excellent source of information. However, open issues carry on. It´s difficult obtain relevant information in short time. Moreover, there is no accuracy for retrieving this information. Servers such as Google, Altavista and Cadê, can retrieve a huge amount of information. Nonetheless, the retrieved information could be not relevant. The information filtering systems arise to aim users in the searching for relevant information. This work proposes a hybrid model of filtering information based on content-based filtering and collaborative filtering. This model has been used into a collaborative learning system named NetClass and it was developed using the PASSI methodology. A case study done with CEFET´s students is presented as well.
A Web é uma excelente fonte de informação, mas um dos problemas que surgem com a grande disseminação de informações é a dificuldade de se obter informação relevante em tempo hábil e de forma precisa. Mecanismos que auxiliem o usuário na recuperação de informações tais como o Google.com, Altavista e Cadê, muitas das vezes retornam uma grande quantidade de conteúdo, sem garantir uma boa efetividade de recuperação, com excesso de informações recuperadas ou com informações irrelevantes. Os Sistemas de Filtragem de Informação surgem como alternativa de auxílio aos usuários na busca de informações relevantes. Este trabalho propõe a criação de um modelo de sistema de filtragem híbrido de informação baseados nos métodos: Filtragem Baseada em Conteúdo e Filtragem Colaborativa. O modelo proposto é aplicado a um ambiente colaborativo de ensinoaprendizagem, o NetClass, e foi desenvolvido com a metodologia PASSI. Um estudo de caso feito com alunos do CEFET-MA também é descrito.

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Books on the topic "Techniques of Filtering Information"

Chambers, Brian D. Adaptive bayesian information filtering. Toronto: Univsity of Toronto, Dept. of Computer Science, 1999.

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Lai, Cristian, Alessandro Giuliani, and Giovanni Semeraro, eds. Information Filtering and Retrieval. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46135-9.

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Aldama, Alvaro A. Filtering Techniques for Turbulent Flow Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990.

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Kalman filtering techniques for radar tracking. New York: Marcel Dekker, 2000.

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Aldama, Alvaro A. Filtering Techniques for Turbulent Flow Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84091-3.

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Aldama, A. A. Filtering techniques for turbulent flow simulation. Berlin: Springer-Verlag, 1990.

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Ma, Hongbin, Liping Yan, Yuanqing Xia, and Mengyin Fu. Kalman Filtering and Information Fusion. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0806-6.

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Nitzberg, M. Filtering, segmentation, and depth. Berlin: Springer-Verlag, 1993.

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Herlocker, Jonathan Lee. Collaborative filtering for digital libraries. [Corvallis, OR: Oregon State University, Dept. of Computer Science, 2003.

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Weiss, Stephan. On adaptive filtering in oversampled subbands. Aachen: Shaker, 1998.

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Book chapters on the topic "Techniques of Filtering Information"

Baltrunas, Linas, and Francesco Ricci. "Item Weighting Techniques for Collaborative Filtering." In Knowledge Discovery Enhanced with Semantic and Social Information, 109–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01891-6_7.

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Marwala, Tshilidzi. "Filtering Irrelevant Information for Rational Decision Making." In Artificial Intelligence Techniques for Rational Decision Making, 111–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11424-8_7.

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Nguyen, Phuong T., Juri Di Rocco, and Davide Di Ruscio. "Building Information Systems Using Collaborative-Filtering Recommendation Techniques." In Lecture Notes in Business Information Processing, 214–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20948-3_19.

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Amorim, Paulo, Thiago Moraes, Jorge Silva, and Helio Pedrini. "Adaptive Filtering Techniques for Improving Hyperspectral Image Classification." In New Advances in Information Systems and Technologies, 889–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31232-3_84.

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O’Donovan, John, and John Dunnion. "Evaluating Information Filtering Techniques in an Adaptive Recommender System." In Lecture Notes in Computer Science, 312–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27780-4_40.

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Morzy, Tadeusz, Marek Wojciechowski, and Maciej Zakrzewicz. "Efficient Constraint-Based Sequential Pattern Mining Using Dataset Filtering Techniques." In Databases and Information Systems II, 297–309. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-015-9978-8_23.

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Agarwal, Diwakar, and Atul Bansal. "Non-adaptive and Adaptive Filtering Techniques for Fingerprint Pores Extraction." In Advances in Data and Information Sciences, 643–54. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0694-9_59.

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Micol, Daniel, Óscar Ferrández, and Rafael Muñoz. "Information Retrieval Techniques for Corpus Filtering Applied to External Plagiarism Detection." In Natural Language Processing and Information Systems, 100–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22327-3_10.

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Lin, Yufeng, Biplob Ray, Dennis Jarvis, and Jia Wang. "False Signal Injection Attack Detection of Cyber Physical Systems by Event-Triggered Distributed Filtering over Sensor Networks." In Applications and Techniques in Information Security, 142–53. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2741-3_12.

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Akhter, Khaleda, and Sheikh Muhammad Sarwar. "Analysis of the Adaptive Nature of Collaborative Filtering Techniques in Dynamic Environment." In Communications in Computer and Information Science, 175–86. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26123-2_17.

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Conference papers on the topic "Techniques of Filtering Information"

Jones, Daniel E., Brian T. Kirby, Gabriele Riccardi, Cristian Antonelli, Antonio Mecozzi, and Michael Brodsky. "Filtering biphoton quantum states to recover quantum information (Conference Presentation)." In Advanced Optical Techniques for Quantum Information, Sensing, and Metrology, edited by Zameer U. Hasan, Philip R. Hemmer, and Alan L. Migdall. SPIE, 2020. http://dx.doi.org/10.1117/12.2546684.

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Popescu, Dan C., Mark Hedley, and Thuraiappah Sathyan. "Data filtering techniques for manifold flattening anchorless localization." In 2012 International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2012. http://dx.doi.org/10.1109/iscit.2012.6380849.

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Karthika, C. P., and C. S. Somu. "A multilevel approach for generating report based on information filtering." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755608.

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Chantrapornchai, C., C. Promsombat, T. Charuenrutsatien, and K. Suttirut. "Experimental studies on pornographic web filtering techniques." In 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2008. http://dx.doi.org/10.1109/ecticon.2008.4600384.

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Jung, Yong J., Jeong H. Yoon, Seung J. Yang, Hee Kyung Lee, and Yong M. Ro. "Contents based image filtering technique for information filtering agent." In Electronic Imaging 2002, edited by Nasser Kehtarnavaz. SPIE, 2002. http://dx.doi.org/10.1117/12.458526.

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Deshpande, Vikas P., Robert F. Erbacher, and Chris Harris. "An Evaluation of Naive Bayesian Anti-Spam Filtering Techniques." In 2007 IEEE SMC Information Assurance and Security Workshop. IEEE, 2007. http://dx.doi.org/10.1109/iaw.2007.381951.

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Baaqeel, Hind, and Rachid Zagrouba. "Hybrid SMS Spam Filtering System Using Machine Learning Techniques." In 2020 21st International Arab Conference on Information Technology (ACIT). IEEE, 2020. http://dx.doi.org/10.1109/acit50332.2020.9300071.

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Sharma, Lakhan Dev, Rakesh Asery, Ramesh Kumar Sunkaria, and Deepti Mittal. "Comparative study of fetal ECG elicitation using adaptive filtering techniques." In 2016 2nd International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). IEEE, 2016. http://dx.doi.org/10.1109/aeeicb.2016.7538399.

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Qingzhen Wen, Yan Zhou, Lan Hu, Jianxun Li, and Dongli Wang. "Comparison of filtering techniques for simultaneous localization and tracking." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280229.

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Fahnun, Budi Utami, A. Benny Mutiara, Eri Prasetyo Wibowo, Johan Harlan, Apriyadi Abdullah, and Muhammad Abdul Latief. "Filtering Techniques for Noise Reduction in Liver Ultrasound Images." In 2018 2nd East Indonesia Conference on Computer and Information Technology (EIConCIT). IEEE, 2018. http://dx.doi.org/10.1109/eiconcit.2018.8878547.

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Reports on the topic "Techniques of Filtering Information"

Zhang, Yi. Adaptive Information Filtering. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada563638.

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Kuperman, Gilbert G. Bandpass Spatial Filtering and Information Content. Fort Belvoir, VA: Defense Technical Information Center, July 1985. http://dx.doi.org/10.21236/ada303025.

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Mu, Ruihui. Integrating Rating Information and Social Information for Collaborative Filtering Recommendation. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, May 2019. http://dx.doi.org/10.7546/crabs.2019.05.03.

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Zseby, T., M. Molina, N. Duffield, S. Niccolini, and F. Raspall. Sampling and Filtering Techniques for IP Packet Selection. RFC Editor, March 2009. http://dx.doi.org/10.17487/rfc5475.

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Knoblock, Craig A. Intelligent Agents for Retrieving, Filtering, and Managing Information. Fort Belvoir, VA: Defense Technical Information Center, December 2000. http://dx.doi.org/10.21236/ada387501.

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Arellano, J., J. M. Hernandez, and J. Brase. Impulse radar imaging for dispersive concrete using inverse adaptive filtering techniques. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10117045.

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Yan, Tak W., and Hector Garcia-Molina. Index Structures for Information Filtering Under the Vector Space Model,. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada326033.

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Young, Teresa. Using Digital Filtering Techniques as an Aid in Wind Turbine Data Analysis. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/10113493.

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George, R., B. Leiba, and A. Melnikov. Sieve Email Filtering: Use of Presence Information with Auto-Responder Functionality. RFC Editor, July 2011. http://dx.doi.org/10.17487/rfc6133.

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E. B. Cummings. Image processing, adaptive gridding, and optimal nonlinear filtering techniques for particle-image velocimetry. Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/750922.

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