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Journal articles on the topic 'Data filtering'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Павло Б. Олійник. "DATA FILTERING METHODS FOR HYDROGRAPHIC SURVEY DATA." MECHANICS OF GYROSCOPIC SYSTEMS, no. 27 (October 6, 2014): 10–18. http://dx.doi.org/10.20535/0203-377127201437908.

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Current trends in navigation are characterized by the further increase of demands on the precision of hydrographic information, especially of the nautical maps. Thus, precision of both spatial position and depth bathymetric data is important for ensuring safe navigation, and so problem of data filtering and elimination of outliers arises.In the present work, comparison of methods, used for postprocessing of depth data, measured by echosounder, is done.First of all, review of commonly used data filtering and outlier elimination methods is done, and their advantages and disadvantages are analyzed.As improved outlier elimination algorithm and median filtering has their flaws, Kalman filtering is considered as a measure of outlier elimination and real data estimation. It’s shown that Kalman filter can both effectively filter noise and eliminate outliers; however, quality of the filtered data strongly depends on measurement noise covariation and process noise covariation estimates, and respectively. At that, the lower is, the better noise is filtered and the smoother depth profile is; the higher is, the better outliers are eliminated. However, care must be taken, as depth profile is distorted at high values, and noise is almost not filtered at low ones.It’s shown that noise covariation estimate has more influence on data filtering; therefore, one should pay attention to correct estimation. For practical reasons, values of ; =10 are recommended.In the recent works, wavelet filtering is considered as a promising method of data filtering in postprocessing. Therefore, as a next step, comparison of Kalman filtering and wavelet filtering is done using the real-world data. To that end, white noise is added to filtered and smoothed data, and then those data are filtered by methods, mentioned above. Corellation of source and denoised data is chosen as a criterion of filter effectiveness.It’s shown that Kalman filter is somewhat less effective in data postprocessing than wavelet filter. However, as Kalman filter allows one both to filter noises form the measured data and to eliminate outliers, and can be used for “on-the-fly” data filtering, it’s advisable to use Kalman filtering for real-time measurements during surveys, and wavelets for data postprocessing.Future studies may be devoted to improvement of existing and introduction of new data filtering and postrprocessing methods.

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2

Iske, Armin. "Progressive scattered data filtering." Journal of Computational and Applied Mathematics 158, no. 2 (September 2003): 297–316. http://dx.doi.org/10.1016/s0377-0427(03)00449-7.

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3

Gdanskiy, N. I., А. М. Кarpov, and P. Y. Коmova. "Using prediction in filtering data for solving model tasks." Contemporary problems of social work 1, no. 2 (June 30, 2015): 81–91. http://dx.doi.org/10.17922/2412-5466-2015-1-2-81-91.

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4

Kim, DaeYoub. "A Study on Fake Data Filtering Method of CCN." Journal of the Korea Institute of Information Security and Cryptology 24, no. 1 (February 28, 2014): 155–63. http://dx.doi.org/10.13089/jkiisc.2014.24.1.155.

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5

Xiaohui, Cheng, Feng Li, and Gui Qiong. "Collaborative Filtering Algorithm based on Data Mixing and Filtering." International Journal of Performability Engineering 15, no. 8 (2019): 2267. http://dx.doi.org/10.23940/ijpe.19.08.p27.22672276.

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6

Kamaludin, Hazalila, Hairulnizam Mahdin, and Jemal H. Abawajy. "Filtering Redundant Data from RFID Data Streams." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7107914.

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Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.

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7

Li, Jianchao, and Ken Larner. "Differential‐equation‐based seismic data filtering." GEOPHYSICS 58, no. 12 (December 1993): 1809–19. http://dx.doi.org/10.1190/1.1443396.

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Suppressing noise and enhancing useful seismic signal by filtering is one of the important tasks of seismic data processing. Conventional filtering methods are implemented through either the convolution operation or various mathematical transforms. We describe a methodology for studying and implementing filters, which, unlike conventional filtering methods, is based on solving differential equations in the time and space domain. We call this differential‐equation‐based filtering (DEBF). DEBF does not require that seismic data be stationary, so filtering parameters can vary with every time and space point. Examples with two‐dimensional (2-D) synthetic and field seismic data demonstrate that the DEBF method accomplishes the desired time‐ and space‐varying temporal and move‐out filtering at lower cost than conventional frequency‐wavenumber‐domain filtering. The computational advantage in 3-D would be much greater.

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8

Burguera, Antoni, Yolanda González, and Gabriel Oliver. "PROBABILISTIC FILTERING OF SONAR DATA." IFAC Proceedings Volumes 40, no. 15 (2007): 49–54. http://dx.doi.org/10.3182/20070903-3-fr-2921.00011.

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9

Grzesiak, M. "Wavelet filtering of chaotic data." Nonlinear Processes in Geophysics 7, no. 1/2 (June 30, 2000): 111–16. http://dx.doi.org/10.5194/npg-7-111-2000.

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Abstract. Satisfactory method of removing noise from experimental chaotic data is still an open problem. Normally it is necessary to assume certain properties of the noise and dynamics, which one wants to extract, from time series. The wavelet based method of denoising of time series originating from low-dimensional dynamical systems and polluted by the Gaussian white noise is considered. Its efficiency is investigated by comparing the correlation dimension of clean and noisy data generated for some well-known dynamical systems. The wavelet method is contrasted with the singular value decomposition (SVD) and finite impulse response (FIR) filter methods.

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10

Elliott, P. J. "Digital Filtering of Sirotem Data." Exploration Geophysics 19, no. 1-2 (March 1988): 258–59. http://dx.doi.org/10.1071/eg988258.

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11

Dentith, Mike. "Textural Filtering of Aeromagnetic Data." Exploration Geophysics 26, no. 2-3 (June 1, 1995): 209–14. http://dx.doi.org/10.1071/eg995209.

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12

Mitrofanov, Georgy, and Viatcheslav Priimenko. "Prony Filtering of Seismic Data." Acta Geophysica 63, no. 3 (June 2015): 652–78. http://dx.doi.org/10.1515/acgeo-2015-0012.

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13

Durand, Jean, Bernard Gimonet, and Jacqueline Perbos. "SAR Data Filtering for Classification." IEEE Transactions on Geoscience and Remote Sensing GE-25, no. 5 (September 1987): 629–37. http://dx.doi.org/10.1109/tgrs.1987.289842.

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14

Sheth, Amit, and Pavan Kapanipathi. "Semantic Filtering for Social Data." IEEE Internet Computing 20, no. 4 (July 2016): 74–78. http://dx.doi.org/10.1109/mic.2016.86.

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15

Vu, T. Thuy, and Mitsuharu Tokunaga. "Filtering Airborne Laser Scanner Data." Photogrammetric Engineering & Remote Sensing 70, no. 11 (November 1, 2004): 1267–74. http://dx.doi.org/10.14358/pers.70.11.1267.

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16

Rydell, Joakim, Hans Knutsson, and Magnus Borga. "Bilateral Filtering of fMRI Data." IEEE Journal of Selected Topics in Signal Processing 2, no. 6 (December 2008): 891–96. http://dx.doi.org/10.1109/jstsp.2008.2007826.

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17

Diniz, Paulo S. R. "On Data-Selective Adaptive Filtering." IEEE Transactions on Signal Processing 66, no. 16 (August 15, 2018): 4239–52. http://dx.doi.org/10.1109/tsp.2018.2847657.

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18

Wolkenstein, M., H. Hutter, and M. Grasserbauer. "Wavelet filtering for analytical data." Fresenius' Journal of Analytical Chemistry 358, no. 1-2 (May 21, 1997): 165–69. http://dx.doi.org/10.1007/s002160050373.

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19

Lew, Roger R., and Charles L. Schauf. "Fractal filtering of channel data." Biochimica et Biophysica Acta (BBA) - Biomembranes 1023, no. 2 (April 1990): 305–11. http://dx.doi.org/10.1016/0005-2736(90)90427-p.

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20

Trad, Daniel O., and Jandyr M. Travassos. "Wavelet filtering of magnetotelluric data." GEOPHYSICS 65, no. 2 (March 2000): 482–91. http://dx.doi.org/10.1190/1.1444742.

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A method is described for filtering magnetotelluric (MT) data in the wavelet domain that requires a minimum of human intervention and leaves good data sections unchanged. Good data sections are preserved because data in the wavelet domain is analyzed through hierarchies, or scale levels, allowing separation of noise from signals. This is done without any assumption on the data distribution on the MT transfer function. Noisy portions of the data are discarded through thresholding wavelet coefficients. The procedure can recognize and filter out point defects that appear as a fraction of unusual observations of impulsive nature either in time domain or frequency domain. Two examples of real MT data are presented, with noise caused by both meteorological activity and power‐line contribution. In the examples given in this paper, noise is better seen in time and frequency domains, respectively. Point defects are filtered out to eliminate their deleterious influence on the MT transfer function estimates. After the filtering stage, data is processed in the frequency domain, using a robust algorithm to yield two sets of reliable MT transfer functions.

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21

Ahmed, Samir A. "Operational filtering of traffic data." Journal of Forecasting 8, no. 1 (January 1989): 19–32. http://dx.doi.org/10.1002/for.3980080103.

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22

Rauch, B. J., and R. E. Rowlands. "Filtering thermoelastically measured isopachic data." Experimental Mechanics 37, no. 4 (December 1997): 387–92. http://dx.doi.org/10.1007/bf02317302.

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23

Wu, Zifeng, Zhouxiang Wu, and Laurence R. Rilett. "Innovative Nonparametric Method for Data Outlier Filtering." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (September 18, 2020): 167–76. http://dx.doi.org/10.1177/0361198120945697.

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Outlier filtering of empirical travel time data is essential for traffic analyses. Most of the widely applied outlier filtering algorithms are parametric in nature and based on assumed data distributions. The assumption, however, might not hold under unstable traffic conditions. This paper proposes a nonparametric outlier filtering method based on a robust locally weighted regression scatterplot smoothing model. The proposed method identifies outliers based on a data point’s standard residual in the robust local regression model. This approach fits a regression surface with no constraint on parametric distributions and limited influence from outliers. The proposed outlier filtering algorithm can be applied to various data collection technologies and for real-time applications. The performance of the new outlier filtering algorithm is compared with the moving standard deviation method and other traditional filtering algorithms. The test sites include GPS data of an Interstate highway in Indiana and Bluetooth data of an urban arterial roadway in Texas. It is shown that the proposed filtering algorithm has several advantages over the traditional filtering algorithms.

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24

Mcdade, Kevin K., Uma Chandran, and Roger S. Day. "Improving Cancer Gene Expression Data Quality through a TCGA Data-Driven Evaluation of Identifier Filtering." Cancer Informatics 14 (January 2015): CIN.S33076. http://dx.doi.org/10.4137/cin.s33076.

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Data quality is a recognized problem for high-throughput genomics platforms, as evinced by the proliferation of methods attempting to filter out lower quality data points. Different filtering methods lead to discordant results, raising the question, which methods are best? Astonishingly, little computational support is offered to analysts to decide which filtering methods are optimal for the research question at hand. To evaluate them, we begin with a pair of expression data sets, transcriptomic and proteomic, on the same samples. The pair of data sets form a test-bed for the evaluation. Identifier mapping between the data sets creates a collection of feature pairs, with correlations calculated for each pair. To evaluate a filtering strategy, we estimate posterior probabilities for the correctness of probesets accepted by the method. An analyst can set expected utilities that represent the trade-off between the quality and quantity of accepted features. We tested nine published probeset filtering methods and combination strategies. We used two test-beds from cancer studies providing transcriptomic and proteomic data. For reasonable utility settings, the Jetset filtering method was optimal for probeset filtering on both test-beds, even though both assay platforms were different. Further intersection with a second filtering method was indicated on one test-bed but not the other.

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25

Waldherr, Annie, Daniel Maier, Peter Miltner, and Enrico Günther. "Big Data, Big Noise." Social Science Computer Review 35, no. 4 (May 9, 2016): 427–43. http://dx.doi.org/10.1177/0894439316643050.

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In this article, we focus on noise in the sense of irrelevant information in a data set as a specific methodological challenge of web research in the era of big data. We empirically evaluate several methods for filtering hyperlink networks in order to reconstruct networks that contain only webpages that deal with a particular issue. The test corpus of webpages was collected from hyperlink networks on the issue of food safety in the United States and Germany. We applied three filtering strategies and evaluated their performance to exclude irrelevant content from the networks: keyword filtering, automated document classification with a machine-learning algorithm, and extraction of core networks with network-analytical measures. Keyword filtering and automated classification of webpages were the most effective methods for reducing noise, whereas extracting a core network did not yield satisfying results for this case.

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26

Prevost, Paoline, Kristel Chanard, Luce Fleitout, Eric Calais, Damian Walwer, Tonie van Dam, and Michael Ghil. "Data-adaptive spatio-temporal filtering of GRACE data." Geophysical Journal International 219, no. 3 (September 19, 2019): 2034–55. http://dx.doi.org/10.1093/gji/ggz409.

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SUMMARY Measurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter. In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability. We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements. Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.

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27

Konstantinopoulos, Stavros, Genevieve M. De Mijolla, Joe H. Chow, Hanoch Lev-Ari, and Meng Wang. "Synchrophasor Missing Data Recovery via Data-Driven Filtering." IEEE Transactions on Smart Grid 11, no. 5 (September 2020): 4321–30. http://dx.doi.org/10.1109/tsg.2020.2986439.

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28

Economou, Nikos. "Time-varying band-pass filtering GPR data by self-inverse filtering." Near Surface Geophysics 14, no. 2 (April 1, 2015): 207–17. http://dx.doi.org/10.3997/1873-0604.2015025.

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29

Smirnova, Ekaterina, Snehalata Huzurbazar, and Farhad Jafari. "PERFect: PERmutation Filtering test for microbiome data." Biostatistics 20, no. 4 (June 18, 2018): 615–31. http://dx.doi.org/10.1093/biostatistics/kxy020.

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Summary The human microbiota composition is associated with a number of diseases including obesity, inflammatory bowel disease, and bacterial vaginosis. Thus, microbiome research has the potential to reshape clinical and therapeutic approaches. However, raw microbiome count data require careful pre-processing steps that take into account both the sparsity of counts and the large number of taxa that are being measured. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. Despite progress in the number and quality of filtering approaches, there is no consensus on filtering standards and quality assessment. This can adversely affect downstream analyses and reproducibility of results across platforms and software. We introduce PERFect, a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. Methods are assessed on three “mock experiment” data sets, where the true taxa compositions are known, and are applied to two publicly available real microbiome data sets. The method correctly removes contaminant taxa in “mock” data sets, quantifies and visualizes the corresponding filtering loss, providing a uniform data-driven filtering criteria for real microbiome data sets. In real data analyses PERFect tends to remove more taxa than existing approaches; this likely happens because the method is based on an explicit loss function, uses statistically principled testing, and takes into account correlation between taxa. The PERFect software is freely available at https://github.com/katiasmirn/PERFect.

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KRYVENCHUK, Yurii, and Mykhailo-Yurii KHANAS. "ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 115–18. http://dx.doi.org/10.31891/2307-5732-2022-311-4-115-118.

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We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual way viewing. Accordingly, there is a need for technologies that can quickly process large amounts of data and highlight only the information that is useful to a particular user. This technology called recommender systems. It automatically suggest items to users that might be interesting for them. Due to the desire to unite people with common interests, it is relevant to develop a recommendation system based on social networks that help in personification of the user and compilation of his psychotype using his profile. The paper has description and results of the creation of recommendation system. The basis of this work is one of the algorithms used in recommendation systems – the recommendation system is based on content filtering. It analyzes users’ Twitter posts and calculates their interests. If we consider all the words, our model will not have good results and do not pay attention to what is important to use. Therefore, the most important step is always filtering data, so the number one task is to speed up the time of filtering text and retrieving data from the social network for further processing. The feature of this system is that this algorithm uses parallel calculations and frequency analysis of the text.

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31

Ashikawa, Masayuki, Takahiro Kawamura, and Akihiko Ohsuga. "Speech Synthesis Data Collection for Visually Impaired Person." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2 (October 14, 2014): 3–5. http://dx.doi.org/10.1609/hcomp.v2i1.13206.

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Crowdsourcing platforms provide attractive solutions for collecting speech synthesis data for visually impaired person. However, quality control problems remain because of low-quality volunteer workers. In this paper, we propose the design of a crowdsourcing system that allows us to devise quality control methods. We introduce four worker selection methods; preprocessing filtering, real-time filtering, post-processing filtering, and guess-processing filtering. These methods include a novel approach that utilizes a collaborative filtering technique in addition to a basic approach involving initial training or use of gold-standard data. These quality control methods improved the quality of collected speech synthesis data. Moreover, we have already collected 140,000 Japanese words from 500 million web data for speech synthesis data.

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32

Abdulhafiz, Waleed A., and Alaa Khamis. "Handling Data Uncertainty and Inconsistency Using Multisensor Data Fusion." Advances in Artificial Intelligence 2013 (November 3, 2013): 1–11. http://dx.doi.org/10.1155/2013/241260.

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Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the individual sensor. This paper presents an approach to multisensor data fusion in order to decrease data uncertainty with ability to identify and handle inconsistency. The proposed approach relies on combining a modified Bayesian fusion algorithm with Kalman filtering. Three different approaches, namely, prefiltering, postfiltering and pre-postfiltering are described based on how filtering is applied to the sensor data, to the fused data or both. A case study to find the position of a mobile robot by estimating its x and y coordinates using four sensors is presented. The simulations show that combining fusion with filtering helps in handling the problem of uncertainty and inconsistency of the data.

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33

Vicsek, Maria, Sharon L. Neal, and Isiah M. Warner. "Time-Domain Filtering of Two-Dimensional Fluorescence Data." Applied Spectroscopy 40, no. 4 (May 1986): 542–48. http://dx.doi.org/10.1366/0003702864508773.

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Four time-domain filtering methods are applied to simulated and experimental two-dimensional fluorescence data in order to evaluate their performance. The methods that were evaluated are (1) moving average, (2) Savitsky-Golay polynomial smoothing, (3) Chebyshev filtering, and (4) bicubic spline filtering. The methods are compared with the use of mean square error analysis and the difference in the amplitudes of the filtered noisy and ideal data. The two-dimensional version of the Savitzky-Golay filtering and the spline method produced the best overall results.

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34

Yin, Xuesong, Jie Yu, and Rongrong Jiang. "Neighbor-based Data Weight Collaborative Filtering." IOSR Journal of Computer Engineering 16, no. 4 (2014): 06–10. http://dx.doi.org/10.9790/0661-16410610.

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35

Wang, Xinyu, Sumit Gulwani, and Rishabh Singh. "FIDEX: filtering spreadsheet data using examples." ACM SIGPLAN Notices 51, no. 10 (December 5, 2016): 195–213. http://dx.doi.org/10.1145/3022671.2984030.

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36

Cowan, D. R., and S. Cowan. "Separation Filtering Applied to Aeromagnetic Data." Exploration Geophysics 24, no. 3-4 (September 1993): 429–36. http://dx.doi.org/10.1071/eg993429.

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37

Verstrepen, Koen, Kanishka Bhaduriy, Boris Cule, and Bart Goethals. "Collaborative Filtering for Binary, Positiveonly Data." ACM SIGKDD Explorations Newsletter 19, no. 1 (September 2017): 1–21. http://dx.doi.org/10.1145/3137597.3137599.

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38

Jinsong Chen, Yun Shao, Huadong Guo, Weiming Wang, and Boqin Zhu. "Destriping CMODIS data by power filtering." IEEE Transactions on Geoscience and Remote Sensing 41, no. 9 (September 2003): 2119–24. http://dx.doi.org/10.1109/tgrs.2003.817206.

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Deineka, I. G., D. A. Egorov, A. A. Makarenko, and M. V. Mekhren’gin. "Digital filtering in FOG data processing." Gyroscopy and Navigation 6, no. 1 (January 2015): 61–65. http://dx.doi.org/10.1134/s2075108715010022.

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40

Gerig, G., O. Kubler, R. Kikinis, and F. A. Jolesz. "Nonlinear anisotropic filtering of MRI data." IEEE Transactions on Medical Imaging 11, no. 2 (June 1992): 221–32. http://dx.doi.org/10.1109/42.141646.

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41

Balter, M. "Filtering a river of cancer data." Science 267, no. 5201 (February 24, 1995): 1084–86. http://dx.doi.org/10.1126/science.7855585.

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42

Westin, C. F., J. Richolt, V. Moharir, and R. Kikinis. "Affine adaptive filtering of CT data." Medical Image Analysis 4, no. 2 (June 2000): 161–77. http://dx.doi.org/10.1016/s1361-8415(00)00011-6.

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Brinkhuis, Matthieu J. S., Marjan Bakker, and Gunter Maris. "Filtering Data for Detecting Differential Development." Journal of Educational Measurement 52, no. 3 (September 2015): 319–38. http://dx.doi.org/10.1111/jedm.12078.

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44

Sasgen, I., Z. Martinec, and K. Fleming. "Wiener optimal filtering of GRACE data." Studia Geophysica et Geodaetica 50, no. 4 (October 2006): 499–508. http://dx.doi.org/10.1007/s11200-006-0031-y.

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45

Reynolds, R. G. "Optimal filtering of FIR prefiltered data." IEEE Transactions on Automatic Control 35, no. 5 (May 1990): 608–10. http://dx.doi.org/10.1109/9.53536.

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46

Cron, Andrew, Liang Zhang, and Deepak Agarwal. "Collaborative filtering for massive multinomial data." Journal of Applied Statistics 41, no. 4 (October 14, 2013): 701–15. http://dx.doi.org/10.1080/02664763.2013.847072.

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47

Yun, Sehyun, and Renato Zanetti. "Nonlinear filtering of light-curve data." Advances in Space Research 66, no. 7 (October 2020): 1672–88. http://dx.doi.org/10.1016/j.asr.2020.06.024.

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48

Delany, Sarah Jane, Mark Buckley, and Derek Greene. "SMS spam filtering: Methods and data." Expert Systems with Applications 39, no. 10 (August 2012): 9899–908. http://dx.doi.org/10.1016/j.eswa.2012.02.053.

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Liu, Ning, and Thomas Schumacher. "Improved Denoising of Structural Vibration Data Employing Bilateral Filtering." Sensors 20, no. 5 (March 5, 2020): 1423. http://dx.doi.org/10.3390/s20051423.

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With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified.

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Parasuraman, Desabandh, and Sathiyamoorthy Elumalai. "Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation." Journal of information and organizational sciences 45, no. 2 (December 15, 2021): 535–51. http://dx.doi.org/10.31341/jios.45.2.10.

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Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online. Especially in the area of e-commerce, recommender systems provide suggestions to users on the items that are likely based upon user’s true interest. Collaborative Filtering and Content Based Filtering are the main techniques of recommender systems. Collaborative Filtering is considered to be the best in all domains and always outperforms Content Based filtering. But, both the techniques have some limitations like data sparsity, cold start, gray sheep and scalability issues. To overcome these limitations, Hybrid Recommender Systems are used by combining Collaborative Filtering and Content Based Filtering. This paper proposes such kind of hybrid system by combining Collaborative Filtering and Content Based Filtering using time variance and machine learning algorithm.

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