Relevant bibliographies by topics / Co-occurrence database

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  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Co-occurrence database'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "Co-occurrence database"

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Yamanishi, Ryosuke, Naoki Shino, Yoko Nishihara, Junichi Fukumoto, and Aya Kaizaki. "Alternative-ingredient Recommendation Based on Co-occurrence Relation on Recipe Database." Procedia Computer Science 60 (2015): 986–93. http://dx.doi.org/10.1016/j.procs.2015.08.138.

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Lameijer, Eric-Wubbo, Joost N. Kok, Thomas Bäck, and Ad P. IJzerman. "Mining a Chemical Database for Fragment Co-occurrence: Discovery of “Chemical Clichés”." Journal of Chemical Information and Modeling 46, no. 2 (2006): 553–62. http://dx.doi.org/10.1021/ci050370c.

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Islam, Md Saiful, Md Emdadul Haque, and Md Ekramul Hamid. "Multidimensional Markov Stationary Feature for Image Retrival Systems." Rajshahi University Journal of Science and Engineering 44 (November 19, 2016): 113–22. http://dx.doi.org/10.3329/rujse.v44i0.30396.

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Markov Stationary Features (MSF) not only considers the distribution of colors like histogram method does, also characterizes the spatial co-occurrence of histogram patterns. However, handling large scale database of images, simple MSF method is not sufficient to discriminate the images. In this paper, we have proposed a robust content based image retrieval algorithm that enhances the discriminating capability of the original MSF. The proposed Multidimensional MSF (MMSF) algorithm extends the MSF by generating multiple co-occurrence matrices with different quantization levels of an image. Publicly available WANG1000 and Corel10800 databases are used to evaluate the performance of the proposed algorithm. The experimental result justifies the effectiveness of the proposed method.
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Gollub, Erica L., Ruth Trino, Melinda Salmon, Len Moore, James L. Dean, and Bruce L. Davidson. "Co-occurrence of AIDS and Tuberculosis: Results of a Database"Match" and Investigation." Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 16, no. 1 (1997): 44–49. http://dx.doi.org/10.1097/00042560-199709010-00007.

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Nguyen, Hoang Duc, Thuong Tien Le, Tuan Hong Do, and Cao Thu Bui. "A NEW DESCRIPTOR FOR IMAGE RETRIEVAL USING CONTOURLET COOCCURRENCE." Science and Technology Development Journal 15, no. 2 (2012): 5–16. http://dx.doi.org/10.32508/stdj.v15i2.1785.

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In this paper, a new descriptor for the feature extraction of images in the image database is presented. The new descriptor called Contourlet Co-Occurrence is based on a combination of contourlet transform and Grey Level Co-occurrence Matrix (GLCM). In order to evaluate the proposed descriptor, we perform the comparative analysis of existing methods such as Contourlet [2], GLCM [14] descriptors with Contourlet Co-Occurrence descriptor for image retrieval. Experimental results demonstrate that the proposed method shows a slight improvement in the retrieval effectiveness.
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Nor Paizin, Muhsin, Siti Maziah Ab Rahman, Khalid Abdul Wahid, Mohd Noor Azam Nafi, Suryani Awang, and Mariam Setapa. "Bibliometric Analysis of Zakat Research in Scopus Database." International Journal of Zakat 6, no. 1 (2021): 13–24. http://dx.doi.org/10.37706/ijaz.v6i1.253.

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Scopus research paper on the zakat was systematically analyzed using the VOSviewer bibliometric measurement. A total of 492 citation data was exported from Scopus on the query of Zakat, and from the initial result, twelve journals were selected in the expanded query process. The journals are Journal of Islamic Accounting and Business Research, International Journal of Islamic and Middle Eastern Finance and Management, International Journal of Innovation Creativity and Change, Advanced Science Letters, and Iop Conference Series Earth and Environmental Science were selected in the query expansion and exported for data visualization in VOSviewer. Results from the journal query returned 492 documents specializing in research of zakat payment. Co-word or co-occurrence analysis was used to identify key themes, and potential future research direction was highlighted.
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NIKAM, SHANKAR BHAUSAHEB, and SUNEETA AGARWAL. "CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION." International Journal of Image and Graphics 09, no. 02 (2009): 171–99. http://dx.doi.org/10.1142/s0219467809003393.

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Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.
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Jayadevan, R., and V. S. Sheeba. "A Semantic Image Retrieval Technique Through Concept Co-occurrence Based Database Organization and DeepLab Segmentation." Journal of Computer Science 16, no. 1 (2020): 56–71. http://dx.doi.org/10.3844/jcssp.2020.56.71.

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Milman, Boris L. "Literature-Based Generation of Hypotheses on Chemical Composition Using Database Co-occurrence of Chemical Compounds." Journal of Chemical Information and Modeling 45, no. 5 (2005): 1153–58. http://dx.doi.org/10.1021/ci049716u.

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Ganapathy, Nagarajan, Diana Baumgärtel, and Thomas Deserno. "Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning." Sensors 21, no. 10 (2021): 3542. http://dx.doi.org/10.3390/s21103542.

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Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length V, word-size W, and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA’s performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (p < 0.005). The DSA feature with W=3 and V=3 yield maximum performance. In terms of F-measure (F), rotation forest and ensemble learning classifier are the most accurate for AFPDB (F = 94.6%) and AFTDB (F = 99.8%). Our method is effective for short-length signals and supports cross-data analysis. The DSA is capable of capturing the dynamics of varied-lengths ECG signals. Particularly, the optimal parameters-based DSA feature and ensemble learning could help to detect PAF in long-term ECG signals. Our method maps time series into a symbolic representation and identifies abnormalities in noisy, varied-length, and pathological ECG signals.
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Dissertations / Theses on the topic "Co-occurrence database"

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Křesťanová, Jitka. "Porovnání výsledků kookurenční databanky (CCDB) a kookurenční analýzy." Master's thesis, 2017. http://www.nusl.cz/ntk/nusl-369912.

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This paper deals with corpus linguistics. There are two applications under its scrutiny. Both of these applications are processing data from the corpus DeReKo via corpus-driven approach. It is a co-occurrence analysis and a Co-occurrence database. The aim of the work is to evaluate whether the results obtained by the co-occurrence analysis of the current scope of DeReKo are different from the results of the Co-occurrence database, which was created on a basis of a smaller scale corpus. In addition, this thesis offers illustrative examples of the use of both applications and the evaluation of their effectiveness, depending on the purpose of the research. The theoretical part of the thesis deals with the terminology of corpus linguistics and with the mentioned corpuses, which serve as a basis for the practical part of the thesis. The empirical part of the thesis consists of analyses of the randomly picked words (one from each word class) in both applications. The results confirm that the data obtained with Co-occurrence database and co-occurrence analysis are in many respects different and thus confirm the hypothesis that the corpus size plays a crucial role in the results. Both applications have their advantages and disadvantages. The paper offers a comprehensive overview and by doing so it...
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Book chapters on the topic "Co-occurrence database"

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Kunttu, Iivari, Leena Lepistö, Juhani Rauhamaa, and Ari Visa. "Binary Co-occurrence Matrix in Image Database Indexing." In Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_143.

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Suwanapong, Thawatchai, Thanaruk Theeramunkong, and Ekawit Nantajeewarawat. "The Vector Space Models for Finding Co-occurrence Names as Aliases in Thai Sports News." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12145-6_13.

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Andrzejewski, Witold, and Paweł Boinski. "Maximal Mixed-Drove Co-Occurrence Patterns." In Advances in Databases and Information Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82472-3_3.

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Aklouche, Billel, Ibrahim Bounhas, and Yahya Slimani. "Pseudo-Relevance Feedback Based on Locally-Built Co-occurrence Graphs." In Advances in Databases and Information Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28730-6_7.

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Ali, Reem Y., Venkata M. V. Gunturi, Andrew J. Kotz, Shashi Shekhar, and William F. Northrop. "Discovering Non-compliant Window Co-Occurrence Patterns: A Summary of Results." In Advances in Spatial and Temporal Databases. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22363-6_21.

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Taifi, Khaddouj, Naima Taifi, Mohamed Fakir, Said Safi, and Muhammad Sarfraz. "Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix." In Advances in Library and Information Science. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1021-6.ch013.

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This chapter explores diagnosis of the breast tissues as normal, benign, or malignant in digital mammography, using computer-aided diagnosis (CAD). System for the early diagnosis of breast cancer can be used to assist radiologists in mammographic mass detection and classification. This chapter presents an evaluation about performance of extracted features, using gray-level co-occurrence matrix applied to all detailed coefficients. The nonsubsampled contourlet transform (NSCT) of the region of interest (ROI) of a mammogram were used to be decomposed in several levels. Detecting masses is more difficult than detecting microcalcifications due to the similarity between masses and background tissue such as F) fatty, G) fatty-glandular, and D) dense-glandular. To evaluate the system of classification in which k-nearest neighbors (KNN) and support vector machine (SVM) used the accuracy for classifying the mammograms of MIAS database between normal and abnormal. The accuracy measures through the classifier were 94.12% and 88.89% sequentially by SVM and KNN with NSCT.
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Ribeiro, Maria Isabel Barreiro, António José Gonçalves Fernandes, and Isabel Maria Lopes. "Digital Marketing." In Advances in Business Strategy and Competitive Advantage. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2963-8.ch003.

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This research aimed to identify the most developed themes in the field of digital marketing from 2010 to 2019. A total of 898 publications were selected from the Scopus database from the Business, Management and Accounting and Economics, Econometrics, and Finance domains. A bibliometric analysis was carried out using VOSviewer software and the term co-occurrence technique was used. Three clusters were identified. The first cluster relates digital marketing to the changes and adaptations of society and the economy since the emergence of the internet. The second cluster relates digital marketing to information technologies, e-commerce, and consumer behavior. Finally, the third cluster relates digital marketing with markets, social media, users, tourism, and electronic word-of-mouth (e-WOM).
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Stanco, Filippo, Davide Tanasi, Giuseppe Claudio Guarnera, and Giovanni Gallo. "Automatic Classification of Decorative Patterns in the Minoan Pottery of Kamares Style." In Geographic Information Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch068.

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An important feature of the Minoan culture is the pottery of Kamares style, that documents the Cretan cultural production between the first half of the 2nd millennium BC. This high level painted production, characterized by the combination of several diverse motifs, presents an enormous decorative repertoire. The extraordinary variety of combinations between elementary motifs according to a complex visual syntax makes interesting the automatic identification of the motifs, particularly upon potsherds. A complete pipeline to accomplish this task is still a challenge to Computer Vision and Pattern Recognition. Starting from a digital image ROI identification, motif extraction, robust contour detection should be performed to obtain a bag of digital shapes. In a second phase each of the extracted shapes has to be classified according to prototypes in a database produced by an expert. The co-occurrence of the different shapes in a specimen will, in turn, be used to help the archaeologists in the cultural and even chronological setting.
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Khennak, Ilyes, and Habiba Drias. "A Novel Hybrid Correlation Measure for Query Expansion-Based Information Retrieval." In Advances in Library and Information Science. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1021-6.ch001.

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Query expansion (QE) is one of the most effective techniques to enhance the retrieval performance and to retrieve more relevant information. It attempts to build more useful queries by enriching the original queries with additional expansion terms that best characterize the users' information needs. In this chapter, the authors propose a new correlation measure for query expansion to evaluate the degree of similarity between the expansion term candidates and the original query terms. The proposed correlation measure is a hybrid of two correlation measures. The first one is considered as an external correlation and it is based on the term co-occurrence, and the second one is considered as an internal correlation and it is based on the term proximity. Extensive experiments have been performed on MEDLINE, a real dataset from a large online medical database. The results show the effectiveness of the proposed approach compared to prior state-of-the-art approaches.
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Gonçalves, Paolo J. S., Rui J. Almeida, João R. Caldas Pinto, Susana M. Vieira, and João M. C. Sousa. "Image Based Classification Platform." In Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3990-4.ch031.

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The high number of exams that is done in healthcare institutions increases the medical doctors’ workload, leading to poor working conditions and the increase of wrong diagnoses. As consequence, an automatic system that can help medical doctors in diagnostic tasks is of major interest to any healthcare institution. The chapter proposes an Image Based Classification Platform suitable to help Medical Doctors diagnosing breast cancer, based on mammograms, i.e., to detect if a tumor is present in the image. The platform is twofold, i.e., in the first part the image descriptors are extracted from the image using image-processing algorithms. The obtained descriptors are used in the second part. The second part is related to classification, where computational intelligence methods are used to classify a given image, based on the descriptors obtained in the first phase. Texture analysis based on co-occurrence matrices are applied to obtain the descriptors from the MIAS database of mammograms. From these descriptors, fuzzy models, neural networks, and support vector machines are successfully used to classify the mammograms and obtain a diagnosis.
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Conference papers on the topic "Co-occurrence database"

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Wartena, Christian, Rogier Brussee, and Wout Slakhorst. "Keyword Extraction Using Word Co-occurrence." In 2010 21st International Conference on Database and Expert Systems Applications (DEXA). IEEE, 2010. http://dx.doi.org/10.1109/dexa.2010.32.

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Mohite, Anuradha, and Varunakshi Bhojane. "Natural language interface to database using modified co-occurrence matrix technique." In 2015 International Conference on Pervasive Computing (ICPC). IEEE, 2015. http://dx.doi.org/10.1109/pervasive.2015.7087045.

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Ghouila, Amel, Olivier Gascuel, Sadok Ben Yahia, and Laurent Brehelin. "Enhancing Protein Domain Detection Using Domain Co-occurrence and Domain Exclusion." In 2012 23rd International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2012. http://dx.doi.org/10.1109/dexa.2012.45.

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Kohonen, Oili, Markku Hauta-Kasari, Jussi Parkkinen, and Timo Jaaskelainen. "Co-occurrence matrix and self-organizing map-based query from spectral image database." In ICO20:Illumination, Radiation, and Color Technologies, edited by Dazun Zhao, M. R. Luo, and Hirohisa Yaguchi. SPIE, 2006. http://dx.doi.org/10.1117/12.668059.

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Liu, Nan, Yanxiang He, Qiang Chen, Mn Peng, and Wenqi Fang. "Multi-document Biased Summarization based on topic-oriented characteristic database of term-pair Co-occurrence." In 2013 IEEE Third International Conference on Information Science and Technology (ICIST). IEEE, 2013. http://dx.doi.org/10.1109/icist.2013.6747670.

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Saralegi, Xabier, and Maddalen Lopez de Lacalle. "Comparing Different Approaches to Treat Translation Ambiguity in CLIR: Structured Queries vs. Target Co-occurrence Based Selection." In 2009 20th International Workshop on Database and Expert Systems Application. DEXA 2009. IEEE, 2009. http://dx.doi.org/10.1109/dexa.2009.58.

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Shino, Naoki, Ryosuke Yamanishi, and Junichi Fukumoto. "Recommendation System for Alternative-Ingredients Based on Co-occurrence Relation on Recipe Database and the Ingredient Category." In 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2016. http://dx.doi.org/10.1109/iiai-aai.2016.187.

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Zaleski, Martin, Gerald Ferris, and Alex Baumgard. "Near-Real-Time Seismic Monitoring for Pipelines." In 2018 12th International Pipeline Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/ipc2018-78013.

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Earthquake hazard management for oil and gas pipelines should include both preparedness and response. The typical approach for management of seismic hazards for pipelines is to determine where large ground motions are frequently expected, and apply mitigation to those pipeline segments. The approach presented in this paper supplements the typical approach but focuses on what to do, and where to do it, just after an earthquake happens. In other words, we ask and answer: “Is the earthquake we just had important?”, “What pipeline is and what sites might it be important for?”, and “What should we do?” In general, modern, high-pressure oil and gas pipelines resist the direct effects of strong shaking, but are vulnerable to large co-seismic differential permanent ground displacement (PGD) produced by surface fault rupture, landslides, soil liquefaction, or lateral spreading. The approach used in this paper employs empirical relationships between earthquake magnitude, distance, and the occurrence of PGD, derived from co-seismic PGD case-history data, to prioritize affected pipeline segments for detailed site-specific hazard assessments, pre-event resiliency upgrades, and post-event response. To help pipeline operators prepare for earthquakes, pipeline networks are mapped with respect to earthquake probability and co-seismic PGD susceptibility. Geological and terrain analyses identify pipeline segments that cross PGD-susceptible ground. Probabilistic seismic models and deterministic scenarios are considered in estimating the frequency of sufficiently large and close causative earthquakes. Pipeline segments are prioritized where strong earthquakes are frequent and ground is susceptible to co-seismic PGD. These may be short-listed for mitigation that either reduces the pipeline’s vulnerability to damage or limits failure consequences. When an earthquake occurs, pipeline segments with credible PGD potential are highlighted within minutes of an earthquake’s occurrence. These assessments occur in near-real-time as part of an online geohazard management database. The system collects magnitude and location data from online earthquake data feeds and intersects them against pipeline network and terrain hazard map data. Pipeline operators can quickly mobilize inspection and response resources to a focused area of concern.
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Phan, Raymond, John Chia, and Dimitrios Androutsos. "Unconstrained logo and trademark retrieval in general color image databases using Color Edge Gradient Co-occurrence Histograms." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517836.

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Phan, Raymond, and Dimitrios Androutsos. "COntent-based unconstrained logo and trademark retrieval in color image databases with Color Edge Gradient Co-occurrence Histograms." In 2009 16th International Conference on Digital Signal Processing (DSP). IEEE, 2009. http://dx.doi.org/10.1109/icdsp.2009.5201266.

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