Готові списки джерел за темами / News classification

Добірка наукової літератури з теми "News classification"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "News classification"

1

Blackman, Michael. "Classification News." World Patent Information 33, no. 3 (September 2011): 294. http://dx.doi.org/10.1016/j.wpi.2011.04.010.

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2

Юдина, И., I. Yudina, Д. Косяков, D. Kosyakov, Е. Базылева, E. Bazyleva, З. Вахрамеева, Z. Vahrameeva, О. Федотова, and O. Fedotova. "On the Classification of Scientific News Information." Scientific Research and Development. Modern Communication Studies 7, no. 5 (September 25, 2018): 16–21. http://dx.doi.org/10.12737/article_5b9f9bf14d1cc7.47427505.

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Анотація:
The present research aims to study modern approaches to the classification of news information in general, and of science news in particular. Based on the analysis of Russian scientific publications and information systems, several classifications used to systematize news (mostly universal) are described. The article proposes the author’s classification of scientific news as an element of modern scientific communication system. This classification can potentially be used as another tool for assessing the effectiveness of scientific institution research results.
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3

Ahmad, Malik Shahzad, and Muhammad Azhar Bhatti. "News Location Classification." iRASD Journal of Computer Science and Information Technology 2, no. 1 (December 31, 2021): 52–62. http://dx.doi.org/10.52131/jcsit.2021.0201.0010.

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Анотація:
Every day there are a lot of things that happen around the world. There are various ways to record every event that is occurring around the world, such as news, blogs, and articles. Over the past few years, there are multiple news available on every event that has occurred. It adds to the size of information that is available for human beings to consume. People are, moving from paper-based newspapers to digital newspapers to get their daily feed of news and digitization has a role to play in this behaviour. These days every person is preoccupied with a lot of work, online and offline, as mentioned earlier the amount of information is being increased with every passing day. For this reason, people are only interested in news that match their interests. A large amount of data in the form of text is available online, hence its classification based on its hidden features can lead to the better recommendation of news to individuals. In this research work, we have used focus area and temporal features to classify news using a Convolutional Neural Network (CNN). The results of the proposed methodology in the form of precision, accuracy, recall, and F1-Score show that these features indeed can be used for recommender systems.
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4

Blackman, Michael. "News on classification." World Patent Information 35, no. 3 (September 2013): 250–51. http://dx.doi.org/10.1016/j.wpi.2013.05.005.

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5

Blackman, Michael. "News on classification." World Patent Information 35, no. 4 (December 2013): 328–29. http://dx.doi.org/10.1016/j.wpi.2013.07.004.

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6

Nagy, Kitti, and Jozef Kapusta. "Improving fake news classification using dependency grammar." PLOS ONE 16, no. 9 (September 14, 2021): e0256940. http://dx.doi.org/10.1371/journal.pone.0256940.

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Анотація:
Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.
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7

Demirsoz, Orhan, and Rifat Ozcan. "Classification of news-related tweets." Journal of Information Science 43, no. 4 (June 1, 2016): 509–24. http://dx.doi.org/10.1177/0165551516653082.

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Анотація:
It is important to obtain public opinion about a news article. Microblogs such as Twitter are popular and an important medium for people to share ideas. An important portion of tweets are related to news or events. Our aim is to find tweets about newspaper reports and measure the popularity of these reports on Twitter. However, it is a challenging task to match informal and very short tweets with formal news reports. In this study, we formulate this problem as a supervised classification task. We propose to form a training set using tweets containing a link to the news and the content of the same news article. We preprocess tweets by removing unnecessary words and symbols and apply stemming by means of morphological analysers. We apply binary classifiers and anomaly detection to this task. We also propose a textual similarity-based approach. We observed that preprocessing of tweets increases accuracy. The textual similarity method obtains results with the highest recognition rate. Success increases in some cases when report text is used with tweets containing a link to the news report within the training set of classification studies. We propose that this study, which is made directly in consideration of tweet texts that measure the trends of national newspaper reports on social media, has a higher significance when compared to Twitter analyses made by using a hashtag. Given the limited number of scientific studies on Turkish tweets, this study makes a contribution to the literature.
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8

Dunne, Edward, and Klaus Hulek. "Mathematics Subject Classification 2020." EMS Newsletter 2020-3, no. 115 (March 3, 2020): 5–6. http://dx.doi.org/10.4171/news/115/2.

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9

MAHAJAN, SHWETA. "News Classification Using Machine Learning." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (May 31, 2021): 23–27. http://dx.doi.org/10.17762/ijritcc.v9i5.5464.

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Анотація:
There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.
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10

Majeed, Fiaz, Muhammad Waqas Asif, Muhammad Awais Hassan, Syed Ali Abbas, and M. Ikramullah Lali. "Social Media News Classification in Healthcare Communication." Journal of Medical Imaging and Health Informatics 9, no. 6 (August 1, 2019): 1215–23. http://dx.doi.org/10.1166/jmihi.2019.2735.

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Анотація:
The trend of news transmission is rapidly shifting from electronic media to social media. Currently, news channels in general, while health news channels specifically send health related news on social media sites. These news are beneficial for the patients, medical professionals and the general public. A lot of health related data is available on the social media that may be used to extract significant information and present several predictions from it to assist physicians, patients and healthcare organizations for decision making. However, A little research is found on health news data using machine learning approaches, thus in this paper, we have proposed a framework for the data collection, modeling, and visualization of the health related patterns. For the analysis, the tweets of 13 news channels are collected from the Twitter. The dataset holds approximately 28k tweets available under 280 hashtags. Furthermore, a comprehensive set of experiments are performed to extract patterns from the data. A comparative analysis is carried among the baseline method and four classification algorithms which include Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (J48). For the evaluation of the results, the standard measures accuracy, precision, recall and f-measure have been used. The results of the study are encouraging and better than the other studies of such kind.
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Дисертації з теми "News classification"

1

Kroha, Petr, and Ricardo Baeza-Yates. "Classification of Stock Exchange News." Universitätsbibliothek Chemnitz, 2004. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200401576.

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Анотація:
In this report we investigate how much similarity good news and bad news may have in context of long-terms market trends. We discuss the relation between text mining, classification, and information retrieval. We present examples that use identical set of words but have a quite different meaning, we present examples that can be interpreted in both positive or negative sense so that the decision is difficult as before reading them. Our examples prove that methods of information retrieval are not strong enough to solve problems as specified above. For searching of common properties in groups of news we had used classifiers (e.g. naive Bayes classifier) after we found that the use of diagnostic methods did not deliver reasonable results. For our experiments we have used historical data concerning the German market index DAX 30
In diesem Bericht untersuchen wir, wieviel Ähnlichkeit gute und schlechte Nachrichten im Kontext von Langzeitmarkttrends besitzen. Wir diskutieren die Verbindungen zwischen Text Mining, Klassifikation und Information Retrieval. Wir präsentieren Beispiele, die identische Wortmengen verwenden, aber trotzdem recht unterschiedliche Bedeutungen besitzen; Beispiele, die sowohl positiv als auch negativ interpretiert werden können. Sie zeigen Probleme auf, die mit Methoden des Information Retrieval nicht gelöst werden können. Um nach Gemeinsamkeiten in Nachrichtengruppen zu suchen, verwendeten wir Klassifikatoren (z.B. Naive Bayes), nachdem wir herausgefunden hatten, dass der Einsatz von diagnostizierenden Methoden keine vernünftigen Resultate erzielte. Für unsere Experimente nutzten wir historische Daten des Deutschen Aktienindex DAX 30
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2

Sandsmark, Håkon. "Spoken Document Classification of Broadcast News." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-19226.

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Two systems for spoken document classification are implemented by combining an automatic speech recognizer with the two classification algorithms naive Bayes and logistic regression. The focus is on how to handle the inherent uncertainty in the output of the speech recognizer. Feature extraction is performed by computing expected word counts from speech recognition lattices, and subsequently removing words that are found to carry little or noisy information about the topic label, as determined by the information gain metric. The systems are evaluated by performing cross-validation on broadcast news stories, and the classification accuracy is measured with different configurations and on recognition output with different word error rates. The results show that a relatively high classification accuracy can be obtained with word error rates around 50%, and that the benefit of extracting features from lattices instead of 1-best transcripts increases with increasing word error rates.
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3

Blein, Florent. "Automatic Document Classification Applied to Swedish News." Thesis, Linköping University, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-3065.

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Анотація:

The first part of this paper presents briefly the ELIN[1] system, an electronic newspaper project. ELIN is a framework that stores news and displays them to the end-user. Such news are formatted using the xml[2] format. The project partner Corren[3] provided ELIN with xml articles, however the format used was not the same. My first task has been to develop a software that converts the news from one xml format (Corren) to another (ELIN).

The second and main part addresses the problem of automatic document classification and tries to find a solution for a specific issue. The goal is to automatically classify news articles from a Swedish newspaper company (Corren) into the IPTC[4] news categories.

This work has been carried out by implementing several classification algorithms, testing them and comparing their accuracy with existing software. The training and test documents were 3 weeks of the Corren newspaper that had to be classified into 2 categories.

The last tests were run with only one algorithm (Naïve Bayes) over a larger amount of data (7, then 10 weeks) and categories (12) to simulate a more real environment.

The results show that the Naïve Bayes algorithm, although the oldest, was the most accurate in this particular case. An issue raised by the results is that feature selection improves speed but can seldom reduce accuracy by removing too many features.

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4

Kolluru, BalaKrishna. "Broadcast news processing: Structural Classification, Summarisation and Evaluation." Thesis, University of Sheffield, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485892.

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Анотація:
This thesis describes the automation and evaluation of structural classification and summarisation of audio documents, specifically broadcast news programmes. News broadcasts are typically 30-minute episodes consisting of several stories describing various events, incidents and current affairs. Some of these news stories are annotated to train the statistical models. Structural classification techniques use speaker-role (eg. anchor, reporter etc) information to categorise these stories into different broad classes such as reader and interview. A few carefully drafted set of rules assign a specific speaker-role to each utterance, which are subsequently used to classify the news stories. It is argued in this thesis that selecting the most relevant subsentence linguistic components is ari efficient information gathering mechanism for summarisation. Short to intermediate sized (15 to 50 word) summaries are automatically generated by employing an iterative decremental refining process that first decomposes a story into sentences and then further divides them into chunks or phrases. The most relevant parts are retained at each iteration until the desired number of words is reached. These chunks are then joined using a set of junction words which are decided by a combination of language model and probabilistic parser scores to generate a fluent summary. The performance of this approach is measured using a novel bipartite evaluation mechanism. It is shown that the summaries need to be measured for informativeness and therefore an approach based on a comprehension test is employed to calculate such scores. The evaluation mechanism uses afiuency scale which is based on comprehensibility and coherence to quantify the fluency of summaries. In experiments, human-authored summaries were analysed to quantify the subjectivity using the comprehension test. Experimental results indicate that the iterative refining approach is a lot more informative than a baseline constructed from first sentence or the 50 words of a news story. The results indicate that the use ofjunction words improved fluency in the summaries.
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5

Rozman, Darija. "UDC in 2008 - Brief news from Slovenia." UDC Consortium, 2008. http://hdl.handle.net/10150/106214.

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The Slovenian translation of the UDC version of MRF 2001 - in the online edition available at http://www.nuk.uni-lj.si/udk/ - was presented at the UDC Seminar in The Hague, in June 2007. In the autumn of 2008 it was updated according to the UDC MRF 2006, including changes approved from 2002 to 2006.
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6

Kurasinski, Lukas. "Machine Learning explainability in text classification for Fake News detection." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20058.

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Анотація:
Fake news detection gained an interest in recent years. This made researchers try to findmodels that can classify text in the direction of fake news detection. While new modelsare developed, researchers mostly focus on the accuracy of a model. There is little researchdone in the subject of explainability of Neural Network (NN) models constructed for textclassification and fake news detection. When trying to add a level of explainability to aNeural Network model, allot of different aspects have to be taken under consideration.Text length, pre-processing, and complexity play an important role in achieving successfully classification. Model’s architecture has to be taken under consideration as well. Allthese aspects are analyzed in this thesis. In this work, an analysis of attention weightsis performed to give an insight into NN reasoning about texts. Visualizations are usedto show how 2 models, Bidirectional Long-Short term memory Convolution Neural Network (BIDir-LSTM-CNN), and Bidirectional Encoder Representations from Transformers(BERT), distribute their attentions while training and classifying texts. In addition, statistical data is gathered to deepen the analysis. After the analysis, it is concluded thatexplainability can positively influence the decisions made while constructing a NN modelfor text classification and fake news detection. Although explainability is useful, it is nota definitive answer to the problem. Architects should test, and experiment with differentsolutions, to be successful in effective model construction.
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7

Svärd, Mikael, and Philip Rumman. "COMBATING DISINFORMATION : Detecting fake news with linguistic models and classification algorithms." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209755.

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Анотація:
The purpose of this study is to examine the possibility of accurately distinguishing fabricated news from authentic news stories using Naive Bayes classification algorithms. This involves a comparative study of two different machine learning classification algorithms. The work also contains an overview of how linguistic text analytics can be utilized in detection purposes and an attempt to extract interesting information was made using Word Frequencies. A discussion of how different actors and parties in businesses and governments are affected by and how they handle deception caused by fake news articles was also made. This study further tries to ascertain what collective steps could be made towards introducing a functioning solution to combat fake news. The result swere inconclusive and the simple Naive Bayes algorithms used did not yieldfully satisfactory results. Word frequencies alone did not give enough information for detection. They were however found to be potentially useful as part of a larger set of algorithms and strategies as part of a solution to handling of misinformation.
Syftet med denna studie är att undersöka möjligheten att på ett pålitligt sättskilja mellan fabricerade och autentiska nyheter med hjälp av Naive bayesalgoritmer,detta involverar en komparativ studie mellan två olika typer avalgoritmer. Arbetet innehåller även en översikt över hur lingvistisk textanalyskan användas för detektion och ett försök gjordes att extrahera information medhjälp av ordfrekvenser. Det förs även en diskussion kring hur de olika aktörernaoch parterna inom näringsliv och regeringar påverkas av och hur de hanterarbedrägeri kopplat till falska nyheter. Studien försöker vidare undersöka vilkasteg som kan tas mot en fungerande lösning för att motarbeta falska nyheter. Algoritmernagav i slutändan otillfredställande resultat och ordfrekvenserna kundeinte ensamma ge nog med information. De tycktes dock potentiellt användbarasom en del i ett större maskineri av algoritmer och strategier ämnade att hanteradesinformation.
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8

Gravenhorst, Claus. "News media processing and interactive presentation." Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden, 2017. https://slub.qucosa.de/id/qucosa%3A16574.

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9

Arevian, Garen Zohrab. "Recurrent neural networks for text classification of news articles from the Reuters Corpus." Thesis, University of Sunderland, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439972.

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10

Lagerkrants, Eleonor, and Jesper Holmström. "Using machine learning to classify news articles." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-59449.

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Анотація:
In today’s society a large portion of the worlds population get their news on electronicdevices. This opens up the possibility to enhance their reading experience bypersonalizing news for the readers based on their previous preferences. We have conductedan experiment to find out how accurately a Naïve Bayes classifier can selectarticles that a user might find interesting. Our experiments was done on two userswho read and classified 200 articles as interesting or not interesting. Those articleswere divided into four datasets with the sizes 50, 100, 150 and 200. We used a NaïveBayes classifier with 16 different settings configurations to classify the articles intotwo categories. From these experiments we could find several settings configurationsthat showed good results. One settings configuration was chosen as a good generalsetting for this kind of problem. We found that for datasets with a size larger than 50there were no significant increase in classification confidence.
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Книги з теми "News classification"

1

Rieger, J. H. On the classification of news of piecewise smooth objects. London: Queen Mary College, Department of Computer Science and Statistics, 1987.

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2

Pritchett, William Christopher. Neural networks for classification. Springfield, Va: Available from National Technical Information Service, 1998.

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3

Huber, John T. The species groups of Gonatocerus Nees in North America with a revision of the sulphuripes and ater groups (Hymenoptera, Mymaridae). Ottawa: Entomological Society of Canada, 1988.

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4

A, Kulikowski Casimir, ed. Computer systems that learn: Classification and prediction methods from statistics, neural nets, machine learning, and expert systems. San Mateo, Calif: M. Kaufmann Publishers, 1991.

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5

Company, Arthur Young &. State of New York classification and compensation study. [New York, N.Y.]: Arthur Young, 1985.

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6

Baram, Yoram. Estimation and classification by sigmoids based on mutual information. [Washington, D.C: National Aeronautics and Space Administration, 1994.

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7

Ammann, Raymond. Les danses kanak: Une introduction : description, classification et analyse. Nouméa, Nouvelle-Calédonie: Agence de Développement de la Culture Kanak avec les concours de la Province Nord, de la Province des Iles Loyauté et de la Délégation aux Affaires Culturelles, 1994.

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8

Classification. Class D. Subclasses DT-DX. History of Africa, Australia, New Zealand, etc. 3rd ed. Washington: Library of Congress, 1989.

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9

Ohrnberger, D. The bamboos of the world: A preliminary study of the names and distribution of the herbaceous and woody bamboos (Bambusoideae Nees V. Esenb.) documented in lists and maps. 3rd ed. Langweid am Lech, Federal Republic of Germany: D. Ohrnberger, 1989.

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10

M, May Dennis. New tree-classification system used by the Southern Forest Inventory and Analysis Unit. New Orleans, La: U.S. Dept. of Agriculture, Forest Service, Southern Forest Experiment Station, 1990.

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Частини книг з теми "News classification"

1

Wu, Jun. "Cosines and news classification." In The Beauty of Mathematics in Computer Science, 109–16. Boca Raton, FL : Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315169491-14.

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2

Weber, Heinz J. "Generating Topic-Based Links in a Hypertext-System for News." In Information and Classification, 366–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-50974-2_37.

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3

García-Mendoza, Consuelo-Varinia, and Omar Gambino Juárez. "News Article Classification of Mexican Newspapers." In Communications in Computer and Information Science, 101–9. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03763-5_9.

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4

Schnörr, C. "Traffic News by Dynamic Fuzzy Classification." In Traffic and Granular Flow ’99, 327–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59751-0_30.

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5

De Santo, M., G. Percannella, C. Sansone, and M. Vento. "Unsupervised News Video Segmentation by Combined Audio-Video Analysis." In Multimedia Content Representation, Classification and Security, 273–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11848035_37.

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6

Böhning, D., and E. Dietz. "Some News about C.A.MAN Computer Assisted Analysis of Mixtures." In Classification, Data Analysis, and Data Highways, 113–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72087-1_13.

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7

Ko, Hon-Man, and Wai Lam. "A New Approach for Semi-supervised Online News Classification." In Lecture Notes in Computer Science, 238–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11527725_25.

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8

Antonellis, Ioannis, Christos Bouras, and Vassilis Poulopoulos. "Personalized News Categorization Through Scalable Text Classification." In Frontiers of WWW Research and Development - APWeb 2006, 391–401. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11610113_35.

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9

Cao, Yukun, Xiaofei Xu, Ye Du, Jun He, and Li Li. "Hybrid Decision Based Chinese News Headline Classification." In Web and Big Data, 3–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01298-4_1.

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Truong, Thanh Cong, Quoc Bao Diep, Ivan Zelinka, and Roman Senkerik. "Supervised Classification Methods for Fake News Identification." In Artificial Intelligence and Soft Computing, 445–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61534-5_40.

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Тези доповідей конференцій з теми "News classification"

1

Cecchini, David, and Li Na. "Chinese News Classification." In 2018 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2018. http://dx.doi.org/10.1109/bigcomp.2018.00125.

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Kirindage, Gayashan, and Navod Godewithana. "Automatic Sinhala News Classification Approach for News Platforms." In 2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2020. http://dx.doi.org/10.1109/icetas51660.2020.9484277.

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Ghosh, Souvick, and Chirag Shah. "Toward Automatic Fake News Classification." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2019. http://dx.doi.org/10.24251/hicss.2019.273.

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Dilrukshi, Inoshika, Kasun De Zoysa, and Amitha Caldera. "Twitter news classification using SVM." In 2013 8th International Conference on Computer Science & Education (ICCSE). IEEE, 2013. http://dx.doi.org/10.1109/iccse.2013.6553926.

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Gao, Yang, Li Zhou, Yong Zhang, Chunxiao Xing, Yigang Sun, and Xianzhong Zhu. "Sentiment classification for stock news." In 2010 5th International Conference on Pervasive Computing and Applications (ICPCA). IEEE, 2010. http://dx.doi.org/10.1109/icpca.2010.5704082.

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Blackledge, Ciara, and Amir Atapour-Abarghouei. "Transforming Fake News: Robust Generalisable News Classification Using Transformers." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671970.

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Esmaeili, Leila, Mohammad Kazem Akbari, Vahid Amiry, and Saeed Sharifian. "Distributed classification of Persian News (Case study: Hamshahri News dataset)." In 2013 3th International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, 2013. http://dx.doi.org/10.1109/iccke.2013.6682829.

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Kilic, Erdal, Mustafa Resit Tavus, and Zehra Karhan. "Classification of breaking news taken from the online news sites." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7129834.

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Katari, Rohan, and Madhu Bala Myneni. "A Survey on News Classification Techniques." In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, 2020. http://dx.doi.org/10.1109/iccsea49143.2020.9132866.

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Septian, Garin, Ajib Susanto, and Guruh Fajar Shidik. "Indonesian news classification based on NaBaNA." In 2017 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2017. http://dx.doi.org/10.1109/isemantic.2017.8251865.

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Звіти організацій з теми "News classification"

1

Papka, Ron. Learning Threshold Parameters for Event Classification in Broadcast News. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada477671.

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Cihlar, J., G. Okouneva, J. Beaubien, and R. Latifovic. A new histogram quantization algorithm for land cover classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219323.

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3

Madani, Farshad. Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6116.

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Glushko, E. Ya, and A. N. Stepanyuk. The multimode island kind photonic crystal resonator: states classification. SME Burlaka, 2017. http://dx.doi.org/10.31812/0564/1561.

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In this work, we consider a new calculation method to solve the eigenvalue problem for electromagnetic field in finite 2D structures including the modes distribution through the system. The field amplitude distribution is valuable if the signal energy inside the system should be transformed in most effective way. The method proposed for finite resonators operates with open boundary conditions that are important to account the electromagnetic field non-periodicity in a finite system.
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Fincher, James M., and Marie-Louise Smith. A discrimlnant function approach to ecological site classification in northern New England. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station, 1994. http://dx.doi.org/10.2737/ne-rp-686.

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Moyer, Elisabeth, Ian Foster, James Franke, Rob Jacob, Rebecca Willett, and Takuya Kuihana. New Understanding of Cloud Processes via Unsupervised Cloud Classification in Satellite Images. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769754.

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May, Dennis M., John S. Vissage, and D. Vince Few. New Tree-Classification System Used by the Southern Forest Inventory and Analysis Unit. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1990. http://dx.doi.org/10.2737/so-gtr-076.

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Johnson, Cecil D., Joseph Zeldner, and Dolores Scholarios. Developing New Test Selection and Weight Stabilization Techniques for Designing Classification Efficient Composites. Fort Belvoir, VA: Defense Technical Information Center, July 1995. http://dx.doi.org/10.21236/ada298740.

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May, Dennis M., John S. Vissage, and D. Vince Few. New Tree-Classification System Used by the Southern Forest Inventory and Analysis Unit. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1990. http://dx.doi.org/10.2737/so-gtr-76.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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Анотація:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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