Relevant bibliographies by topics / Search engines; machine learning

Contents

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

Academic literature on the topic 'Search engines; machine learning'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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Journal articles on the topic "Search engines; machine learning"

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Reddy, Mr D. Ranadeep. "Creating Search Engine Using Machine Learning Methods." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 667–72. http://dx.doi.org/10.22214/ijraset.2024.59841.

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Abstract: The vast and ever-expanding amount of information available in the WWW has led to the widespread usage of search engines for data retrieval. It can be challenging to locate information that is actually relevant and helpful, even while ordinary search engines offer users an intuitive interface for entering queries and retrieving web page links as results. To rectify that problem, which paper presents a novel search engine that work ML techniques. The target is to come up users with most relevant web sites when they query the engine. The suggested search engine improves the relevancy a
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Jha, Radhika. "Xperia Search Engine." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6331–40. http://dx.doi.org/10.22214/ijraset.2023.52996.

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Abstract: This research paper delves into the innerworkings of search engines and introduces Xperia, a personalized search engine aimed at enhancing information retrieval. The paper explores the fundamentaltechnologies employed by search engines, tracing their evolution and growth over time. It presents the development and implementation of Xperia, highlighting its unique feature set that goes beyond traditional search engines by providing users with not only relevant resourcelinks but also extracted information from various web sources. The paper begins with an introduction, providing the bac
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Priyanka, R., and G. S. Megha. "Using Machine Learning To Build A Search Engine." Journal of Advance Research in Mobile Computing 3, no. 2 (2021): 1–5. https://doi.org/10.5281/zenodo.5215150.

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The Internet is a massive server and the most preferred abundant data source. We use search engine as a popular method to retrieve information from the internet. A search engine is a website through which users can search the content of the Internet. It is one of the primary ways that internet users find to obtain suitable information. Now a days search engine providers grows in popularity because they offer increased accuracy and extra functionality which is not possible in the general. Searching for information on the internet differs in several ways. In this paper we propose Page Ranking (P
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T, Anuradha, and Tayyaba Nousheen. "MACHINE LEARNING BASED SEARCH ENGINE WITH CRAWLING, INDEXING AND RANKING." International Journal of Computer Science and Mobile Computing 10, no. 7 (2021): 76–83. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.011.

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The web is the heap and huge collection of wellspring of data. The Search Engine are used for retrieving the information from World Wide Web (WWW). Search Engines are helpful for searching user keywords and provide the accurate result in fraction of seconds. This paper proposed Machine Learning based search engine which will give more relevant user searches in the form of web pages. To display the user entered query search engine plays a major role of basic interface. Every site comprises of the heaps of site pages that are being made and sent on the server.
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Saxena, Dr Saurabh. "A Review on Machine Learning Algorithm." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49303.

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ABSTRACT Machine learning (ML) is a branch of artificial intelligence focused on the development of algorithms and statistical models that enable computer systems to perform specific tasks without explicit programming. ML algorithms are widely used in everyday applications. For instance, when using a web search engine like Google, a learning algorithm plays a crucial role in ranking web pages based on relevance. Beyond search engines, ML is applied in various domains such as data mining, image processing, and predictive analytics. One of its key advantages is the ability to automate tasks once
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Muangprathub, Jirapond, Patthamaphon Kaewmanee, Jarunee Sealee, Pattaraporn Warintarawej, and Wichuta Sae-jie. "Forecasting Trends in Foreign Tourism by Machine Learning." Tourism 73, no. 3 (2025): 392–409. https://doi.org/10.37741/t.73.3.1.

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Tourists frequently use online search engines for travel planning, making search data a valuable predictor of future tourism volume. This study employs machine learning to analyse the predictive power of keyword search data for forecasting tourist arrivals, incorporating a lag time between searches and arrivals. The dataset is collected and prepared from two sources: a search engine and government agencies, covering the years 2014-2019, to be analysed by machine learning. The SARIMA model effectively forecasts trends in keyword searches and tourist numbers, while SVM (Support Vector Machine) a
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Neogy, Taposh Kumar, and Harish Paruchuri. "Machine Learning as a New Search Engine Interface: An Overview." Engineering International 2, no. 2 (2014): 103–12. http://dx.doi.org/10.18034/ei.v2i2.539.

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The essence of a web page is an inherently predisposed issue, one that is built on behaviors, interests, and intelligence. There are relatively a ton of reasons web pages are critical to the new world, as the matter cannot be overemphasized. The meteoric growth of the internet is one of the most potent factors making it hard for search engines to provide actionable results. With classified directories, search engines store web pages. To store these pages, some of the engines rely on the expertise of real people. Most of them are enabled and classified using automated means but the human factor
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Kameni Homte, Jaurès Styve, Bernabé Batchakui, and Roger Nkambou. "Search Engines in Learning Contexts: A Literature Review." International Journal of Emerging Technologies in Learning (iJET) 17, no. 02 (2022): 254–72. http://dx.doi.org/10.3991/ijet.v17i02.26217.

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The web is one of the primary sources of information for finding learning oriented documents. In addition, the main suitable way to find information and documents on the Internet is by using search engines. Search engines are constantly improving in terms of selection algorithms and in terms of the Human Machine interface (HMI). Also, these search engines are the basis of a new field of research called Search-As-Learning. The Search-As-Learning explores information search environments to enhance learning during user search tasks. This work focuses on our view of the state of the art in the fie
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M, VASUKI. "Using Machine Learning in Web Page Categorization for Search Engine optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34167.

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This research introduces an innovative approach to classifying websites based on their compliance with SEO standards. By merging expert insights with machine learning algorithms, the study develops classifiers capable of accurately sorting web pages into three categories. These classifiers pinpoint key factors that impact the level of page optimization. The training phase entails experts manually labeling data. Experimental findings underscore the efficacy of machine learning in gauging a web page's adherence to SEO guidelines. This method holds significance as it automates the identification
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Singh, Saumya, Shivani Chauhan, and Er Mahendra Kumar. "Framework for Designing Questionnaire Using Machine Learning." Research & Reviews: Machine Learning and Cloud Computing 1, no. 1 (2022): 23–29. http://dx.doi.org/10.46610/rrmlcc.2022.v01i01.004.

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For a long time, people have been trying to find a way to retrieve information from a large text database. Convert data into information we need. In current search engines, when we search about something rather than giving the precise answer it takes out keywords from our search and gives us documents or web pages related to those words but what we want is the exact answer, why does the user have to search for it. That is, search engines deal more with whole document retrieval. However, a user often wants an exact or specific answer to the question. For instance, given the question "When is Ho
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Dissertations / Theses on the topic "Search engines; machine learning"

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Harbert, Christopher W. Shang Yi. "An application of machine learning techniques to interactive, constraint-based search." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4324.

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Thesis (M.S.)--University of Missouri-Columbia, 2005.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (December 12, 2006) Includes bibliographical references.
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Westerdahl, Simon, and Larsson Fredrik Lemón. "Optimization for search engines based on external revision database." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-21000.

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The amount of data is continually growing and the ability to efficiently search through vast amounts of data is almost always sought after. To efficiently find data in a set there exist many technologies and methods but all of them cost in the form of resources like cpu-cycles, memory and storage. In this study a search engine (SE) is optimized using several methods and techniques. Thesis looks into how to optimize a SE that is based on an external revision database.The optimized implementation is compared to a non-optimized implementation when executing a query. An artificial neural network (
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Kruger, Andries F. "Machine learning, data mining, and the World Wide Web : design of special-purpose search engines." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53492.

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Thesis (MSc)--Stellenbosch University, 2003.<br>ENGLISH ABSTRACT: We present DEADLINER, a special-purpose search engine that indexes conference and workshop announcements, and which extracts a range of academic information from the Web. SVMs provide an efficient and highly accurate mechanism for obtaining relevant web documents. DEADLINER currently extracts speakers, locations (e.g. countries), dates, paper submission (and other) deadlines, topics, program committees, abstracts, and affiliations. Complex and detailed searches are possible on these fields. The niche search engine was const
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Jing, Yushi. "Learning an integrated hybrid image retrieval system." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43746.

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Current Web image search engines, such as Google or Bing Images, adopt a hybrid search approach in which a text-based query (e.g. "apple") is used to retrieve a set of relevant images, which are then refined by the user (e.g. by re-ranking the retrieved images based on similarity to a selected example). This approach makes it possible to use both text information (e.g. the initial query) and image features (e.g. as part of the refinement stage) to identify images which are relevant to the user. One limitation of these current systems is that text and image features are treated as independent c
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Movin, Maria. "Spelling Correction in a Music Entity Search Engine by Learning from Historical Search Queries." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229716.

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Query spelling correction is an important component of modern search engines that can help users to express their intent, and thus improve search quality. In this study, we investigated with what accuracy a sequence-to-sequence recurrent neural network (RNN) can recognise and correct misspellings in a music search engine, when the model is trained with old search queries. A sequence-to-sequence RNN was chosen as the model in this study since it has achieved state-of-the-art performance on similar tasks, such as machine translation and speech recognition. The findings from the study imply that
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Popescu, Ana-Maria. "Information extraction from unstructured web text /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6935.

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Bahceci, Oktay. "Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210252.

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Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements an
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Nilsson, Olof. "Visualization of live search." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-102448.

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The classical search engine result page is used for many interactions with search results. While these are effective at communicating relevance, they do not present the context well. By giving the user an overview in the form of a spatialized display, in a domain that has a physical analog that the user is familiar with, context should become pre-attentive and obvious to the user. A prototype has been built that takes public medical information articles and assigns these to parts of the human body. The articles are indexed and made searchable. A visualization presents the coverage of a query o
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Скиданенко, Максим Сергійович, Максим Сергеевич Скиданенко, Maksym Serhiiovych Skydanenko, and A. S. Skidanenko. "Visual search engines as a search tool in the learning process." Thesis, Сумський державний університет, 2012. http://essuir.sumdu.edu.ua/handle/123456789/29424.

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The main objective of the University is the promotion of successful professionals who have practical skills, can predict, model, process information, and integrate the knowledge obtained in a higher educational establishment. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/29424
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Segal, Richard B. "Machine learning as massive search /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6863.

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Books on the topic "Search engines; machine learning"

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Pillay, Nelishia, and Rong Qu, eds. Automated Design of Machine Learning and Search Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72069-8.

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Pesch, Erwin. Learning in automated manufacturing: A local search approach. Physica-Verlag, 1994.

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Knoblock, Craig A. Generating abstraction hierarchies: An automated approach to reducing search in planning. Kluwer Academic Publisher, 1993.

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Hearst, Marti. Search user interfaces. Cambridge University Press, 2009.

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Hearst, Marti. Search user interfaces. Cambridge University Press, 2009.

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Ackermann, Ernest C. Learning to use the Internet and World Wide Web with revitalized URLs. Franklin, Beedle & Associates, 2004.

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Lim, Edward H. Y. Knowledge Seeker - Ontology Modelling for Information Search and Management: A Compendium. Springer-Verlag Berlin Heidelberg, 2011.

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Inc, ebrary, ed. Learning the Yahoo! user interface library: Get started and get to grips with the YUI JavaScript development library! Packt, 2008.

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1947-, Miller William, and Pellen Rita M, eds. Libraries and Google. Haworth Information Press, 2005.

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Recommendation Engines. MIT Press, 2020.

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Book chapters on the topic "Search engines; machine learning"

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Martin, Eric, Samuel Kaski, Fei Zheng, et al. "Search Engines: Applications of ML." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_744.

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Aggarwal, Charu C. "Information Retrieval and Search Engines." In Machine Learning for Text. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73531-3_9.

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Aggarwal, Charu C. "Information Retrieval and Search Engines." In Machine Learning for Text. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96623-2_9.

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Martin, Eric. "Search Engines: Applications of ML." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_750-1.

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Martin, Eric. "Search Engines: Applications of ML." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_750.

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Khoussainov, Rinat, and Nicholas Kushmerick. "Optimising Performance of Competing Search Engines in Heterogeneous Web Environments." In Machine Learning: ECML 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39857-8_21.

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Luan, Xi-Dao, Yu-Xiang Xie, Ling-Da Wu, Chi-Long Mao, and Song-Yang Lao. "Information Assistant: An Initiative Topic Search Engine." In Advances in Machine Learning and Cybernetics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_33.

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Güngör, Tunga. "A Machine Learning Approach for Displaying Query Results in Search Engines." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40261-6_21.

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Ziegenbein, Timon, Shahbaz Syed, Martin Potthast, and Henning Wachsmuth. "Objective Argument Summarization in Search." In Robust Argumentation Machines. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63536-6_20.

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AbstractDecision-making and opinion formation are influenced by arguments from various online sources, including social media, web publishers, and, not least, the search engines used to retrieve them. However, many, if not most, arguments on the web are informal, especially in online discussions or on personal pages. They can be long and unstructured, subjective and emotional, and contain inappropriate language. This makes it difficult to find relevant arguments efficiently. We hypothesize that, on search engine results pages, “objective snippets” of arguments are better suited than the commonly used extractive snippets and develop corresponding methods for two important tasks: snippet generation and neutralization. For each of these tasks, we investigate two approaches based on (1) prompt engineering for large language models (LLMs), and (2) supervised models trained on existing datasets. We find that a supervised summarization model outperforms zero-shot summarization with LLMs for snippet generation. For neutralization, using reinforcement learning to align an LLM with human preferences for suitable arguments leads to the best results. Both tasks are complementary, and their combination leads to the best snippets of arguments according to automatic and human evaluation.
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Amudha, S., and I. Elizabeth Shanthi. "Phrase Based Information Retrieval Analysis in Various Search Engines Using Machine Learning Algorithms." In Data Management, Analytics and Innovation. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9364-8_21.

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Conference papers on the topic "Search engines; machine learning"

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Morgan, Jay Paul, and Frederic Boy. "Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage." In 2024 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2024. https://doi.org/10.1109/icmla61862.2024.00127.

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Wang, Suyi. "Research on Keyword Selection and Search Engine Optimization Strategies for Online Marketing Based on Machine Learning." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00139.

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Shmalko, Elizaveta, Askhat Diveev, and Ivan Gromov. "Function Search Automated by Evolutionary Machine Learning." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708558.

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Dai, Xin, Tzu-Chieh Wei, Shinjae Yoo, and Samuel Yen-Chi Chen. "Quantum Machine Learning Architecture Search via Deep Reinforcement Learning." In 2024 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2024. https://doi.org/10.1109/qce60285.2024.00179.

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Ahamed, H. Riaz, and D. Kerana Hanirex. "Enhanced Keyword Search Optimization in E-Learning Using Machine Learning Models." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725374.

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Fang, Yi, Hongfu Liu, Zhiqiang Tao, and Mikhail Yurochkin. "Fairness of Machine Learning in Search Engines." In CIKM '22: The 31st ACM International Conference on Information and Knowledge Management. ACM, 2022. http://dx.doi.org/10.1145/3511808.3557501.

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Zu-Kuan Wei and Jiang-Ping Du. "Research on several key issues about Search Engines." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4621011.

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Bokhari, Mohammad Ubaidullah, Mohd Kashif Adhami, and Rashid Ali. "Machine Learning Approach to Evaluate News Search Engines." In 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2019. http://dx.doi.org/10.1109/upcon47278.2019.8980002.

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Ghadge, Nikhil. "Machine Learning: Enhancing Intelligent Search and Information Discovery." In 5th International Conference on Advanced Natural Language Processing. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141021.

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Machine learning algorithms are revolutionizing intelligent search and information discovery capabilities. By incorporating techniques like supervised learning, unsupervised learning, reinforcement learning, and deep learning, systems can automatically extract insights and patterns from vast data repositories. Natural language processing enables deeper comprehension of text, while image recognition unlocks knowledge from visual data. Machine learning powers personalized recommendation engines and accurate sentiment analysis. Integrating knowledge graphs enriches machine learning models with ba
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Karwa, Rushikesh, and Vikas Honmane. "Building Search Engine Using Machine Learning Technique." In 2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019. http://dx.doi.org/10.1109/iccs45141.2019.9065846.

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Reports on the topic "Search engines; machine learning"

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th
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Adam, Gaelen P., Melinda Davies, Jerusha George, et al. Machine Learning Tools To (Semi-) Automate Evidence Synthesis. Agency for Healthcare Research and Quality (AHRQ), 2025. https://doi.org/10.23970/ahrqepcwhitepapermachine.

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Introduction. Tools that leverage machine learning, a subset of artificial intelligence, are becoming increasingly important for conducting evidence synthesis as the volume and complexity of primary literature expands exponentially. In response, we have created a living rapid review and evidence map to understand existing research and identify available tools. Methods. We searched PubMed, Embase, and the ACM Digital Library from January 1, 2021, to April 3, 2024, for comparative studies, and identified older studies using the reference lists of existing evidence synthesis products (ESPs). We p
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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Banco de España, 2023. http://dx.doi.org/10.53479/29594.

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic lite
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Roldán-Ferrín, Felipe, and Julián A. Parra-Polania. ENHANCING INFLATION NOWCASTING WITH ONLINE SEARCH DATA: A RANDOM FOREST APPLICATION FOR COLOMBIA. Banco de la República, 2025. https://doi.org/10.32468/be.1318.

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This paper evaluates the predictive capacity of a machine learning model based on Random Forests (RF), combined with Google Trends (GT) data, for nowcasting monthly inflation in Colombia. The proposed RF-GT model is trained using historical inflation data, macroeconomic indicators, and internet search activity. After optimizing the model’s hyperparameters through time series cross-validation, we assess its out-of-sample performance over the period 2023–2024. The results are benchmarked against traditional approaches, including SARIMA, Ridge, and Lasso regressions, as well as professional forec
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Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic database
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Fitz, Julie, Marjorie E. Wechsler, and Stephanie Levin. State approaches to developing educational leaders. Learning Policy Institute, 2024. http://dx.doi.org/10.54300/795.572.

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The purpose of this study was to understand the infrastructure that states have built for leadership-relevant professional learning by identifying the long-term leadership development initiatives supported by states and analyzing their purposes, target audiences, and scope. We conducted a scan between March and May of 2023 using search engines, state department of education websites, and other web-based documents. We found that at least 26 states support ongoing statewide leadership development initiatives to build the knowledge and skills of in-service leaders. This report provides examples o
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Griffiths, Rachael M. Handwritten Text Recognition (HTR) for Tibetan Manuscripts in Cursive Script. Verlag der Österreichischen Akademie der Wissenschaften, 2024. http://dx.doi.org/10.1553/tibschol_erc_htr.

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The use of advanced computational methods for the analysis of digitised texts is becoming increasingly popular in humanities and social science research. One such technology is Handwritten Text Recognition (HTR), which generates transcripts from digitised texts with machine learning approaches, to enable full-text search and analysis. Up to now, HTR models for Tibetan manuscripts in cursive script have not been available. This paper introduces work carried out as part of the The Dawn of Tibetan Buddhist Scholasticism (11th-13th) TibSchol) project at the Austrian Academy of Sciences, which is u
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Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Pipeline Research Council International, Inc. (PRCI), 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM.
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Hunter, R., S. Ross, and Jing-Ru Cheng. A general-purpose multiplatform GPU-accelerated ray tracing API. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47260.

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Real-time ray tracing is an important tool in computational research. Among other things, it is used to model sensors for autonomous vehicle simulation, efficiently simulate radiative energy propagation, and create effective data visualizations. However, raytracing libraries currently offered for GPU platforms have a high level of complexity to facilitate the detailed configuration needed by gaming engines and high-fidelity renderers. A researcher wishing to take advantage of the performance gains offered by the GPU for simple ray casting routines would need to learn how to use these ray traci
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