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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 and accuracy of search results by utilizing machine learning algorithms. This system seeks to grasp user intent, adjust to individual preferences, and deliver contextually relevant information by going beyond the bounds of traditional search engines. By using machine learning models, the search engine may learn dynamically and enhance the standard of its results over time by continuously refining its understanding. More sophisticated and adaptable search engines are being developed as an outcome of these combination of state-of-the-art machine learning libraries, tools for processing natural language, and effective indexing systems. The target of the research is too advance information retrieval systems by providing a more advanced and user-focused method of tackling the difficulties presented by the WWW's immense scope
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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 background and motivation for the research, as well as outlining the research objectives and scope. It then delves into the fundamentals of search engines, discussing their components, crawling and indexing processes, ranking algorithms, and user interfaces. The evolution of search engine technologies is examined, from the early stages to the current advancements in semantic search, natural language processing, and the incorporation of machine learning and artificial intelligence.
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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 (PR), Weighted PR(WPR) and Hyperlink Induced Topic Search (HITS) algorithms using machine learning technique to greatly automate the methods and classification of Web pages. Search engines play a critical role in the growth of the internet; they assist many internet users in quickly finding relevant information. It can be used to do the basic process of retrieving information.
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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 the algorithm has been trained on relevant data. This paper provides a brief review of machine learning applications and explores future prospects in this rapidly evolving field.
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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 is dominant in their success. From experimental results, we can deduce that the most effective and critical way to automate web pages for search engines is via the integration of machine learning.
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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 field of Search Engines in learning context and Search-As-Learning, stressing on the most recent research. We conclude by highlighting the current shortcomings on improvement of the learning aspect within search engines, and present next work which will be the association of a layer above the traditional search engines to promote the appropriation of content during search task for a learning context
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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 of pages needing optimization to enhance search engine rankings. Moreover, the research sheds light on the optimal arrangement of ranking variables utilized by search engines, reinforcing previous research. Additionally, the establishment of a new dataset comprising manually annotated web pages proves invaluable for future research initiatives. KEYWORDS: Machine learning, on-page optimization, classification, SEO optimization, Search engine optimization.
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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 Holi festival this year?", what he wants is the answer "March 9, 2022", rather than to read through lots of web pages that contain the words "Holi", "festival", "year", etc. to find the date of the festival. That is, what a user needs is information retrieval, rather than current document retrieval. We handle the task of answering questions, where the answers are in documents in an extensive text database. We take on a machine learning technique to answer questions. In particular, answer candidates are classified and ranked by a classifier trainee donaset of question-answerpairs.
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Chen, Kun, Xiaowen Ji, and Huaiqing Wang. "A search index-enhanced feature model for news recommendation." Journal of Information Science 43, no. 3 (2016): 328–41. http://dx.doi.org/10.1177/0165551516639801.
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General news recommendations are important but have received limited attention because of the difficulties of measuring public interest. In public search engines, the objects of search terms reflect the issues that interest or concern search engine users. Because of the popularity of search engines, search indexes have become a new measure for describing public interest trends. With the help of a public search index provided by search engines, we construct a news topic search feature and a news object search feature. These features measure the public attention on key elements of the news. In the experiment, we compare various feature models with machine learning algorithms with respect to financial news recommendations. The results demonstrate that the topic search features perform best compared with other feature models. This research contributes to both the feature generation and news recommendation domains.
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M. Karthica. "Enhanced Reinforcement Algorithm for Topic Categorization Using Machine Learning Method." Journal of Information Systems Engineering and Management 10, no. 10s (2025): 490–98. https://doi.org/10.52783/jisem.v10i10s.1411.
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Information seekers rely heavily on search engines to extract relevant information because of the Internet's exponential development in users and traffic. The availability of a vast amount of textual, audio, video, and other content has expanded search engines' duty. Users of the Internet can obtain pertinent information about their query from the search engine by using factors like content and link structure. It does not, however, imply that the information is accurate. The link structure of web sites is analyzed using Web Structure Mining (WSM), and their content is analyzed using Web Content Mining (WCM), which determines how well the ranking module performs.A multitude of content-based recommender systems are currently in use, and they are well-researched in both text acquisition and filtering. These systems recommend documents based on text analysis. The management information system can benefit from Web Content Ming technology. Web content mining is the process of extracting or mining knowledge or useful information from web pages. The purpose of this work is to investigate web content extraction technology enhanced Reinforcement algorithm which anticipates user interest by analyzing the page according to user view related topic.This ERIM procedure involves locating web sites linked to user queries and using hyperlinks to locate a collection of related web pages and find the topic categorization using machine learning method.
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Oh, Se Eun, Shuai Li, and Nicholas Hopper. "Fingerprinting Keywords in Search Queries over Tor." Proceedings on Privacy Enhancing Technologies 2017, no. 4 (2017): 251–70. http://dx.doi.org/10.1515/popets-2017-0048.
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AbstractSearch engine queries contain a great deal of private and potentially compromising information about users. One technique to prevent search engines from identifying the source of a query, and Internet service providers (ISPs) from identifying the contents of queries is to query the search engine over an anonymous network such as Tor.In this paper, we study the extent to which Website Fingerprinting can be extended to fingerprint individual queries or keywords to web applications, a task we call Keyword Fingerprinting (KF). We show that by augmenting traffic analysis using a two-stage approach with new task-specific feature sets, a passive network adversary can in many cases defeat the use of Tor to protect search engine queries.We explore three popular search engines, Google, Bing, and Duckduckgo, and several machine learning techniques with various experimental scenarios. Our experimental results show that KF can identify Google queries containing one of 300 targeted keywords with recall of 80% and precision of 91%, while identifying the specific monitored keyword among 300 search keywords with accuracy 48%. We also further investigate the factors that contribute to keyword fingerprintability to understand how search engines and users might protect against KF.
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Matošević, Goran, Jasminka Dobša, and Dunja Mladenić. "Using Machine Learning for Web Page Classification in Search Engine Optimization." Future Internet 13, no. 1 (2021): 9. http://dx.doi.org/10.3390/fi13010009.
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This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.
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Zahrawi, Mohammad, and Ahmad Mohammad. "Implementing Recommender Systems using Machine Learning and Knowledge Discovery Tools." Knowledge-Based Engineering and Sciences 2, no. 2 (2021): 44–53. http://dx.doi.org/10.51526/kbes.2021.2.2.44-53.
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The current research offers a novel use of machine learning strategies to create a recommendation system. At recent era, recommender systems (RSs) have been used widely in e-commerce, entertainment purposes, and search engines. In more general, RSs are set of algorithms designed to recommend relevant items to users (movies to watch, books to read, products to buy, songs to listen, and others). This article discovers the different characteristics and features of many approaches used for recommendation systems in order to filter and prioritize the relevant information and work as a compass for searching. Recommender engines are crucial in some companies as they can create a big amount of income when they are effective or be a way to stand out remarkably from other rivals. As a proof of the importance of recommender engine, it can be stated that Netflix arrange a challenge (the “Netflix prize”) where the mission was to create a recommender engine that achieves better than its own algorithm with a prize of 1 million dollars to win.
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Journal, IJSREM. "Webpage Duplication using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27459.
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Web search engines face challenges with nearly identical and duplicate webpages, leading to increased storage requirements and slower results. Researchers have addressed this issue by exploring methods to detect such similarities. One approach involves utilizing sentence-level characteristics alongside fingerprinting techniques. The proposed method employs K-mode clustering for large sets of web documents and compares fingerprints and sentence features to identify almost identical web pages quickly and accurately. Experimental results on web page collections validate the effectiveness of this approach.
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Mahesh, T. R., Vivek V, and Kumar Vinoth. "Classification-based Collaborative filtering: A Machine Learning Recommendation System." International Journal of Information Technology, Research and Applications (IJITRA) 1, no. 2 (2022): 9–15. https://doi.org/10.5281/zenodo.7007641.
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Social networking platforms like, Twitter, Face book etc., have now emerged as a major forum for the application of Recommended Systems (RSs). Of course these famous sites are taken as the main source of people-related knowledge and thus to be a great choice for exploiting modern and creative approaches to the recommendation, backing the old methods, in order to improve accuracy It was thought that helping users cope with the issue of data overload was the original role of information retrieval systems or search engines, but what separates recommended systems from the existing search engines is the requirements of personalized useful and interesting. The "intelligence" aspect is what suggests more interesting and useful. Intelligence is one of the main routes of personalization to know the interests of the user, anticipate the unknown favorites of the user, and eventually provide suggestions by matching the question and the content beyond a basic search. This article provides simple approaches to Recommendation Systems, provides recommendation for similar items based on the correlation and classification methods of machine learning to build a collaborative filtering system by making use of Logistic Regression model.
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Tirado Vilela, Nicolás Alejandro, Adriana Maemi Ueunten Acevedo, Marcos Fernando Ruiz-Ruiz, and Wilfredo Yushimito. "Gender Biases in Professions: A Machine Learning – Powered Search Engines Analysis." International Journal of Engineering Trends and Technology 72, no. 9 (2024): 367–83. http://dx.doi.org/10.14445/22315381/ijett-v72i9p134.
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Kucukyilmaz, Tayfun, B. Barla Cambazoglu, Cevdet Aykanat, and Ricardo Baeza-Yates. "A machine learning approach for result caching in web search engines." Information Processing & Management 53, no. 4 (2017): 834–50. http://dx.doi.org/10.1016/j.ipm.2017.02.006.
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Researcher. "EVOLUTION AND FUTURE OF SEARCH: HOW AI IS TRANSFORMING INFORMATION RETRIEVAL." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 107–17. https://doi.org/10.5281/zenodo.13134112.
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This article examines the transformative impact of artificial intelligence on search engines, enhancing query processing and information retrieval. It addresses the limitations of traditional keyword-based algorithms. It traces the evolution of search engines from early keyword-based models to the integration of AI, enabling semantic understanding and context-aware search. The article delves into crucial AI techniques like Natural Language Processing, deep learning, and reinforcement learning, highlighting their impact on query processing and retrieval accuracy. It further explores how AI facilitates semantic search, leverages knowledge graphs, and enables personalized search results. Real-world applications are illustrated through examples like Google's BERT model and AI-driven enhancements in e-commerce. Finally, the article addresses challenges such as data privacy, bias in AI models, and computational demands while exploring future directions like multimodal search, explainable AI, and continual learning. Ultimately, the article underscores the profound impact of AI in shaping the future of search engines and their crucial role in navigating the digital age.
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Butcher, Lola. "Artificial Intelligence and Machine Learning: What Physician Leaders Need to Know." Physician Leadership Journal 10, no. 3 (2023): 31–33. http://dx.doi.org/10.55834/plj.6915833702.
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Artificial intelligence and machine learning, already ubiquitous in search engines, online shopping, and other everyday activities, will soon become commonplace in healthcare delivery. Clinicians need support in evaluating, trusting, and using the emerging tools.
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Ghadge, Nikhil. "Leveraging Machine Learning to Enhance Information Exploration." Machine Learning and Applications: An International Journal 11, no. 2 (2024): 17–27. http://dx.doi.org/10.5121/mlaij.2024.11203.
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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 background knowledge for enhanced accuracy and explainability. Applications span voice search, anomaly detection, predictive analytics, text mining, and data clustering. However, interpretable AI models are crucial for enabling transparency and trustworthiness. Key challenges include limited training data, complex domain knowledge requirements, and ethical considerations around bias and privacy. Ongoing research that combines machine learning, knowledge representation, and human-centered design will advance intelligent search and discovery. The collaboration between artificial and human intelligence holds the potential to revolutionize information access and knowledge acquisition.
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Nikhil, N. K. "Leveraging Machine Learning to Enhance Information Exploration." Machine Learning and Applications: An International Journal (MLAIJ) 11, no. 6 (2024): 17–27. https://doi.org/10.5121/mlaij.2024.11203.
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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 background knowledge for enhanced accuracy and explainability. Applications span voice search, anomaly detection, predictive analytics, text mining, and data clustering. However, interpretable AI models are crucial for enabling transparency and trustworthiness. Key challenges include limited training data, complex domain knowledge requirements, and ethical considerations around bias and privacy. Ongoing research that combines machine learning, knowledge representation, and human-centered design will advance intelligent search and discovery. The collaboration between artificial and human intelligence holds the potential to revolutionize information access and knowledge acquisition.
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Gyasoddin, Imran Ahemad, and Prof. D. G. Ingale. "META SEARCH ENGINE." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 311–14. https://doi.org/10.5281/zenodo.13991669.
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Meta search engines are designed to improve the efficiency and breadth of online searches by aggregating results from multiple search engines. Unlike traditional search engines, which rely on their own indexed databases and algorithms, meta search engines query several search engines simultaneously and present a unified set of results. This approach addresses limitations in single search engines, such as limited coverage, ranking biases, and incomplete results (Zhang & Wu, 2005; Jansen & Pooch, 2001). Meta search engines work by distributing user queries to various search engines, collecting the results, and then aggregating and ranking them based on their own algorithms (Kumar & Desai, 2021). This enables users to access a broader range of information, combining results from different sources and increasing the likelihood of finding relevant content. Additionally, meta search engines offer features like result filtering, customization, and privacy protection to enhance the overall search experience. By combining results from multiple sources, meta search engines help mitigate ranking biases present in individual search engines (Yuwono & Lee, 1997).
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Baklanov, Oleksii, Volodymyr Bezkorovainyi, and Liudmyla Kolesnyk. "Studying cognitive services for websites search engine optimization." Bulletin of Kharkov National Automobile and Highway University, no. 97 (September 5, 2022): 7. http://dx.doi.org/10.30977/bul.2219-5548.2022.97.0.7.
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The subject of research in the article is machine learning models for classifying web-pages by quality and compliance with SEO rules. The goal of the article is improving the efficiency of search engines by establishing and using factors that have the greatest impact on the degree of SEO optimization of web pages. The article solves the following tasks: study of the effectiveness of using machine learning methods to build a classification model that automatically classifies web pages according to the degree of adaptation to SEO optimization recommendations; assessment of the influence of relevant page factors (text on a web page, text in meta tags, links, image, HTML code) on the degree of SEO optimization using the developed classification models. The following methods are used: machine learning methods, classification methods and statistical methods. The following results were obtained: analysis of the effectiveness of the application of machine learning methods to determine the degree of adaptation of a web page to SEO recommendations was carried out; classifiers were trained on a data set of web pages randomly selected from the DMOZ catalog and rated by three independent SEO experts in the categories: “low SEO”, “medium SEO” and “high SEO”; five main classifiers were tested (decision trees, naive Bayes, logistic regression, KNN and SVM), on the basis of which it was revealed that all the studied models received greater accuracy (from 54.69% to 69.67%) than the accuracy of the baseline (48.83%); the results of the experiments confirm the hypothesis about the effectiveness of adapting web pages to SEO recommendations using classification algorithms based on machine learning. Conclusions. It was confirmed that with the help of classification algorithms built on the basis of machine learning and the knowledge of experts, it is possible to effectively adjust web pages to SEO recommendations. The considered methods can be adapted for various search engines and applicable to different languages, provided that a stamping or lemmatization algorithm has been developed for them. The results of the study can be used in the development of automated software to support the work of SEO in audit technologies to identify web pages in need of optimization and in spam detection processes.
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Mikhailov, Alexei, and Mikhail Karavay. "Pattern Inversion as a Pattern Recognition Method for Machine Learning." Journal of Physics: Conference Series 2224, no. 1 (2022): 012002. http://dx.doi.org/10.1088/1742-6596/2224/1/012002.
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Abstract Artificial neural networks use a lot of coefficients that take a great deal of computing power for their adjustment, especially if deep learning networks are employed. However, there exist coefficients-free extremely fast indexing-based technologies that work, for instance, in Google search engines, in genome sequencing, etc. The paper discusses the use of indexing-based methods for pattern recognition. It is shown that for pattern recognition applications such indexing methods replace with inverse patterns the fully inverted files, which are typically employed in search engines. Not only such inversion provides automatic feature extraction, which is a distinguishing mark of deep learning, but, unlike deep learning, pattern inversion supports almost instantaneous learning, which is a consequence of absence of coefficients. The paper discusses a pattern inversion formalism that makes use on a novel pattern transform and its application for unsupervised instant learning. Examples demonstrate a view-angle independent recognition of three-dimensional objects, such as cars, against arbitrary background, prediction of remaining useful life of aircraft engines, and other applications. In conclusion, it is noted that, in neurophysiology, the function of the neocortical mini-column has been widely debated since 1957. This paper hypothesizes that, mathematically, the cortical mini-column can be described as an inverse pattern, which physically serves as a connection multiplier expanding associations of inputs with relevant pattern classes.
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B. Sangamithra. "An Improved Information Retrieval System using Hybrid RNN LSTM for Multiple Search Engines." Communications on Applied Nonlinear Analysis 31, no. 5s (2024): 167–80. http://dx.doi.org/10.52783/cana.v31.1011.
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When searching for data on the internet, every user has their own personal context for doing so. The job of a search engine is to find the most relevant content from all the blogs on the internet based on the user's query. Information retrieval systems, both locally and globally, have been profoundly affected by the advent of the internet, and this includes the value of information left as comments on a page of SNS (Social Network Services). The concept of emotional and social connections is translated into a logical framework using the terms "node" and "link" to describe the structure of a social network. Efficient semantic models are more extensive training and evaluation materials, are needed to improve social network search capabilities. When compared with machine learning algorithms, the efficacy of traditional keyword-based search engines at understanding users' intentions is low. Recently, neural networks have gained popularity in the field of information retrieval because of their impressive vector representation learning capabilities. The usage of deep learning techniques for this purpose has recently been seen, and they have proven to be more effective than traditional machine learning techniques such artificial neural networks (ANNs). Improved results have been seen specifically using deep-learning techniques like long short-term memory (LSTM) & Recurrent Neural Network (RNN). In order to create a more personalized Information Retrieval(IR) system, this research suggests using a deep learning Hybrid RNN - LSTM model. Finally, the suggested method takes user comments into account and uses a hybrid RNN - LSTM to re-rank the data so that everyone is happy. Web search contest dataset is used for the implementation. Statistics like accuracy, precision, & recall are used to evaluate the data set on Bing and Duckduck go, two of the most prominent search engines. According to the findings, the proposed Hybrid method outperformed more traditional methods.
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V, K. Raj Anishaa, Sathvika P, and Rawat Sandeep. "Identifying Similar Question Pairs Using Machine Learning Techniques." Indian Journal of Science and Technology 14, no. 20 (2021): 1635–41. https://doi.org/10.17485/IJST/v14i20.312.
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Abstract <strong>Background/Objectives</strong>: Every day millions of people visit search engines like Quora, reedit, stack overflow, etc., the demand for new intelligent techniques is growing, to help individuals find better solutions. <strong>Methods</strong>: In our proposed system, the Quora datasets were filtered using SQLite which takes one-quarter of the time taken to pre-process the same dataset using existing approaches like python functions. We used machine learning techniques namely the Random Forest, Logistic Regression, Linear SVM (Support Vector Machine) and XGBoost to analyze and identify the most suitable model. <strong>Findings</strong>: The error log loss functions (0.887, 0.521, 0.654 and 0.357) of the above machine learning techniques were analyzed and compared respectively. The performance of XGBoost is the best among the other models, hence XGBoost is the most efficient model. <strong>Conclusion/Future Scope</strong>: It is concluded that XGBoost has outperformed other machine learning techniques discussed in the study. It is also found that pre-processing using SQLite has improved the response time. In the future, we would like to apply a similar approach for various other search engines that are available like reedit, stack overflow, etc. and one could ensemble the best models of each type (linear, tree-based, and neural network). <strong>Keywords</strong> Machine Learning, Question Pair Similarity, XGBoost, Linear SVM, Logistic Regression, Random Forest
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Menczer, Filippo, Le-Shin Wu, and Ruj Akavipat. "Intelligent Peer Networks for Collaborative Web Search." AI Magazine 29, no. 3 (2008): 35. http://dx.doi.org/10.1609/aimag.v29i3.2155.
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Collaborative query routing is a new paradigm for Web search that treats both established search engines and other publicly available indices as intelligent peer agents in a search network. The approach makes it transparent for anyone to build their own (micro) search engine, by integrating established Web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment— the existence, knowledge, diversity, reliability, and trustworthiness of other agents — by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and high-quality search results. A prototype of 6S is available for public use and is intended to assist in the evaluation of different AI techniques employed by the networked agents.
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Ankit, Srivastava. "AI/ML in Search Engine Optimizer." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 9, no. 6 (2023): 1–6. https://doi.org/10.5281/zenodo.14507578.
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This paper focuses on how AI and Machine Learning influence SEO. This paper will explain what all these cutting-edge technologies are doing for SEO by discussing how they can be used, how they could help, how they can hinder, and what they can mean for the future of digital marketing. Based on an overview of the available literature, in this paper, we cover the major SEO uses of AI/ML — data mining, content management, automated decision making and so on. Moreover, the paper discusses the ethical and privacy issues involved in the use of such technologies as well as the wider influence they have on the marketing sector. Artificial Intelligence (AI) and Machine Learning (ML) are changing the online world and SEO is one of them. Such technology helps search engines better understand, index and present information based on intent. This blog is about AI and ML reshaping SEO techniques, the instruments harnessing these trends, and the threats and opportunities they hold for marketers and web developers.
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Sun, Jingtong. "Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application." Computational Intelligence and Neuroscience 2022 (August 1, 2022): 1–9. http://dx.doi.org/10.1155/2022/2541421.
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With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job information, has also increased unprecedentedly, which makes human resource services face information overload. Especially with the gradual increase of the amount of information, this method is not enough for the acquisition and classification of massive data. After that, experts developed search engines to deal with the retrieval problem, and the first ones were Google and Baidu. As long as the search engine is clear about the direction of the search, it is indeed very convenient for the retrieval of massive data. However, in many cases, most users cannot clearly recognize the content they need or how to accurately express their needs. Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user’s experience and meet their own needs more easily. Based on the main workflow of the recommender system, this paper designs the overall architecture of the human resources recommendation system and implements a human resources recommendation prototype system based on deep learning. The system can better overcome the cold start problem and provide real-time recommendation results, improving the quality of HR personalized recommendation results.
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Hien, Ngo Le Huy, Thai Quang Tien, and Nguyen Van Hieu. "Web Crawler: Design And Implementation For Extracting Article-Like Contents." Cybernetics and Physics, Issue Volume 9, 2020, Number 3 (November 30, 2020): 144–51. http://dx.doi.org/10.35470/2226-4116-2020-9-3-144-151.
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The World Wide Web is a large, wealthy, and accessible information system whose users are increasing rapidly nowadays. To retrieve information from the web as per users’ requests, search engines are built to access web pages. As search engine systems play a significant role in cybernetics, telecommunication, and physics, many efforts were made to enhance their capacity.However, most of the data contained on the web are unmanaged, making it impossible to access the entire network at once by current search engine system mechanisms. Web Crawler, therefore, is a critical part of search engines to navigate and download full texts of the web pages. Web crawlers may also be applied to detect missing links and for community detection in complex networks and cybernetic systems. However, template-based crawling techniques could not handle the layout diversity of objects from web pages. In this paper, a web crawler module was designed and implemented, attempted to extract article-like contents from 495 websites. It uses a machine learning approach with visual cues, trivial HTML, and text-based features to filter out clutters. The outcomes are promising for extracting article-like contents from websites, contributing to the search engine systems development and future research gears towards proposing higher performance systems.
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D, Lande, Soboliev A, and Dmytrenko O. "Intelligent technologies in information retrieval systems." Artificial Intelligence 27, jai2022.27(1) (2022): 260–68. http://dx.doi.org/10.15407/jai2022.01.260.
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This paper considers the use of modern intelligent technologies in information retrieval systems. A general scheme for the implementation of Internet search engines is presented. The existing and prospective approaches to the intellectualization of individual components of this scheme are presented. An approach to the creation of a system of intelligent agents for information collection is presented. These agents are combined into teams and exchange the results of their work with each other. They form a reliable basis for the information base of search engines, ensure uninterrupted operation of the system in case of failure of individual agents. Methods for the formation of semantic networks corresponding to the texts of individual documents are also considered. These networks are considered as search patterns of documents for information retrieval and detection of duplicates or similar documents. Machine learning methods are used to conduct sentiment analysis. The paper describes an approach that made it possible to make the transition from the use of a naive Bayesian model to a modern machine learning system. The issues of cluster analysis and visualization of search results are also considered.
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Segeda, Oleksii. "Building Intelligent Search Systems: Advances in AI-Based Information Retrieval." American Journal of Applied Sciences 07, no. 06 (2025): 06–11. https://doi.org/10.37547/tajas/volume07issue06-02.
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The exponential growth of digital content has driven the need for more intelligent, context-aware information retrieval systems. While traditional keyword-based search engines remain foundational, they often fall short of capturing deeper semantic meaning. This article explores the evolution, methodologies, and recent developments in intelligent information retrieval systems powered by artificial intelligence. Special attention is given to the use of machine learning, natural language processing (NLP), and neural networks to improve relevance, personalization, and contextual understanding, including the application of learning-to-rank techniques. The paper contrasts the strengths and limitations of conventional search technologies with those of AI-driven models. A critical part of the study focuses on potential risks associated with AI-based search engines, including environmental concerns linked to the heavy water consumption of data centers relying on water-based cooling systems. The research concludes that a holistic approach is needed in the design and implementation of AI-powered search systems—one that integrates ethical, cognitive, and environmental considerations. This article will be of interest to professionals in media and information technology, researchers, and developers engaged in building intelligent search infrastructures.
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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (2021): 932. http://dx.doi.org/10.3390/s21030932.
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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.
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Le, Khac Binh, Minh Thai Duong, Dao Nam Cao, and Van Vang Le. "Application of supervised machine learning and Taylor diagrams for prognostic analysis of performance and emission characteristics of biogas-powered dual-fuel diesel engine." International Journal of Renewable Energy Development 13, no. 6 (2024): 1175–90. http://dx.doi.org/10.61435/ijred.2024.60724.
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In the ongoing search for an alternative fuel for diesel engines, biogas is an attractive option. Biogas can be used in dual-fuel mode with diesel as pilot fuel. This work investigates the modeling of injecting strategies for a waste-derived biogas-powered dual-fuel engine. Engine performance and emissions were projected using supervised machine learning methods including random forest, lasso regression, and support vector machines (SVM). Mean Squared Error (MSE), R-squared (R²), and Mean Absolute Percentage Error (MAPE) were among the criteria used in evaluations of the models. Random Forest has shown better performance for Brake Thermal Efficiency (BTE) with a test R² of 0.9938 and a low test MAPE of 3.0741%. Random Forest once more exceeded other models with a test R² of 0.9715 and a test MAPE of 4.2242% in estimating Brake Specific Energy Consumption (BSEC). With a test R² of 0.9821 and a test MAPE of 2.5801% Random Forest emerged as the most accurate model according to carbon dioxide (CO₂) emission modeling. Analogous results for the carbon monoxide (CO) prediction model based on Random Forest obtained a test R² of 0.8339 with a test MAPE of 3.6099%. Random Forest outperformed Linear Regression with a test R² of 0.9756% and a test MAPE of 7.2056% in the case of nitrogen oxide (NOx) emissions. Random Forest showed the most constant performance overall criteria. This paper emphasizes how well machine learning models especially Random Forest can prognosticate the performance of biogas dual-fuel engines.
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Siji, Jose Pulluparambil, and Bhat Subrahmanya. "Application of Machine Learning in Google Services- A Case Study." International Journal of Case Studies in Business, IT, and Education (IJCSBE) 5, no. 2 (2021): 24–37. https://doi.org/10.5281/zenodo.5145505.
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<strong>Purpose:</strong> <em>Google Search is currently the most preferred search engine worldwide, making it one of the websites with the highest traffic. It assists people in discovering the content they are searching for, from the large repository of the World Wide Web. Google has grown to be the best in the search engine market that it is the single most important variable to be considered when optimizing a website for search. There are many ranking algorithms used by Google to make the searching process more precise. Google has the vision </em><em>“to provide access to the world's information in one click”. Machine learning is the most popular methodology applied in predicting future outcomes or organizing information to assist people in making required decisions.ML algorithms are trained over instances or examples through which they analyze the historical data available and learn from past experiences. By repeatedly training over the samples, the patterns in the data can be identified in order to make predictions about the future. Google, as an organization, can be a pioneer in ML, and as a technology product, can be a use case for machine learning. Here, a case analysis has been prepared on few applications of machine learning in the products and services of Google. Within this paper, we highlight their technological history, services with machine learning applications, financial plans, and challenges. The paper also tries to examine the various products of Google which apply ML, such as Google Maps, Gmail, Google Photos, Google Assistant, and review the algorithms used in each service.</em> <strong>Approach:</strong> <em>The detailed survey method on secondary data is used for analysing the data.</em> <strong>Findings: </strong><em>Based on the developed case study, it is clearly evident that Google is using machine learning algorithms with few artificial intelligence features to enhance the quality of the services they provide.</em> <strong>Originality:</strong><em> A new way of analysis was performed to identify the methods used in the organization’s services. </em> <strong>Paper Type: </strong><em>Descriptive Research</em>
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Castelo, Sonia, Rémi Rampin, Aécio Santos, Aline Bessa, Fernando Chirigati, and Juliana Freire. "Auctus." Proceedings of the VLDB Endowment 14, no. 12 (2021): 2791–94. http://dx.doi.org/10.14778/3476311.3476346.
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The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions. However, finding relevant data is difficult. While search engines have addressed this problem for Web documents, there are many new challenges involved in supporting the discovery of structured data. We demonstrate how the Auctus dataset search engine addresses some of these challenges. We describe the system architecture and how users can explore datasets through a rich set of queries. We also present case studies which show how Auctus supports data augmentation to improve machine learning models as well as to enrich analytics.
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Nagarajan, G., and K. K. Thyagharajan. "A Machine Learning Technique for Semantic Search Engine." Procedia Engineering 38 (2012): 2164–71. http://dx.doi.org/10.1016/j.proeng.2012.06.260.
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Tonkovic, Petar, Slobodan Kalajdziski, Eftim Zdravevski, et al. "Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review." Biology 9, no. 12 (2020): 453. http://dx.doi.org/10.3390/biology9120453.
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Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries’ search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement.
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Arif Ali, Zeravan, Ziyad H. Abduljabbar, Hanan A. Tahir, Amira Bibo Sallow, and Saman M. Almufti. "eXtreme Gradient Boosting Algorithm with Machine Learning: a Review." Academic Journal of Nawroz University 12, no. 2 (2023): 320–34. http://dx.doi.org/10.25007/ajnu.v12n2a1612.
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The primary task of machine learning is to extract valuable information from the data that is generated every day, process it to learn from it, and take useful actions. Original language process, pattern detection, search engines, medical diagnostics, bioinformatics, and chemical informatics are all examples of application areas for machine learning. XGBoost is a recently released machine learning algorithm that has shown exceptional capability for modeling complex systems and is the most superior machine learning algorithm in terms of prediction accuracy and interpretability and classification versatility. XGBoost is an enhanced distributed scaling enhancement library that is built to be extremely powerful, adaptable, and portable. It uses augmented scaling to incorporate machine learning algorithms. it is a parallel tree boost that addresses a variety of data science problems quickly and accurately. Python remains the language of choice for scientific computing, data science, and machine learning, which boosts performance and productivity by enabling the use of clean low-level libraries and high-level APIs. This paper presents one of the most prominent supervised and semi-supervised learning (SSL) machine learning algorithms in a Python environment.
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Saputra, Dwi Fajar, Zayyin Abdul Quddus, Putra Pertama Budianto, and Bintang Mahaputra Nararya Rabbani. "Federated Search in Libraries: An Overview in Academic Libraries." AL Maktabah 9, no. 1 (2024): 34. http://dx.doi.org/10.29300/mkt.v9i1.3559.
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One essential piece of technology that simplifies the process of finding information across a variety of databases and sources in academic libraries is federated search systems. By using a single query interface, these systems, also known as meta-search engines, allow users to simultaneously search through a variety of resources, such as databases, books, and electronic journals. By combining results from several publishers and platforms into a single format, this feature greatly improves the efficacy and efficiency of academic research while also saving time and streamlining user interactions. An overview of federated search technology is given in this study, with particular attention on how it might be integrated into academic libraries. It talks about how these systems have developed, the technological difficulties they provide, and the advantages they have for educational institutions. The research also looks at case studies from different university libraries to showcase effective implementations. It also discusses the possibility for federated search systems in the future, including the incorporation of machine learning and artificial intelligence to enhance and customize search results. The purpose of this overview is to highlight the value of federated search engines in improving the usability and accessibility of scholarly materials, therefore assisting academic institutions in their larger endeavors to promote research and learning.
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Tay, Christina. "The Impact of Artificial Intelligence on International Trade." Journal of Technological Advancements 1, no. 1 (2021): 1–20. http://dx.doi.org/10.4018/jta.20210101.oa6.
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This paper investigates the impact of artificial intelligence on international trade. We use data on neural machine translation & search engines dominating domestic markets from 2016 to 2019, comprising 196 countries to test for their impact on international trade. Three variations of international trade are used: (1) manufacturing trade (sum of manufacturing exports & imports), (2) manufacturing export, and (3) manufacturing import. We cross-breed artificial intelligence theories with that of international economics. We find that artificial intelligence shows significant results at the 1% level for manufacturing trade, at the 10% level for manufacturing export, and at the 1% level for manufacturing import. We also find that as increasing number of languages are introduced through neural machine learning, there is a decreased need to comprehend the language of another country, which in turn, have significant impact on all three variations of international trade. We also find that domestic search engines are increasingly dominating domestic and global market shares.
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Wang, Yuhua, Jinlong Li, Guiyong Wang, et al. "Multiobjective Optimization of Diesel Particulate Filter Regeneration Conditions Based on Machine Learning Combined with Intelligent Algorithms." International Journal of Intelligent Systems 2024 (April 1, 2024): 1–28. http://dx.doi.org/10.1155/2024/7775139.
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To reduce diesel emissions and fuel consumption and improve DPF regeneration performance, a multiobjective optimization method for DPF regeneration conditions, combined with nondominated sorting genetic algorithms (NSGA-III) and a back propagation neural network (BPNN) prediction model, is proposed. In NSGA-III, DPF regeneration temperature (T4 and T5), O2,NOx, smoke, and brake-specific fuel consumption (BSFC) are optimized by adjusting the engine injection control parameters. An improved seagull optimization algorithm (ISOA) is proposed to enhance the accuracy of BPNN predictions. The ISOA-BP diesel engine regeneration condition prediction model is established to evaluate fitness. The optimized fuel injection parameters are programmed into the engine’s electronic control unit (ECU) for experimental validation through steady-state testing, DPF active regeneration testing, and WHTC transient cycle testing. The results demonstrate that the introduced ISOA algorithm exhibits faster convergence and improved search abilities, effectively addressing calculation accuracy challenges. A comparison between the SOA-BPNN and ISOA-BPNN models shows the superior accuracy of the latter, with reduced errors and improved R2 values. The optimization method, integrating NSGA-III and ISOA-BPNN, achieves multiobjective calibration for T4 and T5 temperatures. Steady-state testing reveals average increases of 3.14%, 2.07%, and 10.79% in T4, T5, and exhaust oxygen concentrations, while NOx, smoke, and BSFC exhibit average decreases of 8.68%, 12.07%, and 1.03%. Regeneration experiments affirm the efficiency of the proposed method, with DPF regeneration reaching 88.2% and notable improvements in T4, T5, and oxygen concentrations during WHTC transient testing. This research provides a promising and effective solution for calibrating the regeneration temperature of DPF, thus reducing emissions and fuel consumption of diesel engines while ensuring safe and efficient DPF regeneration.
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Alshdadi, Abdulrahman A., Ahmed S. Alghamdi, Ali Daud, and Saqib Hussain. "Blog Backlinks Malicious Domain Name Detection via Supervised Learning." International Journal on Semantic Web and Information Systems 17, no. 3 (2021): 1–17. http://dx.doi.org/10.4018/ijswis.2021070101.
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Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.
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Titus, Vyasakh. "'Being Human is Being Who You Are': The Being of Technology and the Becoming of Humans." Vidyankur: Journal of Philosophical and Theological Studies Jan-June 2019, no. XXI/1 (2019): 31–30. https://doi.org/10.5281/zenodo.4159424.
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Artificial Intelligence is occupying an inevitable place in our lives. There is steady progress in research too. Machine learning, AI domains, and algorithms are taught even in schools. Knowingly or unknowingly we encounter AI in our everyday life. And the world is slowly becoming dependent on AI. The recent pandemic has highlighted the importance of search engines. At this juncture, there are many relevant questions. What does it mean to be human in the age of Artificial Intelligence? What does it mean to be with others in the age of Artificial Intelligence? What does mean to be unique? Are we losing our identity with the use of search engines? This paper addresses the very essence of Being Human and the essence of Being who You Are in the age of Artificial Intelligence.
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UshaRani, Dr Y., Teja Abhinav Reddy Kaipu, T. Koushik, U. Sai Kiran, and K. Satwik. "Image Based Search Engine Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 1766–70. http://dx.doi.org/10.22214/ijraset.2023.51663.
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Abstract: Machine the learning's field of image recognition as well as classification is one that is expanding quickly. The business ramifications of object identification, which is a crucial component of picture categorization, are enormous. A procedure to identify and recognize an item or property in a digital video or picture, image recognition is a subset of artificial intelligence. A larger phrase used to describe techniques for obtaining, processing, and analyzing data from the actual environment is computer vision. The highly dimensional data generates judgements that are expressed as numerical or pictorial information. Artificial intelligence (AI) also encompasses event detection, object identification, learning, picture the rebuilding process, and tracking of video in addition to image classification. This project outlines a methodical strategy to organizing using machine learning. digital photos. Convolutional neural network (CNN) and deep neural networks are two classifiers that may be combined to improve classification performance. Over the past several years, Convolutional Neural Networks (CNNs) have emerged as the leading approach for image classification and object recognition tasks. On several of the picture categorization databases, they now outperform humans. Most of these datasets are built around the idea of tangible classes; photographs are categorized according to the kind of item they include. Using Abstract classes, this project will propose a unique picture classifying dataset that should be simple for humans to solve but difficult for CNNs to fully understand. This dataset and potential variants of the dataset are used to assess the classifications performance of common CNN designs. Interesting topics for future study are found
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Pugliese Viloria, Angelly de Jesus, Andrea Folini, Daniela Carrion, and Maria Antonia Brovelli. "Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review." Remote Sensing 16, no. 18 (2024): 3374. http://dx.doi.org/10.3390/rs16183374.
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With the increase in climate-change-related hazardous events alongside population concentration in urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing for such events. Machine learning (ML) and deep learning (DL) techniques have increasingly been employed to model susceptibility of hazardous events. This study consists of a systematic review of the ML/DL techniques applied to model the susceptibility of air pollution, urban heat islands, floods, and landslides, with the aim of providing a comprehensive source of reference both for techniques and modelling approaches. A total of 1454 articles published between 2020 and 2023 were systematically selected from the Scopus and Web of Science search engines based on search queries and selection criteria. ML/DL techniques were extracted from the selected articles and categorised using ad hoc classification. Consequently, a general approach for modelling the susceptibility of hazardous events was consolidated, covering the data preprocessing, feature selection, modelling, model interpretation, and susceptibility map validation, along with examples of related global/continental data. The most frequently employed techniques across various hazards include random forest, artificial neural networks, and support vector machines. This review also provides, per hazard, the definition, data requirements, and insights into the ML/DL techniques used, including examples of both state-of-the-art and novel modelling approaches.
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Rahmadani, Mutiara, Sintia Andriani, and Rita Elfina. "Teknologi AI Dalam Meningkatkan Akurasi Sistem Pencarian Informasi Kesehatan." LIBRIA 15, no. 1 (2023): 89. http://dx.doi.org/10.22373/21712.
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AbstrakMesin pencari merupakan alat yang amat berguna untuk menemukan informasi didunia Maya. Mesin pencari informasi ini dari berbagai dokumen yang tidak terstruktur. Kemampuan ini sangat berfungsi ketika kita ingin mencari dan mendapatkan suatu informasi dari dokumen yang memiliki struktur yang memiliki perbedaan (Rila Mandala, 2006). Semua aktivitas yang di lakukan setiap manusia membutuhkan informasi yang sebelumnya, mulai dari tidur sampai bangun pun manusia akan membutuhkan informasi. Penelitian ini bertujuan untuk mengidentifikasi pencarian informasi kesehatan melalui machine learning dan pencarian informasi tanpa machine learning, juga akan memaparkan kelebihan dan kekurangan dari machine learning tersebut. Dalam mengidentifikasi peneliti juga mencari keakuratan dan keefisienan masing-masing cara pencarian tersebut. metode study literature dengan melakukan tinjauan literature yang komprehensif dalam mengumpulkan informasi dari berbagai sumber referensi. bahwa pencarian informasi melalui machine learning dapat memberikan manfaat dan kelebihannya dalam mencari informasi kesehatan, machine learning tidak menggantikan tindakan untuk mengunjungi profesional. Kesimpulan mengenai pencarian informasi kesehatan menggunakan metode machine learning dinilai lebih efisien dikarenakan tidak memakan waktu yang lama dalam percariannya. Dan juga dapat dilakukan kapanpun, dimanapun, dan oleh siapapun. Pencarian informasi di perpustakaan atau dengan mengunjungi profesional informasi memang tidak terlalu efisien tapi hal ini tentu akan menciptakan kepuasan tersendiri kepada para pencarinya.Kata Kunci: Pencarian Informasi Kesehatan, Machine Learning, AI AbstractThe search engine is a very useful tool for finding information in the virtual world. This information search engine from various unstructured documents. The capability is very useful when we want to find and obtain information from documents that have different structures (Rila Mandala, 2006). All activities carried out by every human being actually need information beforehand, from sleeping to waking up, humans will need information. This study aims to identify health information search through machine learning and information search without machine learning, and will also explain the advantages and disadvantages of machine learning. In identifying the researcher also looks for the accuracy and efficiency of each of these search methods. literature study method by conducting a comprehensive literature review in collecting information from various reference sources. that information search through machine learning can provide benefits and advantages in searching for health information, machine learning does not replace the act of visiting a professional. The conclusion regarding the search for health information using the machine learning method is considered more efficient because it does not take a long time to search. And also can be done anytime, anywhere, and by anyone. Searching for information in the library or by visiting information professionals is not very efficient but this will certainly create satisfaction for the seekers.Keyword: health information search, Machine Learning, AI
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Zhao, Xuanzheng. "Research on Methods and Applications Related to Question-and-Answer Dialogue Systems." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 9–14. http://dx.doi.org/10.54097/hset.v57i.9885.
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In the face of more and more network data information, search engines have gradually become the main retrieval method to obtain relevant information knowledge. However, in today's increasingly explosive development of information on the Internet, by contrast, traditional search engines have problems such as semantic understanding and complicated answers. Therefore, question answering systems are more important. The automatic question answering system generally adopts natural language processing related technologies. When users ask questions, the system automatically judges and gives answers. It involves computer linguistics, machine learning, artificial intelligence and other popular technology research. According to different classification criteria, the automatic question answering system is roughly divided into open field automatic question answering system and stereotyped automatic question answering system.. This thesis investigates methods and applications related to question-and-answer dialogue systems. On the methodological side, we introduce commonly used datasets and the principles and techniques of text, speech and visual question and answer systems, and analyse in detail the excellent example ChatGPT. In terms of applications, we present the application of Q&A dialogue systems in search engines, smart campuses. There is some reference value.
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Shelke, Priya, Chaitali Shewale, Riddhi Mirajkar, Suruchi Dedgoankar, Pawan Wawage, and Riddhi Pawar. "A Systematic and Comparative Analysis of Semantic Search Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 222–29. http://dx.doi.org/10.17762/ijritcc.v11i11s.8094.
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Users often struggle to discover the information they need online because of the massive volume of data that is readily available as well as being generated every day in the today’s digital age. Traditional keyword-based search engines may not be able to handle complex queries, which could result in irrelevant or insufficient search results. This issue can be solved by semantic search, which utilises machine learning and natural language processing to interpret the meaning and context of a user's query. In this paper we focus on analyzing the BM-25 algorithm, Mean of Word Vectors approach, Universal Sentence Encoder model, and Sentence-BERT model on the CISI Dataset for Semantic Search Task. The results indicate that, the Finetuned SBERT model performs the best.