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What is Recall

Chapter 24
Handbook of Research on Digital Libraries: Design, Development, and Impact
Recall is the ratio of the number of correctly segmented words to the number of all the words in the documents.
Published in Chapter:
Word Segmentation in Indo-China Languages for Digital Libraries
Jin-Cheon Na (Nanyang Technological University, Singapore), Tun Thura Thet (Nanyang Technological University, Singapore), Dion Hoe-Lian Goh (Nanyang Technological University, Singapore), Yin-Leng Theng (Nanyang Technological University, Singapore), and Schubert Foo (Nanyang Technological University, Singapore)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-59904-879-6.ch024
Abstract
This chapter introduces word segmentation methods for Indo-China languages. It describes six different word segmentation methods developed for the Thai, Vietnamese, and Myanmar languages and compare different approaches in terms of their algorithms and results achieved. The discussion and comparison of these word segmentation methods will provide underlying views about how word segmentation can be achieved and employed in Indo-China languages to support search functionality in digital libraries.
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Chapter 13
Utilizing Artificial Intelligence for Text Classification in Communication Sciences: Reliability of ChatGPT Models in Turkish Texts
Recall, also known as sensitivity or true positive rate, measures the proportion of correctly predicted positive cases among all actual positive cases. It answers the question: “Of all the actual positive cases, how many did I correctly predict?”
Published in Chapter: Utilizing Artificial Intelligence for Text Classification in Communication Sciences: Reliability of ChatGPT Models in Turkish Texts; From: Design and Development of Emerging Chatbot Technology
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Chapter 14
A Robust Method for Classification and Localization of Satellite Cyclonic Images Over the Bay of Bengal and the Arabian Sea Using Deep Learning
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Chapter 5
Approximation of Hate Detection Processes in Spanish and Other Non-Anglo-Saxon Languages
Recall, also known as sensitivity, is defined as the ratio of true positives (TP) over all true positives (TP + FN). That is, it measures the ability of the model to detect all the real positives.
Published in Chapter: Approximation of Hate Detection Processes in Spanish and Other Non-Anglo-Saxon Languages; From: News Media and Hate Speech Promotion in Mediterranean Countries
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Chapter 273
Histogram Generation from the HSV Color Space
The number of relevant images retrieved as a percentage of the total number of relevant images in the database.
Published in Chapter: Histogram Generation from the HSV Color Space; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 13
Query Expansion by Taxonomy
The proportion of relevant documents that are retrieved out of all relevant documents available.
Published in Chapter: Query Expansion by Taxonomy; From: Handbook of Research on Fuzzy Information Processing in Databases
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Chapter 12
Statistical Hypothesization and Predictive Modeling of Reactions to COVID-19-Induced Remote Work: Study to Understand the General Trends of Response to Pursuing Academic and Professional Commitments
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Chapter 6
Using Data Science to Predict Hotel Booking Cancellations
Measure of relevant predictions that are retrieved. It can be interpreted as the probability of a randomly selected prediction could be a True Positive.
Published in Chapter: Using Data Science to Predict Hotel Booking Cancellations; From: Handbook of Research on Holistic Optimization Techniques in the Hospitality, Tourism, and Travel Industry
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Chapter 14
Radial Moments for Image Retrieval
Recall is the ability to retrieve relevant images.
Published in Chapter: Radial Moments for Image Retrieval; From: Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing
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Chapter 29
Towards a Model for Evaluating Web Retrieval Systems in Non-English Queries
Recall is an information retrieval performance measure that quantifies the fraction of known relevant documents which are effectively retrieved.
Published in Chapter: Towards a Model for Evaluating Web Retrieval Systems in Non-English Queries; From: Handbook of Research on Web Information Systems Quality
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Chapter 155
Mapping Ontologies by Utilising Their Semantic Structure
The ratio of the number of relevant entities retrieved to the total number of relevant entities.
Published in Chapter: Mapping Ontologies by Utilising Their Semantic Structure; From: Encyclopedia of Artificial Intelligence
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Chapter 162
Public Opinion and the Internet
The ratio of the number of documents that have been retrieved, out of the total number in the collection that should have been retrieved.
Published in Chapter: Public Opinion and the Internet; From: Encyclopedia of Multimedia Technology and Networking, Second Edition
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Chapter 16
Leveraging Wi-Fi Big Data Streams to Support COVID-19 Contact Tracing
The fraction of relevant instances retrieved. It answers the question: “How many correct instances were retrieved?” It is the fraction of the True positives out of the total True cases.
Published in Chapter: Leveraging Wi-Fi Big Data Streams to Support COVID-19 Contact Tracing; From: Encyclopedia of Data Science and Machine Learning
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Chapter 10
Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant
Similar to precision, it is also a statistical measure. It is the ratio of valid outputs to total number of relevant samples.
Published in Chapter: Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant; From: Applications of Image Processing and Soft Computing Systems in Agriculture
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Chapter 17
Recognition of Face Biometrics
Recall is the ability to retrieve relevant images.
Published in Chapter: Recognition of Face Biometrics; From: Handbook of Research on Advanced Concepts in Real-Time Image and Video Processing
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Chapter 7
Development of Accurate and Timely Students' Performance Prediction Model Utilizing Heart Rate Data
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Chapter 10
TempClass: Implicit Temporal Queries Classifier
The fraction of the relevant documents in the collection of returned results.
Published in Chapter: TempClass: Implicit Temporal Queries Classifier; From: Handbook of Research on Contemporary Perspectives on Web-Based Systems
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Chapter 3
Comprehensive Overview of Autonomous Vehicles and Their Security Against DDoS Attacks
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Chapter 4
Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection
Also known as sensitivity, it measures how often a model correctly identifies an observation for which both the predicted and the actual class labels are positive.
Published in Chapter: Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection; From: Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things
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Chapter 3
Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index
The fraction of relevant instances that are retrieved. High recall means that an algorithm returned most of the relevant results.
Published in Chapter: Using Big Data Opinion Mining to Predict Rises and Falls in the Stock Price Index; From: Handbook of Research on Organizational Transformations through Big Data Analytics
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Chapter 83
Emulating Subjective Criteria in Corpus Validation
Measure that indicates the percentage of correctly classified cases of one class with regard to the total number of cases that actually belong to this class. This measure says if the classifier is ignoring cases that should be classified as members of one specific class when doing a classification.
Published in Chapter: Emulating Subjective Criteria in Corpus Validation; From: Encyclopedia of Artificial Intelligence
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Chapter 386
Information Retrieval
A quality measure for information retrieval evaluation. It can be calculated by dividing the number of relevant documents which were found by the number of relevant documents in the collection. The second figure can often only be estimated.
Published in Chapter: Information Retrieval; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 8
Machine Learning in Python: Diabetes Prediction Using Machine Learning
Recall is the ratio of true positives to the sum of true positives and false negatives.
Published in Chapter: Machine Learning in Python: Diabetes Prediction Using Machine Learning; From: Handbook of Research on Applications and Implementations of Machine Learning Techniques
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Chapter 17
Text Summarization and Its Types: A Literature Review
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Chapter 587
Technologies for Information Access and Knowledge Management
Recall is a quality measure for information retrieval evaluation. It can be calculated by dividing the number of relevant documents that were found by the number of relevant documents in the collection. The second figure can often only be estimated.
Published in Chapter: Technologies for Information Access and Knowledge Management; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 12
Machine Learning and Its Application in Monitoring Diabetes Mellitus
It is an accuracy measure calculated as ratio of the number of true positives predictions to the total of number of true positives and the number of false negatives. True positives cases are those cases where data point is classified as positive by the classifier that actually are positive and false negatives are cases with such data points that are identified as negative by classifier but actually are positive.
Published in Chapter: Machine Learning and Its Application in Monitoring Diabetes Mellitus; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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