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Multimodal Information Retrieval
In today’s rapidly evolving digital landscape, the wealth of available information has expanded beyond the boundaries of traditional text-based...
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Outlook
While multimodal information retrieval has several exciting applications and a high potential for impact on important problems, there are several...
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Multimodal Content Generation
In this chapter, we will review the advances that are being made in this new field of multimodal content generation and also discuss several...
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Retrieval Augmented Modeling
Till this point in our book, we have discussed the fundamental principles of information retrieval, exploring its key elements, and various...
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Transformer-Driven Models for Language, Vision, and Multimodality
In this chapter, we will learn about the modeling and learning techniques that drive multimodal applications. We will focus specifically on the...
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Introduction
In this book, our emphasis is on multimodal information retrieval, specifically concentrating on text and image data. The traditional unimodal...
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Introduction and General Concepts of Machine Learning and Data Science
As a first step toward understanding how and why different machine learning algorithms work, we will begin this chapter with a discussion and...
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Exploratory Data Analysis
Exploratory data analysis (EDA) is an important item in the toolbox of statistics that is often used at the beginning of any data analysis workflow....
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A First Approach to Machine Learning with Linear Regression
Linear regression is one of the most accessible machine learning methods which has strong roots in the field of statistics. Problems of interest...
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Combinatorics and Probabilities
Probability deals with predicting the likelihood of future events, while statistics involves the analysis of the frequency of past events. Without...
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Materials Science Datasets and Data Generation
In this chapter, we explain how to obtain high-quality datasets for statistical and machine learning experiments. In particular, we present our...
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Machine Learning Techniques
The success of Machine Learning benefits from the availability of various numerical techniques and specialized technical concepts that help to make...
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Projects
This is a special chapter dealing with security projects. We have arranged the projects in three parts. Part 1 consists of projects that can be done...
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Standardization and Security Criteria: Security Evaluation of Computer Products
Our growing dependence on technology and the corresponding skyrocketing security problems arising from it have all created a high demand for...
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Deep Learning-Based Solution for Intrusion Detection in the Internet of Things
Securing the Internet of Things-based environment is a top priority for consumers, businesses, and governments. There are billions of devices...
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Introduction to Data Science
Data science is a new interdisciplinary field that has attracted a lot of interest from the media in recent years, where it has been presented as a...
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Regression Analysis
In this chapter, we introduce regression analysis andRegression analysis some of its applications in data scienceData science. Regression is related...
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Social Recommendation Using Deep Auto-encoder and Confidence Aware Sentiment Analysis
The development of online social networks has attracted increasing interest in social recommendation. On the other hand, recommender systems based on...
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Finding a Second Wind: Speeding Up Graph Traversal Queries in RDBMSs Using Column-Oriented Processing
Recursive queries and recursive derived tables constitute an important part of the SQL standard. Their efficient processing is important for many...
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Investigating the Perceived Usability of Entity-Relationship Quality Frameworks for NoSQL Databases
Quality assessment of data models can be a challenging task due to its subjective nature. For the schemaless, heterogeneous and diverse group of...