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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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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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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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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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Machine Learning Approaches for Multi-omics Data Integration in Medicine
Cells are a fundamental unit of life, and the ability to study the phenotypes and behavior of cells is crucial to understanding the functioning of...
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Introduction to Multiomics Technology
Multi-omics refers to the integration of multiple omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics,...
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Multimodal Methods for Knowledge Discovery from Bulk and Single-Cell Multi-Omics Data
Multi-omics measurements (bulk and single-cell) are essential to depict the cellular states comprehensively and thus could derive a deep...
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Knowledge Graph Publishing with Anatomy, Toward a New Privacy and Utility Trade-Off
The impact of the Big and Open data phenomena, as well as the need for a certain confidentiality, obliges the emergence of new knowledge management...
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Clustering
This chapter provides a comprehensive overview of traditional clustering algorithms, which have been fundamental in the field of unsupervised...
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Deep clustering techniques
This chapter serves as a comprehensive guide to deep clustering techniques, offering a deeper understanding of their underlying principles,...
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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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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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Deep Learning Based on TensorFlow and Keras for Predictive Monitoring of Business Process Execution Delays
In order to enhance their performance and responsiveness, organizations must identify, manage, and monitor all business processes that involve...
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Discovering Relationships Between Heterogeneous Declarative Map**s for RDF Knowledge Graph
Nowadays, Knowledge Graphs (KGs) are extensively used in companies, they are created using different techniques, map** languages among them, and...
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Approach Based on Bayesian Network and Ontology for Identifying Factors Impacting the States of People with Psychological Problems from Data on Social Media
Nowadays, social networks provide relevant information that is used in many contexts for different objectives. However, the major challenges remain...
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AI-LMS: AI-Based Long-Term Monitoring System for Patients in Pandemics: COVID-19 Case Study
In the context of the ongoing COVID-19 pandemic, the need for robust health monitoring systems has become increasingly evident, especially for...
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Towards an Effective Attribute-Based Access Control Model for Neo4j
The graph data model is increasingly used in practice due to its flexibility in modeling complex real-life data. However, some security features...