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Mining biomedical text, images and visual features for information retrieval /

Mining Biomedical Text, Images and Visual Features for Information Retrieval provides broad coverage of the concepts, themes, and instrumentalities of the important, evolving area of biomedical text, images, and visual features towards information retrieval. The book aims to encourage an even wider...

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Bibliographic Details
Other Authors: Dash, Sujata, Kumar Pani, Subhendu, Dos Santos, Wellington Pinheiro, Chen, Jake Y.
Format: Electronic eBook
Language:English
Published: London : Academic Press is an imprint of Elsevier, [2025]
Subjects:
Data mining.
Information retrieval.
Medical informatics.
Data Mining
Information Storage and Retrieval
Exploration de données (Informatique)
Recherche de l'information.
Médecine > Informatique.
information retrieval.
Data mining
Information retrieval
Medical informatics
Electronic books.
Online Access:ScienceDirect
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Table of Contents:
  • Front Cover
  • MINING BIOMEDICAL TEXT, IMAGES AND VISUAL FEATURES FOR INFORMATION RETRIEVAL
  • MINING BIOMEDICAL TEXT, IMAGES AND VISUAL FEATURES FOR INFORMATION RETRIEVAL
  • Copyright
  • Dedication
  • Contents
  • Contributors
  • Acknowledgments
  • I
  • IoT for biomedical and health informatics
  • 1
  • Introduction to IoT and health informatics
  • 1. Introduction
  • 1.1 History of the IoT
  • 1.2 The concept of integrated Internet of Things (IoT) technology with healthcare devices
  • 1.3 Health informatics
  • 1.4 Trends in health informatics
  • 1.5 Principle of using IoT in healthcare
  • 1.6 Examples of biosignals used by IoT devices to measure the human body parameters
  • 1.7 Security and privacy in IoT healthcare
  • 1.7.1 Ethical consideration regarding IoT in healthcare
  • 1.7.2 Compliance with regulation
  • 1.7.3 Impact of IoT on healthcare professionals
  • References
  • 2
  • Wireless internet of medical things: Technology and architectural design
  • 1. Introduction
  • 1.1 Increasing the hardware and wireless flexibility
  • 2. WIoMT architecture
  • 3. IoMT wireless technologies
  • 3.1 WiFi/WLAN (IEEE 802.11) standard
  • 3.2 WPAN/Bluetooth (IEEE 802.15.1) standard
  • 3.3 LR-WPAN/ZigBee (IEEE 802.15.4) protocol
  • 3.4 IEEE 802.15.6 protocol
  • 4. Selection of WIoMT technology
  • 5. WIoMT optimization models
  • 5.1 WIoMT performance optimization models
  • 5.1.1 Routing optimization
  • 5.1.2 Back-off mechanism optimization
  • 5.1.3 Channel access optimization
  • 6. Potentiality for WIoMT
  • 7. Conclusion
  • References
  • 3
  • An automated human behavior analysis using AI based predictive model in health care
  • 1. Introduction
  • 2. Literature review
  • 3. Methodology
  • 3.1 PCA algorithm
  • 3.2 Brief of RNNs
  • 3.3 HAR datasets
  • 3.3.1 Sensors-based accelerometer
  • 3.3.2 Sensors-based gyroscope
  • 3.4 Integrated PCA-RNN model.
  • 3.4.1 Sequential model
  • 3.4.2 Dense layer
  • 3.4.3 Dropout
  • 4. Result analysis
  • 5. Conclusion
  • References
  • 4
  • Survey on security and privacy issues in IoT healthcare
  • 1. Introduction
  • 1.1 Overview of IoT in healthcare
  • 1.1.1 Definition and scope
  • 1.2 Importance and applications
  • 1.3 Significance of security and privacy [19]
  • 2. Security threats in IoT healthcare
  • 2.1 Overview of security threat landscape
  • 2.2 Common types of security threats
  • 2.2.1 Unauthorized access
  • 2.2.2 Data breaches
  • 2.2.3 Malware and ransomware
  • 2.2.4 Denial of service (DoS) [4] attacks
  • 2.3 Vulnerabilities in IoT healthcare devices and systems
  • 3. Privacy challenges in IoT healthcare
  • 3.1 Overview of privacy concerns
  • 3.2 Data privacy issues
  • 3.2.1 Collection and storage of sensitive data
  • 3.2.2 Consent and user awareness
  • 3.3 Regulatory compliance and legal implications
  • 3.3.1 HIPAA compliance
  • 3.3.2 GDPR and other privacy regulations
  • 4. Mitigation strategies
  • 4.1 Security measures
  • 4.1.1 Encryption and authentication
  • 4.1.2 Intrusion detection and prevention systems
  • 4.2 Privacy enhancing technologies
  • 4.2.1 Anonymization and pseudonymization
  • 4.2.2 Differential privacy
  • 4.2.3 Privacy-preserving data-sharing mechanisms
  • 5. Case studies and examples
  • 5.1 Notable security breaches in IoT healthcare
  • 5.2 Successful implementation of security measures
  • 5.3 Impact of security and privacy on patient care
  • 6. Future directions and challenges
  • 6.1 Ethical considerations and societal implications
  • 7. Conclusion
  • References
  • 5
  • Smart health monitoring system to prevent complications during pregnancy using IoT and Hadoop
  • 1. Introduction
  • 2. Literature review
  • 3. Integrated model for prenatal health monitoring
  • 3.1 Sensor data collection
  • 3.2 Data transmission to cloud.
  • 3.3 Cloud storage and Hadoop
  • 3.4 Machine learning and deep learning
  • 3.5 Risk determination
  • 3.6 Result distribution
  • 3.7 Alert notifications
  • 4. Dataset and experimental setup
  • 4.1 Data source
  • 4.2 Experimental setup
  • 5. Result and analysis
  • 5.1 Result summary
  • 5.2 Analysis summary
  • 5.3 Overall assessment
  • 6. Limitations and future scope
  • 7. Conclusion
  • References
  • 6
  • Methodical IoT-based information system in healthcare industry
  • 1. Introduction
  • 2. Chapter organization
  • 3. Related work
  • 4. Objectives
  • 5. Methodology
  • 6. Components of health information system
  • 7. Challenges to e-health systems
  • 8. Security threats to IoT devices
  • 8.1 Security guidelines
  • 9. Conclusions
  • References
  • 7
  • Role of IoT in developing smart healthcare monitoring systems
  • 1. Introduction
  • 2. Internet of Things (IoT)
  • 2.1 Smart buildings with IoT
  • 3. Analytics and big data management
  • 3.1 Challenges
  • 3.2 Cybersecurity
  • 3.3 Personal information
  • 4. Process of legacy construction and retrofit
  • 4.1 Interoperability
  • 4.2 Opportunities
  • 4.2.1 Collaboration and joint ventures
  • 4.3 Wellness and good health
  • 4.4 Sustainability
  • 5. Basics of malware
  • 5.1 Emotet
  • 5.2 Service denial
  • 5.3 Man in the center
  • 5.4 Phishing Attack
  • 5.5 Injection of SQL data
  • 5.6 Attacks on passwords
  • 6. Case study
  • 6.1 Content management system
  • 6.2 Which states have the most (and the least) nursing facilities?
  • 7. Conclusion and future scope
  • References
  • 8
  • Internet of things application to study the spontaneous quantitative drugs detoxification through biosensor
  • 1. Introduction
  • 2. Internet of things
  • 3. Opportunities as well as challenges for biosensing and IoT in smart healthcare systems
  • 3.1 The medical issues that elderly adults, older workers, and infants face
  • 3.1.1 Hearing loss.
  • 3.1.2 Cardiovascular diseases
  • 3.1.3 Cognitive impairments
  • 3.1.4 Mental health issues
  • 3.1.5 Balance disorders
  • 4. Innovation healthcare: Benefits and problems
  • 4.1 Low-cost technology
  • 4.1.1 A scalable, extendable, and compatible approach
  • 4.1.2 Big data and automated starting to learn
  • 5. Applied research through IoT: Study on spontaneous quantitative drugs detoxification through biosensor
  • 5.1 Case
  • 5.1.1 Biosensor application to quantify
  • 5.1.2 Tissue-specified sensor devices
  • 5.1.2.1 Molecular semiconductors
  • 5.1.3 Ionic conductor materials for detecting gas molecules
  • 5.1.4 Molecular semiconductors on biosensor design
  • 5.1.5 Ionic conductor materials for detecting gas molecules
  • 6. IoT and IoMT building blocks for applications in health and wellness
  • 6.1 Enablers of a smart environment
  • 6.1.1 Ubiquitous and supportive medical devices
  • 6.1.2 Mobile devices
  • 6.1.3 IoT infrastructure and environmental control
  • 6.1.4 Human surveillance using cameras
  • 7. Preparation of allicin extracts formulation to detoxify cumulative body system
  • 7.1 Calibration on detoxification assessments
  • 7.1.1 Detoxification concept involved
  • 7.1.2 Garlic roles in detoxification
  • 7.2 Biosensor to detect allicin-based detoxification
  • 7.2.1 Spontaneous storing of data information and auto-evaluation process
  • 7.2.2 Data gathering and analysis software as BioSense
  • 7.3 Technical control and management
  • 7.4 Running software snap images (Fig. 8.8)
  • 8. Miscellaneous
  • 8.1 Modern research activities for smart healthcare applications
  • 8.1.1 Monitoring body heat
  • 8.1.2 Activity recognition
  • 8.1.3 Monitoring blood glucose levels and hemoglobin levels
  • 8.1.4 Monitoring and detecting respiration rates
  • 8.1.5 Sleep monitoring
  • 8.1.6 Blood-oxygen saturation detection
  • 8.2 Use cases/applications.
  • 8.2.1 Stroke, cardiac monitoring devices, and heart-rate analysis
  • 9. IoT-based healthcare monitoring systems: Its importance
  • 9.1 IoT-based healthcare monitoring systems
  • 9.2 IoT healthcare wireless network devices
  • 9.2.1 Systems for monitoring healthcare with wearable sensors
  • 9.2.2 Case study sensors for health monitoring: Application cases through IoT
  • References
  • II
  • Computational intelligence for medical image processing
  • 9
  • Supervised and unsupervised techniques for biomedical image classification
  • 1. Introduction
  • 2. General classification process
  • 2.1 Preprocessing
  • 2.1.1 Fast Fourier transform
  • 2.1.2 Gabor filters
  • 2.1.3 Principal component analysis
  • 2.1.4 Local binary pattern
  • 2.1.5 Discrete cosine transform
  • 2.1.6 Discrete wavelet transform
  • 2.2 Feature extraction
  • 2.3 Classifiers
  • 2.3.1 Unsupervised classification
  • 2.3.1.1 K-means clustering algorithm
  • 2.3.1.2 Fuzzy C-means clustering
  • 2.3.1.3 Gaussian mixture model
  • 2.3.2 Supervised classification
  • 2.3.2.1 Linear regression
  • 2.3.2.2 Bayes classifier
  • 2.3.2.3 Decision tree classifier
  • 2.3.2.3.1 Building decision tree
  • 2.3.2.3.2 Determination of best attribute selection
  • 2.3.2.3.3 Split procedure termination
  • 2.3.2.3.4 Problems faced in decision tree classifier
  • 2.3.2.4 Support vector machine
  • 2.3.2.5 Artificial neural network
  • 2.3.2.6 K-nearest neighbor
  • 2.3.2.6.1 Algorithm for KNN classifier
  • 2.3.2.7 Multilayered perceptron
  • 2.3.2.8 Random forest classifier
  • 2.3.2.8.1 Tree bagging
  • 2.3.2.8.2 Bagging to random forests
  • 2.3.2.8.3 Extra trees
  • 2.3.2.9 AdaBoost classifier
  • 3. Applications
  • 4. Classification outcomes
  • 4.1 Performance evaluation parameters
  • 5. Conclusion
  • References
  • 10
  • Image segmentation and parameterization for automatic diagnostics of medical images
  • 1. Introduction.

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