Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practices: IBCFHA

Image Based Computing for Food and Health Analytics: Requirements, Challenges, Solutions and Practices: IBCFHA

Description:
Increase in consumer awareness of nutritional habits has placed automatic food analysis in the spotlight in recent years. However, food-logging is cumbersome and requires sufficient knowledge of the food item consumed. Additionally, keeping track of every meal can become a tedious task. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. Food understanding from digital media has become a challenge with important applications in many different domains. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information effectively and efficiently remains a challenging and open research problem. The provided recommendations could be based on calorie counting, healthy food and specific nutritional composition. In addition, if we also consider a system able to log the food consumed by every individual along time, it could provide health-related recommendations in the long-term.

Computer Vision specialists have developed new methods for automatic food intake monitoring and food logging. Fourth Industrial Revolution [4.0 IR] technologies such as deep learning and computer vision robotics are key for sustainable food understanding. The need for AI based technologies that allow tracking of physical activities and nutrition habits are rapidly increasing and automatic analysis of food images plays an important role. Computer vision and image processing offers truly impressive advances to various applications like food analytics and healthcare analytics and can aid patients in keeping track of their calorie count easily by automating the calorie counting process. It can inform the user about the number of calories, proteins, carbohydrates, and other nutrients provided by each meal. The information is provided in real-time and thus proves to be an efficient method of nutrition tracking and can be shared with the dietician over the internet, reducing healthcare costs. This is possible by a system made up of, IoT sensors, Cloud-Fog based servers and mobile applications. These systems can generate data or images which can be analyzed using machine learning algorithms.

Image Based Computing for Food and Health Analytics covers the current status of food image analysis and presents computer vision and image processing based solutions to enhance and improve the accuracy of current measurements of dietary intake. Many solutions are presented to improve the accuracy of assessment by analyzing health images, data and food industry based images captured by mobile devices. Key technique innovations based on Artificial Intelligence and deep learning-based food image recognition algorithms are also discussed. This book examines the usage of 4.0 industrial revolution technologies such as computer vision and artificial intelligence in the field of healthcare and food industry, providing a comprehensive understanding of computer vision and intelligence methodologies which tackles the main challenges of food and health processing. Additionally, the text focuses on the employing sustainable 4 IR technologies through which consumers can attain the necessary diet and nutrients and can actively monitor their health. In focusing specifically on the food industry and healthcare analytics, it serves as a single source for multidisciplinary information involving AI and vision techniques in the food and health sector. Current advances such as Industry 4.0 and Fog-Cloud based solutions are covered in full, offering readers a fully rounded view of these rapidly advancing health and food analysis systems.

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Table of contents :
Preface
Contents
Food Computing Research Opportunities Using AI and ML
1 Introduction
2 Food Computing Research Opportunities
2.1 Food and Nutrition
2.2 Healthcare
2.3 Agriculture
2.4 Packaging
2.4.1 Packaging Technologies
3 Data Acquisition
3.1 Food Dataset
3.1.1 Food101
3.1.2 Ingredient 101
3.1.3 Fruitveg81
3.2 Healthcare Dataset
3.2.1 UHDDS
3.2.2 UACDS
3.2.3 MDS
3.3 Agriculture Datasets
3.3.1 CNR Dataset
3.3.2 Istat Data Sets
3.3.3 Sensor-Based Datasets
4 Object Recognition
5 Image Processing
6 Segmentation
6.1 Food Segmentation
6.2 Disease Image Segmentation
6.3 Crop/Leaf Disease Image Segmentation
6.4 Packaging Segmentation
7 Object Identification
7.1 Food Identification
7.2 Disease Identification
7.3 Leaf/Crop Disease Identification
7.4 Packaging Identification
References
Estimating the Risk of Diabetes Using Association Rule Mining Based on Clustering
1 Introduction
2 State of Art
3 Proposed Approach
3.1 Rules Generation
3.2 Clustering the Diabetes Data
3.3 Risk of Diabetes
4 Implementation and Results
5 Conclusion
References
Digital Twins for Food Nutrition and Health Based on Cloud Communication
1 Introduction
2 Recent Related Work
2.1 Development Status of Intelligent Detection of Dietary Nutrition Intake
2.2 Application Status and Development Trend of DTs
2.3 Analysis and Summary of Existing Research
3 Analysis of the Nutrition Evaluation Model of Food DTs Based on Cloud Computing and DL
3.1 Analysis of Demand for Intelligent Assessment of Food Nutrition
3.2 Application of Cloud Computing and Communication Technology to Human Nutrition and Health Analysis
3.3 Application of DL in Food Nutrition Evaluation and Analysis
3.4 Construction and Analysis of the DTs Evaluation Model of Food Nutrition and Health Based on Cloud Computing and AlexNet
3.5 Experimental Testing and Evaluation
4 Research Results
4.1 Comparative Analysis of Human Health Data Evaluation under Different Cloud Communication Technologies
4.2 Prediction Performance Analysis of Food Nutrition Classification Under Different Algorithms
5 Conclusion
References
Smart Healthcare Systems: An IoT with Fog Computing based Solution for Healthcared
1 Introduction
1.1 Problem Formation with Background
1.2 Motivation
1.3 Contribution
2 Related Work
2.1 Work Done by Different Researchers
2.2 Comparative Study (Table 1)
3 Proposed Research Work
3.1 Problem Definition
3.2 Proposed Solution
3.3 Proposed System Architecture
4 Methodology and Concepts
4.1 Technology and Concept
4.2 Mathematical Equations and Derivations
4.3 Pseudocode
5 Implementation and Execution Flow
5.1 Dataset Selection (Table 2)
5.2 Dataset Pre-processing
5.3 Characteristics of Dataset
5.4 Execution Environment
5.5 Execution Flow Explanation
6 Results and Discussion
7 Conclusion
References
An Intelligent and Secure Real-Time Environment Monitoring System for Healthcare Using IoT and Cloud Computing with the Mobile Application Support
1 Introduction
2 Motivation and Contribution
3 Related Work
3.1 Summary of the Literature
4 Proposed Methodology
4.1 Advanced Encryption Standard for Data Security
5 Experimental Setup and Results
5.1 Hardware Implementation
5.2 ThingSpeak Cloud Platform and Mobile Application
5.3 Result Analysis and Discussion
6 Conclusion and Future Work
References
Efficient BREV Ensemble Framework: A Case Study of Breast Cancer Prediction
1 Introduction
2 Related Work
3 Experimental Investigation
3.1 Dataset
3.2 Experiment Setting
4 Methods
4.1 Machine Learning Models
4.2 Ensemble Techniques
4.3 Proposed Framework
5 Results and Discussion
5.1 Performance Evaluation
5.2 Experimental Results
6 Conclusion
References
Current and Future Trends of Cloud-based Solutions for Healthcare
1 Introduction
2 Methodology
3 Results and Trends Analysis
3.1 Year Wise Documents’ Publishing Trends
3.2 Types of Published Documents
3.3 Countries contributing documents on Cloud in healthcare sector
3.4 Funding Agencies Contributing Documents on Cloud in Healthcare
3.5 Top Institutes Contributing Research on Cloud Health Applications
3.6 Author Wise Publications’ Trend
3.7 Top 100 Title Words’ Analysis
3.8 Top 100 Abstract Words’ Analysis
3.9 Top 100 Keywords’ Analysis
3.10 Most Cited Publications for Cloud in Health Care
4 Future Trends of Cloud Computing in Healthcare
5 Conclusion and Future Aspects
References
Secure Authentication in IoT Based Healthcare Management Environment Using Integrated Fog Computing Enabled Blockchain System
1 Background
2 Literature Review
3 Proposed Methodology for Secure Authentication
4 Proposed Algorithm for Secure Access Control
5 Conclusion
References
Sentiment Analysis of COVID-19 Tweets Using Voting Ensemble-Based Model
1 Introduction
2 Literature Review
3 Theoretical Background
4 Proposed Method
5 Experiment and Results
6 Conclusion and Future Scope
References
Cloud and Machine Learning Based Solutions for Healthcare and Prevention
1 Introduction
2 The Health System Analytics Group
2.1 Data, Big Data and Health Records
2.2 Precision Health
3 AI Cloud Computing Transform the Healthcare Space
3.1 Enhanced Clinical Productivity and Improved Access to Care
3.2 Greater Healthcare Cost Savings
3.3 Better Use of Healthcare Data
3.4 Types of Learning
4 Impacts Healthcare
4.1 Record Keeping
4.2 Data Integrity
4.3 Predictive Analytics
5 Applications of ML in Healthcare
5.1 Disease Identification and Diagnosis
5.2 Medical Imaging Diagnosis
5.3 Robotic Surgery
5.4 Robotic Patient Support Tasks
5.5 Personalized Medicine
5.6 Ethics of AI in Healthcare
5.7 Sharing Patient Information
5.8 Patient Autonomy
5.9 Patient Safety and Outcomes
5.10 Future of Healthcare Technology
5.11 Virtual Reality in Healthcare
5.12 Augmented Reality in Healthcare
5.13 Wearable Tech
5.14 Genome Sequencing
5.15 Nanotechnology
6 Transforming the Healthcare Industry
6.1 Regulatory Considerations
6.2 Disease Prediction
7 Applications of AI in Healthcare
7.1 Brain-Computer Interfaces
7.2 Medical Diagnosis
7.3 Drug Development
7.4 Analyzing Health Records
7.5 Virtual Assistants
7.6 AI-Enabled Hospitals
8 Research Challenges in Healthcare
8.1 Edge Medical Cloud Based on 5G
8.2 Edge Device Security and Privacy
8.3 Edge Caching and Energy Consumption
8.4 Optimization of AI
8.5 Knowledge Representation
9 Conclusion
References
Interoperable Cloud-Fog Architecture in IoT-Enabled Health Sector
1 Introduction
2 Structure of Healthcare Solutions
2.1 General Cloud-Based Solutions
2.2 Interoperable Fog-Based Solutions
3 Combined Structure
4 Calculation of Execution
4.1 Scheme to Problem
5 Conclusion
References
COVID-19 Wireless Self-Assessment Software for Rural Areas in Nigeria
1 Introduction
2 Related Research
3 Methodology
4 Proposed Program Flow
4.1 Advantages of Using the Self-Testing
4.2 Step by Step Guide on how to Use the Software
5 Conclusion
References
Efficient Fog-to-Cloud Internet-of-Medical-Things System
1 Introduction
2 Associated Work
3 Planned Energy-Efficient FC-IoMT System
4 Results
4.1 Evaluation Metrics and Modeling Factors
4.2 Experimental Outcomes
5 Conclusion
References
Index