Applications of Computational Intelligence in Multi-Disciplinary Research

Applications of Computational Intelligence in Multi-Disciplinary Research
Copyright
Contents
List of contributors
1 Iris feature extraction using three-level Haar wavelet transform and modified local binary pattern
Abbreviations
1.1 Introduction
1.2 Related works
1.3 Iris localization
1.4 Iris normalization
1.5 The proposed feature extraction scheme
1.6 Matching results
1.7 Performance evaluation
1.8 Conclusion
References
2 A novel crypt-intelligent cryptosystem
2.1 Introduction
2.2 Related work
2.2.1 Machine learning contributions in cryptology
2.2.1.1 Analogy between machine learning and cryptography
2.2.1.2 Application of machine learning in cryptography
2.2.1.3 Application of machine learning in cryptanalysis
2.2.1.4 Analysis of existing contributions of machine learning in cryptology
2.2.2 Genetic algorithm contributions in cryptology
2.2.2.1 Applications of genetic algorithm in Cryptography
2.2.2.2 Applications of genetic algorithm in cryptanalysis
2.2.2.3 Analysis of existing contributions of genetic algorithms in cryptology
2.2.3 Neural network contributions in cryptology
2.2.3.1 Applications of neural networks in cryptography
2.2.3.2 Applications of neural networks in cryptanalysis
2.2.3.3 Analysis of contribution of neural network in cryptology
2.2.4 Background of DNA cryptography
2.2.4.1 Analysis of existing work in DNA cryptography
2.3 Proposed methodology
2.3.1 Proposed encryption scheme
2.3.2 Proposed decryption scheme
2.4 Discussion
2.5 Conclusion and future work
References
3 Behavioral malware detection and classification using deep learning approaches
3.1 Introduction
3.1.1 Digital forensics—malware detection
3.1.2 Malware evolution and its taxonomy
3.1.3 Machine learning techniques for malware analysis
3.1.4 Behavioral analysis of malware detection
3.2 Deep learning strategies for malware detection
3.2.1 Feature extraction and data representation
3.2.2 Static Analysis
3.2.2.1 Byte code n-gram features
3.2.2.2 Opcode n-gram features
3.2.2.3 Portable executables
3.2.2.4 String feature
3.2.3 Dynamic analysis
3.2.4 Hybrid analysis
3.2.5 Image processing techniques
3.3 Architecture of CNNs for malware detection
3.3.1 Preprocessing
3.3.2 Classification using CNNs
3.3.3 Evaluation
3.4 Comparative analysis of CNN approaches
3.5 Challenges and future research directions
3.6 Conclusion
References
4 Optimization techniques and computational intelligence with emerging trends in cloud computing and Internet of Things
4.1 Introduction
4.1.1 Introduction to optimization
4.1.2 Introduction to cloud computing with emphasis on fog/edge computing
4.2 Optimization techniques
4.2.1 An optimization problem
4.2.1.1 Defining an optimization problem
4.2.1.2 Elements of an optimization problem
4.2.1.3 Classification of the optimization problem
4.2.1.3.1 On the basis of types of constraints
4.2.1.3.2 On the basis of the physical structure of the problem
4.2.1.3.3 On the basis of the nature of the design variables
4.2.1.3.4 On the basis of the nature of the equations (constraints and objective functions)
4.2.1.3.5 On the basis of the separable nature of the variables
4.2.1.3.6 On the basis of the deterministic nature of the variables
4.2.1.3.7 On the basis of the permissible values of the decision variables
4.2.1.3.8 On the basis of the number of objectives
4.2.2 Solution to the optimization problem
4.2.2.1 Classical optimization techniques
4.2.2.2 Advanced optimization techniques
4.3 Understanding fog/edge computing
4.3.1 What is fog?
4.3.2 Prelude to our framework
4.3.3 Our goal
4.3.4 Framework for fog computing
4.4 Optimizing fog resources
4.4.1 Defining optimization problem for fog layer resources
4.4.2 Optimization techniques used
4.5 Case studies
4.5.1 Case study I: floorplan optimization
4.5.2 Case study II: Gondwana—optimization of drinking water distribution system
4.6 Scope of advancements and future research
4.7 Conclusion
References
5 Bluetooth security architecture cryptography based on genetic codons
5.1 Introduction
5.1.1 Bluetooth
5.1.2 Bluetooth security architecture
5.2 Survey of literature
5.3 Plaintext-to-ciphertext conversion process
5.3.1 Basic workflow
5.3.1.1 Encryption
5.3.1.2 Decryption
5.3.2 Algorithm
5.3.2.1 Encryption
5.3.2.1.1 Plaintext to DNA/RNA codon conversion
5.3.2.2 Promoter addition
5.3.2.2.1 Generation of promoters
5.3.2.2.2 Promoter addition
5.3.2.3 Intron addition
5.3.2.3.1 Intron number generation
5.3.2.3.2 Position to place the introns
5.3.2.3.3 Placing the introns at their positions
5.3.2.4 Masking of the ciphertext
5.3.2.5 Extra data
5.3.2.6 Decryption
5.3.2.6.1 Removal of the mask
5.3.2.6.2 Removal of the introns
5.3.2.6.3 Removal of the promoter
5.3.2.6.4 Conversion of the ciphertext without the intron and promoter to plaintext
5.3.3 Analysis and discussion
5.4 Conclusion
5.5 Future work
References
6 Estimation of the satellite bandwidth required for the transmission of information in supervisory control and data acquis...
Abbreviations
6.1 Introduction
6.2 Supervisory control and data acquisition systems
6.3 The very small aperture terminal networks
6.3.1 The satellite communication systems
6.3.2 Architecture very small aperture terminal networks
6.3.3 Connectivity
6.3.4 Multiple access
6.4 Algorithm for estimating the satellite bandwidth
6.4.1 Determining the bandwidth required for data transmission
6.4.2 Case study
6.4.3 Overview of some recent algorithms in detail
6.4.4 Validation of bandwidth calculations
6.5 Challenges and future work
6.6 Conclusions
References
7 Using artificial intelligence search in solving the camera placement problem
Nomenclature
7.1 Introduction
7.1.1 The roles of visual surveillance systems
7.1.2 The camera placement problem from an artificial intelligence perspective
7.1.3 Chapter description
7.2 Background
7.3 Modeling the visual sensors
7.3.1 The sensor space modeling
7.3.2 The camera coverage modeling
7.3.3 The analysis of camera visibility
7.4 Solving the camera placement problem using artificial intelligence search
7.4.1 Generate and test algorithm
7.4.2 Uninformed search
7.4.3 Hill climbing strategy
7.5 Further discussion
7.5.1 The efficiency of the algorithms
7.5.2 The performance of the algorithms
7.6 Conclusion
References
8 Nanotechnology and applications
8.1 Introduction
8.2 Nanoscience and nanotechnology
8.3 Computational nanotechnology
8.3.1 Molecular modeling
8.3.1.1 Molecular mechanics
8.3.1.2 Quantum methods
8.3.1.3 Semiempirical
8.3.1.4 Molecular dynamics
8.3.2 Nanodevice simulation
8.3.3 Nanoinformatics
8.3.4 High-performance computing
8.3.5 Computational intelligence
8.3.5.1 Genetic algorithms
8.3.5.2 Artificial neural networks
8.3.5.3 Fuzzy system
8.4 Applications of computational nanotechnology
8.4.1 Nanotube-based sensors and actuators
8.4.2 Nanoinformatics for drugs
8.4.3 Molecular docking
8.4.4 Nanotoxicology
8.4.5 Other applications
8.5 Conclusion
References
9 Advances of nanotechnology in plant development and crop protection
9.1 Introduction
9.2 Agriculture’s nanofarming: a modern frontier
9.3 Synthesis of green nanoparticles and its sources
9.4 Good distribution possibilities allowed by nanoparticles: a modern sustainable agriculture portal
9.5 Nanofertilizers: a good food supply for crops
9.6 Germination, field production, and efficiency enhancement of seed nanomaterials
9.7 Plant sensory systems and responses to radical climate change influences nanomaterials
9.8 Nanosensors and nanomaterials: perturbation detection and control
9.9 Pesticide-based plant safety nanomaterials
9.10 Nanotechnology in pesticides and fertilizers
9.11 Control of plant pests
9.12 Concluding remarks
Consent for publication
Conflict of interest
References
10 A methodology for designing knowledge-based systems and applications
10.1 Introduction
10.2 Related work
10.3 Design the knowledge-based system
10.3.1 The architecture of a knowledge-based system
10.3.2 The process for designing the knowledge-based system
10.4 Knowledge base and inference engine of a knowledge-based system
10.4.1 Design the knowledge base
10.4.1.1 Organize the knowledge base
10.4.1.2 Basic knowledge manipulations
10.4.1.2.1 Updating the knowledge base
10.4.1.2.2 Checking the consistency of the knowledge base
10.4.1.2.3 Unification of facts
10.4.2 Design the Inference engine
10.4.2.1 The process for designing the inference engine
10.4.2.1.1 The principles of an inference engine
10.4.2.1.2 Criteria of an inference engine
10.4.2.1.3 The process for designing an inference engine
10.4.2.2 The reasoning methods
10.4.2.2.1 Forward chaining
10.4.2.2.2 Backward chaining
10.4.2.2.3 Reasoning with pattern problems and sample problems
10.5 Applications
10.5.1 Design an intelligent problem solver for solving solid geometry at high school
10.5.1.1 Collect the knowledge domain
10.5.1.2 Build the knowledge model
10.5.1.3 Organize the knowledge base
10.5.1.4 Design the inference engine
10.5.1.5 Testing
10.5.2 Consultancy system for designing housing architecture
10.5.2.1 Organize the knowledge base of the consultancy system
10.5.2.2 Design the inference engine of the consultancy system
10.5.2.3 Testing
10.6 Conclusion and Future work
References
11 IoT in healthcare ecosystem
11.1 Introduction
11.2 Applications of  Internet of Things in healthcare
11.2.1 Patient-centric IoT
11.2.1.1 Remote patient care
11.2.1.2 Pathology and fatal viral/bacterial diseases
11.2.1.3 Critical and emergency patient care
11.2.1.4 Food and workout monitoring
11.2.1.5 Affective computing
11.2.2 Hospital-centric IoT applications
11.2.2.1 Real-time location of medical equipment
11.2.2.2 Deployment of medical staff
11.2.2.3 Drugs management
11.2.2.4 Reducing the charting time
11.2.3 IoT benefitting health insurance companies
11.2.4 Pharmaceutical governance
11.3 Implementation methodologies
11.3.1 Fog computing
11.3.1.1 Architecture
11.3.1.1.1 Smart IoT devices/applications
11.3.1.1.2 Fog nodes
11.3.1.1.3 Cloud
11.3.1.2 Advantages
11.3.2 Edge computing
11.3.2.1 Architecture
11.3.2.1.1 IoT nodes/applications
11.3.2.1.2 Edge nodes
11.3.2.1.3 Cloud
11.3.2.2 Advantages
11.3.2.3 Empowering edge computing
11.4 Implementation models
11.4.1 Heart disease prediction
11.4.2 Healthcare IoT-based affective state mining using deep convolutional neural networks
11.4.2.1 Electrodermal activity
11.4.2.2 Electromyography
11.4.2.3 Electrocardiogram
11.5 Challenges in healthcare IoT
11.5.1 Technology-oriented challenges
11.5.1.1 Risking the patient’s life
11.5.1.2 Incorrect results
11.5.1.3 No planned downtime
11.5.1.4 Need for a specialized tool to handle diversified protocols
11.5.1.5 Remote places with a lack of infrastructure and connectivity
11.5.2 Adapting to remote healthcare and telehealth
11.5.3 Data security
11.6 Security issues and defense mechanisms and IoT
11.6.1 Security requirements in healthcare IoT
11.6.1.1 Confidentiality
11.6.1.2 Integrity
11.6.1.3 Authentication
11.6.2 Attacks on IoT devices
11.6.2.1 Sinkhole attack
11.6.2.2 Blackhole attack
11.6.2.3 Selecting forwarding attack (grayhole attack)
11.6.2.4 Wormhole attack
11.6.2.5 Sybil attack
11.6.2.6 Denial-of-service attack
11.6.3 Defensive mechanism
11.6.3.1 Key management
11.6.3.2 User/device authentication and authorization
11.6.3.3 Intrusion detection
11.6.3.4 Fault tolerance
11.6.3.5 Blockchain technology
11.7 Covid 19—how IoT rose to the global pandemic
11.7.1 About Covid 19
11.7.2 Decoding the outbreak and identifying patient zero
11.7.3 Quarantined patient care
11.7.4 Public surveillance
11.7.5 Safeguarding hygiene
11.7.6 IoT and robotics
11.7.7 Smart disinfection and sanitation tunnel
11.7.8 Smart masks and smart medical equipment
11.8 Future of IoT in healthcare
11.8.1 IoT and 5G
11.8.2 IoT and artificial intelligence
11.9 Conclusion
References
Index