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标题:Automated Tax Return Verification with Blockchain Technology
时间:2020-06-30 19:44:49
DOI:10.1007/978-981-15-3607-6_4
大小:103329 kb
页数:643 PAGES
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目录:
  • Preface
  • Contents
  • About the Editors
  • 1 CerebLearn: Biologically Motivated Learning Rule for Artificial Feedforward Neural Networks
    • 1 Introduction
    • 2 Neuroscience Background
    • 3 CerebLearn
    • 4 Experimentation
    • 5 Conclusion
    • 6 Future Work
    • References
  • 2 Conceptual Content in Deep Convolutional Neural Networks: An Analysis into Multi-faceted Properties of Neurons
    • 1 Introduction
    • 2 Method
      • 2.1 Conceptual Encoding
      • 2.2 Neuron Visualization
    • 3 Results
      • 3.1 Multi-faceted Versus Single-Faceted Neurons
      • 3.2 Neuronal Similarity/Dissimilarity Matrices
    • 4 Conclusion
    • References
  • 3 Toward Lexicon-Free Bangla Automatic Speech Recognition System
    • 1 Introduction
    • 2 Background Study
    • 3 Methodology
      • 3.1 System Architecture
      • 3.2 Data Preprocessing and Experimental Setup
      • 3.3 Feature Extraction
      • 3.4 Acoustic Modeling
      • 3.5 Joint Decoding
    • 4 Experiment and Results Analysis
    • 5 Conclusion
    • References
  • 4 Automated Tax Return Verification with Blockchain Technology
    • 1 Introduction
    • 2 Background and Literature Review
      • 2.1 Current Bangladesh Tax System
      • 2.2 Blockchain Overview
      • 2.3 Literature Review
    • 3 Proposed Architecture
      • 3.1 Actors in Tax System
      • 3.2 The Tax Return Process
    • 4 Simulation
      • 4.1 Environment
      • 4.2 Environment Setup
      • 4.3 Simulation
    • 5 Result Analysis
    • 6 Conclusion
    • References
  • 5 Voice-Enabled Intelligent IDE in Cloud
    • 1 Introduction
    • 2 Related Works
      • 2.1 Cloud-Based IDEs
      • 2.2 Speech Recognition
    • 3 Proposed Method
      • 3.1 Building the IDE
      • 3.2 Speech Recognizer
    • 4 Experimental Results
    • 5 Sample Workflow
    • 6 Conclusion
    • References
  • 6 Recognition and Classification of Fruit Diseases Based on the Decomposition of Color Wavelet and Higher-Order Statistical Texture Features
    • 1 Introduction
    • 2 Literature Review
    • 3 System Architecture
      • 3.1 Proposed Algorithm
      • 3.2 Pre-processing
      • 3.3 Defect Segmentation Using K-Means Clustering
      • 3.4 Feature Extraction Module
      • 3.5 Classification
    • 4 Experimental Results
    • 5 Discussion
    • 6 Conclusion and Future Work
    • References
  • 7 Diabetes Mellitus Risk Prediction Using Artificial Neural Network
    • 1 Introduction
    • 2 Related Works
    • 3 Methodology
      • 3.1 Data Collection
      • 3.2 Data Preprocessing
      • 3.3 Data Training
      • 3.4 Applications of Artificial Neural Network (ANN)
      • 3.5 R Packages
    • 4 Outcomes
      • 4.1 Statistical Parameters
    • 5 Conclusion
    • References
  • 8 IoT-Based Smart Agriculture Monitoring System with Double-Tier Data Storage Facility
    • 1 Introduction
    • 2 Related Works
    • 3 Materials and Methods
      • 3.1 System Overview and Architecture
      • 3.2 Monitoring Network
      • 3.3 Management Software
    • 4 Results and Discussion
      • 4.1 Sensors Data
      • 4.2 Data Visualization
    • 5 Conclusion
    • References
  • 9 Bangla Phoneme Recognition: Probabilistic Approach
    • 1 Introduction
    • 2 Research Methods
    • 3 Experiment Procedure
      • 3.1 Rectangular Window
      • 3.2 Blackman Window
      • 3.3 Hamming Window
      • 3.4 Hanning Window
      • 3.5 Preprocessing
      • 3.6 FFT, LPC and MFCC for Feature Extraction
      • 3.7 Creating Data
      • 3.8 Two-Layer Feed-Forward Neural Network
    • 4 Experiment Results
      • 4.1 Bangla Phoneme Recognition in FFT
      • 4.2 Bangla Phoneme Recognition in LPC
      • 4.3 Bangla Phoneme Recognition in MFCC
    • 5 Conclusions
    • References
  • 10 EEG Motor Signal Analysis-Based Enhanced Motor Activity Recognition Using Optimal De-noising Algorithm
    • 1 Introduction
    • 2 Proposed Model
      • 2.1 Signal De-noising
      • 2.2 Empirical Mode Decomposition
      • 2.3 Discrete Wavelet Transformation
      • 2.4 Savitzky–Golay Filter
      • 2.5 Feature Extraction
      • 2.6 Classification Using Support Vector Machine (SVM)
    • 3 Experimental Results and Discussion
      • 3.1 Dataset
      • 3.2 Result Analysis and Performance Comparison
    • 4 Conclusion
    • References
  • 11 Automatic Missing-Child Recovery System using Eigenfaces
    • 1 Introduction
    • 2 Related Work
    • 3 Proposed Method
      • 3.1 Preparing Training Image
      • 3.2 Prioritize Camera Sequence
      • 3.3 Proposed Recovery Algorithm
    • 4 Implementation
    • 5 Performance Analysis
      • 5.1 The Reporting Delay
      • 5.2 Initiation Delay
      • 5.3 Scanning Delay
      • 5.4 Recovery Delay
      • 5.5 Overall Recovery Rate
    • 6 Experiment
    • 7 Challenges and Limitations
    • 8 Conclusion and Future Work
    • References
  • 12 Prediction of Financial Distress in Bangladesh’s Banking Sector Using Data Mining and Machine-Learning Technique
    • 1 Introduction
    • 2 Literature Review
    • 3 Methodology
      • 3.1 Importance of Artificial Neural Networks (ANN) as Classification
      • 3.2 Bayesian Neural Network for Classification
      • 3.3 Importance of Using SVMs as a Classification Technique
      • 3.4 How Z-Score Works in Distress of a Bank
    • 4 Experimental Analysis and Result
      • 4.1 Data Exploration
      • 4.2 Items Selected from Various Financial Statements
      • 4.3 Feature Selection
      • 4.4 Range of Altman Z-Score
      • 4.5 Result Analysis
    • 5 Conclusion
      • 5.1 Future Recommendation
    • References
  • 13 Pedestrian Age and Gender Identification from Far View Images Using Convolutional Neural Network
    • 1 Introduction
    • 2 Related Works
    • 3 Methodology
      • 3.1 PETA Dataset
      • 3.2 Image Preprocessing
      • 3.3 Real Data Preprocessing
      • 3.4 Proposed CNN Model
      • 3.5 Classification of Loss Function
      • 3.6 Optimization Algorithm
    • 4 Model Evaluation
      • 4.1 Learning Rate Experimentation
      • 4.2 CNN Model Selection Experimentation
      • 4.3 Experimental Results
      • 4.4 Discussion
    • 5 Conclusion
    • References
  • 14 Handwritten Numeral Superposition to Printed Form Using Convolutional Auto-Encoder and Recognition Using Convolutional Neural Network
    • 1 Introduction
    • 2 Superposition of Handwritten Numeral to Printed Form and Recognition
      • 2.1 Convolutional Auto-Encoder (CAE) as a Superposition Method
      • 2.2 Convolutional Neural Network (CNN) as a Classifier
    • 3 Experimental Studies
      • 3.1 Dataset Description and Preprocessing
      • 3.2 Experimental Setup
      • 3.3 Experimental Results and Analysis
    • 4 Conclusion
    • References
  • 15 Chemical Reaction Optimization for Mobile Robot Path Planning
    • 1 Introduction
      • 1.1 Problem Statement and Objective Function
    • 2 Related Works
    • 3 Chemical Reaction Optimization (CRO) for MRPP
      • 3.1 Population Generation
      • 3.2 Operator Design
      • 3.3 Repair Operators
      • 3.4 Parameter Settings
    • 4 Experimental Results
      • 4.1 Comparison with Two-Phase ACO
      • 4.2 Comparison with PSO
      • 4.3 Comparison with AGA
      • 4.4 Graphical Comparison of Algorithms
    • 5 Conclusions
    • References
  • 16 An Automated Wireless Irrigation System by Using Moisture Sensor and DTMF Technology
    • 1 Introduction
    • 2 Related Work
    • 3 Preliminaries
      • 3.1 Arduino UNO
      • 3.2 DTMF Decoder
      • 3.3 Moisture Sensor
      • 3.4 Relay
      • 3.5 DC Power Source
    • 4 System Methodology
    • 5 Procedure Algorithm
    • 6 Proposed System Design and Implementation
    • 7 System Design and Implementation
    • 8 System Evaluation
    • 9 Conclusion
    • References
  • 17 Human Age Prediction from Facial Image Using Transfer Learning in Deep Convolutional Neural Networks
    • 1 Introduction
    • 2 Related Works
    • 3 Age Prediction Using Deep CNNs
      • 3.1 Review of Deep CNN Models
      • 3.2 Age Prediction Using Transfer Learning in Deep CNNs
    • 4 Experimental Studies
      • 4.1 Benchmark Datasets and Preprocessing
      • 4.2 Experimental Setup
      • 4.3 Experimental Results and Comparison
    • 5 Conclusions
    • References
  • 18 Semantic Segmentation of Retinal Blood Vessel via Multi-scale Convolutional Neural Network
    • 1 Introduction
    • 2 Related Work
    • 3 Proposed Method
      • 3.1 Pre-processing of Fundus Image
      • 3.2 Multi-scale Convolutional Neural Network
      • 3.3 Post-processing
    • 4 Experimental Results
      • 4.1 Databases
      • 4.2 Experimental Evaluation
      • 4.3 Results
    • 5 Conclusion
    • References
  • 19 Drug–Protein Interaction Network Detection and Analysis of Cardiovascular Disease-Related Genes: A Bioinformatics Approach
    • 1 Introduction
    • 2 Methodology
      • 2.1 Gene Collection and Filtering
      • 2.2 Data Mining and Top-Weighted Gene Selection
      • 2.3 Protein–Protein Association and Interaction Network
      • 2.4 Drug–Protein Interaction
    • 3 Result and Analysis
      • 3.1 Gene Collection and Filtering
      • 3.2 Protein–Protein Association Network
      • 3.3 Protein–Protein Interaction
      • 3.4 Drug–Protein Interaction Network and GO Analysis
    • 4 Conclusion
    • References
  • 20 An Approach for Detecting Heart Rate Analyzing QRS Complex in Noise and Saturation Filtered ECG Signal
    • 1 Introduction
    • 2 Literature Review
    • 3 Proposed Model
      • 3.1 Preprocess ECG Signal
      • 3.2 QRS Complex Detection
      • 3.3 Calculate Heart Rate
    • 4 Experimental Results
    • 5 Conclusion
    • References
  • 21 Identification of Genetic Links of Thyroid Cancer to the Neurodegenerative and Chronic Diseases Progression: Insights from Systems Biology Approach
    • 1 Introduction
    • 2 Materials and Methods
      • 2.1 Dataset Employed and Statistical Methods Used
      • 2.2 Methods
    • 3 Results and Discussions
      • 3.1 Results
      • 3.2 Discussion
    • 4 Conclusions
    • References
  • 22 DNA Motif Discovery Using a Hybrid Algorithm
    • 1 Introduction
      • 1.1 Basic Concept
    • 2 Related Works
    • 3 Proposed Methods
      • 3.1 GA for DNA Motif Discovery Problem
      • 3.2 SA for DNA Motif Discovery Problem
      • 3.3 GA_SA for DNA Motif Discovery Problem
    • 4 Experimental Results and Analysis
      • 4.1 Experimental Setup
    • 5 Conclusions
    • References
  • 23 Implementation of GSM Cellular Network Using USRP B200 SDR and OpenBTS
    • 1 Introduction
    • 2 Background Study
      • 2.1 GSM Architecture
      • 2.2 OpenBTS Architecture
      • 2.3 Related Work
    • 3 Experimental Setup
    • 4 Experimental Results
      • 4.1 OpenBTS
      • 4.2 Spectrum Analyzer (QT GUI Sink)
      • 4.3 FM Radio Transmit
    • 5 Conclusion
    • References
  • 24 Sources and Impact of Uncertainty on Rule-Based Decision-Making Approaches
    • 1 Introduction
    • 2 Sources of Uncertainties
      • 2.1 Model Setup
      • 2.2 Attribute Selection
      • 2.3 Knowledge Base Construction
    • 3 Review of Existing Rule-Based Models
    • 4 Impact of Uncertainty in Rule-Based Decision Modeling
    • 5 Research Gaps and Direction for the Future Works
    • 6 Conclusion
    • References
  • 25 A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array
    • 1 Introduction
    • 2 Architecture
    • 3 Implementation
      • 3.1 Encoding Process
      • 3.2 Decoding Process
    • 4 Results and Discussions
    • 5 Conclusion and Future Work
    • References
  • 26 A Closer Look into Paintings' Style Using Convolutional Neural Network with Transfer Learning
    • 1 Introduction
    • 2 Related Work
    • 3 Dataset
    • 4 Methodology
      • 4.1 Convolutional Neural Network
      • 4.2 CNN with Transfer Learning
    • 5 Result and Discussion
      • 5.1 Selection of Models
      • 5.2 Visualization from Confusion Matrix
      • 5.3 Comparison with Others' Contribution
    • 6 Conclusion
    • References
  • 27 How Can a Robot Calculate the Level of Visual Focus of Human's Attention
    • 1 Introduction
    • 2 Literature Review
    • 3 Analysis
      • 3.1 Overall System Performance Analysis
    • 4 Methodology
      • 4.1 Eyeball Detection
      • 4.2 Gaze Communication
      • 4.3 Level of Visual Focus of Attention Calculation
    • 5 Experimental Setup
      • 5.1 Experimental Setup for Gaze Pattern Detection
      • 5.2 Experimental Setup for LVFOA Calculation
    • 6 Experimental Result
    • 7 Conclusion
    • References
  • 28 A Computer Vision Approach for Jackfruit Disease Recognition
    • 1 Introduction
    • 2 Literature Review
    • 3 Research Methodology
      • 3.1 Followed Research Strategy
      • 3.2 Description of Diseases and Features
    • 4 Experimental Evaluation
    • 5 Comparative Performance Analysis
    • 6 Conclusion and Future Work
    • References
  • 29 A Novel Hybrid Swarm Intelligence Algorithm Combining Modified Artificial Bee Colony and Firefly Algorithms
    • 1 Introduction
    • 2 Related Works
      • 2.1 Modified Artificial Bee Colony with Parameter Tuning (ABC-T)
      • 2.2 Firefly Algorithm (FA)
    • 3 Proposed Hybrid Algorithm
      • 3.1 Hybrid ABC-IFA
      • 3.2 Migration Scheme
    • 4 Benchmark Functions Used for Optimization
    • 5 Analysis and Comparison of Obtained Experimental Results
      • 5.1 Results Obtained from FA
      • 5.2 Results Obtained from Hybrid ABC-IFA
      • 5.3 Comparison Between FA and Hybrid ABC-IFA
    • 6 Main Contribution, Limitations and Future Scopes
    • 7 Conclusion
    • References
  • 30 Prediction of DNA-Binding Protein from Profile-Based Hidden Markov Model Feature
    • 1 Introduction
    • 2 Related Works
    • 3 Materials and Methods
      • 3.1 Dataset
      • 3.2 Feature Extraction
      • 3.3 StandardScaler
      • 3.4 Cross-Validation
      • 3.5 Description of the Classifier
    • 4 Performance Evaluation
    • 5 Result
    • 6 Conclusion
    • References
  • 31 A Subword Level Language Model for Bangla Language
    • 1 Introduction
    • 2 Related Works
      • 2.1 On Bangla Language
      • 2.2 On Language Models
      • 2.3 On Neural Network Architectures
      • 2.4 On Training Neural Networks
    • 3 Corpus
    • 4 Methodology
      • 4.1 Proposed Architecture
      • 4.2 Subword Tokenization
      • 4.3 Training the Language Model
    • 5 Experiments
      • 5.1 Bi-gram Language Model
      • 5.2 LSTM and CNN Language Models
      • 5.3 AWD-LSTM Word-Level Language Model
    • 6 Results and Discussion
    • 7 Conclusion
    • References
  • 32 Sentiment Analysis Based on Users' Emotional Reactions About Ride-Sharing Services on Facebook and Twitter
    • 1 Introduction
    • 2 Related Works
    • 3 Methodology
      • 3.1 Data Acquisition
      • 3.2 Classifiers
      • 3.3 Training and Text Classification
    • 4 Results
    • 5 Conclusion and Future Work
    • References
  • 33 An SDN-Enabled IoT Architecture with Fog Computing and Edge Encryption Support
    • 1 Introduction
    • 2 State of the Art
      • 2.1 Internet of Things (IoT)
      • 2.2 Software-Defined Networking (SDN)
      • 2.3 Fog Computing
    • 3 IoT Architecture
    • 4 Security Issues and Challenges
    • 5 Architectural Design
      • 5.1 Architectural Requirements
      • 5.2 Security Requirements
      • 5.3 Proposed Architecture
      • 5.4 Description of the Proposed Architecture
      • 5.5 Use Cases
    • 6 Conclusion and Future Work
    • References
  • 34 Upgrading YouTube Video Search by Generating Tags Through Semantic Analysis of Contextual Data
    • 1 Introduction
    • 2 Exploring Tag Recommendation Strategy
      • 2.1 Data Accumulation
      • 2.2 Dataset Enrichments
      • 2.3 Formulating Appropriate Tags
    • 3 Evaluation
    • 4 Conclusion
    • References
  • 35 Internet of Things-Based Smart Security Provisioning Using Voice-Controlled Door Locking System
    • 1 Introduction
    • 2 Literature Review
    • 3 Voice-Controlled Door Locking System for Smart Home
      • 3.1 Identifying User Using Device
      • 3.2 Proposed System Architecture
    • 4 Implementation and Discussion
      • 4.1 Required Devices and Sensors
      • 4.2 Implementation Architecture
      • 4.3 Android Application
      • 4.4 Project Demonstration
    • 5 Conclusion
    • References
  • 36 A Novel Hybrid Machine Learning Model to Predict Diabetes Mellitus
    • 1 Introduction
    • 2 Proposed Methodology
    • 3 Experimental Results
    • 4 Discussion and Conclusion
    • References
  • 37 An Optimized Pruning Technique for Handling Uncertainty in Decision-Making Process
    • 1 Introduction
    • 2 Related Works
    • 3 Proposed Methodology
      • 3.1 Attribute Pruning for Handling Uncertainty
      • 3.2 Belief Flattening for Capturing Uncertainty
      • 3.3 Uncertainty Assessment in Decision-Making Process
    • 4 Numerical Study
    • 5 Result and Discussion
    • 6 Conclusion
    • References
  • 38 Design Exploration of LH-CAM with Updating Mechanism
    • 1 Introduction
    • 2 LH-CAM Architecture
      • 2.1 Mapping/Writing Operation
      • 2.2 Lookup Operation
    • 3 Update Mechanism
    • 4 LH-CAM Design Exploration
    • 5 Comparison with RAM-based CAMs
      • 5.1 Power Consumption
    • 6 Conclusions
    • References
  • 39 Computational Techniques for Structure Preserving Model Reduction of Constrain Dynamical Models
    • 1 Introduction
    • 2 Balanced Truncation for Standard Second-Order Systems
    • 3 Balanced Truncation for Second-Order Index 3 Descriptor Systems
      • 3.1 Elimination of Algebraic Part from the Dynamical Systems
      • 3.2 Model Reduction of Projected System
    • 4 Numerical Results
    • 5 Conclusions
    • References
  • 40 A Digital Platform Design for Supply Chain of Existing Fish Market in Bangladesh
    • 1 Introduction
    • 2 Related Works
    • 3 Survey Data Analysis
      • 3.1 Survey Area
      • 3.2 Survey Method
      • 3.3 Findings from Fishermen and Consumers
      • 3.4 Results
    • 4 Proposed Infrastructure
      • 4.1 Current Fish Distribution Channel
      • 4.2 Proposed System
    • 5 Design Process
      • 5.1 Design Flow of the App for Consumers
      • 5.2 Design Flow of the App for Fishermen
    • 6 User Interface Design of the proposed App
      • 6.1 Fisherman End Window
      • 6.2 Consumer End Window
    • 7 Results and Analysis
    • 8 Evaluation
    • 9 Conclusion
    • References
  • 41 End-to-End Optical Character Recognition Using Sythetic Dataset Generator for Noisy Conditions
    • 1 Introduction
    • 2 Relevant Work
      • 2.1 Bangla OCR-Related Studies
      • 2.2 Isolated Printed and Handwritten Bangla Numerals and Character Recognition
      • 2.3 Neural Networks and Related Topics
    • 3 The Dataset
      • 3.1 Generating Synthetic Noisy Data
      • 3.2 Real-World Data
    • 4 Methodology
      • 4.1 Model Architecture
      • 4.2 Dataset Generation and Training Method
    • 5 Result Analysis of the Experiments
      • 5.1 Experiment: E-1
      • 5.2 Experiment: E-2
      • 5.3 Experiment: E-3
      • 5.4 Experiment: E-4
    • 6 Conclusion
    • References
  • 42 Unsupervised Pretraining and Transfer Learning-Based Bangla Sign Language Recognition
    • 1 Introduction
    • 2 Related Work
    • 3 Proposed Method and Methodology
      • 3.1 Dataset
      • 3.2 Splitting the Dataset
      • 3.3 Dataset Preprocessing
      • 3.4 Proposed Methods
      • 3.5 Experiment Environment Setup
    • 4 Experimental Results
      • 4.1 Unsupervised Pretraining Method
      • 4.2 Transfer Learning Method
    • 5 Conclusion
    • References
  • 43 Facilitating Hard-to-Defeat Car AI Using Flood-Fill Algorithm
    • 1 Introduction
    • 2 Related Work
    • 3 Overview of the Proposed Methodology
      • 3.1 Flood-Fill Algorithm
      • 3.2 Context Diagram
      • 3.3 Data Flow Diagram
    • 4 Implementation Details
      • 4.1 Working Procedure
      • 4.2 Graphical Visualization
    • 5 Experimental Result and Analysis
      • 5.1 Experimental Results
      • 5.2 User Survey and Analysis
      • 5.3 Comparison with Related Work
    • 6 Conclusion and Future Recommendations
    • References
  • 44 A Novel Method for Ghost Removal in High-Dynamic Range Images
    • 1 Introduction
    • 2 Khan's Algorithm
    • 3 Improvement of Khan's Algorithm
    • 4 Deghosting Algorithm Based on Ratio of Intensity Values
    • 5 Conclusion
    • References
  • 45 Internet of Things-Based Household Water Quality Monitoring System Using Wireless Sensor
    • 1 Introduction
    • 2 Literature Review
    • 3 IoT-Based Household Water Quality Monitoring System
      • 3.1 WHO Standard Level of Water Elements
      • 3.2 Proposed System
      • 3.3 Implementation of the System
    • 4 Conclusion
    • References
  • 46 Developing a Fuzzy Feature-Based Online Bengali Handwritten Word Recognition System
    • 1 Introduction
    • 2 Related Work
    • 3 Proposed Bengali Word Recognition System
      • 3.1 Segmentation
      • 3.2 Preprocessing
      • 3.3 Word Segmentation and Clustering
      • 3.4 Fuzzy Feature Extraction
      • 3.5 Learning Phase
      • 3.6 Recognition Phase
    • 4 Experimental Results
      • 4.1 Results
    • 5 Conclusion
    • References
  • 47 Automatic Summarization of Scientific Articles from Biomedical Domain
    • 1 Introduction
      • 1.1 Motivation
      • 1.2 Problem Definition
      • 1.3 Background
    • 2 Related Works
      • 2.1 TextRank
      • 2.2 TF-IDF
      • 2.3 Paragraph Extraction
    • 3 Methodology
      • 3.1 WordRank
      • 3.2 Hybrid
    • 4 Results
      • 4.1 Dataset
      • 4.2 Experimental Environment
      • 4.3 Evaluation Metrics
      • 4.4 Result Analysis
    • 5 Discussion
    • 6 Conclusion and Future Work
    • References
  • 48 Bringing a Change in Digital Mobile Banking Through Distributed Technology
    • 1 Introduction
      • 1.1 Our Contribution
      • 1.2 Paper Organization
    • 2 Related Works
    • 3 Methodology
      • 3.1 Protocol Design
      • 3.2 Entity Description
      • 3.3 Stepwise Working Procedure
    • 4 Brief Discussion
      • 4.1 Registration Process in the Application
      • 4.2 Transaction Process of the Application
    • 5 Security Analysis
      • 5.1 Comparison Between Proposed System and Other Related Works
    • 6 Conclusion
    • References
  • 49 Internal Abnormalities’ Detection of Human Body Analyzing Skin Images Using Convolutional Neural Network
    • 1 Introduction
    • 2 Related Works
    • 3 Our Approach
      • 3.1 Data Collection
    • 4 Methodology
      • 4.1 Preprocessing
      • 4.2 CNN Architecture
    • 5 Algorithms
    • 6 Experimental Result
      • 6.1 Confusion Matrix
    • 7 Result Analysis
    • 8 Conclusion
    • References
  • 50 Orchestration-Based Task Offloading for Mobile Edge Computing in Small-Cell Networks
    • 1 Introduction
    • 2 Background of Mobile Edge Computing
      • 2.1 Mobile Cloud Computing
      • 2.2 Cloudlet
      • 2.3 Fog Computing
      • 2.4 Mobile Edge Computing
    • 3 Related Works
    • 4 Architecture and System Model
      • 4.1 Need for Orchestration-Based Task Offloading
      • 4.2 Architecture of Orchestration-Based Task Offloading
      • 4.3 Task Offloading Model
    • 5 Results and Discussions
    • 6 Conclusions
    • References
  • 51 DEB: A Delay and Energy-Based Routing Protocol for Cognitive Radio Ad Hoc Networks
    • 1 Introduction
    • 2 Background Study
    • 3 Proposed DEB Protocol
      • 3.1 Routing Metric
      • 3.2 Route Discovery and Selection
    • 4 Simulation Results
    • 5 Conclusion and Future Works
    • References

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