Advances in Computing and Network Communications: Proceedings of CoCoNet 2020, Volume 2 (Lecture Notes in Electrical Engineering, 736)

✍ Scribed by Sabu M. Thampi (editor), Erol Gelenbe (editor), Mohammed Atiquzzaman (editor), Vipin Chaudhary (editor), Kuan-Ching Li (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 604
• Edition: 1st ed. 2021
• Category: Library

✦ Synopsis

This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Computing and Network Communications (CoCoNet'20), October 14–17, 2020, Chennai, India. The papers presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical sections on Signal, Image and Speech Processing, Wireless and Mobile Communication, Internet of Things, Cloud and Edge Computing, Distributed Systems, Machine Intelligence, Data Analytics, Cybersecurity, Artificial Intelligence and Cognitive Computing and Circuits and Systems. The book is directed to the researchers and scientists engaged in various fields of computing and network communication domains.

✦ Table of Contents

Preface
Organized by
Contents
About the Editors
Machine Learning, Visual Computing and Signal Processing
Computational Reconstructions of Extracellular Action Potentials and Local Field Potentials of a Rat Cerebellum Using Point Neurons
1 Introduction
2 Methodology
2.1 Point Neuron Model of Cerebellum Granule Neuron
2.2 Estimation of EAP Using Point Source Approximation
2.3 Convolution-Based Method for the Reconstruction of LFP
3 Results
3.1 Intracellular Potentials Shape Extracellular Potentials
3.2 Convolution-Based LFP Reconstruction
4 Discussion
5 Conclusion
References
Iris Recognition Using Integer Wavelet Transform and Log Energy Entropy
1 Introduction
1.1 Main Contribution of the Work
2 Related Study
3 Proposed System
3.1 Iris Localization
3.2 Iris Normalization
3.3 Block Partitioning
3.4 Integer Wavelet Transform
3.5 Computation of Log Energy Entropy
3.6 Iris Code Generation
3.7 Identification or Verification
4 Experimental Results
4.1 Database Description
4.2 Experimental Setup
4.3 Performance Analysis
5 Conclusion and Future Scope
References
Deep Learning-Based Approach for Skin Burn Detection with Multi-level Classification
1 Introduction
2 Literature Survey
3 Proposed Method
3.1 Convolutional Neural Networks
4 Results and Discussion
5 Conclusion
References
Semantic Retrieval of Microbiome Information Based on Deep Learning
1 Introduction
2 Related Works
3 Methodology
3.1 Microbiome Dataset
3.2 NER Model
3.3 Evaluation Metrics
4 Experimental Result and Discussion
4.1 Baseline
4.2 Microbiome
5 Conclusion
References
Early Detection of COVID-19 from CT Scans Using Deep Learning Techniques
1 Introduction
2 Literature Survey
2.1 Motivation
3 Methodology
3.1 Work Flow of the Design
3.2 Model Architecture of Proposed Method1
3.3 Model Architecture of Proposed Method2:
4 Results
4.1 Results: Method1
4.2 Results: Method2
5 Conclusion and Future Work
5.1 Conclusion
5.2 Future Work
References
Towards Protein Tertiary Structure Prediction Using LSTM/BLSTM
1 Introduction
2 Related Works
2.1 LSTM and BLSTM Networks
3 Methodology
3.1 Data Collection
3.2 Data Pre-processing
3.3 Proposed Models
3.4 Implementation Details
4 Results and Discussion
4.1 Method 1: LSTM
4.2 Method 2: BLSTM Along with PSSMs
4.3 Method 3: Two-layer BLSTM
4.4 Method 4: Two-layer BLSTM
5 Conclusion and Future Work
References
An Android-Based Smart Home Automation System in Native Language
1 Introduction
2 Related Works
3 Implementation Details
3.1 Node MCU ESP32
3.2 Relay Module
3.3 Wire Pieces
3.4 LED Bulb
3.5 Arduino IDE
3.6 Android Studio
4 Applications
5 Results and Discussions
6 Conclusion
References
Live Acoustic Monitoring of Forests to Detect Illegal Logging and Animal Activity
1 Introduction
2 Objectives
3 Related Works
4 Methodology
4.1 Dataset
4.2 Data Preprocessing and Augmentation
4.3 System Architecture
4.4 Feature Extraction
4.5 Training
4.6 Deployment
4.7 Technology Stack
5 Result and Discussions
6 Conclusion
Appendix
References
CATS: Cluster-Aided Two-Step Approach for Anomaly Detection in Smart Manufacturing
1 Introduction
2 Related Work
3 CATS—Clustering Aided Two-Step Approach
3.1 Techniques Used for CATS
3.2 Mechanism of CATS
4 Model and Experimental Results
4.1 Experimental Model
4.2 Experimental Results
5 Conclusion
References
Prediction of Energy Consumption Using Statistical and Machine Learning Methods and Analyzing the Significance of Climate and Holidays in the Demand Prediction
1 Introduction
2 Related Work
3 Data Collection and Preprocessing
3.1 Data Preprocessing
4 Implementation and Results Discussion
4.1 Comparison of all the considered models
5 Conclusion and Future Work
References
Ranking of Educational Institutions Based on User Priorities Using AHP-PROMETHEE Approach
1 Introduction
2 Literature Survey
3 Problem Description
4 Results and Discussions
4.1 Crawler
4.2 AHP-Based Weight Derivation
4.3 Data Processing
4.4 PROMETHEE Calculations for Sub-criteria
4.5 PROMETHEE Calculations for Criteria
5 Results
6 Conclusion
7 Future Work
References
Using AUDIT Scores to Identify Synbiotic Supplement Effect in High-Risk Alcoholics
1 Introduction
2 Materials and Methods
2.1 Study Design
2.2 Participants
2.3 Preparation of Synbiotic Intervention
2.4 Intervention
2.5 Statistical Analysis
3 Results
4 Discussion
5 Conclusion
References
Learning-Based Macronutrient Detection Through Plant Leaf
1 Introduction
2 Proposed Framework
3 Convolution Neural Network in Keras
3.1 FastAI in Pytorch
4 Results and Analysis
5 Conclusion
References
Characteristics of Karawitan Musicians’ Brain: sLORETA Investigation
1 Introduction
2 Materials and Methods
2.1 Participants
2.2 Stimuli
2.3 Electroencephalographic Recording
2.4 Data Pre-processing and Feature Extraction
2.5 Spatial Analysis
2.6 Statistical Analysis
3 Results
4 Discussion
5 Conclusion
References
Automatic Detection of Parkinson Speech Under Noisy Environment
1 Introduction
2 Literature Survey
3 Experimental Approach
3.1 Database
3.2 Feature Extraction
3.3 Classification
3.4 Performance Metric
4 Experimental Analysis and Results
4.1 Comparative Analysis with Existing Work on PD Detection
5 Conclusion and Future Scope
References
Voice Conversion Using Spectral Mapping and TD-PSOLA
1 Introduction
2 Literature Review
3 Proposed Methodologies
3.1 Method 1
3.2 Method 2
3.3 Summary
4 Experimental Setup
5 Results and Performance Evaluation
5.1 Performance Evaluation
6 Conclusion
References
Haze Removal Using Generative Adversarial Network
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Generator
3.2 Discriminator
3.3 Loss Functions
4 Experiments
5 Conclusion
References
Natural Language Processing
Fake News Detection Using Passive-Aggressive Classifier and Other Machine Learning Algorithms
1 Introduction
2 Methodology
2.1 Dataset
2.2 Text Preprocessing Techniques
2.3 Machine Learning Classifiers
2.4 Performance Metrics
3 Results and Discussions
4 Conclusion
References
Generative Adversarial Network-Based Language Identification for Closely Related Same Language Family
1 Introduction
2 Related Works
2.1 Hand-Crafted LI Methods
2.2 Deep Learning-Based Methods
3 GAN-Based LI Architecture
3.1 Generator
3.2 Discriminator
3.3 Classifier
3.4 Algorithm
4 Experiments and Results
4.1 Data Pre-processing
4.2 Implementation Details
4.3 Language Dataset
4.4 LI for Pair of Languages
4.5 LI for Overall Testing Data
4.6 Perplexity and Fake Sentence Generation
5 Conclusions and Future Work
References
Statistical and Neural Machine Translation for Manipuri-English on Intelligence Domain
1 Introduction
2 Related Works
3 System Description
3.1 Data Preparation
3.2 Phrase-Based Statistical Machine Translation
3.3 NMT System
3.4 Morphological Analysis
4 Result and Analysis
5 Conclusion
References
Fake Review Detection Using Hybrid Ensemble Learning
1 Introduction
2 Related Work
2.1 Linguistic Based Approaches
2.2 Behavioral-Based Approaches
2.3 Graph-Based Approaches
3 Hybrid Ensemble Learning Architecture
3.1 Data Preprocessing
3.2 Detection
3.3 Ensemble Learning
4 Experiments and Results
4.1 Datasets
4.2 Results and Discussion
5 Conclusions
References
Utilizing Corpus Statistics for Assamese Word Sense Disambiguation
1 Introduction
2 Challenges in Indian Languages WSD
3 Related Work
4 Methodology
4.1 Simplified Lesk Algorithm
4.2 WSD Algorithm Using Word Co-occurrence
5 Dataset and Experiments
5.1 Experiment 1
5.2 Experiment 2
5.3 Experiment 3
6 A Few Close Observations
6.1 Very Short Sentence
6.2 Spelling Error
6.3 Scarcity of Information in Assamese WordNet
7 Conclusion
References
A Novel Approach to Text Summarisation Using Topic Modelling and Noun Phrase Extraction
1 Introduction
2 Literature Review
3 Proposed Model
3.1 Score Calculation Method
3.2 Total Score
3.3 Algorithm
4 Experimental Results and Evaluation
4.1 Validating Models
4.2 Evaluation Metrics
4.3 Reference Summary: Sumy Models
4.4 Dataset Used
4.5 Results
5 Conclusion
References
Part of Speech Tagging Using Bi-LSTM-CRF and Performance Evaluation Based on Tagging Accuracy
1 Introduction
2 Background and Related Work
3 Models
3.1 LSTM Networks
3.2 Bidirectional LSTM Networks
3.3 LSTM-CRF Networks
3.4 Bi-LSTM-CRF Networks
4 Dataset Description
5 Training Procedure
6 Experimental Results
7 Conclusion
References
Clustering Research Papers: A Qualitative Study of Concatenated Power Means Sentence Embeddings over Centroid Sentence Embeddings
1 Introduction
2 Related Work
2.1 Citation-Based Clustering
2.2 Tag-Based Clustering
2.3 Natural Language Processing-Based Clustering
3 Methodology
3.1 Dataset
3.2 Sentence Embeddings
3.3 Optimal Number of Clusters
3.4 Clustering
4 Results
4.1 Clustering Quality with Respect to Dataset Size
4.2 Clustering Quality for Word and Sentence Embeddings
4.3 Cluster Distributions of Datasets
4.4 Similarity for CORE Dataset
5 Discussion
5.1 Clustering Quality as a Function of Dataset Size
5.2 Clustering Quality for Word and Sentence Embeddings
5.3 Cluster Distributions of Datasets
5.4 Similarity
6 Conclusion
7 Future Work
References
Semantic Sensitive TF-IDF to Determine Word Relevance in Documents
1 Introduction
2 Related Works
2.1 An Overview of TF-IDF
2.2 Related Works
3 Materials and Method
4 Results and Discussion
5 Conclusion
References
Web-Based Interactive Neuro-Psychometric Profiling to Identify Human Brain Communication and Miscommunication Processing
1 Introduction
2 Materials and Methods
2.1 Participants
2.2 Design
2.3 Statistical Analysis
3 Results
3.1 Demographic Information of the Respondents
3.2 Potentially Miscommunication Process Identification
3.3 Chaotic Brain Communication Process
3.4 Linear Brain Communication Process
3.5 Relational Brain Communication Process
3.6 Intuitive Brain Communication Process
4 Discussion
5 Conclusion
References
Seventh International Symposium on Computer Vision and the Internet (VisionNet’20)
Deep Visual Attention Based Transfer Clustering
1 Introduction
2 Related Work
2.1 Deep Adaptive Clustering
2.2 Deep Embedded Clustering
2.3 Deep Transfer Clustering
3 Deep Visual Attention Based Transfer Clustering
4 Results
5 Conclusion
References
Video Retrieval Using Residual Networks
1 Introduction
2 Proposed Framework for Video Retrieval Using Residual Network
2.1 Video Segmentation and Extraction of Key Frames
2.2 Residual Network
2.3 Locality Sensitive Hashing
2.4 Why ResNet-34 and Locality Sensitive Hashing?
2.5 Computational Complexity of Locality Sensitive Hashing (LSH)
3 Results and Discussions
4 Conclusion
References
Dynamic Search Paths for Visual Object Tracking
1 Introduction
2 The Proposed Method
3 Results
4 Conclusion
References
Thermal Facial Expression Recognition Using Modified ResNet152
1 Introduction
2 Related Works
3 Proposed Method
4 Results and Analysis
5 Conclusion
References
Real-Time Retail Smart Space Optimization and Personalized Store Assortment with Two-Stage Object Detection Using Faster Regional Convolutional Neural Network
1 Introduction
2 State of the Art
3 Proposed Approach
4 Architecture
4.1 Faster RCNN
5 Methodology
6 Machine Environment
7 Results
7.1 Object Detection
7.2 Misplaced Recognition and Empty Space Localization
8 Conclusion
Annexure—A—Abbreviations
References
2D-Image Super-Resolution on Heritage Site
1 Introduction
2 Related Work
2.1 Convolutional Neural Networks Design
2.2 Loss Functions
2.3 Contribution
3 Method
3.1 Perceptual Loss Function
3.2 Content Loss
3.3 Adversarial Loss
4 Comparison
5 Training and Results
6 Conclusion and Future Work
References
Automated Detection of Liver Tumor Using Deep Learning
1 Introduction
2 Related Works
3 Methodology
3.1 Data
3.2 Deep Learning Model-U-net
3.3 Implementation
3.4 Training and Validation
4 Experimental Results
5 Conclusion
References
Breast Mass Classification Using Classic Neural Network Architecture and Support Vector Machine
1 Introduction
2 Related Works
3 Proposed Method
3.1 Dataset Collection
3.2 Dataset Preprocessing
3.3 Feature Extraction
3.4 Classification
3.5 Transfer Learning
3.6 Fuzzy System
4 Experiment and Results
5 Conclusion and Future Work
References
Symposium on Emerging Topics in Computing and Communications (SETCAC’20)
Providing Software Asset Management Compliance in Green Deployment Algorithm
1 Introduction
2 Related Work
3 GreenSAM: Energy and Software Asset Management
3.1 Servers Attributes
3.2 Multi-objective Optimization
4 Oracle Database Enterprise Edition Use Case
5 OpenStack Use Case
6 Conclusion
References
An Analysis of Rainstreak Modeling as a Noise Parameter Using Deep Learning Techniques
1 Introduction
2 Methodology
2.1 Proposed Work and Dataset
2.2 Model Design
3 Analysis and Results
3.1 Network Architecture
3.2 Metrics
4 Conclusion and Future Scope
References
Diverting Tantrum Behavior Using Percussion Instrument on Autistic Spectrum Disorders
1 Introduction
2 Materials and Methods
2.1 Subject
2.2 Research Design
2.3 Procedure
3 Results
4 Discussion
5 Conclusion
References
Text Sentiment Analysis Using Artificial Intelligence Techniques
1 Introduction
2 Literature Review
3 Proposed Work
3.1 Data Preprocessing
3.2 Feature Extraction
3.3 Classification and Prediction Using Machine Learning
4 Observations and Results
5 Conclusion
References
Internet Performance Profiling of Countries
1 Introduction
2 Background
3 Related Work (CAIDA vs RIPE)
4 Research Methodology
5 Results and Analysis
6 Conclusions
References
Modelling a Folded N-Hypercube Topology for Migration in Fog Computing
1 Introduction
2 Folded N-Hypercube
3 Model
4 Host
4.1 Preamble
4.2 Coding Model
4.3 ACP Model
5 Switch
5.1 Preamble
5.2 Arithmetic Model
5.3 Logical Model
5.4 ACP Model
5.5 Redundant Paths
6 Verification
7 Conclusions
References
Brain Electric Microstate of Karawitan Musicians’ Brain in Traditional Music Perception
1 Introduction
2 Materials and Methods
2.1 Participants
2.2 Stimuli
2.3 Electroencephalographic Recording
2.4 Data Pre-processing and Feature Extraction
2.5 Assessment of Map Landscape
2.6 Statistical Analysis
3 Results
4 Discussion
5 Conclusion
References
MIMO-Based 5G Data Communication Systems
1 Introduction
1.1 5G
2 5G Cellular Network Architecture
3 Technology Emerging for 5G Broadband Networks
3.1 Device-to-Device (D2D)
3.2 Massive Machine Communications (MMC)
3.3 Moving Networks (MN)
3.4 Ultra-Dense Networks (UDN)
3.5 Ultra-Reliable Networks (URNs)
4 Massive MIMO
4.1 Massive MIMO Potential Energy and Power
4.2 Flexibility of Low Power and Cost Materials for Massive MIMO Systems
4.3 Degree of Freedom for a Massive MIMO Device
4.4 Massive MIMO Calls Reduction in Air Connection Latency
4.5 Massive MIMO Multi-layer Exposure
4.6 Massive MIMO Power Against Unintentional Intrusion and Intentional Jamming
5 A Millimeter Wireless Network Wave Approach
6 Service Management Trends and Quality to 5G
7 Conclusion
References
Android Malware Classification Based on Static Features of an Application
1 Introduction
2 Android Applications
3 Related Works
4 Types of Malware
5 Methodology
6 Results and Discussions
6.1 Dataset
6.2 Feature Selection
6.3 Feature Vector
6.4 System Setup and Configuration
6.5 Evaluation Metrics
7 Results
8 Conclusions
References
Performance Evaluation of Cross-Layer Routing Metrics for Multi-radio Wireless Mesh Network
1 Introduction
2 Related Work
3 Cross-Layer Routing Metrics
3.1 Metric of Interference and Channel Switching (MIC)
3.2 Interference Aware Routing Metric (iAWARE)
3.3 Contention Aware Transmission Time (CATT)
3.4 Metric for Interference and Channel Diversity (MIND)
3.5 Passive Interference and Delay Aware (P-IDA) Metric
4 Performance Evaluation
4.1 Simulation Model
4.2 Results and Discussion
5 Conclusion
References
Modelling a Plain N-Hypercube Topology for Migration in Fog Computing
1 Introduction
2 Plain N-Hypercube
3 Model
4 Host
4.1 Preamble
4.2 Coding Model
4.3 ACP Model
5 Switch
5.1 Preamble
5.2 Arithmetic Model
5.3 Logical Model
5.4 ACP Model
5.5 Redundant Paths
6 Verification
7 Conclusions
References
Author Index