Smart Technologies in Data Science and Communication

Name: Smart Technologies in Data Science and Communication | Proceedings of SMART-DSC 2021
Brand: Springer
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Proceedings of SMART-DSC 2021

Sanjoy Kumar Saha Paul S. Pang Debnath Bhattacharyya(Editor)

Springer (Publisher)

1st Edition

Published on 7. June 2021

XXIV, 398 pages

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978-981-16-1773-7 (ISBN)

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Dr. Sanjoy Kumar Saha is currently associated as Professor with the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India. He did his B.E. and M.E. from Jadavpur University and completed his Ph.D. from IIEST Shibpur, West Bengal, India. His research interests include image, video and audio data processing, physiological sensor signal processing, and data analytics. He published more than hundred articles in various international journals and conferences of repute. He has guided eleven Ph.D. students. He holds four US patents. He is a member of IEEE Computer Society, Indian Unit for Pattern Recognition and Artificial Intelligence, ACM. He has served TCS innovation Lab, Kolkata, India, as an advisor for the signal processing group.

Dr. Pang is Associate Professor of cybersecurity at the School of Engineering, Information Technology and Physical Sciences, Federation University Australia. Before joining Federation University, he was Professor of Data Analytics and Director of Center Computational Intelligence for Cybersecurity at the Unitec Institute of Technology, New Zealand. He acted as Principle Investigator for over 13 research grant projects, totaling more than NZD$3.5 million in funding by the Ministry of Business, Employment and Innovation, NZ (MBIE); the Ministry for Primary Industries, NZ (MPI); the Health Research Council, NZ (HRC); the National Institute of Information and Communications Technology, Japan (NICT); Telecom NZ, Mitsubishi Electric, Japan; LuojiaDeyi Technology, China; and Lucent & Bell Lab, USA.

Dr. Debnath Bhattacharyya received Ph.D. (Tech., CSE) from University of Calcutta, Kolkata, India. Currently, Dr. Bhattacharyya is associated with Koneru Lakshmaiah Education Foundation, K L Deemed to be University, Vaddeswaram, Guntur, AP, India, as Professor from May 2021 and Dean R&D, VIIT from the year 2015-May 2020. His research areas include image processing, pattern recognition, bio-informatics, computational biology, evolutionary computing, and security. He published 200+ research papers in various reputed international journals and conferences. He published six textbooks for computer science as well. He is the member of IEEE, ACM, ACM SIGKDD, IAENG, and IACSIT.

Content

Intro
Conference Committee Members
Preface
Acknowledgements
Contents
Editors and Contributors
A Detailed Study on Optimal Traffic Flow in Tandem Communication Networks
1 Introduction
2 Literature Review
3 Forked Communication Network Model
4 Methodology and Working of the Current Model
5 Conclusion
References
Detection of Brain Stroke Based on the Family History Using Machine Learning Techniques
1 Introduction
1.1 Stroke
2 Related Work
3 System Architecture and Methodology
3.1 Data Sources
3.2 Feature Selection
3.3 Data Preprocessing
3.4 Naïve Bayes
3.5 Support Vector Machine (SVM)
3.6 Decision Tree
3.7 Logistic Regression
3.8 Bagging and Boosting
3.9 Random Forest (RF)
4 Results and Discussion
4.1 AUC-ROC Curve
5 Conclusion and Future Scope
References
Classification of Brain Tumors Using Deep Learning-Based Neural Networks
1 Introduction
2 Synopsis of Deep Learning
3 Problem Statement
4 Proposed Model
5 Experiment Results and Discussions
6 Conclusion and Forthcoming Efforts
References
Deep Learning for Lung Cancer Prediction: A Study on NSCLC Patients
1 Background
2 Techniques and Results
3 Data Analysis
4 Preprocessing of Data for Deep Learning
5 Deep Learning
6 Findings
6.1 Tumor Categorization Utilizing Three-Dimensional Deep Learning Networks
6.2 Threshold Values Against Clinical Factors and Highlights of Designed Imaging
7 Discussion
8 An Outline of Author
8.1 Reason for This Research Conducted
8.2 Findings and Work Carried Out by Author
References
Lung Cancer Detection Using Improvised Grad-Cam++ With 3D CNN Class Activation
1 Introduction
2 Related Work
3 Convolutional Neural Network
4 Proposed Model
4.1 Gradient Weight Class Activation
4.2 Study Design
5 Experimental Results and Discussions
5.1 Luna 16 Dataset
5.2 Split and Pre-process Data
5.3 Training Process
5.4 Model Evaluation
6 Conclusions and Future Work
References
An Automatic Perception of Blood Sucker on Thin Blood Splotch Using Graphical Modeling Methods
1 Introduction
1.1 Blood Sucker
2 Literature Survey
2.1 Observations from Previous Work
3 Proposed Work
3.1 Methodology
3.2 Flow Chart
4 Results
5 Conclusions
References
Dual Detection Procedure to Secure Flying Ad Hoc Networks: A Trust-Based Framework
1 Introduction
2 FANET Security
2.1 Attacks According to Source
2.2 Attacks According to Target
3 Related Work
4 Proposed Methodology
4.1 Packet Drop-Based Detection (PDD)
4.2 Content-Based Detection (CD)
4.3 Trust Model (TM)
4.4 Malicious Node Information (MNI)
5 Simulation and Results
5.1 Simulation Setup
5.2 Performance Analysis
6 Conclusion
References
An E-Waste Collection System Based on IoT Using LoRa Open-Source Machine Learning Framework
1 Introduction
2 Related Work
3 Implementation
3.1 Algorithm for Detection of E-Waste Management of Bin
4 Outcomes and Discussion of Object Detection System
5 Conclusion
References
Deep Learning Techniques for Air Pollution Prediction Using Remote Sensing Data
1 Introduction
2 Major Air Pollutants That Cause Mortality and Morbidity
3 Deep Learning Architectures
3.1 CNN Models
3.2 Recurrent Neural Network
3.3 Long Short-term Memory
4 State of Art
5 Remote Sensing Data
5.1 Properties of Remote Sensing
5.2 Types of Remote Sensing
5.3 Meteorological Data
6 Survey Report
7 Future Work
8 Conclusion
References
A Real and Accurate Diabetes Detection Using Voting-Based Machine Learning Approach
1 Introduction
2 Literacy Survey
3 Methodology
3.1 Diabetes UCI Repository Dataset
3.2 Visualizing the Dataset
3.3 Pre-processing and Splitting the Dataset
3.4 Classifiers Used for Diabetes Detection
4 Identifying the Best Three Models by Their Performance
5 Voting Classifier
6 Results
7 Conclusions
References
Performance Analysis of a Simulation-Based Communication Network Model with NS2 Simulator
1 Introduction
2 Proposed System
3 System Design
3.1 Favourable Circumstances
3.2 Route Discovery
4 Results
5 Conclusion
References
A Comparative Analysis on Resource Allocation in Load Balancing Optimization Algorithms
1 Introduction
2 Literature Survey
3 Algorithms Used
3.1 Heterogeneous Earliest Finish Time
3.2 Ant Colony Optimization (ACO)
4 Methodology
4.1 Gathering Project Requirements
4.2 Testing HEFT Algorithm
4.3 Testing Ant Colony Optimization
5 Results
6 Analysis
7 Conclusion
References
Artificial Intelligence-Based Vehicle Recognition Model to Control the Congestion Using Smart Traffic Signals
1 Introduction
1.1 Picture Processing
1.2 Smart Traffic Signal
2 Literature Review
3 Proposed Work
3.1 Algorithms
3.2 Working Mechanism
4 Results and Discussion
5 Conclusion
References
A Literature Review on: Handwritten Character Recognition Using Machine Learning Algorithms
1 Introduction
2 Overview of Handwritten Character Recognition
3 Phases of Handwritten Character Recognition
4 Machine Learning and Deep Learning Technique
5 Character Recognition System
6 Classification Algorithms
7 Conclusion and Future Work
References
Tweet Data Analysis on COVID-19 Outbreak
1 Introduction
2 Literature Review
3 Research Methodology
4 Related Work
4.1 VADER in NLP
5 Results and Findings
6 Conclusion
References
An IoT Model-Based Traffic Lights Controlling System
1 Introduction
2 Methodology Used in the Current Model
3 Existing System
4 Proposed System
4.1 Modules of the Current System
5 Working of the Current Model
6 Results and Discussions
7 Conclusion
References
A Comparative Study on Construction of 3D Objects from 2D Images
1 Introduction
2 Literature Review
3 Results and Analysis
4 Conclusion
5 Future Scope
References
A Deep Learning-Based Object Detection System for Blind People
1 Introduction
1.1 Computer Vision
1.2 Object Detection
1.3 Deep Learning
2 Related Work
3 System Architecture and Methodology
3.1 U-Net Architecture
3.2 ResNet Architecture
3.3 Loss Function
4 Results and Discussion
5 Conclusion and Future Work
References
Pothole Detection Using Deep Learning
1 Introduction
1.1 Objective
1.2 About the Project
1.3 Purpose
1.4 Scope
2 Methodology
2.1 Functional Requirements
2.2 Non-functional Requirements
3 Module Description
3.1 Flask Framework
4 Algorithm Analysis
5 Results
References
Automatic Determination of Harassment in Social Network Using Machine Learning
1 Introduction
2 Related Work
3 Problem Statement
4 Proposed System
4.1 Data Extraction
4.2 Feature Selection
5 Pre-processing
6 Results and Discussion
7 Conclusion
References
Enhanced Performance of ssFFT-Based GPS Acquisition
1 Introduction
2 ssFFT-Based Acquisition
3 Proposed Technique IssFFT-Based Acquisition
4 Result and Discussion
5 Conclusion
References
Optimized Deep Neural Model for Cancer Detection and Classification Over ResNet
1 Introduction
2 Related Work
3 Proposed Work
3.1 Model Description
3.2 Algorithm
3.3 Performance Metrics
4 Experiment and Results
4.1 Dataset and Execution
4.2 Graphical Representation
4.3 Comparison Table
5 Conclusion
References
A Steady-State Analysis of a Forked Communication Network Model
1 Introduction
2 Literature Review
3 Communication Network Model
4 Performance Analysis of the Model Under Equilibrium State
5 Evaluation Metrics of the Current Model Under Equilibrium State
6 Conclusions
References
A Comparative Analysis of With and Without Clustering to Prolonging the Life of Wireless Sensor Network
1 Introduction
2 Comparative Analysis for Wireless Sensor Networks with Clustering and Without Clustering
2.1 Plotting of Sensor Nodes in the X and Y axes
2.2 Formation of Clusters
3 Comparison Graph for Wireless Sensor Networks with and Without Clustering
4 Conclusion
References
A Study on Techniques of Soft Computing for Handling Traditional Failure in Banks
1 Introduction
2 Research Gap and Research Problem
3 Objectives of the Study
4 The Concept: What is Soft Computing?
4.1 Fuzzy Logic
4.2 Evolutionary Computation (EC)-Genetic Algorithms
4.3 Expert Systems
5 Rationale in Financial World
6 Application of Soft Computing
7 Conclusion
References
Lesion Detection and Classification Using Sematic Deep Segmentation Network
1 Introduction
2 Motivation
2.1 Literature Review
2.2 Challenges
3 Proposed DeepSegNet Model for Liver Lesion Detection
3.1 Thresholding to Extract the Interesting Regions
3.2 Proposed DeepSegNet Model for Extracting Lesions of the Liver from CT Image
4 Results and Discussion
4.1 Experimental Setup
4.2 Dataset Description
4.3 Evaluation Metrics
4.4 Experimental Results
4.5 Performance Analysis
4.6 Comparative Methods
4.7 Comparative Discussion
5 Conclusion
References
Automatic Gland Segmentation for Detection of CRC Using Enhanced SegNet Neural Network
1 Introduction
2 Related Work
3 Proposed Architecture for Gland Segmentation
4 Experimental Results and Analysis
4.1 Dataset
4.2 Data Augmentation
4.3 Model Evaluation
4.4 Experimental Setup
4.5 Results and Analysis
5 Conclusion and Future Work
References
Smart Cyclones: Creating Artificial Cyclones with Specific Intensity in the Dearth Situations Using IoT
1 Introduction
2 Literature Review
3 Implementation
4 Results
5 Conclusion
References
Machine Learning-Based Application to Detect Pepper Leaf Diseases Using HistGradientBoosting Classifier with Fused HOG and LBP Features
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Data Pre-processing
3.2 Feature Extraction
3.3 Dimensionality Reduction
3.4 Classification
4 Proposed Methodology
4.1 Pepper Leaf Disease Dataset
4.2 Performance Evaluation Measures
4.3 Result Analysis
5 Conclusion
References
Tracking Missing Objects in a Video Using YOLO3 in Cloudlet Network
1 Introduction
2 Related Work
2.1 Object Detection Using CNN, R-CNN, Faster R-CNN, and YOLO
2.2 Offloading Computations onto the Cloudlet
2.3 Object Detection in Edge Computing
3 Methodology
4 Results and Discussion
5 Conclusion
References
Face Hallucination Methods-A Review
1 Introduction
2 Literature Review
3 Conclusion
References
Image Encryption for Secure Internet Transfer
1 Introduction
2 Materials and Experimental Procedures
2.1 Materials
2.2 Methods
2.3 Testing and Analysis
3 Results and Discussion
4 Conclusions
References
Author Index
Correction to: Smart Cyclones: Creating Artificial Cyclones with Specific Intensity in the Dearth Situations Using IoT
Correction to: Chapter "Smart Cyclones: Creating Artificial Cyclones with Specific Intensity in the Dearth Situations Using IoT" in: S. K. Saha et al. (eds.), Smart Technologies in Data Science and Communication, Lecture Notes in Networks and Systems 210, https://doi.org/10.1007/978-981-16-1773-728

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Smart Technologies in Data Science and Communication

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