Keywords:
Machine learning.
;
Electronic books.
Type of Medium:
Online Resource
Pages:
1 online resource (381 pages)
Edition:
1st ed.
ISBN:
9783031243523
Series Statement:
Communications in Computer and Information Science Series ; v.1762
URL:
https://ebookcentral.proquest.com/lib/geomar/detail.action?docID=7179476
DDC:
780
Language:
English
Note:
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Machine Learning and Computational Intelligence -- Cardiac Arrhythmia Classification Using Cascaded Deep Learning Approach (LSTM & -- RNN) -- 1 Introduction -- 2 Deep Learning Techniques -- 2.1 Convolutional Neural Network (CNN) -- 2.2 Recurrent Neural Network -- 2.3 Long Short-Term Memory (LSTM) -- 2.4 Gated Recurrent Unit (GRU) -- 3 Proposed Work and Methodology -- 4 Experimental Setup -- 5 Result and Future Scope -- References -- A Computational Approach to Identify Normal and Abnormal Persons Gait Using Various Machine Learning and Deep Learning Classifier -- 1 Introduction -- 2 Related Work -- 3 Data Acquisition -- 4 Proposed Method -- 5 Result Evaluation -- 6 Conclusion and Future Work -- References -- Hierarchical-Based Binary Moth Flame Optimization for Feature Extraction in Biomedical Application -- 1 Introduction -- 2 Related Work -- 3 Proposed HBMFO Model -- 3.1 Moth Flame Optimization (MFO) -- 3.2 Hierarchical Binary MFO (HBMFO) -- 3.3 Evaluation -- 4 Results and Discussions -- 5 Conclusion -- References -- Distinctive Approach for Speech Emotion Recognition Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Proposed Scheme -- 3.1 Data Collection -- 3.2 Visualization -- 3.3 Augmentation -- 3.4 Feature Extraction -- 3.5 Classification Approaches -- 4 Experimental Analysis -- 5 Conclusion -- References -- A Survey on Human Activity Recognition Using Deep Learning Techniques and Wearable Sensor Data -- 1 Introduction -- 2 Overview of Human Activity Recognition -- 2.1 Data Acquisition -- 2.2 Data Segmentation -- 2.3 Feature Extraction and Classification -- 3 Wearable Sensors -- 3.1 Inertial Measurement Unit (IMU) -- 3.2 Electromyography (EMG) -- 3.3 Electrocardiography (ECG) -- 3.4 Electroencephalography (EEG).
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4 Applications of HAR Using Wearable Sensor Data -- 5 Datasets -- 5.1 UCI-HAR -- 5.2 PAMAP2 -- 5.3 WISDM -- 5.4 MHEALTH -- 5.5 Opportunity -- 5.6 Daphnet -- 6 Deep Learning Techniques Applied for HAR in Literature -- 6.1 Autoencoder -- 6.2 CNN -- 6.3 Recurrent Neural Network (RNN) -- 6.4 Deep Belief Network (DBN) -- 7 Challenges and Future Scope -- 8 Conclusions -- References -- Refined-Para Forming Question Generation System Using Lamma -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 3.1 Important Flow Steps -- 4 Implementation Details -- 5 Results and Discussion -- 5.1 Evaluation -- 6 Objective Evaluation -- 7 Conclusion -- 8 Future Work -- References -- Estimation of Dynamic Balancing Margin of the 10-DOF Biped Robot by Using Polynomial Trajectories -- 1 Introduction -- 2 Mathematical Modelling of the Biped Robot -- 2.1 Forward Kinematics -- 2.2 Inverse Kinematics -- 2.3 Foot Trajectory -- 2.4 Dynamic Balance Margin (DBM) -- 3 Experimentation -- 4 Results and Discussion -- 5 Conclusions -- 6 Future Scope -- References -- A Review on: Deep Learning and Computer Intelligent Techniques Using X-Ray Imaging for the Early Detection of Knee Osteoarthritis -- 1 Introduction -- 2 Literature Review -- 3 Comparative Analysis -- 3.1 Region of Interest (ROI) and Segmentation -- 3.2 Deep Learning -- 3.3 As a Feature Descriptor, Convolutional Neural Network -- 3.4 Oriented Gradient Histogram -- 3.5 Pattern of Local Binary -- 4 Experimental Evaluation -- 4.1 Dataset -- 4.2 State of Art Comparisons -- 5 Conclusion -- References -- Effective Real Time Disaster Management Using Optimized Scheduling -- 1 Introduction -- 1.1 Real Time Scheduling for Disaster Management -- 2 Background Work -- 3 Methodology -- 4 Implementation -- 4.1 Case Study of Flash Flood Crisis Management -- 4.2 Disaster Management System Model.
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4.3 Cluster Based Scheduling Algorithm -- 5 Result and Discussion -- 6 Conclusions -- References -- Design System of Urban Residential Environment Based on Interactive Genetic Algorithm -- 1 Introduction -- 2 Research on the Optimal Design System of Urban Living Environment Based on Interactive Genetic Algorithm -- 2.1 The Role of Interactive Genetic Algorithm in the Optimization of Urban Living Environment -- 2.2 Problems Existing in the Urban Living Environment -- 2.3 Establishment of the Fitness Function -- 2.4 Algorithm Process -- 3 System Design Experiment of Optimization Design of Urban Living Environment Based on Interactive Genetic Algorithm -- 3.1 Residential Environment Survey -- 3.2 Simulation System Operation -- 4 Experimental Analysis of the Systematic Optimization Design of Urban Living Environment Based on Interactive Genetic Algorithm -- 4.1 Factors and Analysis of Resident Satisfaction -- 4.2 Program Satisfaction Analysis -- 5 Conclusions -- References -- Sensorless Control Algorithm of Permanent Magnet Synchronous Motor on Account of Neural Network -- 1 Introduction -- 2 Design and Exploration of Sensorless Control Algorithm of Permanent Magnet Synchronous Motor on Account of Neural Network -- 2.1 Neural Network Algorithm -- 2.2 Sensorless Control Algorithm of Permanent Magnet Synchronous Motor Based on Neural Network -- 3 Research on the Effect of Sensorless Control Algorithm of Permanent Magnet Synchronous Motor on Account of Neural Network -- 4 Investigation and Research on Sensorless Control Algorithm of Permanent Magnet Synchronous Motor on Account of Neural Network -- 5 Conclusions -- References -- A Techno Aid to Ease in e-Rehabilitation -- 1 Introduction -- 2 Role of Assistive Technology (AT) and Rehabilitative Technology (RT) in Rehabilitation -- 3 AT and RT Related Technologies for Patient Connectivity with Society.
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3.1 Robotic Technology in Rehabilitation -- 3.2 Wearable Technology in Rehabilitation -- 3.3 Virtual Reality Technology in Rehabilitation -- 3.4 Internet of Things (IoT) Technology in Rehabilitation -- 4 System Model for Patient Monitoring System -- 4.1 Keeping Track of Patient Conditions -- 4.2 Collection of Patient's Heath Parameters -- 4.3 Interact with Patient -- 4.4 Patient's Security -- 4.5 Remote Patient's Monitoring -- 5 Methodology and Implementation Details -- 5.1 Design an IoT Framework -- 5.2 Connecting and Storing Data on Cloud -- 5.3 Website Application -- 6 System Working Model Flowchart -- 7 Results and Analysis -- 8 Conclusion -- References -- A Novel Approach to Analyse Lung Cancer Progression and Metastasis Using Page Rank Technique -- 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset Description -- 2.2 PageRank Algorithm -- 3 Results and Discussion -- 4 Conclusion -- References -- Homomorphic Encryption of Neural Networks -- 1 Introduction -- 2 Literature Survey -- 2.1 Homomorphism -- 2.2 Partially Homomorphic Systems -- 2.3 Additive Homomorphic Systems -- 2.4 Multiplicative Homomorphic Systems -- 2.5 Additive and Multiplicative Homomorphic Encryption Systems -- 2.6 Somewhat Homomorphic Encryption -- 2.7 Fully Homomorphic Encryption Systems -- 2.8 Encryption Scheme -- 3 Proposed Algorithm and Its Application -- 3.1 Applications -- 3.2 Medical Records -- 3.3 Finance and Advertising -- 3.4 Neural Network Support -- 3.5 Support for Floating Point Numbers -- 3.6 Encrypting Neural Network -- 4 Result and Analysis -- 5 Conclusion and Future Work -- References -- An Empirical Study to Enhance the Accuracy of an Ensemble Learning Model for Crop Recommendation System by Using Bit-Fusion Algorithm -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset Preparation -- 3.2 Study Area.
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3.3 The Importance of the Base Classifier -- 4 Results of Experimental Assessment and Simulation -- 4.1 Ada Boost Classifier Simulation Results -- 4.2 Gradient Boosting Simulation Results -- 4.3 XGB Classifier Simulation Results -- 5 Proposed Algorithm based on SVM and CNN -- 5.1 Ensemble Method (SVM + CNN) -- 5.2 Description of the Bit-Fusion Algorithm -- 5.3 Comparison Between Proposed Bit-Fusion Ensemble Technique vs Traditional Fusion Methods -- 6 Conclusion and Future Scope -- References -- Bayesian Learning Model for Predicting Stability of System with Nonlinear Characteristics -- 1 Introduction -- 2 Gathering Data for the ML Model -- 3 Bayesian Learning for Machining Stability -- 4 Learning Stability with Non-linear Characteristics -- 4.1 Learning Stability for a Process Exhibiting Bistable Behaviour -- 4.2 Learning Stability with Process Damping -- 5 Conclusion -- References -- Data Sciences -- Novel ABC: Aspect Based Classification of Sentiments Using Text Mining for COVID-19 Comments -- 1 Introduction -- 2 Related Work -- 3 Text Mining: The Process -- 3.1 Steps in Text Mining -- 4 Classification of Sentiments for Text-Based Content -- 5 Frequently Used Methods and Techniques -- 5.1 Basic Methods -- 5.2 Advanced Methods -- 6 Proposed Methodology -- 6.1 Steps for Novel ABC: Aspect Based Classification -- 7 Results and Analysis -- 7.1 Comparative Analysis -- 8 Conclusions and Future Work -- References -- Topic Modeling, Sentiment Analysis and Text Summarization for Analyzing News Headlines and Articles -- 1 Introduction -- 2 Related Work -- 2.1 Topic Modeling -- 2.2 Sentiment Analysis and Emotion Detection -- 2.3 Topic Modeling and Sentiment Analysis -- 2.4 Text Summarization and Other Approaches -- 3 Methodology -- 3.1 Module 1: Topic Modeling -- 3.2 Module 2: Text Summarization -- 3.3 Module 3: Sentiment Classification.
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4 Results and Experiments Discussion.
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