LEADER 11243nam 2200541 450 001 9910580161903321 005 20221230084828.0 010 $a981-16-9416-8 035 $a(MiAaPQ)EBC7026806 035 $a(Au-PeEL)EBL7026806 035 $a(CKB)24100621500041 035 $a(PPN)267815352 035 $a(EXLCZ)9924100621500041 100 $a20221230d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCongress on Intelligent Systems$hVolume 1 $eproceedings of CIS 2021 /$fMukesh Saraswat [and four others], editors 210 1$aSingapore :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (933 pages) 225 1 $aLecture notes on data engineering and communications technologies ;$vVolume 114 311 08$aPrint version: Saraswat, Mukesh Congress on Intelligent Systems Singapore : Springer,c2022 9789811694158 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Contents -- About the Editors -- The Extraction of Automated Vehicles Traffic Accident Factors and Scenarios Using Real-World Data -- 1 Introduction -- 2 Data Collecting and Preprocessing -- 2.1 Collecting AVs Accident Data -- 2.2 Preprocessing AVs Accident Data -- 3 Learning Method and Result -- 3.1 Random Forest -- 3.2 Extraction of AVs Collision Factor Importance -- 4 AVs Traffic Accident Scenarios -- 4.1 Scenario Combinations Based on Collision Situations -- 4.2 AVs Ahead Situation -- 4.3 AVs Rear Situation -- 4.4 Comparison with HVs Traffic Accidents -- 4.5 Summary -- 5 Conclusion and Future Research -- References -- Leaf Disease Identification in Rice Plants Using CNN Model -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Dataset -- 3.2 Pre-processing -- 3.3 Data Normalization -- 3.4 Create a Model -- 3.5 Training and Validation -- 3.6 Test the Model -- 3.7 Convert the Model to .tflite Format -- 3.8 Develop and Deploy Using Mobile App -- 3.9 Test the Mobile App -- 4 Experimental Results -- 5 Experimental Set-Up -- 6 Discussion -- 7 Conclusions -- References -- Twitter Sentiment Analysis Based on Neural Network Techniques -- 1 Introduction -- 2 Literature Survey -- 2.1 Related Work -- 2.2 Opinion Mining -- 2.3 Twitter -- 2.4 Twitter Sentiment Analysis -- 3 Data Description -- 4 Proposed Methodology -- 4.1 Pre-processing -- 4.2 Feature Extraction -- 4.3 Feature Representation -- 4.4 Handling Memory Issues -- 4.5 Implementation -- 5 Classification Techniques Compared -- 5.1 Naďve Bayes -- 5.2 Decision Tree -- 5.3 Support Vector Machine (SVM) -- 5.4 Multilayer Perceptron (MLP) -- 5.5 Recurrent Neural Network (RNN) -- 5.6 Convolutional Neural Network (CNN) -- 6 Results -- 7 Conclusion -- References -- Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble -- 1 Introduction -- 2 Literature Survey. 327 $a3 Dataset -- 4 Proposed Methodology -- 4.1 Ensemble -- 4.2 Bagging -- 4.3 Optimizer -- 4.4 Experimental Settings -- 5 Result -- 6 Conclusion -- References -- Classifying Microarray Gene Expression Cancer Data Using Statistical Feature Selection and Machine Learning Methods -- 1 Introduction -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Feature Selection -- 3.3 Classifiers -- 4 Results and Discussion -- 5 Conclusion -- References -- Pythagorean Fuzzy Information Measure with Application to Multicriteria Decision Making -- 1 Introduction -- 2 Literature Review -- 3 New Pythagorean Fuzzy Information Measure -- 3.1 Properties of ??? ( A ) -- 3.2 Monotonicity of Proposed Pythagorean Fuzzy Information Measure -- 3.3 Multicriteria Decision Making Based on New Pythagorean Fuzzy Information Measure -- 4 Conclusion -- References -- Developing an Improved Software Architecture Framework for Smart Manufacturing -- 1 Introduction -- 2 Literature Review -- 2.1 Industry 4.0 -- 2.2 Software Architecture 4.0 -- 3 Review of Software Architectures -- 3.1 Requirements of Smart Manufacturing -- 4 Results -- 4.1 Centralized Communication -- 4.2 Decentralised Communication -- 4.3 Combined Communication -- 4.4 Assessment of Software Architectures -- 5 Conclusion and Proposal of Software Architecture -- 5.1 Proposed Segments of the Software Architecture -- 5.2 Divisions of Machine Communication in Software Architecture -- 5.3 Proposed Software Architecture Implementation Mock-Up -- 5.4 Predicted Impact of Software Architecture -- 6 Future Work -- References -- Intelligent Water Drops Algorithm Hand Calculation Using a Mathematical Function -- 1 Introduction to Intelligent Water Drops Algorithm -- 2 Working of Intelligent Water Drops Algorithm -- 2.1 The Pseudo-Code of an IWD-Based Algorithm -- 2.2 Case Study. 327 $a3 Convergence of Intelligence Water Drops Optimization Algorithm -- 4 Conclusion -- References -- A Study of Decision Tree Classifier to Predict Learner's Progression -- 1 Introduction -- 2 Related Study -- 3 Material and Method -- 3.1 Data Collection -- 3.2 ID3 Decision Tree Classifier Algorithm Steps -- 3.3 Experimental Work -- 4 Results and Discussion -- 4.1 Example -- 5 Conclusion -- References -- An Overview of Blockchain and IoT in e-Healthcare System -- 1 Introduction -- 2 Blockchain -- 2.1 Blockchain Technology [12] -- 2.2 Blockchain Implementation -- 2.3 Smart Contracts -- 2.4 Consensus -- 3 Combination of Blockchain and Internet of Things -- 3.1 Internet of Things (IoT) -- 4 Applications Internet of Things for Blockchain in e-Healthcare -- 5 Literature Review -- 6 Existing Research on Internet of Things for Blockchain -- 7 Conclusion -- References -- Energy-Efficient ACO-DA Routing Protocol Based on IoEABC-PSO Clustering in WSN -- 1 Introduction -- 2 Related Work -- 2.1 Proposed Approach -- 2.2 Energy Consumption Model -- 2.3 Network Model -- 2.4 Proposed IoEABC-PSO Clustering Algorithm -- 2.5 PSO Algorithm -- 2.6 Algorithm for Proposed IoEABC-PSO Approach -- 2.7 Cluster Head Selection -- 2.8 ACO Algorithm -- 3 Result and Discussion -- 3.1 Network Lifetime -- 3.2 Energy Consumption -- 3.3 Distance Between Node and Base Station -- 3.4 Residual Energy of Nodes -- 4 Conclusion -- References -- Modelling Critical Success Factors for Smart Grid Development in India -- 1 Introduction -- 2 Review of Literature -- 2.1 Smart Grid at a Glance -- 2.2 Overview of Smart Grid Development in India -- 3 Methodology -- 3.1 Identification of Critical Success Factors -- 3.2 Exploratory Factor Analysis (EFA) -- 3.3 Questionnaire Development -- 3.4 Analysis of EFA Result -- 3.5 TISM -- 3.6 TISM Model -- 3.7 Analysis of TISM-Based Model -- 4 Discussion. 327 $a5 Conclusions -- Appendix -- References -- Stability Analysis of Emerged Seaside Perforated Quarter Circle Breakwater Using Soft Computing Techniques -- 1 Introduction -- 1.1 General -- 1.2 Quarter Circle Breakwater -- 1.3 ANN -- 1.4 SVM -- 1.5 AdaBoost -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Separation of Data -- 3.3 Development of Prediction Models -- 3.4 Evaluation of Model Performance and Selection of the Best Model -- 4 Prediction Models -- 4.1 ANN -- 4.2 SVM -- 4.3 AdaBoost -- 5 Results and Discussion -- 5.1 Artificial Neural Network -- 5.2 Support Vector Machine -- 5.3 AdaBoost -- 6 Conclusions -- References -- A Risk-Budgeted Portfolio Selection Strategy Using Novel Metaheuristic Optimization Approach -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Proposed Strategy -- 4.1 Gradient Search Rule -- 4.2 Local Escaping Operator -- 5 Experimental Results -- 6 Conclusions and Future Scope -- References -- An Optimization Reconfiguration Reactive Power Distribution Network Based on Improved Bat Algorithm -- 1 Introduction -- 2 A Model of Reactive Power Optimization of Distribution Network -- 3 Improving Bat Algorithm -- 3.1 Bats Algorithm -- 3.2 Improving Bat Algorithm -- 3.3 The IBA for the RPODN Problem -- 4 Experimental Results -- 5 Conclusion -- References -- Analyzing a Raga-Based Bollywood Song: A Statistical Approach -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Experimental Results and Discussion -- 4.1 Note Duration -- 4.2 IOI (Interonset Interval) -- 4.3 Unconditional Probability -- 4.4 Chi Square Goodness-of-Fit Test -- 4.5 Conditional Probability -- 4.6 Andrews Plot for Statistical Parameters of Music Phrase -- 5 Conclusion -- References -- Security Prioritized Heterogeneous Earliest Finish Time Workflow Allocation Algorithm for Cloud Computing -- 1 Introduction. 327 $a2 Related Work -- 3 Workflow Allocation Problem -- 4 SPHEFT -- 5 Experimental Results -- 6 Conclusions and Future Scope -- References -- Dropout-VGG Based Convolutional Neural Network for Traffic Sign Categorization -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background -- 2.2 Deep Learning in Image Classification -- 3 Experiment -- 3.1 Dataset Description -- 4 Data Preprocessing -- 5 Network Architectures -- 5.1 VGG-16 Model -- 5.2 dVGG Model -- 6 Results and Comparison -- 7 Conclusion -- References -- Internet-Based Healthcare Things Driven Deep Learning Algorithm for Detection and Classification of Cervical Cells -- 1 Introduction -- 2 Literature Survey -- 3 Classification of Cervical Cancer -- 4 Cervical Cancer Screening Methods and Their Characterization -- 4.1 PAP Tests -- 4.2 Visual Inspection -- 4.3 Fluid-Based Cytology -- 4.4 HPV DNA Testing -- 4.5 Antibody -- 5 Approaches Used for Cervical Cytology -- 6 Channel Estimation Parameters -- 6.1 High Resolution Algorithm -- 7 Role of Machine Learning and Internet of Things in Diagnose the Cervical Disorder -- 8 Proposed Solution Strategies for Cervical Cancer Based on Internet of Things and Machine Learning -- 9 Conclusion and Future Scope -- References -- Load Balancing Algorithms in Cloud Computing Environment-An Effective Survey -- 1 Introduction -- 2 Load Balancing in Cloud Computing -- 2.1 Load Balancing -- 2.2 Challenges in Cloud Load Balancing -- 2.3 Types of Load Balancing -- 3 Existing Load Balancing Algorithms: A Review -- 3.1 Examining Static Load Balancing Algorithms -- 3.2 Examining Dynamic Load Balancing Algorithms -- 3.3 Review of Static and Dynamic Load Balancing Algorithms -- 4 Comparative Study of Static and Dynamic Load Balancing Algorithms -- 5 Conclusion -- References. 327 $aAssessment of the Spatial Variability of Air Pollutant Concentrations at Industrial Background Stations in Malaysia Using Self-organizing Map (SOM). 410 0$aLecture notes on data engineering and communications technologies ;$vVolume 114. 606 $aArtificial intelligence$vCongresses 606 $aArtificial intelligence 606 $aInternet of things 615 0$aArtificial intelligence 615 0$aArtificial intelligence. 615 0$aInternet of things. 676 $a006.3 702 $aSaraswat$b Mukesh 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910580161903321 996 $aCongress on intelligent systems$92586081 997 $aUNINA