LEADER 12558nam 22007695 450 001 9910878980403321 005 20240803130436.0 010 $a9789819735235$b(electronic bk.) 010 $z9789819735228 024 7 $a10.1007/978-981-97-3523-5 035 $a(MiAaPQ)EBC31579742 035 $a(Au-PeEL)EBL31579742 035 $a(CKB)33645673300041 035 $a(DE-He213)978-981-97-3523-5 035 $a(EXLCZ)9933645673300041 100 $a20240803d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Distributed Computing and Machine Learning $eProceedings of ICADCML 2024, Volume 2 /$fedited by Umakanta Nanda, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar, Kuan-Ching Li 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (482 pages) 225 1 $aLecture Notes in Networks and Systems,$x2367-3389 ;$v1015 311 08$aPrint version: Nanda, Umakanta Advances in Distributed Computing and Machine Learning Singapore : Springer,c2024 9789819735228 327 $aIntro -- Preface -- Contents -- Editors and Contributors -- OSNR Monitoring for QPSK and QAM in Fiber-Optic Networks Using Machine Learning -- 1 Introduction -- 2 Proposed Method -- 3 Support Vector Machine Algorithms -- 4 Simulation Results and Discussion -- 5 Conclusion and Future Research -- References -- Classification of Star and Galaxy Objects Utilizing Machine Learning Techniques and Deep Neural Networks -- 1 Introduction -- 2 Dataset -- 2.1 Processing Data -- 3 Machine Learning Approach for Star Versus Galaxy Classification -- 4 Convolutional Neural Networks-(CNN) -- 4.1 Convolutional Layers -- 4.2 Implementation Details -- 5 Result and Analysis -- 6 Conclusion -- References -- Probabilistic Forecasting Analysis on Electric Load Systems -- 1 Introduction -- 2 Review of Literature -- 3 Description of the Model -- 4 Sources of Data Generation -- 5 Computational Analysis and Results -- 5.1 Representation of ELG Units -- 5.2 Correlation Analysis -- 5.3 Bivariate Normal Distribution -- 5.4 Linear Regression and ARIMA Models -- 5.5 Electricity Consumption Charges -- 6 Conclusion -- References -- Smart City Survey on AIoT Using Machine Learning, Deep Learning, and Its Computing Tools -- 1 Introduction -- 2 IoT-Oriented Perspective -- 2.1 Smart Infrastructure -- 2.2 Air Management -- 2.3 Traffic Management -- 2.4 Waste Management -- 3 ML-Orient Perspective -- 3.1 Infrastructure -- 3.2 Air Management -- 3.3 Traffic Analysis -- 3.4 Waste Management -- 4 Deep Learning-Oriented Perspective -- 4.1 Supervised Learning -- 4.2 Unsupervised Learning -- 4.3 Reinforcement Learning -- 5 Computing Tools for Smart City -- 5.1 Cloud Computing -- 5.2 Fog Computing -- 5.3 Edge Computing -- 6 Conclusion -- References -- Energy Harvesting Integrated Sensor Node Architecture for Sustainable IoT Networks -- 1 Introduction -- 1.1 Contributions Made in This Research. 327 $a2 Literature Study on Energy Harvesting -- 3 System Architecture -- 3.1 Hardware Requirements -- 3.2 Circuit Implementation -- 3.3 Energy Source: The PV Cell -- 3.4 Energy Storage Structures -- 3.5 Power Management Protocols -- 4 Lifetime Evaluation with Solar Energy Harvester -- 4.1 System Implementation and Analysis -- 5 Conclusion -- References -- Enhancing Real Estate Price Prediction in Smart Cities: A Comparative Analysis of Machine Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 Limitation -- 4 Methodology -- 4.1 Feature Engineering -- 4.2 Model Description and Predicting the Value -- 5 Results -- 6 Conclusion -- 7 Future Work -- References -- Real-Time AI-Based Face-Mask Detection -- 1 Introduction -- 2 Proposed Design Approach -- 2.1 Custom Dataset Gathering -- 2.2 Data Augmentation for Best Results -- 2.3 Training Model -- 3 Methodology -- 3.1 YOLO Algorithm -- 3.2 MobileNetV2 -- 4 Results and Discussion -- 5 Conclusion -- References -- A Logical Model for Multiple People Activity Recognition Using Non-intrusive Sensors for Geriatric Care -- 1 Introduction -- 2 Related Work -- 3 Problem Scenario -- 4 Logical FHMM for Multiple People Activity Recognition -- 4.1 Solution Overview -- 5 Experiments -- 5.1 Experimental Setup -- 6 Conclusion -- References -- From Sea to Table: A Blockchain-Enabled Framework for Transparent and Sustainable Seafood Supply Chains -- 1 Introduction -- 2 Related Work -- 3 Seafood Supply Chain and Blockchain -- 4 Conceptual Blueprint -- 4.1 The Flow of Code Implementation -- 5 Result -- 6 Discussion -- 7 Conclusion and Future Scope -- References -- Distributed State Estimation for GPS Navigation: The Correntropy Extended Kalman Filter Approach -- 1 Introduction -- 2 Literature Study -- 3 Correntropy Extended Kalman Filter -- 4 Results and Discussion -- 5 Conclusion -- References. 327 $aNayantara: Crime Analysis from CCTV Footage Using MobileNet-V2 and Transfer Learning -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 System Architecture -- 3.2 Detection Model -- 3.3 Web Application -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 Working of the Detection Algorithm -- 4.4 CNN Model -- 4.5 Results -- 5 Conclusion -- References -- Bird Detection in Microlight Aircraft Strip Using YOLOv8for Adventure Tourism -- 1 Introduction -- 2 Bigdata Analytics Unlocks for Tourism Industry -- 2.1 Why is Microlight Aircraft Safety Important? -- 3 Literature Review -- 4 Implementation and Discussion -- 4.1 Methodology Used -- 4.2 Dataset Used -- 5 Performance Analysis and Results -- 6 Conclusion -- References -- A Graphical Tuning Method-Based Robust PID Controller for Twin-Rotor MIMO System with Loop Shaping Technique -- 1 Introduction -- 2 Preliminaries -- 2.1 Description of Twin-Rotor MIMO System -- 2.2 Design of Decouplers -- 2.3 FOPDT Model -- 3 upper H Subscript normal infinityHinfty Controller -- 4 Results an Discussions -- 5 Conclusion -- References -- Signature Verification Using Deep Learning: An Empirical Study -- 1 Introduction -- 2 Proposed Method -- 2.1 Data Acquisition -- 2.2 Pre-processing -- 2.3 Feature Extraction -- 2.4 Model and Algorithm Hyperparameters -- 2.5 Optimizing Algorithm -- 2.6 Batch Normalization and Dropout -- 3 Results -- 3.1 Performance Stats -- 3.2 Evaluation Metrics -- 4 Discussion -- 5 Conclusion -- References -- An Intelligent and Automated Machine Learning-Based Approach for Heart Disease Prediction and Personalized Care -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset Description -- 3.2 Data Pre-processing -- 3.3 Handling Imbalanced Classes -- 3.4 Data Normalization -- 3.5 Feature Relevance Analysis -- 4 Results and Discussion. 327 $a4.1 Comparative Analysis -- 5 Conclusion -- References -- Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Pre-processing -- 3.3 LSTM Architecture -- 4 Results and Discussion -- 4.1 Comparative Analysis -- 5 Conclusion -- References -- Polarity Detection of Online News Articles Using Deep Learning Techniques -- 1 Introduction -- 1.1 Deep Learning and Polarity Detection -- 2 Literature Survey -- 2.1 RNN with GRU -- 2.2 RNN with LSTM -- 2.3 Bidirectional RNN -- 2.4 CNN -- 2.5 Dynamic Dictionaries -- 3 Proposed Method -- 4 Experiment and Result Discussion -- 5 Conclusion and Future Work -- References -- Harnessing ResNet50 and EfficientNetB5 for Detection of Diabetic Retinopathy Using Explainable AI -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Results -- 4.1 Model Performance -- 4.2 Interpretation of Result -- 4.3 Model Explainability -- 5 Conclusion -- References -- A Grey Wolf and Rough Set Hybrid Approach for the Detection of Chronic Kidney Disease -- 1 Introduction -- 2 Schematic Representation of Proposed Research -- 3 Experimental Research on Chronic Kidney Disease -- 4 Result Analysis -- 4.1 Proposed GWRSO Data Analysis -- 5 Conclusion -- References -- Efficient Rice Disease Classification Using Intelligent Techniques -- 1 Introduction -- 2 Methodology -- 3 Data Description -- 3.1 Bacterial Leaf Blight -- 3.2 Brown Spot -- 3.3 Blast -- 3.4 Tungro -- 4 Experimental Setup and Performance Analysis -- 5 Conclusion -- References -- Maize Crop Yield Prediction Using Machine Learning Regression Approach -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Feature Selection -- 2.4 Data Transformation -- 2.5 Model Building Algorithms -- 2.6 Evaluation Metrics. 327 $a3 Experiment and Results -- 3.1 Model Building, Training, and Testing -- 3.2 Dimension Reduction Using Principal Component Analysis (PCA) -- 3.3 Comparison of the Results -- 3.4 Identification of Main Features -- 3.5 Discussion of the Findings -- 4 Conclusion -- References -- Mode Division Multiplexing-Based Passive Optical Networks for High-Capacity Data Rate via Radio Over Fiber Technology -- 1 Introduction -- 2 Proposed Mode Division Multiplexing Passive Optical Network -- 3 Mode Division Multiplexing Layout Simulation by Using OptiSystemV20 -- 4 Simulation Design of MDM with QAM and DSPK -- 5 Simulation Design of MDM for Noise Removal Systems -- 6 Result and Discussion -- 7 Conclusion -- References -- Enhancing Urban Connectivity: Free Space Optics as a Resilient Backup Link for Fiber Networks in Urban Environments -- 1 Introduction -- 2 Proposed Block Diagram of FSO-NRZ System Model -- 3 Result and Discussion -- 4 Conclusion -- References -- Integrating ANSYS Simulation and Machine Learning Techniques for Thermo-Mechanical Analysis of PCBs -- 1 Introduction -- 2 Problem Statement and Methodology -- 3 Results and Discussions -- 4 Conclusions -- References -- Automation of Quality Assessment Procedures in School Education -- 1 Introduction -- 2 Software Tool for Quality Evaluation: Design and Software Prototype Development -- 3 Experiments -- 4 Conclusions -- References -- The FGSM Attack on Image Classification Models and Distillation as Its Defense -- 1 Introduction -- 2 Related Work -- 3 Theoretical Background -- 4 Results of the FGSM Attack -- 4.1 The Classification Results in the Absence of the FGSM Attack -- 4.2 The Classification Results in the Presence of the FGSM Attack -- 5 Distillation for Defense Against the FGSM Attack -- 6 Conclusion -- References -- An Experimentation of Firefly Algorithm Using a Different Set of Objective Functions. 327 $a1 Introduction. 330 $aThis book is a collection of peer-reviewed best selected research papers presented at the Fifth International Conference on Advances in Distributed Computing and Machine Learning (ICADCML 2024), organized by School of Electronics and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India, during 5?6 January 2024. This book presents recent innovations in the field of scalable distributed systems in addition to cutting edge research in the field of Internet of Things (IoT) and blockchain in distributed environments. 410 0$aLecture Notes in Networks and Systems,$x2367-3389 ;$v1015 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aMachine learning 606 $aBlockchains (Databases) 606 $aInternet of things 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aBlockchain 606 $aInternet of Things 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aBlockchains (Databases). 615 0$aInternet of things. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aBlockchain. 615 24$aInternet of Things. 676 $a004.36 700 $aNanda$b Umakanta$01742711 701 $aTripathy$b Asis Kumar$01372576 701 $aSahoo$b Jyoti Prakash$01372577 701 $aSarkar$b Mahasweta$01742712 701 $aLi$b Kuan-Ching$01078909 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910878980403321 996 $aAdvances in Distributed Computing and Machine Learning$94169410 997 $aUNINA LEADER 02461pam 2200673 a 450 001 9910495869703321 005 20230829001304.0 010 $a0-585-07879-3 035 $a(CKB)111004366705276 035 $a(MH)002231081-9 035 $a(SSID)ssj0000241593 035 $a(PQKBManifestationID)12086129 035 $a(PQKBTitleCode)TC0000241593 035 $a(PQKBWorkID)10300060 035 $a(PQKB)10713070 035 $a(EXLCZ)99111004366705276 100 $a19910206d1991 ub 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe scar of revolution $eCustine, Tocqueville, and the romantic imagination /$fIrena Grudzinska Gross$b[electronic resource] 210 0 $aBerkeley $cUniversity of California Press$dc1991 215 $a1 online resource (xv, 191 p. ) 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-520-07351-7 320 $aIncludes bibliographical references and index. 531 $aSCAR OF REVOLUTION: CUSTINE, TOCQUEVILLE, AND THE ROMANTIC IMAGINATION 531 $aSCAR OF REVOLUTION 531 $aTHE SCAR OF REVOLUTION: CUSTINE, TOCQUEVILLE, & THE ROMANTIC IMAGINATION 606 $aDemocracy 606 $aRomanticism$zFrance 606 $aDemocracy$zFrance 606 $aRomanticism 606 $aRussia & Former Soviet Republics$2HILCC 606 $aRegions & Countries - Europe$2HILCC 606 $aHistory & Archaeology$2HILCC 607 $aSoviet Union$xDescription and travel 607 $aUnited States$xPolitics and government 607 $aUnited States$xSocial conditions$yTo 1865 607 $aFrance$xHistory$yRevolution, 1789-1799$xInfluence 615 0$aDemocracy. 615 0$aRomanticism 615 0$aDemocracy 615 0$aRomanticism. 615 7$aRussia & Former Soviet Republics 615 7$aRegions & Countries - Europe 615 7$aHistory & Archaeology 676 $a321.09/4 700 $aGrudzi?ska-Gross$b Irena$01168875 801 0$bDLC 801 1$bDLC 801 2$bDLC 906 $aBOOK 912 $a9910495869703321 996 $aThe scar of revolution$92867248 997 $aUNINA 999 $aThis Record contains information from the Harvard Library Bibliographic Dataset, which is provided by the Harvard Library under its Bibliographic Dataset Use Terms and includes data made available by, among others the Library of Congress