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Record Nr. |
UNINA9910865235703321 |
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Autore |
Sharma Harish |
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Titolo |
Communication and Intelligent Systems : Proceedings of ICCIS 2023, Volume 3 |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore Pte. Limited, , 2024 |
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©2024 |
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ISBN |
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9789819720828 |
9789819720811 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (486 pages) |
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Collana |
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Lecture Notes in Networks and Systems Series ; ; v.969 |
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Altri autori (Persone) |
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ShrivastavaVivek |
TripathiAshish Kumar |
WangLipo |
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Disciplina |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Preface -- Contents -- Editors and Contributors -- Deep Learning-Based Attack Prediction for Returns in Supply Chain Management Systems -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Architecture and Statistics -- 3.1 Dataset -- 3.2 Data Prepossessing -- 3.3 Identification of Attributes Using the Recommended PCA-BSO -- 3.4 A Distributed Denial-of-Service Categorization Employing Deep Learning and Machine Learning Strategies -- 4 Result and Discussion -- 4.1 Evaluation Matrices -- 4.2 Examination Utilizing Chosen Features Algorithms -- 4.3 Analysis in the Means of Supervised Algorithm -- 4.4 Analysis in the Means of DL Algorithms -- 5 Conclusion -- References -- An Effective Framework for Early Detection and Classification of Cardiovascular Disease (CVD) Using Machine Learning Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Problem Statement -- 3.2 Proposed Methodology -- 4 Experimental Results and Analysis -- 4.1 Dataset Description -- 4.2 Exploratory Data Analysis -- 4.3 Performance Measures -- 4.4 Simulation Results of the Proposed GB Model -- 4.5 Simulation Results of the Proposed AB Model -- 4.6 Simulation Results of the Proposed MLP Model -- 5 Discussion -- 6 Conclusion and Future |
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Work -- References -- A Coherent Ensemble Modeling Approach for Diabetic Retinopathy Using MIFNET Method -- 1 Introduction -- 2 Literature Survey -- 3 Research Methodology -- 3.1 Image Enhancement -- 3.2 Gray-Level Thresholding -- 3.3 Gradient-Based Segmentation -- 3.4 Boundary Tracking -- 3.5 Laplacian Edge Detection -- 3.6 Region Growing -- 3.7 Wavelet Transforms -- 3.8 Feature Extraction -- 3.9 Image Processing -- 3.10 Convolutional Neural Networks -- 4 Conclusion and Result -- References -- A Novel Approach for Agricultural Crop Classification with Incremental Learning -- 1 Introduction -- 2 Literature Review. |
3 Methodology -- 3.1 Dataset and Pre-processing Techniques -- 3.2 Model Development -- 4 Results Assessment -- 5 Conclusion and Further Improvements -- References -- Coronary Vessel Segmentation in X-ray Using U-Net -- 1 Introduction -- 2 Literature Review -- 3 Dataset and Methodology -- 3.1 U-Net Architecture -- 4 Results and Discussion -- 5 Conclusion -- References -- Unveiling Clarity: A Survey on Haze Removal Techniques Using Deep Learning Approaches -- 1 Introduction -- 2 Technical Background -- 3 Literature Review -- 3.1 Artificial Neural Network (ANN) -- 3.2 Convolutional Neural Network (CNN) -- 3.3 Recurrent Neural Network (RNN) -- 3.4 Autoencoder -- 3.5 Generative Adversarial Network (GAN) -- 4 Datasets -- 5 Assessment Criteria -- 5.1 Mean Squared Error (MSE) -- 5.2 Peak Signal-to-Noise Ratio (PSNR) -- 5.3 Structural SIMilarity (SSIM) -- 6 Approach Analysis -- 7 Conclusion -- References -- Surveying Strategies: Detecting Sinkhole Attacks in IoT Networks -- 1 Introduction -- 2 Background Details -- 2.1 IoT Architecture -- 2.2 Network Connecting Technologies -- 2.3 Sinkhole Attack -- 3 Sinkhole Attack Detection Strategy -- 3.1 Anomaly Based Sinkhole Detection -- 3.2 Signature-Based Sinkhole Detection -- 3.3 Specification-Based Sinkhole Detection -- 3.4 Hybrid-Based Sinkhole Detection -- 3.5 Trust-Based Sinkhole Detection -- 3.6 Machine Learning-Based Sinkhole Detection -- 3.7 Deep Learning-Based Sinkhole Detection -- 3.8 Ensemble Learning-Based Sinkhole Detection -- 3.9 Blockchain-Based Sinkhole Detection -- 4 Prevention Strategies -- 4.1 Bypassing Sinkhole Attack -- 4.2 Mitigate Sinkhole Attack -- 4.3 Isolate Sinkhole Attack -- 4.4 Blacklist Sinkhole Attack -- 5 Performance Metrics -- 5.1 Packet Delivery Ratio (PDR) -- 5.2 True Positive Rate (TPR) -- 5.3 False Positive Rate (FPR) -- 5.4 False Negative Rate (FNR) -- 5.5 Accuracy. |
5.6 Precision -- 5.7 F1 Score -- 5.8 End-to-End Delay -- 5.9 Energy Consumption -- 5.10 Throughput -- 6 Comparative Analysis of Different Detection Strategies -- 7 Conclusion -- References -- Pattern of Lung Injury in CT/HRCT Using Deep Learning Techniques -- 1 Introduction -- 2 Literature Review -- 3 Dataset and Methodology -- 3.1 U-Net Architecture -- 3.2 MultiResUNet Architecture -- 4 Results and Discussion -- 5 Conclusion -- References -- Facial Expression Recognition in ATM Surveillance System -- 1 Introduction -- 2 Literature Survey -- 3 System Overview -- 3.1 Architecture of the Proposed System -- 3.2 Feature Extraction Using Local Binary Pattern (LBP) -- 3.3 Support Vector Machine (SVM) Classifier -- 4 Results and Discussion -- 4.1 Experimental Results of Facial Expression Inside ATM Cabin -- 5 Conclusion -- References -- Challenges, Communications, Routing Protocols and Applications of FANETs-A Systematic Review -- 1 Introduction -- 1.1 MANET -- 1.2 VANET -- 1.3 FANET -- 1.4 SANET -- 2 Communication Protocols -- 3 Routing Protocols -- 4 Challenges -- 5 Applications -- 6 Conclusion -- References -- Prediction of Chronic Venous Insufficiency Condition Using Infrared Thermography and Machine Learning -- 1 Introduction -- 1.1 Chronic Venous Insufficiency -- 1.2 Advancements in CVI |
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Detection -- 2 Literature Review -- 3 Materials and Methods -- 3.1 Participants -- 3.2 Experimental Protocol -- 3.3 Implementation of Machine Learning Algorithms -- 3.4 Proposed Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- A Review of Brain Tumor MRI Classification and Feature Extraction Using Varying Methods -- 1 Introduction -- 1.1 Brain Tumor -- 1.2 Automatic Brain Tumor Identification System -- 2 Related Work -- 3 Analysis and Discussion -- 4 Conclusion -- References. |
Time Domain Specifications of Step Responses of Both Underdamped and Overdamped Systems: In Correction to MATLAB Inbuilt 'Stepinfo' Function -- 1 Introduction -- 2 Literature Review -- 3 Theoretical Background -- 3.1 Time Domain Specifications Formulas -- 4 Proposed Algorithm -- 5 Results and Discussion -- 5.1 Underdamped Response with No Steady State Error -- 5.2 Overdamped Response -- 5.3 Underdamped Response with Steady State Error -- 6 Conclusion -- References -- Transfer Learning-Based Speed Limit Traffic Sign Recognition with Multilingual Audio Alerts -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Dataset Description -- 3.2 Preprocessing -- 3.3 Algorithms and Techniques Used -- 3.4 Generation of Multilingual Audio Alerts -- 4 Results and Discussions -- 4.1 Evaluation Metrics -- 4.2 Performance Comparison -- 4.3 Confusion Matrix -- 4.4 Limitations -- 5 Conclusions and Future Work -- References -- Impact of the Use of Social Media Among University Students Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Proposed System -- 4 Dataset Description -- 4.1 Age -- 4.2 Number of Platform -- 4.3 Spend Time (Social Media vs Physical Activities) -- 4.4 Exposed to Inappropriate Content (1-10) -- 4.5 Victims of Cyber Crimes -- 4.6 Measurement Effect -- 5 Data Preprocessing -- 6 Used Model -- 7 Training and Evaluating Model -- 7.1 Cross Validation -- 7.2 Classification Evaluation -- 8 Result Analysis -- 8.1 Performance -- 8.2 ROC Curve -- 8.3 Confusion Matrix -- 8.4 Prediction -- 9 Future Scope and Conclusion -- References -- A Study on DL for Pulmonary Embolism Prediction Harnessing Multimodal Data -- 1 Introduction -- 2 Literature Review -- 3 Proposed Work -- 3.1 Input Data -- 3.2 Feature Fusion -- 3.3 DL Models -- 3.4 Softmax Classifier -- 4 Experimental Setup -- 4.1 Dataset Description -- 4.2 Performance Metrics. |
5 Results and Discussions -- 6 Conclusion and Future Works -- References -- Performance Analysis of Machine Learning Algorithms Using Information Theoretic Class Based Multi-correlation Dependent Feature Selection -- 1 Introduction -- 1.1 Organization of the Paper -- 2 Literature Survey -- 3 Theoretical Background -- 3.1 Information Measuring Metrics -- 3.2 Feature Correlation Metrics -- 4 Proposed Method -- 5 Data Pre-processing -- 5.1 Dataset Details -- 5.2 Missing Values Removal -- 5.3 Imbalanced Dataset Handling -- 5.4 Feature Selection -- 6 Result Analysis -- 6.1 Without Feature Selection (FS) -- 6.2 With Proposed Feature Selection (FS) Technique -- 7 Conclusion -- References -- Empirical Analysis of Classification Approaches for Indian Language Processing -- 1 Introduction -- 1.1 Background: Linguistic Diversity in India -- 1.2 Objectives -- 2 Optical Character Recognition Workflow -- 2.1 Classification -- 2.2 Post Processing -- 3 Related Work on Indian Language -- 3.1 Devnagari Hindi, Marathi, Gurmukhi, and Gujarati -- 3.2 Telugu -- 3.3 Bangla -- 3.4 Kannada -- 4 Performance Evaluation of Classifiers with K-fold Validation for Indic Language -- 5 Limitations -- 6 Conclusion -- References -- An Overview of Generative Models for EEG Signal Processing -- 1 Introduction -- 2 Previous Works -- 2.1 Machine Learning -- 2.2 Deep Learning -- 2.3 Generative Models -- 3 Normalizing Flows -- 3.1 |
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Change of Variable Formula -- 3.2 Jacobian Matrix -- 3.3 Types of Flows -- 3.4 Popular Normalizing Flow Architectures -- 3.5 Applications of Normalizing Flows -- 4 Scope of Flow-Based Models for EEG Signal Processing -- 5 Key Findings and Conclusion -- References -- Anomalous EEG Signal Time Series Classification Using Modified Metaheuristic Optimized RNN -- 1 Introduction -- 2 Related Works -- 2.1 Recurrent Neural Networks -- 2.2 Metaheuristic Optimization. |
3 Methods. |
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