LEADER 00839nam0-2200277 --450 001 9910689097803321 005 20230508121022.0 100 $a20230508d1936----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $aAlessandro Lamarmora$fRenato Piola Caselli 210 $aMilano$cOberdan Zucchi$dstampa 1936 215 $a410 p., [13] carte di tav.$cill.$d25 cm 517 1 $aAlessandro Lamarmora e i bersaglieri 610 0 $aEsercito italiano$aBersaglieri$aStoria 610 0 $aLa Marmora, Alessandro Ferrero : de 676 $a356.1092$v21 700 1$aPiola-Caselli,$bRenato$01353038 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910689097803321 952 $a4/I H 28$bbibl.14809$fFLFBC 959 $aFLFBC 996 $aAlessandro Lamarmora$93217666 997 $aUNINA LEADER 10905nam 22005053 450 001 9910842296503321 005 20240308080228.0 010 $a981-9997-07-0 035 $a(CKB)30597565400041 035 $a(MiAaPQ)EBC31200920 035 $a(Au-PeEL)EBL31200920 035 $a(EXLCZ)9930597565400041 100 $a20240308d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aProceedings of Fifth International Conference on Computer and Communication Technologies $eIC3T 2023, Volume 2 205 $a1st ed. 210 1$aSingapore :$cSpringer,$d2024. 210 4$d©2024. 215 $a1 online resource (422 pages) 225 1 $aLecture Notes in Networks and Systems Series ;$vv.898 311 $a981-9997-06-2 327 $aIntro -- Committees -- Preface -- Acknowledgements -- Contents -- Editors and Contributors -- Implementation of Digital Down Conversion Technique Using FPGA for Atmospheric RADAR Application -- 1 Introduction -- 2 Literature Review -- 3 Methodologies of Implementing DDC -- 3.1 Existing DDC System -- 3.2 Proposed DDC System -- 4 Analog to Digital Conversion (ADC) by Using AD6654 -- 5 Working with XILINX ISE -- 6 Experimental Setup and Simulation Results -- 7 Conclusions -- References -- Predictive Data Analysis to Support Decision-Making Based on Long-Term Impacts of Disasters -- 1 Introduction -- 2 Significance of Datasets in Disaster Management -- 3 Impacts of Disaster -- 4 Challenges of Regional Administration -- 5 Methodology -- 6 Proposed Algorithm and Implementation -- 7 Discussion -- 8 Conclusion -- References -- Generative Adversarial Networks: Challenges, Solutions, and Evaluation Metrics -- 1 Introduction -- 2 Related Works -- 3 Advantages -- 4 Challenges -- 5 GAN Training Tricks -- 6 Evaluation Metrics -- 7 Applications -- 8 Future Works -- 9 Conclusion -- References -- Improving Accuracy and Robustness in Depression Detection with Ensemble Learning and Optimization Techniques -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Problem Statement -- 3.2 Dataset -- 3.3 Text Processing -- 3.4 Optimization Techniques -- 4 Results -- 5 Conclusion and Future Scope -- References -- Spatial-Spectral Features-Based Dimensionality Reduction Technique for Robust Multivariate Image Classification -- 1 Introduction -- 2 Spatial-Spectral DR Method -- 2.1 GLCM and Fusion Matrix -- 2.2 Principal Component Analysis (PCA) for Dimensionality Reduction -- 2.3 DR Using Locally Linear Embedding (LLE) -- 2.4 Classification Using Support Vector Machine (SVM) -- 3 Experiment and Result -- 4 Conclusion -- References. 327 $aImproving Breast Cancer Prognosis with DL-Based Image Classification -- 1 Introduction -- 2 Literature Review -- 3 Dataset Overview -- 3.1 Data Collection -- 3.2 Image Resizing -- 3.3 Image Normalization -- 3.4 Image Data Augmentation -- 3.5 Image Label Encoding -- 4 Experimental Analysis and Discussion -- 4.1 Performance Analysis of the Models -- 5 Results and Discussion -- 6 Conclusion and Future Work -- References -- A Lightweight Encryption Method for Preserving E-Healthcare Data Privacy Using Dual Signature on Twisted Edwards Curves -- 1 Introduction -- 2 Preliminaries -- 2.1 Healthcare Device -- 2.2 Edge Computing Device -- 2.3 Cloud Storage -- 2.4 Dual Signature -- 3 Proposed Scheme -- 4 Correctness of Scheme -- 5 Performance Analysis and Result Comparison -- 6 Conclusion -- References -- Intrusion Classification and Detection System Using Machine Learning Models on NSL-KDD Dataset -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Linear Support Vector Machine (SVM) -- 3.2 Quadratic Support Vector Machine (SVM) -- 3.3 K-Nearest-Neighbor (KNN) -- 3.4 Linear Discriminant Analysis (LDA) -- 3.5 Quadratic Discriminant Analysis (QDA) -- 3.6 Multi-layer Perceptron (MLP) -- 3.7 Long Short-Term Memory (LSTM) -- 3.8 Auto Encoder -- 4 Implementation -- 4.1 Dataset -- 4.2 Illustrative Example -- 5 Result -- 5.1 Linear Support Vector Machine -- 5.2 Quadratic Support Vector Machine -- 5.3 K-Nearest-Neighbor -- 5.4 Linear Discriminant Analysis -- 5.5 Quadratic Discriminant Analysis -- 5.6 Multi-layer Perceptron -- 5.7 Long Short-Term Memory -- 5.8 Auto Encoder -- 5.9 Comparative Result of Models -- 6 Conclusion -- References -- A Comprehensive Review of the Application of Greenhouse Using the Internet of Things -- 1 Introduction -- 1.1 Problems Occurred in Agriculture Department -- 2 Literature Review -- 3 Observation -- 4 Techniques. 327 $a4.1 Proper Light for Photosynthesis -- 4.2 Proper Irrigation Techniques -- 4.3 Technique for Humidity and Temperature -- 5 Conclusion -- References -- Controlling PowerPoint Slide Presentations Through Hand Gestures -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Collection -- 3.2 Algorithm -- 3.3 Euclidean Distance -- 3.4 Bounding Box -- 4 Result and Discussion -- 5 Conclusion -- References -- Design of Multiband GaN HEMT Power Amplifier for Radar Applications -- 1 Introduction -- 2 Design Methodology -- 2.1 DC Analysis -- 2.2 Stability Analysis -- 2.3 Load Pull Analysis -- 2.4 Input and Output Matching Circuit -- 2.5 Layout Preparation -- 3 Results and Discussions -- 4 Conclusion -- References -- SqueezeNet-Based Model for Subject Identification from Off-Angle Iris Image -- 1 Introduction -- 2 Background Work -- 3 Methodology -- 4 Results and Discussions -- 5 Conclusion -- References -- Brain Tumor Detection and Segmentation Using Deep Learning Models with Dual Attention Mechanism -- 1 Introduction -- 2 Literature Review -- 3 Investigating Deep Models with Dual Attention Mechanism -- 3.1 Dual Attention Mechanism -- 3.2 Proposed Methodology -- 4 Experimental Results and Evaluation -- 5 Conclusions -- References -- Exploring Machine Learning Algorithms for Accurate Breast Cancer Classification: A Comparative Analysis Using F2 Metric -- 1 Introduction -- 2 Related Work -- 3 Research Methodology -- 3.1 Dataset -- 3.2 Work Flow Model -- 4 Performance Metrics -- 4.1 Confusion Matrix -- 4.2 Accuracy -- 4.3 Precision -- 4.4 Recall or Sensitivity -- 4.5 F-Measure -- 4.6 FBeta Measure -- 5 Implementation -- 5.1 Decision Tree Classifier -- 5.2 Logistic Regression -- 5.3 Support Vector Machine -- 5.4 K-Nearest Neighbors -- 5.5 Naïve Bayes -- 6 Results and Discussion -- 7 Conclusion -- References. 327 $aOptimizing Gene Expression Analysis Using Clustering Algorithms -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Prevention and Mitigation of Intrusion Using an Efficient Ensemble Classification in Fog Computing -- 1 Introduction -- 2 Related Works -- 2.1 Literature Survey -- 2.2 Challenges -- 3 Computational System Model -- 4 RSLO for IDS -- 4.1 Training Data Phase via Cloud Layer -- 4.2 End Layer and Fog Layer -- 5 Results and Discussion -- 6 Conclusion -- References -- Fake News Detector Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Flowchart -- 3.2 Tools and Technology Used -- 3.3 Python Libraries -- 3.4 Dataset and Data Preprocessing -- 3.5 Algorithm of Decision Tree -- 3.6 Algorithm of Random Forest -- 3.7 Algorithm of Naïve Bayes -- 3.8 Algorithm of Logistic Regression -- 4 Results and Discussion -- 5 Conclusion -- References -- Electric Vehicle Energy Management System Using Fuzzy Logic Controller -- 1 Introduction -- 2 Literature Survey -- 3 Existing System -- 3.1 PI Controller -- 4 Proposed System -- 4.1 Fuzzy Logic Controller -- 5 Results -- 6 Conclusion -- References -- Optimized Fall Detection Algorithm with Adaptive Sum Vector Magnitude and Axis-Weighted Features from Wearable Accelerometer Data -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Methodology -- 4 Classification -- 5 Performance Evaluation -- 5.1 Dataset Description -- 5.2 Quantitative Analysis -- 6 Conclusion and Future Work -- References -- Deep Learning-Based Real-Time Face Mask Detection for Human Using Novel YOLOv2 with Higher Accuracy -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 YOLOv2 Algorithm-Based Detection Network -- 3.2 Proposed Classification Network Architecture -- 4 Discussion and Experimental Result. 327 $a5 Conclusion -- References -- Smart University Application: Internet of Things (IoT)-Based Smart and Random Method to Collect Waste Management System in a University Campus by Using Ant Colony Optimization (ACO) Algorithm -- 1 Introduction -- 2 Ant Colony Optimization (Aco)-An Overview -- 3 Literature Survey -- 4 Ant Colony Optimization (ACO) Algorithm -- 5 Result and Discussion -- 6 . -- 7 Conclusion -- References -- Tomato Leaf Disease Detection and Classification by Using Novel CNN Model -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Dataset Description -- 3.2 Convolutional Neural Network -- 3.3 Depth-Wise Separable Convolution -- 3.4 Implemented Novel CNN Method for Detection of Tomato Leaf Diseases -- 4 Results -- 4.1 Results of the Experiments -- 4.2 Transfer Learning Models Comparison -- 5 Conclusion -- References -- Adaptive Trajectory Data Stream Clustering -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Statistical Model for JR -- 3.2 Statistical Model for BM -- 3.3 Statistical Model for SP -- 4 Experimental Results and Evaluation -- 5 Conclusions -- References -- Ransomware Classification and Detection: A Supervised Machine Learning Approach -- 1 Introduction -- 1.1 Types of Ransomware -- 1.2 Top Ransomware Targets -- 1.3 Recognition of Attacks -- 1.4 Ransomware Detection Techniques -- 1.5 Prevent Ransomware Attacks -- 2 Proposed Methodology -- 3 Implementation -- 3.1 The Effect of Features Dimension -- 3.2 The Impact of Tree and Seed Numbers -- 4 Conclusion -- References -- Comparing Ensemble Learning Algorithms to Improve Flight Prediction Accuracy and Reliability -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Experimental Framework -- 3.2 Data Balancing and Preprocessing -- 3.3 Feature Selection -- 3.4 Building Ensemble Models -- 4 Results and Discussion. 327 $a4.1 Data Balancing and Preprocessing. 410 0$aLecture Notes in Networks and Systems Series 676 $a004.6 700 $aDevi$b B. Rama$01732670 701 $aKumar$b Kishore$0639205 701 $aRaju$b M$01732671 701 $aRaju$b K. Srujan$01732672 701 $aSellathurai$b Mathini$01732673 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910842296503321 996 $aProceedings of Fifth International Conference on Computer and Communication Technologies$94147004 997 $aUNINA