LEADER 01571nam 2200361 n 450 001 996390788103316 005 20200824121739.0 035 $a(CKB)4940000000106790 035 $a(EEBO)2240884165 035 $a(UnM)99860502e 035 $a(UnM)99860502 035 $a(EXLCZ)994940000000106790 100 $a19850604d1643 uh | 101 0 $aeng 135 $aurbn||||a|bb| 200 13$aAn ordinance of the Lords and Commons assembled in Parliament$b[electronic resource] $econcerning all brewers and makers of beere, ale, cider, or perry, for payment of the excise imposed by an ordinance of Parliament, before the delivering thereof, upon paine of forfeiture of double the value of the said commodities. Die Martis 17 Octobris. 1643. Ordered by the Lords and Commons in Parliament, that this ordinance be forthwith printed and published. Jo. Browne Cler. Parliamentorum. H Elsyng. Cler. Parl. D. Com 210 $aLondon $cPrinted by Richard Cotes, and John Raworth$d1643 215 $a[2], 23-28 p 300 $aAnnotation on Thomason copy: "9 Nouemb:". 300 $aPossibly a fragment. 300 $aSignatures: D?. 300 $aReproduction of the original in the British Library. 330 $aeebo-0018 606 $aInternal revenue$zEngland$vEarly works to 1800 615 0$aInternal revenue 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996390788103316 996 $aAn ordinance of the Lords and Commons assembled in Parliament$92298865 997 $aUNISA LEADER 11106nam 22004933 450 001 9910865234703321 005 20240613080248.0 010 $a9783031622175$b(electronic bk.) 010 $z9783031622168 035 $a(MiAaPQ)EBC31462118 035 $a(Au-PeEL)EBL31462118 035 $a(CKB)32266332200041 035 $a(EXLCZ)9932266332200041 100 $a20240613d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning, Image Processing, Network Security and Data Sciences $e5th International Conference, MIND 2023, Hamirpur, India, December 21-22, 2023, Revised Selected Papers 205 $a1st ed. 210 1$aCham :$cSpringer International Publishing AG,$d2024. 210 4$d©2024. 215 $a1 online resource (372 pages) 225 1 $aCommunications in Computer and Information Science Series ;$vv.2128 311 08$aPrint version: Chauhan, Naveen Machine Learning, Image Processing, Network Security and Data Sciences Cham : Springer International Publishing AG,c2024 9783031622168 327 $aIntro -- Preface -- Organization -- Contents -- Machine Learning -- SynText - Data Augmentation Algorithm in NLP to Improve Performance of Emotion Classifiers -- 1 Introduction -- 2 Literature Review -- 2.1 Emotion Taxonomy -- 2.2 Benchmark Datasets -- 2.3 Data Augmentation -- 3 Methodology -- 4 Experimentation -- 5 Conclusion -- 6 Future Scope -- References -- Internet of Medical Things: Empowering Mobility and Health Monitoring with a Smart Walking Stick -- 1 Introduction -- 2 Related Works -- 3 Material and Design -- 4 Material and Methods -- 4.1 Fall Detection and Step Count -- 4.2 Heart Rate and SpO2 Measurement -- 4.3 Smart Home Control -- 4.4 Stopwatch -- 4.5 Weather and Helpline -- 4.6 Stress Monitoring and Wi-Fi Reset -- 5 Results and Discussion -- 6 Conclusion -- References -- MRI Based Spatio-Temporal Model for Alzheimer's Disease Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Dataset -- 3.2 Spatio-Temporal Model -- 4 Results and Discussion -- 4.1 ConvLSTM -- 4.2 ConvLSTM with Other Spatio-Temporal Model -- 4.3 ConvLSTM with State of the Art -- 5 Conclusion -- References -- Comparative Analysis of Economy-Based Multivariate Oil Price Prediction Using LSTM -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Method -- 3.1 Data Collection -- 3.2 Data Pre-processing and Exploratory Data Analysis (EDA) -- 3.3 Model Training -- 3.4 Model Training -- 4 Results and Discussion -- 5 Conclusion -- References -- Deep Learning Based EV's Charging Network Management -- 1 Introduction -- 2 Literature Review -- 2.1 EV Charging Stations -- 2.2 State of Charge (SoC) -- 3 Methodology -- 3.1 Deploy EV Charging Station -- 3.2 Optimal Path to the EV Charging Station -- 3.3 SoC Estimation -- 4 Result -- 5 Conclusions -- References -- Crop Yield Prediction Using Machine Learning Approaches -- 1 Introduction. 327 $a2 Related Works -- 3 Proposed Work -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Model Selection -- 3.4 Evaluation -- 4 Results -- 5 Conclusion -- 6 Future Work -- References -- Detection and Classification of Waste Materials Using Deep Learning Techniques -- 1 Introduction -- 2 Related Works -- 3 Proposed Model -- 3.1 Data Collection -- 3.2 Pre Processing and Augmentation -- 3.3 Evaluation Metrics -- 3.4 Waste Garbage Detection Algorithms -- 4 Result and Simulation -- 4.1 SSD MobileNet -- 4.2 EfficientDet-D0 -- 4.3 YOLOv7 and YOLOv8 -- 4.4 Comparison of Model -- 5 Conclusion -- References -- A Comparative Analysis of ML Based Approaches for Identifying AQI Level -- 1 Introduction -- 1.1 Arrangement of the Paper -- 2 Related Work -- 3 Materials and Methods -- 3.1 Accumulation of Data and Dataset Description -- 3.2 Pre-processing of Data -- 3.3 Different ML Models -- 4 Results and Discussion -- 4.1 Experimental Result Analysis and Discussion -- 5 Conclusion and Future Scope -- References -- Marker-Based Augmented Reality Application in Education Domain -- 1 Introduction -- 2 AR Approaches -- 3 Related Work -- 4 Proposed Solution -- 4.1 Development Architecture -- 5 Implementation -- 5.1 Building a Raw Mesh on the Marker Image and Marker Detection -- 5.2 Feature Extraction Using Vuforia Image Scanner -- 5.3 Implementing Virtual Buttons with C# Scripting -- 5.4 Creating 3D Models with Blender -- 6 Results -- 7 Conclusion -- References -- Hate Speech Detection Using Machine Learning and Deep Learning Techniques -- 1 Introduction -- 2 Definitions and Taxonomy -- 3 Comprehensive Review of the Literature -- 3.1 Fact-Finding Process -- 3.2 Sources -- 3.3 Study Method Criteria -- 3.4 Research Focus -- 4 Challenges in Defining and Categorizing Hate Speech and Detection with ML/DL -- 4.1 Personalization and Explanation -- 4.2 Evolving Language. 327 $a4.3 Legal and Cultural Variations -- 4.4 Subtlety and Micro-aggression -- 4.5 Data Quality and Labeling -- 4.6 Data Imbalance -- 4.7 Multilingual and Multi Modal Content -- 4.8 Evolution of Hate Speech -- 4.9 Adversarial Attacks -- 4.10 Privacy Concerns -- 4.11 Bias and Fairness -- 4.12 Real-Time Detection -- 4.13 Scalability -- 4.14 User Behavior -- 4.15 Legal and Ethical Considerations -- 4.16 Intersectionality -- 4.17 Ambiguity -- 4.18 Freedom of Speech -- 4.19 Digital Evolution -- 4.20 Diverse Expressions -- 5 Hate Speech Detection Datasets -- 6 Machine Learning-Based Approaches -- 6.1 Data Preprocessing -- 6.2 Feature Extraction -- 6.3 Classification Algorithms -- 6.4 Ensemble Methods -- 6.5 Cross-Validation -- 7 Deep Learning-Based Approaches -- 7.1 Convolutional Neural Networks (CNNs) -- 7.2 Recurrent Neural Networks (RNNs) -- 7.3 Transformers -- 8 Evaluation Metrics -- 9 Result and Discussion -- 10 Conclusion -- References -- Phishing Detection Using 1D-CNN and FF-CNN Models Based on URL of the Website -- 1 Introduction -- 2 Literature Survey -- 2.1 Whitelist-Based Techniques -- 2.2 Blacklist-Based Techniques -- 2.3 Content-Based Techniques -- 2.4 URL-Based Techniques -- 3 Proposed Work -- 3.1 1D Convolutional Neural Network (1D-CNN) -- 3.2 FeedForward-Convolutional Neural Network (FF-CNN) -- 4 Dataset and Pre-processing -- 5 Experimentation and Results -- 5.1 Performance Measures -- 5.2 Experiment 1: Comparison of Performance of Proposed 1D-CNN-based Approach on Different Datasets -- 5.3 Experiment 2: Comparison of the Performance of Proposed 1D-CNN-based Approach with PCA and Without PCA -- 5.4 Experiment 3: Comparison of Performance of the proposed FF-CNN-based Approach on Different Datasets -- 5.5 Experiment 4: Comparison of Performance of the Proposed 1D-CNN-based Approach and FF-CNN-based Approach. 327 $a5.6 Comparison Proposed 1D-CNN-based Approach and FF-CNN-based Approach with Other ML Models -- 6 Conclusion -- References -- Diabetes Prediction Using Machine Learning Classifiers -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 Results and Discussions -- 5 Conclusion -- 6 Future Work -- References -- A Deep Learning Method for Obfuscated Android Malware Detection -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Work -- 3.1 Adversarial Sample Generation -- 3.2 Autoencoder -- 3.3 LSTM Autoencoder -- 3.4 Image Based Autoencoder -- 3.5 Web Application -- 4 Results and Discussions -- 4.1 Dataset Description -- 4.2 LSTM Based Autoencoder -- 4.3 Image-Based Autoencoder -- 5 Result Comparisons -- 5.1 Non-adversarial Training -- 5.2 Adversarial Training -- 6 Conclusion -- References -- Code-Mixed Language Understanding Using BiLSTM-BERT Multi-attention Fusion Mechanism -- 1 Introduction -- 1.1 Contributions -- 2 Related Work -- 3 Proposed Model -- 3.1 Problem Definition -- 3.2 BiLSTM Attention Mechanism for Code-Mixed Intent Classification and Slot Filling -- 3.3 mBERT Code-Mixed Domain Knowledge Adaption -- 3.4 Multi-head Query Attention Mechanism -- 4 Result Analysis -- 4.1 Baseline Methods -- 5 Conclusion -- References -- The Potential of 1D-CNN for EEG Mental Attention State Detection -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Dataset Selection and Description -- 3.2 Pre-processing -- 3.3 The Application of Machine Learning Models -- 4 Results Discussion -- 5 Conclusions -- References -- Potato Leaf Disease Classification Using Deep Learning Model -- 1 Introduction -- 2 Motivation -- 3 Literature Review -- 4 Problem Statement -- 5 Methodology -- 5.1 Dataset -- 5.2 Data Splitting -- 6 Model Architecture -- 6.1 Convolutional Neural Network -- 7 Results and Discussion -- 7.1 Model Evaluation. 327 $a7.2 Model Predictions -- 8 Conclusion -- References -- Breast Cancer Detection: An Evaluation of Machine Learning, Ensemble Learning, and Deep Learning Algorithms -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Pre-processing -- 3.3 Apply Learning Algorithms -- 3.4 Evaluation Criteria -- 3.5 Training Data and Testing Data -- 4 Result and Discussion -- 4.1 Results of Machine Learning Models -- 4.2 Results of Ensemble Learning Models -- 4.3 Results of Deep Learning Model -- 5 Conclusion -- References -- Advancements in Facial Expression Recognition: A Comprehensive Analysis of Techniques -- 1 Introduction -- 2 Background -- 3 Methods for Facial Expression Recognition -- 3.1 Traditional Methods -- 3.2 Deep Learning Methods -- 3.3 Hybrid Methods -- 4 Models Used for Facial Expression Recognition -- 4.1 Model 1: ResNet-50 -- 4.2 Model 2: FERNet -- 4.3 Model 3: Attentional Convolutional Network -- 5 Performance Metrics and Evaluation -- 5.1 ResNet-50 -- 5.2 FERNet -- 5.3 Attentional Convolutional Network -- 6 Comparative Analysis -- 6.1 Model Architectures -- 6.2 Training Approaches -- 6.3 Computational Efficiency -- 6.4 Robustness and Generalization -- 7 Current Implementations -- 8 Conclusion -- 9 Future Scope -- References -- Image Processing -- Sparse Representation with Residual Learning Model for Medical Image Classification -- 1 Introduction -- 2 Related Work -- 2.1 Dictionary Learning -- 2.2 ResNet -- 3 The Proposed Method -- 3.1 Dictionary Learning and Sparse Representation -- 3.2 Residual-CNN Network Features -- 3.3 Dimensionality Reduction with PCA -- 3.4 Deep Neural Network (DNN) for Classification -- 4 Experimental Results -- 4.1 Description of Datasets -- 4.2 System Implementation -- 4.3 Results and Analysis -- 5 Ablation Study -- 6 Conclusion -- References. 327 $aCOVID-19 Detection from Chest X-Ray Images Using GBM with Comparative Analysis. 410 0$aCommunications in Computer and Information Science Series 700 $aChauhan$b Naveen$01743055 701 $aYadav$b Divakar$01743056 701 $aVerma$b Gyanendra K$01741629 701 $aSoni$b Badal$01427340 701 $aLara$b Jorge Morato$01743057 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910865234703321 996 $aMachine Learning, Image Processing, Network Security and Data Sciences$94169772 997 $aUNINA