Kooperativer Bibliotheksverbund

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Format: 1 online resource (651 pages)

Edition: 1st ed.

ISBN: 981-9719-23-2

Series Statement: Lecture Notes in Networks and Systems Series ; v.961

Note: Intro -- Organization -- Preface -- Contents -- Editors and Contributors -- Soft Computing and Computational Intelligence -- Deep Learning Approach to Compose Short Stories Based on Online Hospital Reviews of Tirunelveli Region -- 1 Introduction and Significance -- 2 CVAE Technique for Story Creation -- 3 SSHR Model Approaches -- 4 Evaluation Analysis of SSHR -- 5 Conclusion and Discussion -- References -- Music Emotion Recognition for Intelligent and Efficient Recommendation Systems -- 1 Introduction -- 2 Related Works -- 3 Traditional and Proposed Approach -- 3.1 Recommendation System -- 3.2 Proposed Work -- 4 Methods -- 4.1 Face Recognition and Image Processing -- 4.2 OpenCV Software -- 4.3 Fisherface Classifier -- 5 Results and Discussion -- 5.1 Measurement of Precision, F1-Score, and Recall -- 6 Conclusion -- References -- BScFilter: A Deep Learning Approach for Sports Comments Filtering in a Resource Constraint Language -- 1 Introduction -- 2 Related Work -- 3 Dataset Development -- 4 Methodology -- 5 Results and Analysis -- 6 Conclusion and Future Work -- References -- HABE Secure Access at Cloud-Healthcare Database -- 1 Introduction -- 2 State of the Art -- 3 Problem Formulation -- 4 ABE Framework Overview -- 5 Proposed Mechanism -- 6 Performance Analysis -- 7 Conclusion and Future Work -- References -- Comparative Analysis of Nature-Inspired Algorithms for Task Assignment Problem -- 1 Introduction -- 1.1 Jaya Optimization Algorithm (JOA) -- 1.2 Ant Colony Optimization (ACO) -- 1.3 Genetic Algorithm (GA) -- 1.4 Differential Evolution Algorithm (DE) -- 2 Problem Statement -- 3 Simulation Analysis and Discussion -- 3.1 Experimental Setup -- 3.2 Experimental Outcomes -- 3.3 Discussion of Results -- 4 Conclusion -- References. , A Novel Approach for Object Recognition in Hazy Scenes: Integrating YOLOv7 Architecture with Boundary-Constrained Dehazing -- 1 Introduction -- 2 Literature Review -- 3 Framework -- 4 Implementation and Result -- 5 Conclusion -- References -- Dynamic Multihead Attention for Enhancing Neural Machine Translation Performance -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Results -- 5 Conclusion and Future Scope -- References -- Scratch Vision Transformer Model for Diagnosis Grape Leaf Disease -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Experiment and Analysis -- 4.1 Experimenting with Scratch Vision Transformer -- 4.2 Confusion Matrix -- 4.3 Training Loss and Validation Loss -- 5 Results and Discussion -- 6 Conclusion -- References -- Multilevel Association Mining with Particle Swarm Optimization: A Comprehensive Approach for High-Utility Itemset Discovery -- 1 Introduction -- 2 Related Works -- 3 Methods -- 4 Results and Discussion -- 5 Conclusion -- References -- Attention-Based Neural Machine Translation for Multilingual Communication -- 1 Introduction -- 2 Related Works -- 2.1 Per-language Encoder-Decoder -- 3 Methodology -- 4 Results -- 5 Conclusion -- References -- Use of Deep Learning to Handle Early-Stage Business Data -- 1 Introduction -- 1.1 Background and Motivation -- 1.2 Objective -- 1.3 Scope of Study -- 2 Literature Survey -- 3 Research Methodology -- 3.1 Data Collection and Preprocessing -- 3.2 Feature Extraction and Representation -- 4 Early-Stage Business Data Analysis -- 4.1 Data Characteristics and Challenges -- 4.2 Deep Learning Approaches for Early-Stage Data -- 4.3 Predictive Analysis and Forecasting -- 4.4 Anomaly Detection and Risk Management -- 5 Handling Early-Stage Business Data: Implementation Using Python -- 5.1 Exploring the Dataset and Identifying Predictors -- 5.2 Preprocessing of Data. , 5.3 Balancing the Data -- 5.4 Loading of Pre-processed Data -- 5.5 Learning and Interpreting the Result by Creating a Model -- 5.6 Setting an Early Stopping Mechanism -- 5.7 Testing the Model -- 6 Analysis and Limitations -- 7 Conclusion -- References -- An Efficient Text-Based Document Categorization with k-Means and Cuckoo Search Optimization -- 1 Introduction -- 2 Related Works -- 3 Methods -- 4 Results and Discussion -- 5 Conclusion -- References -- Potato Leaf Disease Detection and Classification Using Deep Learning -- 1 Introduction -- 2 Literature Review -- 3 Dataset Overview -- 4 Proposed Methodology -- 4.1 Image Resizing -- 4.2 Image Augmentation -- 4.3 Histogram Equalization -- 4.4 CLAHE -- 5 Experimental Set-Up -- 5.1 ResNet50 -- 5.2 VGG16 -- 5.3 VGG19 -- 5.4 MobileNet -- 6 Result and Discussion -- 7 Conclusion -- 8 Future Scope -- References -- Disease Detection -- Parkinson's Disease Detection Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Implemented Approach -- 3.1 About Dataset -- 3.2 Data Preprocessing -- 3.3 Algorithms Used for Classification -- 3.4 Metrics Used to Access the Performance of  Implementation -- 4 Result and Discussion -- 5 Conclusion and Future Scope -- References -- Comparative Analysis of ML Models for Brain Tumor Detection -- 1 Introduction -- 2 Related Work -- 3 Dataset Description -- 4 Methodology -- 4.1 Model Selection -- 4.2 Model Creation Using Orange -- 5 Result and Discussion -- 6 Conclusion -- References -- Machine Learning-Based Prediction of COVID-19: A Robust Approach for Early Diagnosis and Treatment -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Gathering -- 3.2 Dataset -- 3.3 Data Preparation -- 3.4 Data Normalization -- 3.5 Data Splitting -- 3.6 The Application of Algorithms -- 3.7 Model Assessment -- 3.8 Choosing the Best Algorithm -- 4 Result Analysis. , 4.1 Accuracy -- 4.2 Jaccard Score -- 4.3 Cross-Validated Score -- 5 Conclusion -- References -- The Prediction of Diabetes Using Machine Learning in the Healthcare System -- 1 Introduction -- 2 Literature Study -- 3 Methodology -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 3.3 Algorithm -- 4 Simulation Result -- 5 Conclusion and Future Scope -- References -- A Fuzzy-Based Vision Transformer Model for Tea Leaf Disease Detection -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Splitting -- 4 Experimental Result -- 4.1 Accuracy -- 4.2 Precision -- 4.3 Recall -- 4.4 Analysis of Model Accuracy and Training Times -- 4.5 Experimenting with Vision Transformer -- 4.6 Confusion Matrix -- 4.7 Training Loss and Validation Loss -- 5 Contribution -- 6 Conclusion -- 7 Limitations and Future Works -- References -- Towards Federated-Deep Learning-Based Glaucoma Detection from Color Fundus Images -- 1 Introduction -- 2 Literature Review -- 3 CNN and Federated Learning -- 3.1 Convolution Neural Network -- 3.2 Federated Learning -- 3.3 Used Dataset -- 3.4 Data Pre-processing -- 3.5 Federated Learning Technique -- 3.6 AlexNet Architecture -- 4 Proposed Architecture -- 5 Performance Analysis -- 6 Limitations and Future Scopes -- 7 Conclusions -- References -- Inventing the Potential of a High-Frequency EEG, Namely Dodecanogram (DDG): Human Subjects' Study -- 1 Introduction -- 2 Methods -- 2.1 Construction of DDG -- 3 Discussion -- 3.1 Triplet-of-Triplet Electromagnetic Radiation Mapping from the Whole Human Body Using Dodecanogram, DDG -- 3.2 Human Subjects Study Using DDG Technology -- 4 Conclusion -- References -- Real-Time Prediction of Diabetes Complications Using Regression-Based Machine Learning Models -- 1 Introduction -- 2 Literature Review -- 3 Proposed Method -- 3.1 Methods for Measuring Blood Glucose Levels. , 3.2 Data Preprocessing -- 3.3 Machine Learning Classifiers -- 4 Performance Evaluation -- 5 Result Analysis -- 6 Conclusion -- References -- Network and Cyber Security -- Exploring Open Access Cybersecurity Datasets for Machine Learning-Based Cyberattack Detection -- 1 Introduction -- 2 Literature Review -- 3 Basic Security Concepts -- 4 Cybersecurity Datasets -- 4.1 CICID2017 -- 4.2 VIRUS-MNIST: A BENCHMARK MALWARE -- 4.3 Dumpware10: A Dataset for Memory Dump Based Malware Image Recognition -- 4.4 CCCS-CIC-AndMal2020 -- 4.5 Malimg Malware Images -- 4.6 KDD Cup'99 -- 4.7 NSL-KDD -- 4.8 DARPA -- 4.9 UNSW-NB15 -- 4.10 IoT-23 -- 4.11 CTU-13 -- 5 Types of Attack in Cyberspace -- 5.1 Cross-Site Scripting -- 5.2 SQL Injection Attacks -- 5.3 Broken Authentication -- 5.4 Drive-By Download -- 5.5 Password-Based Attacks -- 5.6 Fuzzing -- 5.7 Using Components with Known Vulnerabilities -- 5.8 Distributed Denial-of-Service (DDoS) -- 5.9 Man-in-the-Middle (MiTM) -- 5.10 Directory Traversal -- 6 Machine Learning Algorithms -- 6.1 Random Forest -- 6.2 ECOC-SVM -- 6.3 SVM -- 6.4 KNN -- 6.5 Logistic Regression -- 6.6 CatBoost -- 6.7 MDRL-SLSTM -- 6.8 RC-NN -- 6.9 Voting Extreme Learning Machine (V-ELM) -- 6.10 ExtraTrees Classifier -- 7 Limitations and Future Work -- 8 Conclusion -- References -- Design and Development of a Reliable Blockchain-Based Pension System -- 1 Introduction -- 2 Blockchain -- 3 Methodology Adopted to Implement a Blockchain-Secured Pension System -- 4 Implementation of Blockchain-Secured Pension System -- 5 Results and Discussion -- 6 Conclusion and Future Scope -- References -- Comparative Analysis of CatBoost Against Machine Learning Algorithms for Classification of Altered NSL-KDD -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Preprocessing -- 3.2 Classification and Evaluation. , 3.3 Evaluation Metrics and Model Comparison.

Additional Edition: ISBN 981-9719-22-4

Language: English