Intelligent Systems Design and Applications : Deep Learning, Volume 2

• Autore: Abraham Ajith
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (514 pages)
• Altri autori: BajajAnu HanneThomas HongTzung-Pei
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Deep Learning Approach for Autonomous Spacecraft Landing -- 1 Introduction -- 2 Related Work -- 3 Simulation Details -- 4 Training a Deep Neural Network -- 5 Results -- 6 Conclusion and Future Scope -- References -- Deep Learning Approach for Flood Mapping Using Satellite Images Dataset -- 1 Introduction -- 2 Related Works -- 3 Proposed Algorithm -- 4 Data Analysis and Results -- 4.1 Sample Output -- 4.2 Performance Analysis -- 4.3 Performance Graph -- 5 Conclusion -- References -- Large Language Models for Named Entity Recognition (NER) of Skills in Job Postings in German -- 1 Introduction -- 2 Problem Description -- 2.1 Problem -- 2.2 Optimization Methods -- 2.3 Implementation and Comparison of Different Optimization Methods -- 3 Models -- 4 Evaluation Methods -- 5 Standard NER Functionality -- 6 Context Model -- 7 Few-Shot Learning Model -- 8 Cost Considerations -- 9 Conclusions -- References -- Machine Learning Approaches for Investing Strategies in Stock Market -- 1 Introduction -- 2 Literature Review -- 3 System Architecture: Enhancing Investing Strategies with Ml -- 3.1 Architecture and System Architecture for Machine Learning Approaches in Investing Strategies for Stock Market -- 3.2 Data Preprocessing -- 3.3 Model Training -- 3.4 Model Evaluation -- 3.5 Model Deployment -- 3.6 System Architecture -- 3.7 Flowchart -- 4 Description of the Experiment -- 4.1 Model Training -- 4.2 DNN Model Training -- 4.3 Evaluation -- 5 Strategic Stock ML Implementation -- 6 Results and Decision Making: Leveraging Machine Learning for Stock Market Investing -- 6.1 Better Prediction -- 6.2 Entry/Exit Points -- 6.3 Data-Driven Decisions -- 6.4 Portfolio Performance -- 6.5 Decision Making -- 7 Comparison of Machine Learning Techniques for Investing Strategies in the Stock Market -- 7.1 Math Foundations. ; 7.2 Predictive Skills -- 7.3 Graphical Representations: -- 8 Conclusion: Embracing the Future of Stock Market Investing with Machine Learning -- 8.1 Data-Driven Decision Making -- 8.2 Future Scope -- References -- OP-FedELM: One-Pass Privacy-Preserving Federated Classification via Evolving Clustering Method and Extreme Learning Machine Hybrid -- 1 Introduction -- 2 Literature Survey -- 3 Preliminaries -- 3.1 Evolving Clustering Machine -- 3.2 Extreme Learning Machine -- 4 Proposed Methodology -- 4.1 Phase I: Generation of the Perturbed Dataset -- 4.2 PHASE II: OP-FedELM -- 5 Dataset Description -- 6 Results and Discussions -- 6.1 Computational Analysis -- 7 Conclusions and Future Directions -- References -- Gamma Corrected Pyramid Pix2pix - Breast Cancer HE to IHC Image Generation -- 1 Introduction -- 2 Existing Solutions and Objective of Present Study -- 3 Methods and Materials -- 3.1 The Programming Environment -- 4 Proposed Methodology -- 5 Results and Discussions -- 5.1 Projection Profile Comparison -- 5.2 Qualitative Validation -- 6 Conclusions and Future Scope -- References -- Unveiling Deepfakes: Convolutional Neural Networks for Detection -- 1 Introduction -- 2 Related Work -- 3 Dataset -- 4 Proposed Method -- 5 Results and Discussion -- 6 Conclusion -- References -- The Nasdaq Composite Index Prediction Using LSTM and Bi-LSTM Multivariate Deep Learning Approaches -- 1 Introduction -- 2 Related Studies -- 3 Methodology -- 4 Results -- 5 Conclusion -- References -- PlastOcean: Detecting Floating Marine Macro Litter (FMML) Using Deep Learning Models -- 1 Introduction -- 1.1 Effect of FMML on CO2 Cycle -- 1.2 Effect of FMML on Aquatic Life -- 1.3 Estimation of Plastic Production -- 1.4 Non-AI-Based Estimation Methodologies of FMML Identification -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Constructing Datasets. ; 3.2 Enhancing PlastOcean Dataset -- 3.3 Building Deep Neural Network for Object Detection -- 4 Results -- 5 Discussion -- 5.1 Previous Results by the Ocean Cleanup -- 5.2 Improving the Dataset -- 6 Conclusion and Future Work -- References -- Data Augmentation Using Generative Neural Networks Based on Fourier Feature Mapping -- 1 Introduction -- 2 Related Work -- 2.1 Data-Level Approaches -- 2.2 Data-Level Approaches -- 2.3 Cost-Sensitive Learning -- 3 Proposed Approach -- 3.1 Basic Concept -- 3.2 Approach Details -- 4 Experiment -- 4.1 Experimental Data and Environment -- 4.2 Experimental Settings -- 4.3 Experimental Results -- 5 Conclusions -- References -- Delay Risk Detection in Road Construction Projects Utilizing Large Language Model -- 1 Introduction -- 2 Related Work -- 2.1 Delay Risk Identification in Construction Projects -- 2.2 Text Mining in Construction Project Management -- 3 Road Construction Delay Risk Detection System -- 4 Experiment and Results -- 4.1 Experiment Setup -- 4.2 Results -- 5 Conclusion -- References -- Unlocking the Potential of Novel LSTM in Airline Recommendation Prediction -- 1 Introduction -- 2 Literature Review -- 2.1 Customer Recommendations -- 2.2 Online Reviews -- 3 Proposed Methodology -- 3.1 Dataset Description -- 3.2 Data Pre-processing -- 3.3 Feature Selection -- 3.4 Machine Learning Methodologies -- 3.5 Model Architecture -- 4 Result Analysis -- 4.1 Training -- 4.2 Testing -- 5 Conclusion -- References -- Pylung: A Supporting Tool for Comparative Study of ViT and CNN-Based Models Used for Lung Nodules Classification -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Dataset -- 3.2 Preparing the Dataset -- 3.3 Models -- 3.4 Pylung Tool -- 4 Results -- 5 Discussion -- 6 Conclusion -- References. ; Deep Learning Model for Predicting Rice Plant Disease Identification and Classification for Improving the Yield -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Description -- 2.2 Types and Number of Diseases Represented -- 2.3 Description of Neural Architecture Search (NAS) -- 3 Proposed Methodology -- 4 Results and Discussion -- 4.1 Performance of Proposed Method -- 4.2 Comparison with Other Disease Detection Methods -- 5 Conclusion -- References -- Deep Learning-Based Active Fire Detection Using Satellite Imagery -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Data Source -- 2.3 Approach -- 3 Results -- 4 Conclusion -- References -- Evaluating Time Series Classification with GAN-Generated Synthetic Data -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experimental Results -- 5 Conclusion and Future Work -- References -- Word2Vec-GloVe-BERT Embeddings for Query Expansion -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Word2Vec Term Embeddings Creation and Selection -- 3.2 GloVe Term Embeddings Creation and Selection -- 3.3 BERT Term Embeddings Creation and Selection -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusion -- References -- A BERT Based Architecture for Detecting Arabic Fake News -- 1 Introduction -- 2 Related Work -- 2.1 Classical Machine Learning Approaches -- 2.2 Deep Learning Approaches -- 2.3 Bidirectional Encoder Representations from Transformer (BERT) Approaches -- 3 Dataset Details -- 4 Proposed Approach -- 4.1 Bidirectional Encoder Representations from Transformers (BERT) -- 4.2 AraBERT -- 4.3 AraBERT V2.0 -- 4.4 Other Models -- 5 Experiments and Results -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Conclusion -- References -- Deep Learning-Based Approaches for Facial Recognition Technology Through Convolutional Neural Networks -- 1 Introduction. ; 2 Background Details and Related Work -- 3 Proposed Approach -- 4 Implementation of the System -- 5 Conclusions -- References -- Cognizant Prognostication: An In-Depth Comparative Study of Machine Learning Models for Predictive Employee Turnover Analysis in the Realm of Human Resources Analytics -- 1 Introduction -- 2 Literature Review -- 3 Research Methodology -- 3.1 Data Pre-processing -- 3.2 Feature Engineering -- 3.3 Dataset Split and Model Selection -- 4 Model Training and Evaluation -- 4.1 Model Training -- 4.2 Model Evaluation -- 4.3 Results and Comparison -- 5 Discussion -- 6 Results -- 6.1 Model Training -- 6.2 Model Evaluation -- 7 Comparison Table -- 8 Roc Curves -- 9 Discussion -- 10 Conclusion -- References -- Enhancing Road Infrastructure Maintenance Using Deep Learning Approach -- 1 Introduction -- 2 Related Works -- 3 Model Introduction and Improvement -- 4 Dataset -- 5 Design and Implementation -- 5.1 Implementation -- 5.2 Image Dataset -- 5.3 Dataset Preparation -- 5.4 Model Training -- 6 Experimental Results and Discussion -- 6.1 YOLOV8 with Crack Class -- 6.2 YOLOV8 with Pothole Class -- 6.3 YOLOV8 with Two Classes -- 6.4 Visual Analysis -- 7 Conclusion and Future Works -- References -- E-Learning Facial Emotion Recognition Using Deep Learning Models -- 1 Introduction -- 2 Related Work -- 2.1 Approaches to Emotion Recognition (ER) -- 2.2 E-Learnig Emotion Recongition -- 3 Data Collection and Preprocessing -- 3.1 Data Collection -- 3.2 Data Preprocessing -- 4 Methodology -- 4.1 Face Detection -- 4.2 Emotion Recognition (ER) -- 5 Dataset Preparation and Results -- 5.1 Data-Set Preparation -- 5.2 Results -- 6 Comparaison with Others Works -- 7 Conclusion -- References -- Music Recommender Based on the Facial Emotion of the User Identified Using YOLOV8 -- 1 Introduction -- 2 Literature Review -- 3 Proposed Model. ; 3.1 Dataset Description and Pre-processing.
• Titolo autorizzato: Intelligent Systems Design and Applications
• ISBN: 3-031-64836-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878044703321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Networks and Systems Series