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Advances in Data Science and Intelligent Data Communication Technologies for COVID-19 : Innovative Solutions Against COVID-19
Advances in Data Science and Intelligent Data Communication Technologies for COVID-19 : Innovative Solutions Against COVID-19
Autore Hassanien Aboul Ella
Pubbl/distr/stampa Cham : , : Springer International Publishing AG, , 2021
Descrizione fisica 1 online resource (311 pages)
Altri autori (Persone) ElghamrawySally M
ZelinkaIvan
Collana Studies in Systems, Decision and Control Ser.
Soggetto genere / forma Electronic books.
ISBN 3-030-77302-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Altri titoli varianti Advances in Data Science and Intelligent Data Communication Technologies for COVID-19
Record Nr. UNINA-9910497098303321
Hassanien Aboul Ella  
Cham : , : Springer International Publishing AG, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial Intelligence for Environmental Sustainability and Green Initiatives / / edited by Aboul Ella Hassanien, Ashraf Darwish, Sally M. Elghamrawy
Artificial Intelligence for Environmental Sustainability and Green Initiatives / / edited by Aboul Ella Hassanien, Ashraf Darwish, Sally M. Elghamrawy
Autore Hassanien Aboul Ella
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (361 pages)
Disciplina 620
Altri autori (Persone) DarwishAshraf
ElghamrawySally M
Collana Studies in Systems, Decision and Control
Soggetto topico Engineering mathematics
Engineering - Data processing
Chemical engineering
Environmental engineering
Artificial intelligence
Mathematical and Computational Engineering Applications
Environmental Process Engineering
Artificial Intelligence
ISBN 3-031-63451-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Artificial Intelligence for Environmental Sustainability -- Recognizing Aluminum Beverage Cans from Waste Mixtures Based on Densenet121-CNN Model: Deep Learning Methodology -- 1 Introduction -- 2 Related Work -- 3 Aluminum Beverage Cans Recognition Model -- 4 Model Evaluation and Experimental Results -- 5 Discussion -- 6 Conclusion -- References -- Machine Learning-Based Forecasting of Electricity Demand for Sustainable Electricity Planning -- 1 Introduction -- 2 Methods and Background -- 2.1 Support Vector Regression -- 2.2 Random Forest -- 2.3 EXtreme Gradient Boosting -- 2.4 Autoregressive Integrated Moving Average -- 3 Dataset Description -- 4 The Proposed Electricity Demand Forecasting Model -- 4.1 Data Pre-processing Phase -- 4.2 Time Series Forecasting Phase -- 5 Experimental Results and Discussion -- 6 Conclusions and Future Work -- References -- Using Artificial Intelligence Techniques in Water Quality Analysis and Prediction: Towards Sustainability -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Phase 1: Data Collection and Characteristics -- 3.2 Phase 2: Data Preprocessing -- 3.3 Phase 3: Predicting the Potability of Water Using Machine Learning Classification Methods -- 4 Experimental Study -- 4.1 Experimental Results -- 5 Conclusion and Future Work -- References -- Flowers Classification with Low Carbon Footprint Using Deep Learning Pretrained Models -- 1 Introduction -- 2 Related Work -- 3 The Proposed Model -- 3.1 Training Phase -- 3.2 Testing Phase -- 4 Dataset Characteristics and Pre-processing -- 5 Experiments and Results -- 6 Conclusion and Future Work -- References -- Measuring Global Warming Effect by the Prediction of Climate Change on the Different Countries Using Machine Learning Approaches -- 1 Introduction -- 2 Background -- 2.1 Support Vector Machine.
2.2 Artificial Neural Network -- 2.3 Decision Trees -- 2.4 Ensemble Methods -- 3 Climate Change Prediction System Description -- 3.1 Preprocessing -- 3.2 Model Selection -- 3.3 Step Wise Linear Regression -- 3.4 Optimization -- 4 Experimental Results -- 4.1 Evaluation Metrics -- 5 Conclusion and Future work -- References -- Towards Sustainable and Green Agriculture: Integrating Machine Learning and Fuzzy Rough Set Analysis to Enhance Fruit Classification and Ripeness Detection -- 1 Introduction -- 2 Related Work -- 3 The Proposed Model for Discriminating Fruit Types Using Mineral Composition Data -- 4 Experimental Results and Analysis -- 4.1 Dataset Description -- 4.2 Statistical Analysis: Mineral Composition Analysis -- 4.3 Feature Selection Based on FRS -- 4.4 Fruit Discrimination Using Support Vector Machines (SVMs) -- 5 Conclusion and Future Works -- References -- Classifying Bird Songs Based on Chroma and Spectrogram Feature Extraction -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Bird Song Dataset Description -- 3.2 Feature Extraction -- 3.3 Mel Spectrogram -- 3.4 Chroma -- 3.5 Support Vector Machine -- 3.6 CNN -- 3.7 Spectral Features and Feature Learning -- 4 Experimental Results -- 5 Conclusions -- References -- Fish Recognition Using MobileNet-V2 and MAR-Based Metaverse for an Educative Marine Life System -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Extended Reality (XR) -- 3.2 Mobile Augmented Reality (MAR) -- 3.3 The Role of AI in the Metaverse -- 3.4 The Importance of Deep Learning and Image Processing for the Speed of Object Detection in the Metaverse -- 3.5 Machine Learning Technique Transfer Learning -- 4 The Proposed Model -- 4.1 Virtual World -- 4.2 Metaverse Engine -- 4.3 The Physical Worlds -- 5 Experiments Results and Discussion -- 5.1 Experiment I: Training Based Adam Optimization Algorithm.
5.2 Experiment II: Training Based SGD Optimization Algorithm -- 5.3 Experiment III: Training Based RMSprop Optimization Algorithm -- 5.4 The Testing Results -- 6 Conclusion -- References -- Biodiesel Yield Prediction from Sunflower Oil Using Artificial Intelligence: Towards Sustainable, and Renewable Energy Sources -- 1 Introduction -- 2 Literature Review -- 3 Preliminaries -- 3.1 Artificial Intelligence -- 3.2 Biodiesel as a Renewable Energy Source -- 4 Proposed Framework and Methodology -- 4.1 Phase 1: Classification of Sunflower Diseases Using VGG16 Model -- 4.2 Phase 2: Prediction of Biodiesel Yield from Sunflower Oil Using GBR Model -- 5 Results and Discussion -- 5.1 Performance Metrics -- 5.2 Results of VGG16 in Phase 1 -- 5.3 Results of GBR in Phase 2 -- 6 Comparison with Other Related Work Models -- 6.1 Validation of the VGG16 Model Performance -- 6.2 Validation of the GBR Model Performance -- 7 Conclusion and Future Work -- References -- Applications and Innovations in Green Initiatives -- Snake Optimization of Multiclass SVM for Efficient Diagnosis of Heart Disease Risk Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 4 Dataset Details -- 5 Support Vector Machine -- 6 Snake Optimization (SO) Algorithm and Its Mathematical Model -- 6.1 The Proposed OFA for SVM Parameter Optimization Algorithm -- 7 Results and Discussions -- 8 Conclusion -- References -- Efficient Prediction Adverse Drug-Drug Interactions with Deep Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Material and Methods -- 4 Experimental Design -- 4.1 Benchmark Dataset -- 4.2 Pre-processing Data -- 4.3 Technical Processing and Chemical Characteristics -- 4.4 Proposed Model -- 5 Results and Discussions -- 5.1 Statistical Analysis -- 5.2 Comparison and Result Validation for State-of-the-Art Study -- 5.3 Experimental Discussions -- 6 Conclusion.
References -- Drones and Birds Detection Based on InceptionV3-CNN Model: Deep Learning Methodology -- 1 Introduction -- 2 Related Work -- 3 "Drone Versus Bird" Detection Recognition Model: Inception V3-CNN -- 4 Model Evaluation and Experimental Results -- 5 Discussion -- 6 Conclusion -- References -- Deep Artificial Neural Network Regression Model for Synergistic Drug Combination Prediction -- 1 Introduction -- 2 Background -- 2.1 Drugs Combination -- 2.2 Deep Learning -- 3 Related Work -- 4 The Proposed Model -- 4.1 Dataset -- 4.2 Phase 1: Features Extraction -- 4.3 Phase 2: Feature Selection and Data Pre-processing -- 4.4 Phase 3: Implementation and Model Evaluation -- 5 Experimental Results -- 6 Conclusion -- References -- Classification of Benign and Malignant Breast Tumor Based on Machine Learning and Feature Selection Algorithms -- 1 Introduction -- 2 Related Work -- 3 Data Description -- 4 Proposed Model -- 4.1 Data Preprocessing -- 4.2 Feature Selection -- 4.3 Classification -- 5 Experimental Results -- 5.1 Evaluation Metrics -- 5.2 Evaluation -- 6 Conclusion and Future Work -- References -- Enhancing Synergistic Drug Combination Model Through Dimension Reduction in Cancer Cell Lines -- 1 Introduction -- 2 Implementation Approach -- 2.1 Feature Selection Techniques -- 2.2 Implementation Models -- 3 Experimental Results -- 3.1 Dataset Characteristics -- 3.2 Evaluation Metrics -- 3.3 Global Settings -- 3.4 Results and Discussion -- 4 Conclusion -- References -- A Novel Dynamic Chaotic Golden Jackal Optimization Algorithm for Sensor-Based Human Activity Recognition Using Smartphones for Sustainable Smart Cities -- 1 Introduction -- 2 Related Work -- 3 Golden Jackal Algorithm -- 3.1 Initialization -- 3.2 Exploration Phase -- 3.3 Exploitation Phase -- 3.4 Exploration-Exploitation Transition -- 4 Proposed Approach -- 4.1 Dynamic Opposite Learning.
4.2 Chaotic Maps Learning -- 4.3 The Proposed IGJO -- 5 Experimental Results -- 5.1 Parameter Settings and Evaluation Metrics -- 5.2 Experiment 1: Global Optimization Test -- 5.3 Experiment 2: IGJO as Feature Selection for HAR -- 6 Conclusion and Future Work -- References -- Artificial Intelligence in Finance Sector for Risk Prediction -- 1 Introduction -- 2 Artificial Intelligent Techniques for Risk Prediction in Finance -- 3 Related Work -- 4 Methodology and Data -- 4.1 Data Set Description -- 4.2 Data Set Analysis -- 4.3 Modelling Method and Results -- 5 Conclusion -- References -- Ensemble Regression Tree with Bayesian Optimization for Prediction of Biochemical Oxygen Demand and Climate Impact Assessment in Full Scale Waste Water Treatment Plant -- 1 Introduction -- 2 Related Work -- 3 Basics and Background -- 3.1 Feature Selection -- 3.2 Ensemble Regression Tree -- 3.3 Bayesian Optimization -- 3.4 Performance Metric -- 4 The Proposed Optimized Predictive Model -- 5 Results, Discussion and Analysis -- 5.1 Dataset-Statistical Description -- 5.2 Results and Analysis -- 6 Conclusion and Future Work -- References -- Enhancing Dynamic Wind Power Forecasting Using Cluster-Based Intelligence Swarm Optimization Technique -- 1 Introduction -- 2 Related Work -- 3 The Proposed Wind Power Forecasting Technique -- 4 Experimental Results -- 4.1 SDWPF Dataset -- 4.2 Experiment Setup -- 4.3 Evaluation Measures and Comparative Analysis of the CISWP Technique -- 5 Conclusions -- References -- Sustainable Green Cognitive Radio Networks: Optimized Deep Transfer Learning Model for Energy Consumption -- 1 Introduction -- 2 Cognitive Radio Network's System Model -- 3 Related Works -- 4 The Proposed Optimized Deep Transfer Learning Model (ODTL) in GNRNs -- 4.1 The African Vultures Optimization Algorithm (AVOA) -- 5 Experiment Results and Discussion.
5.1 Testing the Energy Consumption After Applying the Proposed ODTL-GCRN.
Record Nr. UNINA-9910878988603321
Hassanien Aboul Ella  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui