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[null,"1.","Record Nr.",null,"UNINA990001077850403321"]
[null,null,"Autore",null,"Markushevich, A.I."]
[null,null,"Titolo",null,"Elementi di teoria delle funzioni analitiche / A.I. Markushevich"]
[null,null,"Pubbl/distr/stampa",null,"Roma : Editori Riuniti, 1988"]
[null,null,"ISBN",null,"88-359-3284-X"]
[null,null,"Descrizione fisica",null,"385 p. ; 25 cm"]
[null,null,"Disciplina",null,"527.4"]
[null,null,"Locazione",null,"FI1"]
[null,null,"Collocazione",null,"S.16-037"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910701346003321"]
[null,null,"Titolo",null,"Do not enter [[electronic resource] ] : how proposed hours of service trucking rules are a dead end for small businesses : hearing before the Subcommittee on Investigations, Oversight, and Regulations of the Committee on Small Business, United States House of Representatives, One Hundred Twelfth Congress, first session, hearing held June 14, 2011"]
[null,null,"Pubbl/distr/stampa",null,"Washington : , : U.S. G.P.O., , 2011"]
[null,null,"Descrizione fisica",null,"1 online resource (iii, 110 pages) : illustrations"]
[null,null,"Soggetti",null,"Truck drivers - Legal status, laws, etc - United States"]
[{"$nulls":4},"Truck drivers - Workload - United States"]
[{"$nulls":4},"Hours of labor - Law and legislation - United States"]
[{"$nulls":4},"Trucking - Safety regulations - United States"]
[{"$nulls":4},"Trucking - Accidents - Prevention - Government policy - United States"]
[{"$nulls":4},"Small business - United States - Cost of operation"]
[{"$nulls":4},"Small business - Law and legislation - Compliance costs - United States"]
[{"$nulls":4},"Delegated legislation - Compliance costs - United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title from title screen (viewed on Jan. 20, 2012)."]
[{"$nulls":4},"Paper version available for sale by the Supt. of Docs., U.S. G.P.O."]
[{"$nulls":4},"\"Small Business Committee document number 112-020.\""]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (pages 103-110)."]
[null,"3.","Record Nr.",null,"UNINA9910878988603321"]
[null,null,"Autore",null,"Hassanien Aboul Ella"]
[null,null,"Titolo",null,"Artificial Intelligence for Environmental Sustainability and Green Initiatives / / edited by Aboul Ella Hassanien, Ashraf Darwish, Sally M. Elghamrawy"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-63451-9"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (361 pages)"]
[null,null,"Collana",null,"Studies in Systems, Decision and Control, , 2198-4190 ; ; 542"]
[null,null,"Disciplina",null,"620"]
[null,null,"Soggetti",null,"Engineering mathematics"]
[{"$nulls":4},"Engineering - Data processing"]
[{"$nulls":4},"Chemical engineering"]
[{"$nulls":4},"Environmental engineering"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Mathematical and Computational Engineering Applications"]
[{"$nulls":4},"Environmental Process Engineering"]
[{"$nulls":4},"Artificial Intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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: "]
[{"$nulls":4},"Predicting the Potability of Water Using Machine Learning Classification Methods -- 4 Experimental Study -- 4.1 Experimental Results -- 5 Conclusion and Future Work -- References -- Flowers Classification with Low Carbon Footprint Using Deep Learning Pretrained Models -- 1 Introduction -- 2 Related Work -- 3 The Proposed Model -- 3.1 Training Phase -- 3.2 Testing Phase -- 4 Dataset Characteristics and Pre-processing -- 5 Experiments and Results -- 6 Conclusion and Future Work -- References -- Measuring Global Warming Effect by the Prediction of Climate Change on the Different Countries Using Machine Learning Approaches -- 1 Introduction -- 2 Background -- 2.1 Support Vector Machine."]
[{"$nulls":4},"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."]
[{"$nulls":4},"5.2 Experiment II: Training Based SGD Optimization Algorithm -- 5.3 Experiment III: Training Based RMSprop Optimization Algorithm -- 5.4 The Testing Results -- 6 Conclusion -- References -- Biodiesel Yield Prediction from Sunflower Oil Using Artificial Intelligence: Towards Sustainable, and Renewable Energy Sources -- 1 Introduction -- 2 Literature Review -- 3 Preliminaries -- 3.1 Artificial Intelligence -- 3.2 Biodiesel as a Renewable Energy Source -- 4 Proposed Framework and Methodology -- 4.1 Phase 1: Classification of Sunflower Diseases Using VGG16 Model -- 4.2 Phase 2: Prediction of Biodiesel Yield from Sunflower Oil Using GBR Model -- 5 Results and Discussion -- 5.1 Performance Metrics -- 5.2 Results of VGG16 in Phase 1 -- 5.3 Results of GBR in Phase 2 -- 6 Comparison with Other Related Work Models -- 6.1 Validation of the VGG16 Model Performance -- 6.2 Validation of the GBR Model Performance -- 7 Conclusion and Future Work -- References -- Applications and Innovations in Green Initiatives -- Snake Optimization of Multiclass SVM for Efficient Diagnosis of Heart Disease Risk Prediction -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 4 Dataset Details -- 5 Support Vector Machine -- 6 "]
[{"$nulls":4},"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."]
[{"$nulls":4},"References -- Drones and Birds Detection Based on InceptionV3-CNN Model: Deep Learning Methodology -- 1 Introduction -- 2 Related Work -- 3 \"Drone Versus Bird\" Detection Recognition Model: Inception V3-CNN -- 4 Model Evaluation and Experimental Results -- 5 Discussion -- 6 Conclusion -- References -- Deep Artificial Neural Network Regression Model for Synergistic Drug Combination Prediction -- 1 Introduction -- 2 Background -- 2.1 Drugs Combination -- 2.2 Deep Learning -- 3 Related Work -- 4 The Proposed Model -- 4.1 Dataset -- 4.2 Phase 1: Features Extraction -- 4.3 Phase 2: Feature Selection and Data Pre-processing -- 4.4 Phase 3: Implementation and Model Evaluation -- 5 Experimental Results -- 6 Conclusion -- References -- Classification of Benign and Malignant Breast Tumor Based on Machine Learning and Feature Selection Algorithms -- 1 Introduction -- 2 Related Work -- 3 Data Description -- 4 Proposed Model -- 4.1 Data Preprocessing -- 4.2 Feature Selection -- 4.3 Classification -- 5 Experimental Results -- 5.1 Evaluation Metrics -- 5.2 Evaluation -- 6 Conclusion and Future Work -- References -- Enhancing Synergistic Drug Combination Model Through Dimension Reduction in Cancer Cell Lines -- 1 Introduction -- 2 Implementation Approach -- 2.1 Feature Selection Techniques -- 2.2 Implementation Models -- 3 Experimental Results -- 3.1 Dataset Characteristics -- 3.2 Evaluation Metrics -- 3.3 Global Settings -- 3.4 Results and Discussion -- 4 Conclusion -- References -- A Novel Dynamic Chaotic Golden Jackal Optimization Algorithm for Sensor-Based Human Activity Recognition Using Smartphones for Sustainable Smart Cities -- 1 Introduction -- 2 Related Work -- 3 Golden Jackal Algorithm -- 3.1 Initialization -- 3.2 Exploration Phase -- 3.3 Exploitation Phase -- 3.4 Exploration-Exploitation Transition -- 4 Proposed Approach -- 4.1 Dynamic Opposite Learning."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Related Work -- 3 The Proposed Wind Power Forecasting Technique -- 4 Experimental Results -- 4.1 SDWPF Dataset -- 4.2 Experiment Setup -- 4.3 Evaluation Measures and Comparative Analysis of the CISWP Technique -- 5 Conclusions -- References -- Sustainable Green Cognitive Radio Networks: Optimized Deep Transfer Learning Model for Energy Consumption -- 1 Introduction -- 2 Cognitive Radio Network's System Model -- 3 Related Works -- 4 The Proposed Optimized Deep Transfer Learning Model (ODTL) in GNRNs -- 4.1 The African Vultures Optimization Algorithm (AVOA) -- 5 Experiment Results and Discussion."]
[{"$nulls":4},"5.1 Testing the Energy Consumption After Applying the Proposed ODTL-GCRN."]
[null,null,null,"Sommario/riassunto","This book discusses AI's applications in sustainability, exploring its potential in sectors such as energy, healthcare, agriculture, transportation, and waste management. Discusses applications and innovations in Green Initiatives such as energy, finance, and drug discovery. Highlights the ethical challenges and benefits of integrating AI into sustainability initiatives."]