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[null,"1.","Record Nr.",null,"UNINA9910130528503321"]
[null,null,"Autore",null,"Panero Francesco <1950->"]
[null,null,"Titolo",null,"Grandi proprietà ecclesiastiche nell'Italia nord-occidentale : tra sviluppo e crisi, secoli X-XIV / / Francesco Panero"]
[null,null,"Pubbl/distr/stampa",null,"Bologna, : CLUEB, 2009"]
[null,null,"ISBN",null,"88-491-3270-0"]
[null,null,"Descrizione fisica",null,"268 p. ; ; 21 cm"]
[null,null,"Collana",null,"Biblioteca di storia agraria medievale ; ; 35"]
[null,null,"Disciplina",null,"333"]
[{"$nulls":4},"271"]
[{"$nulls":4},"261"]
[{"$nulls":4},"945"]
[{"$nulls":4},"282"]
[{"$nulls":4},"726"]
[null,null,"Soggetti",null,"Italy, Northern Church history"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Collection of already publ. writings, now revised."]
[{"$nulls":4},"F. Panero teaches at the University of Turin."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,"2.","Record Nr.",null,"UNINA9910810498603321"]
[null,null,"Autore",null,"Mikulskas Rolandas"]
[null,null,"Titolo",null,"Copular constructions in Lithuanian / / Rolandas Mikulskas"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam, [Netherlands] ; ; Philadelphia, [Pennsylvania] : , : John Benjamins Publishing Company, , 2017"]
[{"$nulls":4},"©2017"]
[null,null,"ISBN",null,"9789027267429 (pdf)"]
[{"$nulls":4},"9789027259127 (hb : alk. paper)"]
[null,null,"Descrizione fisica",null,"1 online resource (281 pages)"]
[null,null,"Collana",null,"Valency, Argument Realization and Grammatical Relations in Baltic, , 2352-0159 ; ; Volume 4"]
[null,null,"Disciplina",null,"491.925"]
[null,null,"Soggetti",null,"Lithuanian language - Grammar"]
[{"$nulls":4},"Lithuanian language - Copula"]
[{"$nulls":4},"Lithuanian language - Coordinate constructions"]
[{"$nulls":4},"Construction grammar"]
[{"$nulls":4},"Balkan Peninsula Language Grammar"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and indexes."]
[null,"3.","Record Nr.",null,"UNINA9910561300503321"]
[null,null,"Titolo",null,"The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022) / / edited by Aboul Ella Hassanien, Rawya Y. Rizk, Václav Snášel, Rehab F. Abdel-Kader"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022"]
[null,null,"ISBN",null,"3-031-03918-1"]
[null,null,"Edizione",null,"[1st ed. 2022.]"]
[null,null,"Descrizione fisica",null,"1 online resource (708 pages)"]
[null,null,"Collana",null,"Lecture Notes on Data Engineering and Communications Technologies, , 2367-4520 ; ; 113"]
[null,null,"Disciplina",null,"006.3"]
[{"$nulls":4},"006.31"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Engineering - Data processing"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Big Data"]
[{"$nulls":4},"Data Engineering"]
[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 -- Organization -- Honorary Chair -- General Chairs -- Co-chairs -- International Advisory Board -- Publication Chair -- Program Chairs -- Publicity Chairs -- Technical Program Committee -- Local Arrangement Chairs -- Contents -- Deep Learning and Applications -- Plant Leaf Diseases Detection and Identification Using Deep Learning Model -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusions -- References -- Reinforcement Learning for Developing an Intelligent Warehouse Environment -- 1 Introduction -- 2 Machine Learning Techniques -- 3 Results and Discussion -- 4 Conclusion and Future Research -- References -- A Low-Cost Multi-sensor Deep Learning System for Pavement Distress Detection and Severity Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Overall System Architecture -- 3.2 Deep Learning Distress Detection -- 3.3 Dataset and Training Information -- 3.4 Projection onto the Depth "]
[{"$nulls":4},"3D Point Cloud and ROI Filtering -- 4 Case Study: Pothole Severity Classification -- 5 Experimental Results -- 5.1 Results for the Distress Detection -- 5.2 Results for Pothole Severity Classification -- 6 Conclusion -- References -- An Intrusion Detection Model Based on Deep Learning and Multi-layer Perceptron in the Internet of Things (IoT) Network -- 1 Introduction -- 2 Related Work -- 2.1 Multi Agent Systems for IDS -- 2.2 Fuzzy Systems for IDS -- 2.3 Game Theory Models for IDS -- 3 Architecture of the Proposed Intrusion Detection System -- 3.1 Pre-processing and Feature Engineering -- 3.2 Deep Learning Layer -- 3.3 Evaluation Layer -- 4 The Experimental Results -- 5 Comparison Between Proposed Models and the Others -- 6 Conclusion -- References -- Transfer Learning and Recurrent Neural Networks for Automatic Arabic Sign Language Recognition -- 1 Introduction."]
[{"$nulls":4},"2 Related Work -- 3 Arabic Sign Language Dataset -- 4 Methodology -- 4.1 Prepare the Dataset -- 4.2 Extract the Spatial Features -- 4.3 Extract the Temporal Features -- 4.4 Video Augmentation -- 5 Experimental and Results -- 5.1 Experiment Settings -- 5.2 Models Results -- 6 Conclusion and Future Works -- References -- Robust Face Mask Detection Using Local Binary Pattern and Deep Learning -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 4 Experimental Results -- 5 Conclusion -- References -- Steganography Adaptation Model for Data Security Enhancement in Ad-Hoc Cloud Based V-BOINC Through Deep Learning -- 1 Introduction -- 1.1 Ad-Hoc Cloud Computing -- 1.2 Deep Steganography -- 1.3 Contribution -- 1.4 Paper Organization -- 2 Literature Review -- 3 Proposed Solution -- 4 Experiment -- 5 Discussion and Analysis -- 6 Conclusion -- References -- Performance of Different Deep Learning Models for COVID-19 Detection -- 1 Introduction -- 2 Deep Learning (DL) -- 2.1 The DL-Algorithms Steps in COVID-19 Diagnosis -- 2.2 DL-Models for COVID-19 Detection -- 3 Discussion -- 4 Conclusion -- References -- Deep Learning-Based Apple Leaves Disease Identification Approach with Imbalanced Data -- 1 Introduction -- 2 Basics and Background -- 2.1 Data Imbalance -- 2.2 Convolutional Neural Networks -- 2.3 Transfer Learning -- 3 The Proposed Approach -- 3.1 Dataset Description -- 3.2 Data Preprocessing Phase -- 3.3 Training Phase -- 3.4 Evaluation Phase -- 4 Experimental Results and Analysis -- 4.1 Data Imbalance Problem -- 4.2 Data Augmentation -- 4.3 Setup of the Experiment -- 4.4 Evaluation of the Model -- 5 Conclusion and Future Work -- References -- Commodity Image Retrieval Based on Image and Text Data -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Image and Text Feature Fusion -- 3.2 Target Function -- 4 Experiment -- 4.1 Evaluation Metrics."]
[{"$nulls":4},"4.2 Datasets -- 4.3 Experimental Details -- 4.4 Experimental Results and Analysis -- 5 Conclusion -- References -- Machine Learning Technologies -- Artificial Intelligence Based Solutions to Smart Warehouse Development: A Conceptual Framework -- 1 Introduction -- 2 SWOT Analysis -- 2.1 Strengths -- 2.2 Weaknesses -- 2.3 Opportunities -- 2.4 Threats -- 3 Proposed Solutions and Current Approaches -- 3.1 WO Strategy (Improve): Testbed as a Trial for Investment Decision -- 3.2 WO Strategy (Improve): AI-Powered Solutions -- 3.3 SO Strategy (Attack): AI Resource Development -- 4 Conclusions and Future Research -- References -- Long-Short Term Memory Model with Univariate Input for Forecasting Individual Household Electricity Consumption -- 1 Introduction -- 2 Related Works -- 3 Deep Learning Models for Load Forecasting -- 3.1 LSTM and LSTM-ED Neural Networks -- 3.2 CNN-LSTM Neural Networks -- 3.3 GRU Neural Networks -- 3.4 BiLSTM Neural Networks -- 3.5 "]
[{"$nulls":4},"ConvLSTM Neural Networks -- 4 Results and Discussion -- 4.1 Dataset Description -- 4.2 Evaluation Metrics -- 4.3 Prediction Results of ConvLSTM -- 4.4 Discussion of the Forecasting Models -- 5 Conclusion and Future Work -- References -- DNA-Binding-Proteins Identification Based on Hybrid Features Extraction from Hidden Markov Model -- 1 Introduction -- 2 Materials and Methods -- 2.1 Datasets -- 2.2 Encoding -- 2.3 Framing -- 2.4 Hybrid Visual HMM Structure -- 2.5 Features Extraction -- 2.6 Classifier -- 3 Results and Discussions -- 4 Conclusions -- References -- Machine Learning Based Mobile Applications for Cardiovascular Diseases (CVDs) -- 1 Introduction -- 2 ML Based m-Health for CVDs -- 3 Characteristics of the Commercially Available CVDs Mobile Applications -- 4 Future Requirements -- 5 Conclusion -- References -- Regression Analysis for Remaining Useful Life Prediction of Aircraft Engines."]
[{"$nulls":4},"1 Introduction -- 2 Related Work -- 3 Aircraft Engine System -- 4 Proposed Model for Predicting the RUL -- 5 Experimental Results and Discussion -- 6 Conclusion and Future Work -- References -- Applying Machine Learning Technology to Perform Automatic Provisioning of the Optical Transport Network -- 1 Introduction -- 2 The Challenges in the Current Model of the Supervision of the OTN -- 3 Proposed Model for the Automatic Provision of the OTN -- 4 Results and Discussion -- 5 Conclusion and Future Work -- References -- Robo-Nurse Healthcare Complete System Using Artificial Intelligence -- 1 Introduction -- 1.1 Related Work -- 2 Research Method -- 2.1 Software Implementation -- 2.2 Hardware Implementation -- 2.3 External Design Implementation -- 3 Results and Discussions -- 4 Conclusion -- References -- Resolving Context Inconsistency Approach Based on Random Forest Tree -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 IoT Data Collection Phase -- 3.2 Context Inconsistency Validator -- 3.3 Best Resolution Selection -- 3.4 Random Forest Tree -- 4 Experimental Results and Evaluations -- 5 Conclusion and Future Directions -- References -- Arduino Line Follower Using Fuzzy Logic Control -- 1 Introduction -- 2 Methodology -- 2.1 Lab Simulation -- 2.2 The ATmega328p Microcontroller -- 2.3 Voltage Regulator -- 2.4 Circuit Diagram Explanation -- 2.5 Microcontroller-Motor Driver IC Interface -- 2.6 Microcontroller-IR Sensor Module Interface -- 2.7 Microcontroller-Variable Resistor Interface -- 2.8 Arduino IDE Interface with Microcontroller -- 3 Summary of Methodology -- 4 Physical Modeling -- 4.1 Block Diagram -- 4.2 Flow Chart -- 4.3 Working Principle -- 5 Result and Analysis -- 6 Conclusion -- References -- Evaluating Adaptive Facade Performance in Early Building Design Stage: An Integrated Daylighting Simulation and Machine Learning."]
[{"$nulls":4},"1 Introduction -- 2 Related Works -- 3 Building as a Machine and Machine Learning in Architecture -- 4 Adaptive Facade -- 5 Methodology -- 5.1 Data Collection: Available Forms of Kinetic Façade Systems -- 5.2 Data Preparation: Applying System Possibility Scores -- 5.3 Data Exploration and Case Study Setup -- 5.4 Prediction Stage: Applying the KNN Algorithm as a Selective Filter -- 6 Systems Modeling and Simulation -- 7 Results and Discussion -- 8 Conclusion -- References -- LTE Downlink Scheduling with Soft Policy Gradient Learning -- 1 Introduction -- 2 Downlink Resource Allocation in LTE -- 3 Related Work -- 4 DSPG Scheduler: The Proposed Scheduling Algorithm -- 4.1 Problem Statement -- 4.2 Model Design -- 5 Simulation Implementation and Results -- 6 Conclusions -- References -- Predicting the Road Accidents Severity Using Artificial Neural Network -- 1 Introduction -- 2 Literature Review -- 3 Dataset -- 4 The Proposed Methodology -- 5 Results and Discussions -- 5.1 Attributes "]
[{"$nulls":4},"vs Accident Severity -- 5.2 Accident Severity Prediction Results -- 6 Conclusion -- References -- Predicting the Intention to Use Audi and Video Teaching Styles: An Empirical Study with PLS-SEM and Machine Learning Models -- 1 Introduction -- 2 Theoretical Framework -- 2.1 Technology Acceptance Model (TAM) -- 2.2 Flow Theory -- 2.3 Virtual Reality Attributes -- 3 Research Methodology -- 3.1 Data Collection -- 3.2 Personal/Demographic Information -- 3.3 Study Instrument -- 3.4 Survey Structure -- 4 Findings and Discussion -- 4.1 Data Analysis -- 4.2 Convergent Validity -- 4.3 Discriminant Validity -- 4.4 Hypotheses Testing Using PLS-SEM -- 4.5 Hypothesis Testing Using Machine Learning Algorithms -- 5 Discussion of Results -- References -- Intellgenet Systems and Applications."]
[{"$nulls":4},"Immunity of Signals Transmission Using Secured Unequal Error Protection Scheme with Various Packet Format."]
[null,null,null,"Sommario/riassunto","This book constitutes the refereed proceedings of the 8th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2022, held in Cairo, Egypt, during May 5-7, 2022. The 8th edition of AMLTA will be organized by the Scientific Research Group in Egypt (SRGE), Egypt, collaborating with Port Said University, Egypt, and VSB-Technical University of Ostrava, Czech Republic. AMLTA series aims to become the premier international conference for an in-depth discussion on the most up-to-date and innovative ideas, research projects, and practices in the field of machine learning technologies and their applications. The book covers current research on advanced machine learning technology, including deep learning technology, sentiment analysis, cyber-physical system, IoT, and smart cities informatics and AI against COVID-19, data mining, power and control systems, business intelligence, social media, digital transformation, and smart systems. ."]