{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":25,"n_cols":5,"rows_kept":25,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[12,2,12,3],[13,2,13,3],[15,2,15,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910754096603321"]
[null,null,"Autore",null,"Dao Nhu-Ngoc"]
[null,null,"Titolo",null,"Intelligence of Things : The Second International Conference on Intelligence of Things (ICIT 2023), Ho Chi Minh City, Vietnam, October 25-27, 2023, Proceedings, Volume 2"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2023"]
[{"$nulls":4},"©2023"]
[null,null,"ISBN",null,"9783031467493"]
[{"$nulls":4},"3031467493"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (368 pages)"]
[null,null,"Collana",null,"Lecture Notes on Data Engineering and Communications Technologies Series ; ; v.188"]
[null,null,"Soggetti",null,"Internet of things"]
[{"$nulls":4},"Artificial intelligence"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents -- AIoT Services and Applications -- Investigating Ensemble Learning Methods for Predicting Water Quality Index -- 1 Introduction -- 2 Methodology -- 2.1 Decision Tree -- 2.2 Bagging (Bootstrapped Aggregating) -- 2.3 Random Forest -- 2.4 Extra Trees -- 2.5 Adaptive Boosting -- 2.6 XGBoost (eXtreme Gradient Boosting) -- 3 Experiments and Results -- 3.1 Data Description -- 3.2 Metrics to Evaluate the Performance of ML Models -- 3.3 The Performance of Ensemble Methods on Predicting the WQI -- 4 Conclusions -- References -- Age-Invariant Face Recognition Based on Self-Supervised Learning -- 1 Introduction -- 2 Related Works -- 2.1 Age-Invariant Face Recognition -- 2.2 Self-Supervised Learning in Face Recognition -- 3 Methods -- 3.1 Data Augmentation -- 3.2 Age-Invariant Face Recognition Models -- 3.3 Self-Supervised Learning -- 4 Experimental Results -- 4.1 Experiment Setup -- 4.2 Experimental Results -- 5 Conclusion -- References -- Detection of Kidney Stone Based on Super Resolution Techniques and YOLOv7 Under Limited Training Samples -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Dataset -- 3.2 Convolutional Block Attention Module -- 3.3 Single Image Super Resolution Techniques -- 3.4 The Proposed Re-Designed YOLOv7 Architecture -- 4 Experimental Results -- 4.1 Environment and Hyperparameters Setup -- 4.2 Evaluation Results -- 5 Conclusion -- References -- Hardware-Based Lane "]
[{"$nulls":4},"Detection System Architecture for Autonomous Vehicles -- 1 Introduction -- 2 Related Work -- 3 Proposed Hough Transform Hardware Design Architecture -- 4 Proposed Design Verification -- 5 Conclusions -- References -- Video Classification Based on the Behaviors of Children in Pre-school Through Surveillance Cameras -- 1 Introduction -- 2 Related Works -- 3 The BCiPS Dataset -- 3.1 Dataset Creation."]
[{"$nulls":4},"3.2 Dataset Analysis -- 4 Baseline Models -- 4.1 CNN+LSTM (ConvLSTM2D) -- 4.2 CNN+SVM -- 4.3 CNN+Random Forest -- 4.4 TimeSformer -- 4.5 MoViNets -- 4.6 (2+1)D Resnet-18 -- 4.7 EfficientNetB0 -- 5 Results -- 5.1 Results of Baseline Models -- 5.2 Error Analysis -- 6 Conclusion-Future Works -- References -- Land Subsidence Susceptibility Mapping Using Machine Learning in the Google Earth Engine Platform -- 1 Introduction -- 2 Study Area -- 3 Materials and Methods -- 3.1 Methods -- 3.2 Materials -- 4 Results and Discussions -- 5 Conclusions -- References -- Building an AI-Powered IoT App for Fall Detection Using Yolov8 Approach -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Network Architecture -- 3.2 Fall Detection Work Flow -- 3.3 Performance Metrics -- 4 Implementation and Result -- 4.1 Data -- 4.2 Comparison Result of Different YOLOv5 Models over 100 Epochs -- 4.3 Fall Detection Web Application Using Streamlit -- 5 Discussion and Conclusion -- References -- Seam Puckering Level Classification Using AIoT Technology -- 1 Introduction -- 2 Background and Related Works -- 3 Methodology -- 3.1 Framework Architecture -- 3.2 Custom Dataset -- 3.3 Models for Seam Puckering Level Classification -- 3.4 System Implementation -- 4 Result and Evaluation -- 4.1 Result -- 4.2 Evaluation -- 5 Discussion, Conclusion, and Future Works -- References -- Classification of Pneumonia on Chest X-ray Images Using Transfer Learning -- 1 Introduction -- 2 Related Works -- 3 Dataset and Methods -- 3.1 Dataset -- 3.2 Proposed Method -- 4 Experimental Results -- 5 Conclusions -- References -- Bayesian Approach for Static Object Detection and Localization in Unmanned Ground Vehicles -- 1 Introduction -- 2 Methodologies -- 2.1 Detecting Algorithms -- 2.2 Noise Reducing and Navigating Procedures -- 2.3 Sensor's Fusion and Target Localization."]
[{"$nulls":4},"3 Experiment and Prelim Results -- 3.1 Simulation Environment -- 3.2 Onsite Testing -- 4 Conclusion and Future Work -- References -- Diabetic Retinopathy Diagnosis Leveraging Densely Connected Convolutional Networks and Explanation Technique -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Data Preprocessing -- 3.2 Data Augmentation -- 3.3 Architectures for Classification -- 3.4 Grad-CAM for Output Visualization From the Trained Model -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Scenarios -- 4.4 Result Summary and Comparison with Some Previous Studies -- 5 Conclusion -- References -- Deep Learning Approach for Inundation Area Detection Using Sentinel Data -- 1 Introduction -- 2 Methodology and Dataset -- 2.1 Methodology -- 2.2 Dataset -- 3 Prelim Results -- 4 Conclusion -- References -- Classification of Raisin Grains Based on Ensemble Learning Techniques in Machine Learning -- 1 Introduction -- 2 Problem and Data -- 3 Research Methodology -- 3.1 Existing Methods -- 3.2 Proposed Method -- 3.3 Data Preprocessing -- 3.4 Experimental Settings -- 4 Results -- 5 Conclusion -- References -- An Effective Deep Learning Model for Detecting Plant Diseases Using a Natural Dataset for the Agricultural IoT System -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Data Augmentation Methods -- 3.2 Datasets -- 3.3 CNN Architecture -- 4 The Proposed Model -- 5 Conclusions -- References -- Real-Time "]
[{"$nulls":4},"Air Quality Monitoring System Using Fog Computing Technology -- 1 Introduction -- 2 Background and Related Works -- 3 Methodology -- 3.1 System Design -- 3.2 Pollution Parameters Selection -- 3.3 Data Acquisition Module -- 3.4 Communication Module -- 3.5 Fog Computing Module -- 4 Implementation and Evaluation -- 4.1 Implementation -- 4.2 Evaluation -- 5 Discussion, Conclusion, and Future Works -- References."]
[{"$nulls":4},"An Intelligent Computing Method for Scheduling Projects with Normally Distributed Activity Times -- 1 Introduction -- 2 Project Scheduling with Crisp Activity Duration Times Using Approach Based on Linear Programming -- 2.1 Program Evaluation and Review Technique: Some Fundamental Definitions and Concepts -- 2.2 Scheduling Projects Using Linear Programming -- 3 Schedule Project with Normally Distributed Activity Times Using an Approach Based on Fuzzy Linear Programming -- 3.1 Solving a Class of Stochastic Programming Problems Using Fuzzy Linear Programming -- 3.2 Computing the Project Completion Time when Project Activity Duration Times are Random Variables following Normal Distributions -- 3.3 Finding Critical Activities when Project Activity Duration Times are Random Variables following Normal Distributions -- 4 Concluding Observations -- References -- Security and Privacy -- An Improved Hardware Architecture of Ethereum Blockchain Hashing System -- 1 Introduction -- 2 Proposed Ethash Hardware Architecture -- 2.1 The Architecture of Ethash -- 2.2 The Architecture of Keccak256_2stage -- 2.3 The Architecture of Main_Loop -- 3 Implementation Results -- 3.1 Keccak Implementation Result -- 3.2 Ethash Implementation Result -- 4 Conclusion -- References -- CSS-EM: A Comprehensive, Secured and Sharable Education Management System for Schools -- 1 Introduction -- 2 Blockchain -- 2.1 Overview -- 2.2 Substrate -- 3 Proposed Scorechain System -- 3.1 Scorechain System Overview -- 3.2 A Comprehensive University Management System -- 3.3 High Secure Data Management Through a Hierarchical Role Assignment Mechanism -- 3.4 Multiple Universities' High Secure Privacy and Data-Sharing Platform -- 4 System Implementation -- 5 Discussion -- 6 Conclusion -- References -- A High-Speed Barret-Based Modular Multiplication with Bit-Correction for the CRYSTAL-KYBER Cryptosystem."]
[{"$nulls":4},"1 Introduction -- 2 The Preliminary Background -- 2.1 Barret Reduction Method -- 2.2 K-RED/K2-RED Reduction Method -- 3 The Proposed Design -- 4 Implementation Result -- 5 Conclusion -- References -- Securing Digital Futures: Exploring Decentralised Systems and Blockchain for Enhanced Identity Protection -- 1 Introduction -- 2 Centralized and Decentralized Identity Models -- 2.1 Definition -- 2.2 Process -- 2.3 Practical Applications -- 2.4 Advantages -- 2.5 Disadvantages -- 2.6 Decentralized Identity Model -- 3 Preliminaries of Blockchain-Based Digital Identity Management System -- 3.1 Blockchain Technology -- 3.2 AI and OCR Technologies -- 3.3 Zero-Knowledge Proof and Smart Contracts -- 4 Proposed System Model and Processes for Managing Privacy Attributes -- 4.1 Scheme Overview -- 4.2 User Registration -- 4.3 Identity Verification -- 4.4 Authentication in Digital Identity Decentralized System on Blockchain -- 4.5 Access Control -- 5 Conclusion and Future Work -- 5.1 Conclusion -- 5.2 Future Work -- References -- Enhancing Blockchain Interoperability Through Sidechain Integration and Valid-Time-Key Data Access Control -- 1 Introduction -- 2 Related Works -- 3 Proposed System -- 3.1 Blockchain -- 3.2 Sidechain -- 3.3 Valid Time Key - VTK -- 3.4 Cross-Chain Data Transfer -- 4 Experiments and Results -- 4.1 Performance Evaluation -- 4.2 Security Analysis -- 5 Conclusions and Future Work -- References -- An IoT Attack Detection Framework Leveraging Graph "]
[{"$nulls":4},"Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Graph Neural Networks (GNNs) -- 4 Data Preparation -- 4.1 Datasets -- 4.2 Data Acquisition and Integration -- 4.3 Data Preprocessing -- 4.4 Feature Selection -- 5 GraphSage-Based Attack Classification Framework -- 6 Performance Evaluation and Discussion -- 7 Conclusion -- References -- Network Attack Detection on IoT Devices Using 2D-CNN Models."]
[{"$nulls":4},"1 Introduction."]
[null,null,null,"Sommario/riassunto","This volume presents the proceedings from the Second International Conference on Intelligence of Things (ICIT 2023), held in Ho Chi Minh City, Vietnam. The conference focused on the integration of artificial intelligence technologies with the Internet of Things, forming the concept known as the Intelligence of Things (AIoT). This area is expected to enhance IoT operations through flexible adaptation, resource optimization, and improved human-machine interactions. The book covers the latest research and developments in AIoT, featuring contributions from scholars around the world. It includes papers on various topics, such as AIoT applications, ensemble learning methods for predicting water quality, and age-invariant face recognition. The publication aims to bridge the gap between fundamental research in data science and practical applications in data engineering, appealing to academics, researchers, and industry professionals interested in the advancement of AIoT technologies."]