{"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],[4,2,4,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[12,2,12,3],[13,2,13,3],[16,2,16,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910555252503321"]
[null,null,"Titolo",null,"Human communication technology : internet-of-robotic-things and ubiquitous computing / / edited by R. Anandan [and four others]"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-119-75215-9"]
[{"$nulls":4},"1-119-75216-7"]
[{"$nulls":4},"1-119-75214-0"]
[null,null,"Descrizione fisica",null,"1 online resource (512 pages)"]
[null,null,"Collana",null,"Artificial Intelligence and Soft Computing for Industrial Transformation Ser."]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Internet of things"]
[{"$nulls":4},"Electronic books."]
[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 index."]
[null,null,"Nota di contenuto",null,"Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Internet of Robotic Things: A New Architecture and Platform -- 1.1 Introduction -- 1.1.1 Architecture -- 1.1.1.1 Achievability of the Proposed Architecture -- 1.1.1.2 Qualities of IoRT Architecture -- 1.1.1.3 Reasonable Existing Robots for IoRT Architecture -- 1.2 Platforms -- 1.2.1 Cloud Robotics Platforms -- 1.2.2 IoRT Platform -- 1.2.3 Design a Platform -- 1.2.4 The Main Components of the Proposed Approach -- 1.2.5 IoRT Platform Design -- 1.2.6 Interconnection Design -- 1.2.7 Research Methodology -- 1.2.8 Advancement Process-Systems Thinking -- 1.2.8.1 Development Process -- 1.2.9 Trial Setup-to Confirm the Functionalities -- 1.3 Conclusion -- 1.4 Future Work -- References -- 2 Brain-Computer Interface Using Electroencephalographic Signals for the Internet of Robotic Things -- 2.1 Introduction -- 2.2 Electroencephalography Signal Acquisition Methods -- 2.2.1 Invasive Method -- 2.2.2 Non-Invasive Method -- 2.3 Electroencephalography Signal-Based BCI -- 2.3.1 Prefrontal Cortex in Controlling Concentration Strength -- 2.3.2 Neurosky Mind-Wave Mobile -- 2.3.2.1 Electroencephalography Signal Processing Devices -- 2.3.3 Electromyography Signal Extraction of Features and Its Signal Classifications -- 2.4 IoRT-Based Hardware for BCI -- 2.5 Software Setup for IoRT -- 2.6 Results and Discussions -- "]
[{"$nulls":4},"2.7 Conclusion -- References -- 3 Automated Verification and Validation of IoRT Systems -- 3.1 Introduction -- 3.1.1 Automating V&amp -- V-An Important Key to Success -- 3.2 Program Analysis of IoRT Applications -- 3.2.1 Need for Program Analysis -- 3.2.2 Aspects to Consider in Program Analysis of IoRT Systems -- 3.3 Formal Verification of IoRT Systems -- 3.3.1 Automated Model Checking -- 3.3.2 The Model Checking Process -- 3.3.2.1 PRISM -- 3.3.2.2 UPPAAL."]
[{"$nulls":4},"3.3.2.3 SPIN Model Checker -- 3.3.3 Automated Theorem Prover -- 3.3.3.1 ALT-ERGO -- 3.3.4 Static Analysis -- 3.3.4.1 CODESONAR -- 3.4 Validation of IoRT Systems -- 3.4.1 IoRT Testing Methods -- 3.4.2 Design of IoRT Test -- 3.5 Automated Validation -- 3.5.1 Use of Service Visualization -- 3.5.2 Steps for Automated Validation of IoRT Systems -- 3.5.3 Choice of Appropriate Tool for Automated Validation -- 3.5.4 IoRT Systems Open Source Automated Validation Tools -- 3.5.5 Some Significant Open Source Test Automation Frameworks -- 3.5.6 Finally IoRT Security Testing -- 3.5.7 Prevalent Approaches for Security Validation -- 3.5.8 IoRT Security Tools -- References -- 4 Light Fidelity (Li-Fi) Technology: The Future Man-Machine-Machine Interaction Medium -- 4.1 Introduction -- 4.1.1 Need for Li-Fi -- 4.2 Literature Survey -- 4.2.1 An Overview on Man-to-Machine Interaction System -- 4.2.2 Review on Machine to Machine (M2M) Interaction -- 4.2.2.1 System Model -- 4.3 Light Fidelity Technology -- 4.3.1 Modulation Techniques Supporting Li-Fi -- 4.3.1.1 Single Carrier Modulation (SCM) -- 4.3.1.2 Multi Carrier Modulation -- 4.3.1.3 Li-Fi Specific Modulation -- 4.3.2 Components of Li-Fi -- 4.3.2.1 Light Emitting Diode (LED) -- 4.3.2.2 Photodiode -- 4.3.2.3 Transmitter Block -- 4.3.2.4 Receiver Block -- 4.4 Li-Fi Applications in Real Word Scenario -- 4.4.1 Indoor Navigation System for Blind People -- 4.4.2 Vehicle to Vehicle Communication -- 4.4.3 Li-Fi in Hospital -- 4.4.4 Li-Fi Applications for Pharmacies and the Pharmaceutical Industry -- 4.4.5 Li-Fi in Workplace -- 4.5 Conclusion -- References -- 5 Healthcare Management-Predictive Analysis (IoRT) -- 5.1 Introduction -- 5.1.1 Naive Bayes Classifier Prediction for SPAM -- 5.1.2 Internet of Robotic Things (IoRT) -- 5.2 Related Work -- 5.3 Fuzzy Time Interval Sequential Pattern (FTISPAM)."]
[{"$nulls":4},"5.3.1 FTI SPAM Using GA Algorithm -- 5.3.1.1 Chromosome Generation -- 5.3.1.2 Fitness Function -- 5.3.1.3 Crossover -- 5.3.1.4 Mutation -- 5.3.1.5 Termination -- 5.3.2 Patterns Matching Using SCI -- 5.3.3 Pattern Classification Based on SCI Value -- 5.3.4 Significant Pattern Evaluation -- 5.4 Detection of Congestive Heart Failure Using Automatic Classifier -- 5.4.1 Analyzing the Dataset -- 5.4.2 Data Collection -- 5.4.2.1 Long-Term HRV Measures -- 5.4.2.2 Attribute Selection -- 5.4.3 Automatic Classifier-Belief Network -- 5.5 Experimental Analysis -- 5.6 Conclusion -- References -- 6 Multimodal Context-Sensitive Human Communication Interaction System Using Artificial Intelligence-Based Human-Centered Computing -- 6.1 Introduction -- 6.2 Literature Survey -- 6.3 Proposed Model -- 6.3.1 Multimodal Data -- 6.3.2 Dimensionality Reduction -- 6.3.3 Principal Component Analysis -- 6.3.4 Reduce the Number of Dimensions -- 6.3.5 CNN -- 6.3.6 CNN Layers -- 6.3.6.1 Convolution Layers -- 6.3.6.2 Padding Layer -- 6.3.6.3 Pooling/Subsampling Layers -- 6.3.6.4 Nonlinear Layers -- 6.3.7 ReLU -- 6.3.7.1 Fully Connected Layers -- 6.3.7.2 Activation Layer -- 6.3.8 LSTM -- 6.3.9 Weighted Combination of Networks -- 6.4 Experimental Results -- 6.4.1 Accuracy -- 6.4.2 Sensibility -- 6.4.3 Specificity -- 6.4.4 A Predictive Positive Value (PPV) -- 6.4.5 Negative Predictive Value (NPV) -- 6.5 Conclusion -- 6.6 Future Scope -- References -- 7 AI, Planning and Control Algorithms for IoRT Systems -- 7.1 Introduction -- 7.2 General "]
[{"$nulls":4},"Architecture of IoRT -- 7.2.1 Hardware Layer -- 7.2.2 Network Layer -- 7.2.3 Internet Layer -- 7.2.4 Infrastructure Layer -- 7.2.5 Application Layer -- 7.3 Artificial Intelligence in IoRT Systems -- 7.3.1 Technologies of Robotic Things -- 7.3.2 Artificial Intelligence in IoRT -- 7.4 Control Algorithms and Procedures for IoRT Systems."]
[{"$nulls":4},"7.4.1 Adaptation of IoRT Technologies -- 7.4.2 Multi-Robotic Technologies -- 7.5 Application of IoRT in Different Fields -- References -- 8 Enhancements in Communication Protocols That Powered IoRT -- 8.1 Introduction -- 8.2 IoRT Communication Architecture -- 8.2.1 Robots and Things -- 8.2.2 Wireless Link Layer -- 8.2.3 Networking Layer -- 8.2.4 Communication Layer -- 8.2.5 Application Layer -- 8.3 Bridging Robotics and IoT -- 8.4 Robot as a Node in IoT -- 8.4.1 Enhancements in Low Power WPANs -- 8.4.1.1 Enhancements in IEEE 802.15.4 -- 8.4.1.2 Enhancements in Bluetooth -- 8.4.1.3 Network Layer Protocols -- 8.4.2 Enhancements in Low Power WLANs -- 8.4.2.1 Enhancements in IEEE 802.11 -- 8.4.3 Enhancements in Low Power WWANs -- 8.4.3.1 LoRaWAN -- 8.4.3.2 5G -- 8.5 Robots as Edge Device in IoT -- 8.5.1 Constrained RESTful Environments (CoRE) -- 8.5.2 The Constrained Application Protocol (CoAP) -- 8.5.2.1 Latest in CoAP -- 8.5.3 The MQTT-SN Protocol -- 8.5.4 The Data Distribution Service (DDS) -- 8.5.5 Data Formats -- 8.6 Challenges and Research Solutions -- 8.7 Open Platforms for IoRT Applications -- 8.8 Industrial Drive for Interoperability -- 8.8.1 The Zigbee Alliance -- 8.8.2 The Thread Group -- 8.8.3 The WiFi Alliance -- 8.8.4 The LoRa Alliance -- 8.9 Conclusion -- References -- 9 Real Time Hazardous Gas Classification and Management System Using Artificial Neural Networks -- 9.1 Introduction -- 9.2 Existing Methodology -- 9.3 Proposed Methodology -- 9.4 Hardware &amp -- Software Requirements -- 9.4.1 Hardware Requirements -- 9.4.1.1 Gas Sensors Employed in Hazardous Detection -- 9.4.1.2 NI Wireless Sensor Node 3202 -- 9.4.1.3 NI WSN Gateway (NI 9795) -- 9.4.1.4 COMPACT RIO (NI-9082) -- 9.5 Experimental Setup -- 9.5.1 Data Set Preparation -- 9.5.2 Artificial Neural Network Model Creation -- 9.6 Results and Discussion."]
[{"$nulls":4},"9.7 Conclusion and Future Work -- References -- 10 Hierarchical Elitism GSO Algorithm For Pattern Recognition -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Methodology -- 10.3.1 Additive Kuan Speckle Noise Filtering Model -- 10.3.2 Hierarchical Elitism Gene GSO of MNN in Pattern Recognition -- 10.4 Experimental Setup -- 10.5 Discussion -- 10.5.1 Scenario 1: Computational Time -- 10.5.2 Scenario 2: Computational Complexity -- 10.5.3 Scenario 3: Pattern Recognition Accuracy -- 10.6 Conclusion -- References -- 11 Multidimensional Survey of Machine Learning Application in IoT (Internet of Things) -- 11.1 Machine Learning-An Introduction -- 11.1.1 Classification of Machine Learning -- 11.2 Internet of Things -- 11.3 ML in IoT -- 11.3.1 Overview -- 11.4 Literature Review -- 11.5 Different Machine Learning Algorithm -- 11.5.1 Bayesian Measurements -- 11.5.2 K-Nearest Neighbors (k-NN) -- 11.5.3 Neural Network -- 11.5.4 Decision Tree (DT) -- 11.5.5 Principal Component Analysis (PCA) t -- 11.5.6 K-Mean Calculations -- 11.5.7 Strength Teaching -- 11.6 Internet of Things in Different Frameworks -- 11.6.1 Computing Framework -- 11.6.1.1 Fog Calculation -- 11.6.1.2 Estimation Edge -- 11.6.1.3 Distributed Computing -- 11.6.1.4 Circulated Figuring -- 11.7 Smart Cities -- 11.7.1 Use Case -- 11.7.1.1 Insightful Vitality -- 11.7.1.2 Brilliant Portability -- 11.7.1.3 Urban Arranging -- 11.7.2 Attributes of the Smart City -- 11.8 Smart Transportation -- 11.8.1 Machine Learning and IoT in Smart Transportation -- 11.8.2 Markov Model -- 11.8.3 Decision Structures "]
[{"$nulls":4},"-- 11.9 Application of Research -- 11.9.1 In Energy -- 11.9.2 In Routing -- 11.9.3 In Living -- 11.9.4 Application in Industry -- 11.10 Machine Learning for IoT Security -- 11.10.1 Used Machine Learning Algorithms -- 11.10.2 Intrusion Detection -- 11.10.3 Authentication -- 11.11 Conclusion -- References."]
[{"$nulls":4},"12 IoT-Based Bias Analysis in Acoustic Feedback Using Time-Variant Adaptive Algorithm in Hearing Aids."]