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Computational Sciences and Sustainable Technologies [[electronic resource] ] : First International Conference, ICCSST 2023, Bangalore, India, May 8–9, 2023, Revised Selected Papers / / edited by Sagaya Aurelia, Chandra J., Ashok Immanuel, Joseph Mani, Vijaya Padmanabha
| Computational Sciences and Sustainable Technologies [[electronic resource] ] : First International Conference, ICCSST 2023, Bangalore, India, May 8–9, 2023, Revised Selected Papers / / edited by Sagaya Aurelia, Chandra J., Ashok Immanuel, Joseph Mani, Vijaya Padmanabha |
| Autore | Aurelia Sagaya |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (516 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
JChandra
ImmanuelAshok ManiJoseph PadmanabhaVijaya |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Artificial intelligence
Database management Machine learning Application software Computer engineering Computer networks Artificial Intelligence Database Management System Machine Learning Computer and Information Systems Applications Computer Engineering and Networks Computer Communication Networks |
| ISBN | 3-031-50993-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Organization -- Contents -- Performance Evaluation of Metaheuristics-Tuned Deep Neural Networks for HealthCare 4.0 -- 1 Introduction -- 2 Background and Related Works -- 3 Methods -- 3.1 Original Sine Cosine Algorithm (SCA) -- 3.2 SCA Bat Search Algorithm (SCA-BS) -- 4 Experiments and Comparative Analysis -- 4.1 Liver Disorder Dataset Details -- 4.2 Dermatology Dataset Details -- 4.3 Hepatitis Dataset Details -- 4.4 Experimental Setup -- 4.5 Evaluation Metrics -- 5 Results and Discussion -- 5.1 Liver Disorder Dataset Results -- 5.2 Dermatology Dataset Results -- 5.3 Hepatitis Dataset Results -- 6 Conclusion -- References -- Early Prediction of At-Risk Students in Higher Education Institutions Using Adaptive Dwarf Mongoose Optimization Enabled Deep Learning -- 1 Introduction -- 2 Motivation -- 2.1 Literature Survey -- 2.2 Major Challenges -- 3 Proposed ADMOADNFN for Prediction At-Risk Students -- 3.1 Data Acquisition -- 3.2 Data Transformation -- 3.3 Feature Selection -- 3.4 Data Augmentation (Oversampling) -- 3.5 Performance Prediction to Determine at Risk Students -- 4 Results and Discussion -- 4.1 Experimental Results -- 4.2 Dataset Description -- 4.3 Evaluation Metrics -- 4.4 Comparative Techniques -- 4.5 Comparative Discussion -- 5 Conclusion -- References -- Decomposition Aided Bidirectional Long-Short-Term Memory Optimized by Hybrid Metaheuristic Applied for Wind Power Forecasting -- 1 Introduction -- 2 Related Works -- 2.1 Variational Mode Decomposition VMD -- 2.2 Bidirectional Long Short-Term Memory (BiLSTM) -- 2.3 Metaheuristics Optimization -- 3 Methods -- 3.1 Original Reptile Search Algorithm (RSA) -- 3.2 Hybrid RSA (HRSA) -- 4 Experimental Setup -- 4.1 Dataset -- 4.2 Metrics -- 4.3 Setup -- 5 Results and Discussion -- 6 Conclusion -- References.
Interpretable Drug Resistance Prediction for Patients on Anti-Retroviral Therapies (ART) -- 1 Introduction -- 2 Background and Motivation -- 3 Literature Review -- 3.1 Research Gaps -- 3.2 Paper Contributions -- 4 Data Analysis and Methods -- 4.1 Dataset Description -- 4.2 Data Preparation and Exploratory Data Analysis -- 4.3 Methodology -- 4.4 Model Evaluation -- 4.5 Feature Importance -- 5 Results and Discussion -- 5.1 ML Model Selection and Optimization -- 5.2 ML Model Selection Accountability -- 6 Conclusion and Future Works -- References -- Development of a Blockchain-Based Vehicle History System -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 4 Design -- 4.1 Manufacturer-Dealer Workflow -- 4.2 Vehicle Sale/ Registration Workflow -- 4.3 Vehicle Transfer Workflow -- 4.4 Vehicle Resale Workflow -- 5 Testing and Evaluation -- 6 Results and Discussion -- 7 Conclusions -- References -- Social Distancing and Face Mask Detection Using YOLO Object Detection Algorithm -- 1 Introduction -- 2 Related Works -- 3 Relevant Methodologies -- 3.1 Convolutional Neural Network (CNN) -- 3.2 Object Detection -- 3.3 YOLO Object Detection Model -- 3.4 Faster R-CNN -- 3.5 Single-Shot Detector (SSD) -- 3.6 AlexNet -- 3.7 Inception V3 -- 3.8 MobileNet -- 3.9 Visual Geometry Group (VGG) -- 4 Implementation -- 4.1 Face Mask Detectıon -- 4.2 Socıal Dıstance Detectıon -- 5 Results -- 6 Conclusion and Future Scope -- References -- Review on Colon Cancer Prevention Techniques and Polyp Classification -- 1 Introduction -- 1.1 Objectives -- 2 Design -- 3 Setting and Participants -- 4 Methods -- 5 Results -- 6 Conclusion and Implications -- References -- Security Testing of Android Applications Using Drozer -- 1 Introduction -- 2 Methodology -- 2.1 Online Questionnaire -- 2.2 Emulation of Penetration Testing -- 3 Implementation -- 3.1 Retrieving Package Information. 3.2 Identifying the Attack Surface -- 3.3 Identifying and Launching Activities -- 3.4 Exploiting Content Providers -- 3.5 Interacting with Services -- 3.6 Listing Broadcast Receivers -- 4 Results and Discussion -- 4.1 Questionnaire Results and Discussion -- 4.2 Vulnerability Testing Results -- 5 Conclusions -- References -- Contemporary Global Trends in Small Project Management Practices and Their Impact on Oman -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Results and Discussion -- 4.1 Participants -- 5 Conclusion -- References -- Early Prediction of Sepsis Using Machine Learning Algorithms: A Review -- 1 Introduction -- 1.1 Description of Sepsis -- 1.2 Challenges -- 2 Methodology -- 2.1 Pathogenesis -- 2.2 Host Response -- 2.3 Analysis and Selection of Patients -- 2.4 Collection of Data -- 2.5 Data Imputation -- 3 Model Design and Technique -- 3.1 Gradient Boosting -- 3.2 Random Forest Model -- 3.3 Support Vector Machine -- 3.4 XG Boost Algorithm -- 4 Conclusion -- References -- Solve My Problem-Grievance Redressal System -- 1 Introduction -- 2 Literature Review -- 3 Problem Statement -- 4 Existing Systems -- 5 Proposed System -- 6 Implementation -- 7 Conclusion -- References -- Finite Automata Application in Monitoring the Digital Scoreboard of a Cricket Game -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Tracking of On-Strike Batsman -- 3.2 Ball Tracking in an Over -- 4 Results/Discussion -- 5 Conclusion -- References -- Diabetes Prediction Using Machine Learning: A Detailed Insight -- 1 Introduction -- 2 Identification of Symptoms for Diabetes Prediction -- 3 Feature Analysis -- 4 Comparative Analysis of Different ML Algorithms in Diabetes Onset Prediction -- 5 Conclusion -- References -- Empirical Analysis of Resource Scheduling Algorithms in Cloud Simulated Environment -- 1 Introduction -- 2 Literature Review. 3 EA of the Results and Their Implications -- 3.1 EA Concerning A.S.T -- 3.2 EA Concerning A.C.T -- 3.3 EA Concerning A.T.A.T -- 3.4 EA Concerning A.C -- 4 Improving Resource Scheduling Using Intelligence Mechanism -- 5 Conclusion -- References -- A Recommendation Model System Using Health Aware- Krill Herd Optimization that Develops Food Habits and Retains Physical Fitness -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Architecture of RecSys -- 3.2 Krill Herd Algorithm for Optimization -- 3.3 Genetic Operators -- 3.4 Recommendation System (Recsys) Using KHO Algorithm -- 3.5 Evaluation of Fitness Value -- 4 Results and Analysis -- 4.1 Quantitative Analysis -- 4.2 Qualitative Analysis -- 5 Conclusion -- References -- Video Summarization on E-Sport -- 1 Introduction -- 2 Literature Review -- 3 Proposed System -- 3.1 Flow Diagram -- 3.2 Algorithmic Steps -- 4 Implementation -- 5 Results -- 6 Conclusion -- References -- SQL Injection Attack Detection and Prevention Based on Manipulating the SQL Query Input Attributes -- 1 Introduction -- 2 SQL-Injection Attacks -- 3 Work Model of SQL Injection -- 4 Related Work -- 5 Proposed Work -- 5.1 Proposed Algorithm for Replacing Special String Constraints Instead of Input Parameter -- 5.2 Levenshtein Method -- 6 Implementation -- 7 Conclusion and Future Work -- References -- Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics -- 1 Introduction -- 2 Problem Statement -- 3 Literature Review -- 3.1 Convolutional Neural Networks -- 3.2 FaceNet -- 3.3 ArcFace -- 3.4 OpenFace -- 3.5 RetinaFace -- 4 Design Methodology -- 4.1 Face Extraction by RetinaFace -- 4.2 Vectorization by FaceNet, ArcFace and OpenFace -- 4.3 Results -- 4.4 Loss Functions -- 5 Conclusion -- References. Hash Edward Curve Signcryption for Secure Big Data Transmission -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Theil-Sen Robust Linear Regression -- 3.2 Pseudoephemeral Kupyna HashEdward-Signcryption-Based Secure Data Transmission -- 4 Assessment Settings -- 5 Performance Comparison -- 6 Conclusion -- References -- An Energy Efficient, Spontaneous, Multi-path Data Routing Algorithm with Private Key Creation for Heterogeneous Network -- 1 Introduction -- 1.1 Difficulties in Formation of a Heterogeneous Network -- 2 Literature Survey -- 2.1 Contribution of Proposed Research Mechanism -- 3 Intelligent Swarm Adapted Colony Based Optimization Methodology -- 4 Spontaneous Energy Proficient Multi-path Data Routing (SEPMDR) -- 4.1 Formation of Basic Network Metrics -- 4.2 Algorithm 1 for SEPMDR -- 4.3 Algorithm 2 for Private Key Creation -- 4.4 Operational Phases of SEPMDR -- 5 Performance Evaluation and Its Results -- 5.1 Power Conservation of Nodes -- 5.2 Comparison of Packet Delivery Ratio (PDR) of the Proposed System -- 5.3 Evaluation of Routing Overhead of the Different Methodologies -- 5.4 Comparison of Network Throughput -- 6 Conclusion and Future Scope -- References -- A Hybrid Model for Epileptic Seizure Prediction Using EEG Data -- 1 Introduction -- 1.1 Organization -- 2 Related Work -- 3 Proposed Methodology -- 3.1 Preprocessing of EEG signals -- 3.2 Feature Extraction -- 3.3 Classification -- 4 Performance Analysis -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Performance Analysis -- 5 Conclusion -- References -- Adapting to Noise in Forensic Speaker Verification Using GMM-UBM I-Vector Method in High-Noise Backgrounds -- 1 Introduction -- 2 Data Acquisition -- 3 Feature Extraction -- 4 Mel-Frequency Cepstral Coefficients (MFCC) -- 5 Speaker Verification System Using GMM-UBM I Vector Frame Work. 6 Modified Feature Extraction for Noise Adapting. |
| Record Nr. | UNINA-9910831008003321 |
Aurelia Sagaya
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Immersive Technology in Smart Cities : Augmented and Virtual Reality in IoT
| Immersive Technology in Smart Cities : Augmented and Virtual Reality in IoT |
| Autore | Aurelia Sagaya |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2021 |
| Descrizione fisica | 1 online resource (270 pages) |
| Altri autori (Persone) | PaivaSara |
| Collana | EAI/Springer Innovations in Communication and Computing Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN | 3-030-66607-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- Chapter 1: Exploring Immersive Technology in Education for Smart Cities -- 1.1 Introduction -- 1.1.1 How Does the Technology Fit and What Are the Benefits? -- 1.2 Augmented Reality in Education -- 1.2.1 Remote Collaborative Classrooms -- 1.2.2 Safer Experiments and Demonstrations -- 1.3 Immersive Technology in Four Cs of Learning -- 1.3.1 Critical Thinking and Problem-Solving -- 1.3.2 Creativity and Innovation -- 1.3.3 Collaboration -- 1.3.4 Effective Communication -- 1.4 Applications of Immersive Technology in Education -- 1.4.1 Engineering Education -- 1.4.2 Medical Education -- 1.4.3 Complex Concepts in Mathematics and Space Technology -- 1.4.4 General Education -- 1.5 Research Method -- 1.5.1 Research Design -- 1.5.2 Sample of Study -- 1.5.3 AstroSolar Application -- 1.5.4 Research Process -- 1.5.5 Data Collection Utilities -- 1.6 Results -- 1.6.1 Expert's Interview -- 1.6.2 SUS Score -- 1.6.3 Usability and Learnability Factor -- 1.6.4 Feedback on Positive and Negative SUS Questionnaire -- 1.7 Conclusion and Future Work -- References -- Chapter 2: Immersive Learning About IC-Engine Using Augmented Reality -- 2.1 Introduction -- 2.2 Literature Review -- 2.3 Problem Statement and Objective -- 2.4 Methodology -- 2.5 Block Diagram -- 2.6 Implementation -- 2.7 Conclusion and Future Work -- References -- Chapter 3: Location-Based Mobile Augmented Reality Systems: A Systematic Review -- 3.1 Introduction -- 3.2 LBMAR Systems: A Walkthrough of Common Applications and Current State -- 3.3 Research Questions -- 3.4 Research Methods -- 3.5 Planning the Review -- 3.5.1 Data Sources -- 3.5.2 Search Terms -- 3.5.3 Inclusion and Exclusion Criteria -- 3.5.4 Categories for Analysis and Data Coding -- 3.6 Conducting the Review and Reporting the Review -- 3.6.1 Future Research -- 3.7 Conclusion -- References.
Chapter 4: Innovative Natural Disaster Precautionary Methods Through Virtual Space -- 4.1 Introduction -- 4.2 Virtual Reality -- 4.2.1 Working of Virtual Reality -- 4.2.2 Key Features of Virtual Reality Technology -- 4.3 Natural Disasters -- 4.3.1 Causes of Natural Disasters -- 4.4 State of the Art -- 4.5 Proposed Model -- 4.5.1 The Type of Natural Disaster -- 4.5.2 Environment -- 4.5.3 Training -- 4.5.4 Precautionary Measures -- 4.5.5 Scenario Simulation -- 4.5.6 Trainee's Interaction -- 4.6 Result and Discussion -- 4.6.1 Simulation Process -- 4.7 Limitation -- 4.8 Future Enhancements -- 4.9 Conclusion -- References -- Chapter 5: Internet of Things: Immersive Healthcare Technologies -- 5.1 Introduction to Internet of Things -- 5.1.1 IoT Ecosystem and Its Components -- 5.2 Architecture of Internet of Things -- 5.3 Internet of Things in Healthcare -- 5.3.1 IoT Services -- 5.3.1.1 Elderly Assistance Through Ambient-Assisted Living Technology -- 5.3.1.2 IoMT (Internet of M Health) -- 5.3.1.3 Assistance Provided for Adverse Drug Reaction Patients -- 5.3.1.4 Health Concerns of the Public -- 5.3.1.5 IoT Healthcare with the Integration of Wearable Devices -- 5.3.1.6 Emergency Healthcare for Natural Disasters -- 5.3.1.7 Configuration of the Embedded Gateways -- 5.3.2 IoT Applications -- 5.3.2.1 Single Condition -- 5.3.2.2 Clustered-Condition Applications -- 5.4 More Details on the Applications in Healthcare -- 5.4.1 IoT Applications in Healthcare -- 5.4.1.1 Sensors and Technology Used for the Diseases -- 5.4.1.2 Apps in Use for Healthcare -- 5.5 Architecture for Healthcare-Based Internet of Things -- 5.5.1 Perception Layer -- 5.5.2 Network Layer -- 5.5.3 Middleware Layer -- 5.5.4 Application Layer -- 5.5.5 Business Layer -- 5.6 Challenges in Deployment of Healthcare System -- 5.6.1 Rules Regarding Standardization of Merchants and Sellers of Medical Devices. 5.6.2 Analyzing the Cost Effectiveness -- 5.6.3 The Process of Developing the Application -- 5.6.4 Low Power Requirements -- 5.6.5 Types of Network: Data Centric, Service, and Patients Centric -- 5.6.6 Issue of Reducing Scalability -- 5.6.7 Arise of New Conditions and Diseases -- 5.6.8 Identification and Managing Resources -- 5.6.9 The Issue of Quality of Service -- 5.6.10 Managing and Protecting the Data -- 5.6.11 Mobility and Heterogeneous Nature -- 5.7 Security in IoT Healthcare -- 5.7.1 Analyzing the Security Requirements in IoT -- 5.7.2 Security Issues in IoT Healthcare -- 5.7.3 Secured IoT Healthcare -- 5.8 Conclusion -- References -- Chapter 6: Implementation of an Intelligent Model Based on Big Data and Decision-Making Using Fuzzy Logic Type-2 for the Car Assembly Industry in an Industrial Estate in Northern Mexico -- 6.1 Introduction -- 6.1.1 Proposal Methodology -- 6.1.2 Main Stakeholders or Interest Groups -- 6.2 Business Simulators as Knowledge Manager -- 6.2.1 Industry 4.0 -- 6.2.2 Big Data -- 6.2.3 Fuzzy Logic Type-2 -- 6.3 Simulation Execution Methodology -- 6.3.1 Measure Knowledge Management -- 6.3.2 The Intellect Model -- 6.4 Results -- 6.5 Conclusions and Future Research -- 6.5.1 Future Research -- References -- Chapter 7: Cloud Computing Model on Wireless Ad Hoc Network Using Clustering Mechanism for Smart City Applications -- 7.1 Introduction -- 7.1.1 Cloud Computing -- 7.1.2 Clustering Mechanism -- 7.1.3 Clustering Mechanism in Cloud Computing -- 7.1.4 Ad Hoc Networks -- 7.2 Related Works -- 7.2.1 Cloud Computing -- 7.2.2 Ad Hoc Networks -- 7.2.3 Clustering Mechanism in Cloud Computing -- 7.3 Clustering in Cloud Computing -- 7.3.1 Pseudocode: Cluster Creation -- 7.3.2 Pseudocode: Clusterhead Election -- 7.3.3 Experimental Results -- 7.4 Cloud Computing Model on Clustered Wireless Ad Hoc Networks. 7.5 Clustered Wireless Ad Hoc Cloud Network for Smart City Applications -- 7.5.1 Storage and Resource Sharing -- 7.5.2 Data Analytics -- 7.5.3 Virtual Machine Clustering -- 7.5.4 Fog Computing or Edge Computing -- 7.5.5 Green Computing -- 7.6 Conclusion -- References -- Chapter 8: Smart Cities New Paradigm Applications and Challenges -- 8.1 Introduction -- 8.2 The Service Delivery Progression from Push Model into the Ecosystems Model -- 8.3 Pillars of the Fourth Industrial Revolution -- 8.4 The Current State of Smart Services -- 8.5 Smart Cities Services Vs. Smart Connected Giant Technology and Services "SCGTS" -- 8.6 Proposed Smart Cities Collaborative Ecosystems -- 8.6.1 Infrastructure Layer -- 8.6.2 Application Layer -- 8.6.2.1 Application to Application Exchange -- 8.6.2.2 Application to Data Zone Exchange -- 8.6.2.3 Application to Grade Service Exchange -- 8.6.2.4 Application to Infrastructure Exchange -- 8.6.3 Services Layer -- 8.6.4 Cloud of Digital Data -- 8.6.5 End Users -- 8.7 Smart Applications -- 8.7.1 Smart Urban Energy Systems -- 8.7.1.1 Energy Infrastructure -- 8.7.1.2 Energy Applications -- 8.7.1.3 Energy Cloud Zone -- 8.7.1.4 End Users -- 8.7.1.5 Energy Services -- 8.7.1.6 Energy Standardization and Protocols -- 8.7.2 Smart Urban Transportation Systems -- 8.7.2.1 Transportations Infrastructure -- 8.7.2.2 Transportation Applications -- 8.7.2.3 Transportation Cloud Zone -- 8.7.2.4 End Users -- 8.7.2.5 Transportation Services -- 8.7.2.6 Transportation Standardized and Protocols -- 8.8 Transition Plan Properties and Challenges -- 8.9 Conclusion -- 8.10 Exercises -- 8.10.1 Short Answers Questions -- 8.10.2 True/False Questions -- References -- Chapter 9: A Survey on IoT Applications in Smart Cities -- 9.1 Introduction -- 9.2 Related Work -- 9.3 Applications of IoT in Smart Cities -- 9.3.1 Smart Security -- 9.3.2 Smart Services. 9.3.3 Smart Infrastructure -- 9.3.4 Smart Home and Buildings -- 9.3.5 Smart Environment -- 9.3.6 Waste Management -- 9.3.7 Smart Grid -- 9.3.8 E-Governance -- 9.3.9 Smart Agriculture and Animal Farming -- 9.4 Conclusion -- References -- Chapter 10: IoT-Based Water Quality and Quantity Monitoring System for Domestic Usage -- 10.1 Introduction -- 10.1.1 Overview of the Existing Systems -- 10.2 Proposed System -- 10.2.1 Ultrasonic Sensor -- 10.2.2 Turbidity Sensor -- 10.2.3 pH Sensor -- 10.2.4 NTC Thermistor -- 10.2.5 Flow Measurement -- 10.2.6 Arduino UNO -- 10.2.7 RF Module -- 10.2.8 LED -- 10.2.9 LCD -- 10.3 Results and Discussion -- 10.3.1 Steps for Connection -- 10.4 Conclusion and Future Scope -- References -- Chapter 11: Threat Modeling and IoT Attack Surfaces -- 11.1 Introduction -- 11.2 IoT Layered Architecture -- 11.3 IoT Technologies and Protocols -- 11.4 IoT Operating Systems -- 11.5 IT Communication Model -- 11.5.1 Device-to-Device Model -- 11.5.2 Device-to-Cloud Model -- 11.5.3 Device to Gateway Model -- 11.5.4 Back-End Data Sharing Model -- 11.6 IoT Issues and Challenges -- 11.6.1 IoT Security Problems -- 11.7 IoT Vulnerabilities and Attack Surfaces -- 11.8 Tools and Techniques -- 11.8.1 Defend IoT Security Issues -- 11.9 Conclusion -- References -- Index. |
| Altri titoli varianti | Immersive Technology in Smart Cities |
| Record Nr. | UNINA-9910497091203321 |
|
Aurelia Sagaya
|
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| Cham : , : Springer International Publishing AG, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||