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Adaptive and Scalable Blockchain Systems / / by Jie Xu, Xiaohua Jia
| Adaptive and Scalable Blockchain Systems / / by Jie Xu, Xiaohua Jia |
| Autore | Xu Jie |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (199 pages) |
| Disciplina | 621.382 |
| Altri autori (Persone) | JiaXiaohua |
| Collana | Wireless Networks |
| Soggetto topico |
Telecommunication
Cooperating objects (Computer systems) Blockchains (Databases) Communications Engineering, Networks Cyber-Physical Systems Blockchain |
| ISBN | 3-031-90811-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Overview of Blockchain -- Blockchain Consensus Protocols -- Adaptive and Scalable Design for Proof of Work Blockchains -- Adaptive and Scalable Design for Proof of Stake Blockchains -- Adaptive and Scalable Design for Sharding-based Blockchains -- Future Work -- Conclusion. |
| Record Nr. | UNINA-9911015969103321 |
Xu Jie
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Distributed Computing and Machine Learning : Proceedings of ICADCML 2023 / / edited by Suchismita Chinara, Asis Kumar Tripathy, Kuan-Ching Li, Jyoti Prakash Sahoo, Alekha Kumar Mishra
| Advances in Distributed Computing and Machine Learning : Proceedings of ICADCML 2023 / / edited by Suchismita Chinara, Asis Kumar Tripathy, Kuan-Ching Li, Jyoti Prakash Sahoo, Alekha Kumar Mishra |
| Autore | Chinara Suchismita |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (600 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
TripathyAsis Kumar
LiKuan-Ching SahooJyoti Prakash MishraAlekha Kumar |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Machine learning Blockchains (Databases) Internet of things Computational Intelligence Artificial Intelligence Machine Learning Blockchain Internet of Things |
| ISBN | 981-9912-03-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | NS3-based Performance Assessment of Routing Protocols AODV, OLSR and DSDV for VANETs -- A Novel Blockchain Based Smart Contract for Real estate Management -- A Review on VM Placement Scheme using Optimization Algorithms -- Use of Blockchain to Prevent Distributed Denial-of-service (DDoS) Attack: A Systematic Literature Review -- CS-based Energy-Efficient Service Allocation in Cloud. |
| Record Nr. | UNINA-9910734882003321 |
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Chinara Suchismita
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Distributed Computing and Machine Learning : Proceedings of ICADCML 2024, Volume 2 / / edited by Umakanta Nanda, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar, Kuan-Ching Li
| Advances in Distributed Computing and Machine Learning : Proceedings of ICADCML 2024, Volume 2 / / edited by Umakanta Nanda, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar, Kuan-Ching Li |
| Autore | Nanda Umakanta |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (482 pages) |
| Disciplina | 004.36 |
| Altri autori (Persone) |
TripathyAsis Kumar
SahooJyoti Prakash SarkarMahasweta LiKuan-Ching |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Machine learning Blockchains (Databases) Internet of things Computational Intelligence Artificial Intelligence Machine Learning Blockchain Internet of Things |
| ISBN |
9789819735235
9789819735228 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Editors and Contributors -- OSNR Monitoring for QPSK and QAM in Fiber-Optic Networks Using Machine Learning -- 1 Introduction -- 2 Proposed Method -- 3 Support Vector Machine Algorithms -- 4 Simulation Results and Discussion -- 5 Conclusion and Future Research -- References -- Classification of Star and Galaxy Objects Utilizing Machine Learning Techniques and Deep Neural Networks -- 1 Introduction -- 2 Dataset -- 2.1 Processing Data -- 3 Machine Learning Approach for Star Versus Galaxy Classification -- 4 Convolutional Neural Networks-(CNN) -- 4.1 Convolutional Layers -- 4.2 Implementation Details -- 5 Result and Analysis -- 6 Conclusion -- References -- Probabilistic Forecasting Analysis on Electric Load Systems -- 1 Introduction -- 2 Review of Literature -- 3 Description of the Model -- 4 Sources of Data Generation -- 5 Computational Analysis and Results -- 5.1 Representation of ELG Units -- 5.2 Correlation Analysis -- 5.3 Bivariate Normal Distribution -- 5.4 Linear Regression and ARIMA Models -- 5.5 Electricity Consumption Charges -- 6 Conclusion -- References -- Smart City Survey on AIoT Using Machine Learning, Deep Learning, and Its Computing Tools -- 1 Introduction -- 2 IoT-Oriented Perspective -- 2.1 Smart Infrastructure -- 2.2 Air Management -- 2.3 Traffic Management -- 2.4 Waste Management -- 3 ML-Orient Perspective -- 3.1 Infrastructure -- 3.2 Air Management -- 3.3 Traffic Analysis -- 3.4 Waste Management -- 4 Deep Learning-Oriented Perspective -- 4.1 Supervised Learning -- 4.2 Unsupervised Learning -- 4.3 Reinforcement Learning -- 5 Computing Tools for Smart City -- 5.1 Cloud Computing -- 5.2 Fog Computing -- 5.3 Edge Computing -- 6 Conclusion -- References -- Energy Harvesting Integrated Sensor Node Architecture for Sustainable IoT Networks -- 1 Introduction -- 1.1 Contributions Made in This Research.
2 Literature Study on Energy Harvesting -- 3 System Architecture -- 3.1 Hardware Requirements -- 3.2 Circuit Implementation -- 3.3 Energy Source: The PV Cell -- 3.4 Energy Storage Structures -- 3.5 Power Management Protocols -- 4 Lifetime Evaluation with Solar Energy Harvester -- 4.1 System Implementation and Analysis -- 5 Conclusion -- References -- Enhancing Real Estate Price Prediction in Smart Cities: A Comparative Analysis of Machine Learning Techniques -- 1 Introduction -- 2 Related Work -- 3 Limitation -- 4 Methodology -- 4.1 Feature Engineering -- 4.2 Model Description and Predicting the Value -- 5 Results -- 6 Conclusion -- 7 Future Work -- References -- Real-Time AI-Based Face-Mask Detection -- 1 Introduction -- 2 Proposed Design Approach -- 2.1 Custom Dataset Gathering -- 2.2 Data Augmentation for Best Results -- 2.3 Training Model -- 3 Methodology -- 3.1 YOLO Algorithm -- 3.2 MobileNetV2 -- 4 Results and Discussion -- 5 Conclusion -- References -- A Logical Model for Multiple People Activity Recognition Using Non-intrusive Sensors for Geriatric Care -- 1 Introduction -- 2 Related Work -- 3 Problem Scenario -- 4 Logical FHMM for Multiple People Activity Recognition -- 4.1 Solution Overview -- 5 Experiments -- 5.1 Experimental Setup -- 6 Conclusion -- References -- From Sea to Table: A Blockchain-Enabled Framework for Transparent and Sustainable Seafood Supply Chains -- 1 Introduction -- 2 Related Work -- 3 Seafood Supply Chain and Blockchain -- 4 Conceptual Blueprint -- 4.1 The Flow of Code Implementation -- 5 Result -- 6 Discussion -- 7 Conclusion and Future Scope -- References -- Distributed State Estimation for GPS Navigation: The Correntropy Extended Kalman Filter Approach -- 1 Introduction -- 2 Literature Study -- 3 Correntropy Extended Kalman Filter -- 4 Results and Discussion -- 5 Conclusion -- References. Nayantara: Crime Analysis from CCTV Footage Using MobileNet-V2 and Transfer Learning -- 1 Introduction -- 2 Related Works -- 3 Proposed Methodology -- 3.1 System Architecture -- 3.2 Detection Model -- 3.3 Web Application -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 Working of the Detection Algorithm -- 4.4 CNN Model -- 4.5 Results -- 5 Conclusion -- References -- Bird Detection in Microlight Aircraft Strip Using YOLOv8for Adventure Tourism -- 1 Introduction -- 2 Bigdata Analytics Unlocks for Tourism Industry -- 2.1 Why is Microlight Aircraft Safety Important? -- 3 Literature Review -- 4 Implementation and Discussion -- 4.1 Methodology Used -- 4.2 Dataset Used -- 5 Performance Analysis and Results -- 6 Conclusion -- References -- A Graphical Tuning Method-Based Robust PID Controller for Twin-Rotor MIMO System with Loop Shaping Technique -- 1 Introduction -- 2 Preliminaries -- 2.1 Description of Twin-Rotor MIMO System -- 2.2 Design of Decouplers -- 2.3 FOPDT Model -- 3 upper H Subscript normal infinityHinfty Controller -- 4 Results an Discussions -- 5 Conclusion -- References -- Signature Verification Using Deep Learning: An Empirical Study -- 1 Introduction -- 2 Proposed Method -- 2.1 Data Acquisition -- 2.2 Pre-processing -- 2.3 Feature Extraction -- 2.4 Model and Algorithm Hyperparameters -- 2.5 Optimizing Algorithm -- 2.6 Batch Normalization and Dropout -- 3 Results -- 3.1 Performance Stats -- 3.2 Evaluation Metrics -- 4 Discussion -- 5 Conclusion -- References -- An Intelligent and Automated Machine Learning-Based Approach for Heart Disease Prediction and Personalized Care -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset Description -- 3.2 Data Pre-processing -- 3.3 Handling Imbalanced Classes -- 3.4 Data Normalization -- 3.5 Feature Relevance Analysis -- 4 Results and Discussion. 4.1 Comparative Analysis -- 5 Conclusion -- References -- Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset -- 3.2 Data Pre-processing -- 3.3 LSTM Architecture -- 4 Results and Discussion -- 4.1 Comparative Analysis -- 5 Conclusion -- References -- Polarity Detection of Online News Articles Using Deep Learning Techniques -- 1 Introduction -- 1.1 Deep Learning and Polarity Detection -- 2 Literature Survey -- 2.1 RNN with GRU -- 2.2 RNN with LSTM -- 2.3 Bidirectional RNN -- 2.4 CNN -- 2.5 Dynamic Dictionaries -- 3 Proposed Method -- 4 Experiment and Result Discussion -- 5 Conclusion and Future Work -- References -- Harnessing ResNet50 and EfficientNetB5 for Detection of Diabetic Retinopathy Using Explainable AI -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Results -- 4.1 Model Performance -- 4.2 Interpretation of Result -- 4.3 Model Explainability -- 5 Conclusion -- References -- A Grey Wolf and Rough Set Hybrid Approach for the Detection of Chronic Kidney Disease -- 1 Introduction -- 2 Schematic Representation of Proposed Research -- 3 Experimental Research on Chronic Kidney Disease -- 4 Result Analysis -- 4.1 Proposed GWRSO Data Analysis -- 5 Conclusion -- References -- Efficient Rice Disease Classification Using Intelligent Techniques -- 1 Introduction -- 2 Methodology -- 3 Data Description -- 3.1 Bacterial Leaf Blight -- 3.2 Brown Spot -- 3.3 Blast -- 3.4 Tungro -- 4 Experimental Setup and Performance Analysis -- 5 Conclusion -- References -- Maize Crop Yield Prediction Using Machine Learning Regression Approach -- 1 Introduction -- 2 Methodology -- 2.1 Dataset -- 2.2 Data Preprocessing -- 2.3 Feature Selection -- 2.4 Data Transformation -- 2.5 Model Building Algorithms -- 2.6 Evaluation Metrics. 3 Experiment and Results -- 3.1 Model Building, Training, and Testing -- 3.2 Dimension Reduction Using Principal Component Analysis (PCA) -- 3.3 Comparison of the Results -- 3.4 Identification of Main Features -- 3.5 Discussion of the Findings -- 4 Conclusion -- References -- Mode Division Multiplexing-Based Passive Optical Networks for High-Capacity Data Rate via Radio Over Fiber Technology -- 1 Introduction -- 2 Proposed Mode Division Multiplexing Passive Optical Network -- 3 Mode Division Multiplexing Layout Simulation by Using OptiSystemV20 -- 4 Simulation Design of MDM with QAM and DSPK -- 5 Simulation Design of MDM for Noise Removal Systems -- 6 Result and Discussion -- 7 Conclusion -- References -- Enhancing Urban Connectivity: Free Space Optics as a Resilient Backup Link for Fiber Networks in Urban Environments -- 1 Introduction -- 2 Proposed Block Diagram of FSO-NRZ System Model -- 3 Result and Discussion -- 4 Conclusion -- References -- Integrating ANSYS Simulation and Machine Learning Techniques for Thermo-Mechanical Analysis of PCBs -- 1 Introduction -- 2 Problem Statement and Methodology -- 3 Results and Discussions -- 4 Conclusions -- References -- Automation of Quality Assessment Procedures in School Education -- 1 Introduction -- 2 Software Tool for Quality Evaluation: Design and Software Prototype Development -- 3 Experiments -- 4 Conclusions -- References -- The FGSM Attack on Image Classification Models and Distillation as Its Defense -- 1 Introduction -- 2 Related Work -- 3 Theoretical Background -- 4 Results of the FGSM Attack -- 4.1 The Classification Results in the Absence of the FGSM Attack -- 4.2 The Classification Results in the Presence of the FGSM Attack -- 5 Distillation for Defense Against the FGSM Attack -- 6 Conclusion -- References -- An Experimentation of Firefly Algorithm Using a Different Set of Objective Functions. 1 Introduction. |
| Record Nr. | UNINA-9910878980403321 |
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Nanda Umakanta
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Distributed Computing and Machine Learning : Proceedings of ICADCML 2024, Volume 1 / / edited by Umakanta Nanda, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar, Kuan-Ching Li
| Advances in Distributed Computing and Machine Learning : Proceedings of ICADCML 2024, Volume 1 / / edited by Umakanta Nanda, Asis Kumar Tripathy, Jyoti Prakash Sahoo, Mahasweta Sarkar, Kuan-Ching Li |
| Autore | Nanda Umakanta |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (483 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
TripathyAsis Kumar
SahooJyoti Prakash SarkarMahasweta LiKuan-Ching |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Machine learning Blockchains (Databases) Internet of things Computational Intelligence Artificial Intelligence Machine Learning Blockchain Internet of Things |
| ISBN |
9789819718412
9789819718405 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Comparative Analysis of Deep Learning Based Hybrid Algorithms for Liver Disease Prediction -- Chapter 2: A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method -- Chapter 3: Face Recognition Using CNN for Monitoring and Surveillance of Neurological Disorder Patients -- Chapter 4: A Review on Satellite Image Segmentation using Metaheuristic Optimization Techniques -- Chapter 5: A framework for enabling artificial intelligence inference for the hardware acceleration of IVIS imaging system -- Chapter 6: Cloud-based Anomaly Detection for Broken Rail Track using LSTM Autoencoders and Cross-modal Audio Analysis -- Chapter 7: A Study on the Mental Health among Indian Population in the Post COVID-19 Pandemic using Computational Intelligence -- Chapter 8: Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud Computing -- Chapter 9: Towards Finger Photoplethysmogram Based Non-Invasive Classification of Diabetic versus Normal -- Chapter 10: Evaluation of Weather Forecasting Models and Handling Anomalies in Short-Term Wind Speed Data. etc. |
| Record Nr. | UNINA-9910865241203321 |
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Nanda Umakanta
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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AI and Blockchain in Healthcare / / edited by Bipin Kumar Rai, Gautam Kumar, Vipin Balyan
| AI and Blockchain in Healthcare / / edited by Bipin Kumar Rai, Gautam Kumar, Vipin Balyan |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (241 pages) |
| Disciplina | 610.285 |
| Collana | Advanced Technologies and Societal Change |
| Soggetto topico |
Blockchains (Databases)
Artificial intelligence Medical care Social medicine Health services administration Blockchain Artificial Intelligence Health Care Health, Medicine and Society Health Care Management |
| ISBN |
9789819903771
9819903777 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine Learning for Drug Discovery and Manufacturing -- Knowledge Strategies Influencing on The Epidemiologists Performance of The Qeshm Island’s Health Centers -- Healthcare: In the Era of Blockchain -- Securing Healthcare records using Blockchain: Applications and Challenges -- Authentication Schemes For Healthcare Data Using Emerging Computing Technologies -- Biomedical data classification using fuzzy clustering -- Applications of Machine Learning in healthcare With a Case Study of Lung Cancer Detection Through Deep Learning Approach -- Fetal Health Status Prediction During Labor and Delivery Based on Cardiotocogram Data using Machine and Deep Learning -- Blockchain and AI: Disruptive Digital Technologies in Designing the Potential Growth of Healthcare Industries. |
| Record Nr. | UNINA-9910720098203321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Android Malware Detection and Adversarial Methods / / by Weina Niu, Xiaosong Zhang, Ran Yan, Jiacheng Gong
| Android Malware Detection and Adversarial Methods / / by Weina Niu, Xiaosong Zhang, Ran Yan, Jiacheng Gong |
| Autore | Niu Weina |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (xiv, 190 pages) : illustrations |
| Disciplina | 005.8 |
| Altri autori (Persone) |
ZhangXiaosong <1968->
YanRan GongJiacheng |
| Soggetto topico |
Computer networks - Security measures
Data protection Data protection - Law and legislation Machine learning Blockchains (Databases) Mobile and Network Security Data and Information Security Security Services Privacy Machine Learning Blockchain Cadena de blocs (Bases de dades) Protecció de dades Aprenentatge automàtic |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-9714-59-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Part I The Overview of Android Malware Detection -- 1 Introduction of Android Malware Detection -- 1.1 Android Malware Family -- 1.1.1 Trojan Horse -- 1.1.2 Viruses -- 1.1.3 The Back Door -- 1.1.4 Zombies -- 1.1.5 Espionage -- 1.1.6 Intimidation -- 1.1.7 Extortion -- 1.1.8 Advertising -- 1.1.9 Tracking -- 1.2 History of Android Malware Detection -- 1.3 Android Malware Detection Overview -- 1.4 Challenges and Apps of Android Malware Detection -- 1.5 Domestic and International Android Malware Detection -- 1.5.1 Android Malware Detection Method Based on Static Analysis -- 1.5.2 Android Malware Detection Method Based on Dynamic Analysis -- 1.5.3 Android Malware Detection Method Based on Hybrid Analysis -- 1.6 Chapter Summary -- References -- Part II The General Android Malware Detection Method -- 2 Feature Code Based Android Malware Detection Method -- 2.1 Detection Based on Traditional Feature Codes -- 2.1.1 Introduction -- 2.1.2 DroidAnalyzer: A Case Study in Android Malware Analysis -- 2.1.2.1 Suspicious Android APIs and Keywords 3 -- 2.1.2.2 Main Algorithm of DroidAnalyzer -- 2.2 Detection Based on Semantic Feature Codes -- 2.2.1 Introduction -- 2.2.2 DroidNative: A Case Study in Android Malware Analysis -- 2.2.2.1 Static Analysis in DroidNative -- 2.2.2.2 System Design and Implementation -- 2.3 Chapter Summary -- References -- 3 Behavior-Based Detection Method for Android Malware -- 3.1 Privacy Disclosure -- 3.2 Permission Escalation -- 3.2.1 Permission Escalation Method -- 3.2.2 Authorization Based on Configuration Files -- 3.2.3 Code Analysis -- 3.2.4 Taint Analysis -- 3.3 Machine Learning Technology and Malicious Behavior of Android Software -- 3.4 Chapter Summary -- References -- 4 AI-Based Android Malware Detection Methods.
4.1 Detection Based on Permissions, APIs, and Components -- 4.1.1 Permissions in Android System -- 4.1.1.1 Permissions in Android System -- 4.1.1.2 Overview of Permission-Based Detection Methods -- 4.1.2 Detection Based on API -- 4.1.3 Component-Based Detection -- 4.1.3.1 Components of an Application -- 4.1.3.2 Overview of Component-Based Detection Methods -- 4.1.4 Specific Case: Drebin -- 4.1.4.1 Static Analysis of Applications -- 4.1.4.2 Embedding in Vector Space -- 4.1.4.3 Learning-Based Detection -- 4.1.4.4 Explanation -- 4.2 Detection Anchored in Dynamic Runtime Features -- 4.2.1 Dynamic Analysis and Runtime Features -- 4.2.2 Overview of Detection Methods Based on Dynamic Runtime Features -- 4.2.3 Specific Case: EnDroid -- 4.2.3.1 Training Phase -- 4.2.3.2 Detection Phase -- 4.3 Detection Through Semantic Code Analysis -- 4.3.1 Dalvik Bytecode -- 4.3.2 Overview of Code Semantic-Based Detection Methods -- 4.3.3 Specific Case: MviiDroid -- 4.3.3.1 Static Analysis Phase -- 4.3.3.2 Feature Generation Phase -- 4.3.3.3 Model Training Phase -- 4.4 Detection via Image Analysis -- 4.4.1 Overview of Image-Based Detection Methods -- 4.4.2 Specific Case: R2-D2 -- 4.5 Detection Through Graph Analysis -- 4.5.1 Overview of Homogeneous Graph-Based Detection Methods -- 4.5.2 Overview of Heterogeneous Graph-Based Detection Methods -- 4.5.3 Case Study: HAWK -- 4.5.3.1 Feature Engineering -- 4.5.3.2 Constructing Heterogeneous Information Network (HIN) -- 4.5.3.3 Constructing Application Graph from HIN -- 4.6 Chapter Summary -- References -- Part III The Adversarial Method for Android Malware Detection -- 5 Static Adversarial Method -- 5.1 Static Obfuscation -- 5.1.1 Code Obfuscation -- 5.1.2 Resource Obfuscation -- 5.1.3 Manifest File Obfuscation -- 5.1.4 Control Flow Obfuscation -- 5.2 Common APK Static Obfuscation Tools -- 5.2.1 Obfuscapk -- 5.2.2 ProGuard. 5.2.3 DexGuard -- 5.2.4 Allatori -- 5.2.5 DashO -- 5.2.6 Bangcle -- 5.2.7 Arxan -- 5.2.8 Comparative Analysis -- 5.3 Research on Static Obfuscation -- 5.3.1 Detection Methods Based on New Features -- 5.3.1.1 Static Detection Based on Perceptual Hashing -- 5.3.1.2 Static Detection Based on Semantic Feature Set -- 5.3.1.3 Static Detection Based on Static Data Streams -- 5.3.1.4 Static Detection Based on Grayscale Images -- 5.3.1.5 Static Detection Based on Permission Pairs -- 5.3.1.6 Static Detection Based on Static Sensitive Subgraphs -- 5.3.1.7 Static Detection Based on Malicious URLs -- 5.3.2 Detection Method Based on Binding Method -- 5.3.2.1 Static Detection Combined with Dynamic -- 5.3.2.2 Static Detection Combined with Machine Learning -- 5.3.2.3 Static Detection Combined with Deep Learning -- 5.4 Chapter Summary -- References -- 6 Dynamic Adversarial Method in Android Malware -- 6.1 Automatic Dynamic Analysis Evasion -- 6.1.1 Detection Dependent -- 6.1.1.1 Fingerprint -- 6.1.1.2 Reverse Turing Test -- 6.1.1.3 Target -- 6.1.2 Detection Independent -- 6.1.2.1 Stalling -- 6.1.2.2 Trigger-Based -- 6.1.2.3 Fileless Attack -- 6.2 Manual Dynamic Analysis Evasion -- 6.2.1 Direct Detection -- 6.2.1.1 Read PEB -- 6.2.1.2 Breakpoint Query -- 6.2.1.3 System Artifacts -- 6.2.1.4 Parent Process Detection -- 6.2.2 Deductive Detection -- 6.2.2.1 Trap -- 6.2.2.2 Time-Based Detection -- 6.2.3 Debugger Evasion -- 6.2.3.1 Control Flow Manipulation -- 6.2.3.2 Lockout Evasion -- 6.2.3.3 Debugger Identification -- 6.2.3.4 Fileless Malware -- 6.3 Related Research About Dynamic Analysis Evasion -- 6.3.1 Research About Improving Sandbox -- 6.3.1.1 The Droid is in the Details: Environment-Aware Evasion of Android Sandboxes -- 6.3.1.2 Morpheus: Automatically Generating Heuristics to Detect Android Emulators -- 6.3.2 Research About Detecting Dynamic Evasion. 6.3.2.1 CamoDroid: An Android App Analysis Environment Resilient Against Sandbox Evasion -- 6.3.2.2 Lumus: Dynamically Uncovering Evasive Android apps -- 6.4 Chapter Summary -- References -- 7 AI-Based Adversarial Method in Android -- 7.1 Introduction to Adversarial Examples -- 7.2 Classification of Adversarial Example Generation Methods -- 7.2.1 Gradient-Based Attacks -- 7.2.2 Optimization-Based Attacks -- 7.2.3 GAN-Based Attacks -- 7.2.4 Domain-Specific Attacks (Audio, Images, Text, etc.) -- 7.3 Black-Box Attacks -- 7.3.1 Introduction to Black-Box Attacks -- 7.3.2 Common Black-Box Attack Methods -- 7.3.3 Transfer Learning-Based Black-Box Attacks -- 7.3.4 Meta-Model Based Black-Box Attacks -- 7.3.5 Query-Based Attacks -- 7.3.6 Optimization-Based Attacks -- 7.4 White-Box Attacks -- 7.4.1 Optimization-Based Attacks -- 7.4.1.1 C& -- W Attack -- 7.4.1.2 PGD Attack -- 7.4.2 Gradient-Based Attacks -- 7.4.2.1 FGSM Attack -- 7.4.2.2 BIM Attack -- 7.4.3 App of Adversarial Attacks in Malware Detection -- 7.5 Chapter Summary -- References -- Part IV The Future Trends of Android Malware Detection -- 8 Future Trends in Android Malware Detection -- 8.1 Machine Learning And Deep Learning Techniques -- 8.1.1 Overview of Machine Learning and Deep Learning for Android Malware Detection -- 8.1.2 Challenges Faced -- 8.2 Integrated Solutions -- 8.2.1 Challenges Faced -- 8.3 Blockchain Technology -- 8.3.1 Introduction to Blockchain Technology -- 8.3.2 Examples of Blockchain Technology in the Field of Android Malware Detection -- 8.4 Hardware Technology -- 8.4.1 Advantages of Hardware Technology -- 8.4.2 Challenges to Hardware Technology -- 8.4.3 Examples of Hardware Technologies Applied in the Field of Android Malware Detection -- 8.5 BPF Technology -- 8.5.1 Development of BPF Technology -- 8.5.2 eBPF Technology Overview. 8.5.3 Examples of BPF Techniques in the Field of Android Malware Detection -- 8.6 Chapter Summary -- References. |
| Record Nr. | UNINA-9910864193603321 |
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Niu Weina
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence in Manufacturing : Enabling Intelligent, Flexible and Cost-Effective Production Through AI / / edited by John Soldatos
| Artificial Intelligence in Manufacturing : Enabling Intelligent, Flexible and Cost-Effective Production Through AI / / edited by John Soldatos |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (XXVII, 505 p. 175 illus., 153 illus. in color.) |
| Disciplina | 621.382 |
| Soggetto topico |
Telecommunication
Artificial intelligence Big data Blockchains (Databases) Business information services Communications Engineering, Networks Artificial Intelligence Big Data Blockchain IT in Business |
| ISBN | 3-031-46452-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part I Architectures and Knowledge Modelling for AI in Manufacturing -- Reference Architecture for AI-based Industry 5.0 Applications -- Designing a Marketplace to Exchange AI Models for Industry 4.0 -- Domain Ontology Enrichment through Human-AI Interaction -- Survey of Knowledge Graphs in Industrial Settings -- From Knowledge to Wisdom: Leveraging Semantic Representations via Knowledge Graph Embeddings -- Advancing high value-added networked production through Decentralized Technical Intelligence -- Part II AI-based Digital Twins for Manufacturing Applications -- Digital-Twin enabled framework for training and deploying AI agents for production scheduling -- Digital Twin for Human Machine Interaction -- Learning-based Collaborative Digital Twins -- A Manufacturing Digital Twin Framework -- Part III Agent based Approaches for AI in Manufacturing -- Reinforcement Learning based approaches in manufacturing environments -- A participatory modelling approach to Agents in Industry using AAS -- 4.0 Holonic Multi-Agent Testbed Enabling Shared Production -- Application of a Multi agent system on production and scheduling optimization -- Integrating Knowledge to Conversational Agents for Worker Upskilling -- Part IV Trusted AI for Industry 5.0 Applications -- Wearable sensor-based human activity recognition for worker safety in manufacturing line -- Object detection for human-robot interaction and worker assistance systems -- Application of autoML, XAI and differential privacy method into manufacturing -- Anomaly Detection in Manufacturing -- Towards Industry 5.0 by incorporation of Trustworthy and Human-Centric approaches -- How AI changes human roles in Industry 5.0-enabled environments: Human in the AI loop via xAI and Active Learning for Manufacturing Quality Control -- Multi-Stakeholder Perspective on Human-AI Collaboration in Industry 5.0 -- Conclusion. |
| Record Nr. | UNINA-9910829580903321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Balancing Software Innovation and Regulatory Compliance : 17th International Conference on Software Quality, SWQD 2025, Munich, Germany, May 20–22, 2025, Proceedings / / edited by Jannik Fischbach, Rudolf Ramler, Dietmar Winkler, Johannes Bergsmann
| Balancing Software Innovation and Regulatory Compliance : 17th International Conference on Software Quality, SWQD 2025, Munich, Germany, May 20–22, 2025, Proceedings / / edited by Jannik Fischbach, Rudolf Ramler, Dietmar Winkler, Johannes Bergsmann |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XII, 125 p. 31 illus., 28 illus. in color.) |
| Disciplina | 005.1 |
| Collana | Lecture Notes in Business Information Processing |
| Soggetto topico |
Software engineering
Computer programs - Testing Artificial intelligence Blockchains (Databases) Software Engineering Software Testing Artificial Intelligence Blockchain |
| ISBN | 3-031-89277-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Keynote -- Why Digitalization Will Kill Your Company Too -- Model-Based Software Testing -- Model-Based Test Design in SysML for System Requirements Verification and Validation (Full Paper) -- Effective Black Box Testing of Sentiment Analysis Classification Networks (Short Paper) -- Machine Learning and Large Language Models -- Automating Invariant Filtering: Leveraging LLMs to Streamline Test Oracle Generation (Full Paper) -- Advanced Detection of Source Code Clones via an Ensemble of Unsupervised Similarity Measures (Full Paper) -- Security and Compliance -- Trusted Provenance with Blockchain Technology: A Systematic Literature Review (Short Paper) -- Academic-Industry Collaborations -- Experiences Applying Lean R&D in Industry-Academia Collaboration Projects (Full Paper). |
| Record Nr. | UNINA-9910999675203321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Blockchain and Smart-Contract Technologies for Innovative Applications / / edited by Nour El Madhoun, Ioanna Dionysiou, Emmanuel Bertin
| Blockchain and Smart-Contract Technologies for Innovative Applications / / edited by Nour El Madhoun, Ioanna Dionysiou, Emmanuel Bertin |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (304 pages) |
| Disciplina | 005.74 |
| Soggetto topico |
Telecommunication
Cooperating objects (Computer systems) Blockchains (Databases) Communications Engineering, Networks Cyber-Physical Systems Blockchain |
| ISBN | 3-031-50028-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Fundamentals of blockchain and smart-contracts -- Surveys on blockchain and smart contracts -- IoT Applications -- Healthcare Applications -- Finance Applications -- Government and E-voting Applications -- Energy Applications -- Metaverse and NFT Applications -- Telecommunications Applications -- Conclusion. |
| Record Nr. | UNINA-9910842293203321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Blockchain and Web3.0 Technology Innovation and Application : First Conference, BWTAC 2024, Guangzhou, China, November 6–8, 2024, Proceedings / / edited by Gansen Zhao, Jian Weng, Zhihong Tian, Liehuang Zhu, Zibin Zheng
| Blockchain and Web3.0 Technology Innovation and Application : First Conference, BWTAC 2024, Guangzhou, China, November 6–8, 2024, Proceedings / / edited by Gansen Zhao, Jian Weng, Zhihong Tian, Liehuang Zhu, Zibin Zheng |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XV, 513 p. 180 illus., 149 illus. in color.) |
| Disciplina | 005.3 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Application software
Data protection Blockchains (Databases) Software engineering Computer and Information Systems Applications Data and Information Security Blockchain Software Engineering |
| ISBN | 981-9794-12-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | -- Behavioral Unicity: On the Limits of Anonymized Social Behavior Metadata. -- Research on Effects of Blockchain Pilot Programs in Regional Equity Markets： Evidence from Participant and Non-Participant Institutions. -- Tailoring Noise to Fit: An Adaptive Noise Optimization Mechanism against Gradient Leakage. -- Design of Privacy-Preserving Smart Contracts for Regional Equity Markets. -- Deep Learning Empowered Blockchain Transaction Prediction and Anomaly Detection. -- A Blockchain-based Framework for Crowdsourcing Evaluation of Large Language Models. -- Trusted Data Authorization and Sharing Method Based on Distributed Digital Identity. -- Secure and Efficient Deduplication for Encrypted Image Data in Cloud Storage. -- SolSecure: A Security Analyzer for Integer Bugs in Smart Contracts. -- BBP: Blockchain-enabled Biological Assets Identity Protection System. -- Accountability Mechanism for Reliable Mobile Crowdsourcing with Efficient Blockchain. -- Who will be hooked?: A Phishing Fraud Detection Model Based on Dynamic Graph Temporal Feature Coding in Ethereum. -- Static Analysis Detection of Hyperleger Fabric Read-Write Logic Vulnerability. -- A Dataset Quality Evaluation Algorithm for Data Trading on Blockchain. -- A Variable (n, n) Threshold Secret Sharing Scheme Based on Paillier Cryptosystem. -- Design and Validation of a Hyper-Converged Blockchain Hardware and Software System Based on Domestic Chips. -- DFADNet: A Diverse-Feature Adaptive Network for Web3.0-oriented Deep Forgery Detection. -- Privacy Protection Model of International Cold Chain Trade Blockchain Platform Based on Zero-Knowledge Proofs. -- Blockchain-based Federated Recommendation with Incentive Mechanism. -- A Treatment of EIP-1559: Enhancing Transaction Fee Mechanism through Nth-Price Auction. -- Unravelling Stablecoin-favored Ecosystem: Extracting, Exploring On-chain Data from TRON Blockchain. -- Robust and Efficient Group-Based Ring Federated Learning Framework with Double-Masking Mechanism. -- Centralized Oracle for Smart Contract Applications, Information Output Methods, and Systems. -- DataSafe: Copyright Protection with PUF Watermarking and Blockchain Tracing. -- A GAN anomaly detection method based on multi-scale endogenous enhancement. -- A Comprehensive Review of Blockchain-Enabled Dynamic and Credible Spectrum Sharing. -- Heterogeneous Data Fusion Based Vulnerability Detection for Ethereum Smart Contracts. -- Adaptive Federated Learning Based on Device Performance in a Heterogeneous Environment of Medical Computing Devices. -- MSCV: A Cross-chain Smart Contract State Data Verification Model Based on MTC. -- How Does Hashgraph-based Blockchain Work in MANETs: A Theoretical Analysis Model. -- Heterogeneous Graph Structure Learning Based on Feature and Topology Information Extraction. -- A blockchain-based traceable and verifiable digital circuit trade method with dual-signature strategy. -- Personalized medical federated learning based on mutual knowledge distillation in object heterogeneous environment. -- Cryptocurrency Transaction Anomaly Detection Based on Chebyshev Graph Neural Network. -- Validating the integrity for Deep Learning Models based on Zero-knowledge proof and Blockchain. -- CLB-BAFL: Critical Learning Behaviour Verification Mechanism for Blockchain-Based Asynchronous Federated Learning. -- GeePT: Governance of Efficient and Extensible Privacy-preserving Transaction for Blockchain. -- HetGNN-TF: Self-supervised Learning on Heterogeneous Graph Neural Network via Topology and Feature Reconstruction. -- Cooperative Perception and Decision-Making in Internet of Vehicles: A Comprehensive Review of Federated Learning and Blockchain Technology. -- ETGuard: Malicious Encrypted Traffic Detection in Blockchain-based Power Grid Systems. -- Self-Supervised Heterogeneous Graph Neural Network Based on Deep and Broad Neighborhood Encoding. -- Batch Validation Scheme of Data Feature Requirement in Blockchain-based Data Trading Platform. -- Research on business process representation based on graph convolutional neural network. -- Membership Data Privacy Protection and Poisoning Detection Scheme for Federated Learning. -- Code-based Blockchain Light Node Data Availability Guarantee Method. |
| Record Nr. | UNINA-9910983363103321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Soggetto topico
-
Blockchain
(111)
- Blockchains (Databases) (110)
- Artificial intelligence (35)
- Artificial Intelligence (32)
- Data protection (24)