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Biblioteca

MARC Lista (tabellare)
Blockchain Security in Cloud Computing [[electronic resource] /] / edited by K.M. Baalamurugan, S. Rakesh Kumar, Abhishek Kumar, Vishal Kumar, Sanjeevikumar Padmanaban
Blockchain Security in Cloud Computing [[electronic resource] /] / edited by K.M. Baalamurugan, S. Rakesh Kumar, Abhishek Kumar, Vishal Kumar, Sanjeevikumar Padmanaban
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (XIII, 317 p. 111 illus., 90 illus. in color.)
Disciplina 621.382
Collana EAI/Springer Innovations in Communication and Computing
Soggetto topico Electrical engineering
Computational intelligence
Computer security
Communications Engineering, Networks
Computational Intelligence
Systems and Data Security
Privacy
Soggetto genere / forma Electronic books.
ISBN 3-030-70501-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction -- Cloud Security -- Block Chain -- Block Chain Cloud Paradigm -- Block Chain Security -- Blockchain for Cloud -- Block chain-based cloud data storage security -- Clustering using Blockchain for cloud -- Cloud Assisted Secure Health System using blockchain -- Next Generation AI&ML using Blockchain -- Cloud Key Management for Secure Connection -- Computational Efficiency of Blockchain on cloud paradigm -- Conclusion.
Record Nr. UNINA-9910497110003321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Blockchain, Artificial Intelligence, and the Internet of Things : Possibilities and Opportunities
Blockchain, Artificial Intelligence, and the Internet of Things : Possibilities and Opportunities
Autore Raj Pethuru
Pubbl/distr/stampa Cham : , : Springer International Publishing AG, , 2021
Descrizione fisica 1 online resource (218 pages)
Altri autori (Persone) DubeyAshutosh Kumar
KumarAbhishek
RathorePramod Singh
Collana EAI/Springer Innovations in Communication and Computing Ser.
Soggetto genere / forma Electronic books.
ISBN 9783030776374
9783030776367
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910510565503321
Raj Pethuru  
Cham : , : Springer International Publishing AG, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Blockchain, artificial intelligence, and the Internet of things : possibilities and opportunities / / Pethuru Raj [and three others] editors
Blockchain, artificial intelligence, and the Internet of things : possibilities and opportunities / / Pethuru Raj [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (218 pages)
Disciplina 004.678
Collana EAI/Springer Innovations in Communication and Computing
Soggetto topico Internet of things
Blockchains (Databases)
ISBN 3-030-77637-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910523909503321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Deep Learning and Its Applications Using Python
Deep Learning and Its Applications Using Python
Autore Basha Niha Kamal
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (255 pages)
Altri autori (Persone) Bhatia KhanSurbhi
KumarAbhishek
MashatArwa
ISBN 1-394-16779-2
1-394-16778-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910830198303321
Basha Niha Kamal  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Deep Learning and Its Applications Using Python
Deep Learning and Its Applications Using Python
Autore Basha Niha Kamal
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (255 pages)
Altri autori (Persone) Bhatia KhanSurbhi
KumarAbhishek
MashatArwa
ISBN 1-394-16779-2
1-394-16778-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910841787403321
Basha Niha Kamal  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Deep Learning Techniques for Automation and Industrial Applications
Deep Learning Techniques for Automation and Industrial Applications
Autore Rathore Pramod Singh
Edizione [1st ed.]
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2024
Descrizione fisica 1 online resource (280 pages)
Altri autori (Persone) AhujaSachin
BurriSrinivasa Rao
KhuntetaAjay
BaliyanAnupam
KumarAbhishek
ISBN 1-394-23425-2
1-394-23427-9
1-394-23426-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Text Extraction from Images Using Tesseract -- 1.1 Introduction -- 1.1.1 Areas -- 1.1.2 Why Text Extraction? -- 1.1.3 Applications of OCR -- 1.2 Literature Review -- 1.3 Development Areas -- 1.3.1 React JavaScript (JS) -- 1.3.2 Flask -- 1.4 Existing System -- 1.5 Enhancing Text Extraction Using OCR Tesseract -- 1.6 Unified Modeling Language (UML) Diagram -- 1.6.1 Use Case Diagram -- 1.6.2 Model Architecture -- 1.6.3 Pseudocode -- 1.7 System Requirements -- 1.7.1 Software Requirements -- 1.7.2 Hardware Requirements -- 1.8 Testing -- 1.9 Result -- 1.10 Future Scope -- 1.11 Conclusion -- References -- Chapter 2 Chili Leaf Classification Using Deep Learning Techniques -- 2.1 Introduction -- 2.2 Objectives -- 2.3 Literature Survey -- 2.4 About the Dataset -- 2.5 Methodology -- 2.6 Result -- 2.7 Conclusion and Future Work -- References -- Chapter 3 Fruit Leaf Classification Using Transfer Learning Techniques -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Methodology -- 3.3.1 Image Preprocessing -- 3.3.2 Data Augmentation -- 3.3.3 Deep Learning Models -- 3.3.4 Accuracy Chart -- 3.3.5 Accuracy and Loss Graph -- 3.4 Conclusion and Future Work -- References -- Chapter 4 Classification of University of California (UC), Merced Land-Use Dataset Remote Sensing Images Using Pre-Trained Deep Learning Models -- 4.1 Introduction -- 4.2 Motivation and Contribution -- 4.2.1 Related Work -- 4.3 Methodology -- 4.3.1 Pre-Trained Models -- 4.3.2 Dataset -- 4.3.3 Training Processes -- 4.4 Experiments and Results -- 4.4.1 VGG Family -- 4.4.2 ResNet Family -- 4.4.2.1 ResNet101 -- 4.4.2.2 ResNet152 -- 4.4.3 MobileNet Family -- 4.4.4 Inception Family -- 4.4.5 Xception Family -- 4.4.6 DenseNet Family -- 4.4.7 NasNet Family -- 4.4.8 EfficientNet Family -- 4.4.9 ResNet Version 2.
4.5 Conclusion -- References -- Chapter 5 Sarcastic and Phony Contents Detection in Social Media Hindi Tweets -- 5.1 Introduction -- 5.1.1 Sarcasm in Social Media Hindi Tweets -- 5.2 Literature Review -- 5.2.1 Literature Review of Sarcasm Detection Based on Data Analysis Without Machine Learning Algorithms -- 5.2.1.1 Other Related Works without Machine Learning Algorithms for Sarcasm Detection -- 5.2.2 Literature Review of Sarcasm Detection with Machine Learning Algorithms and Based on Manual Feature Engineering Approach -- 5.3 Research Gap -- 5.4 Objective -- 5.5 Proposed Methodology -- 5.6 Expected Outcomes -- References -- Chapter 6 Removal of Haze from Synthetic and Real Scenes Using Deep Learning and Other AI Techniques -- 6.1 Introduction -- 6.2 Formation of a Haze Model -- 6.3 Different Techniques of Single-Image Dehazing -- 6.3.1 Contrast Enhancement -- 6.3.2 Dark Channel Prior -- 6.3.3 Color Attenuation Prior -- 6.3.4 Fusion Techniques -- 6.3.5 Deep Learning -- 6.4 Results and Discussions -- 6.5 Output for Synthetic Scenes -- 6.6 Output for Real Scenes -- 6.7 Conclusions -- References -- Chapter 7 HOG and Haar Feature Extraction-Based Security System for Face Detection and Counting -- 7.1 Introduction -- 7.1.1 Need for a Better Security System -- 7.2 Literature Survey -- 7.3 Proposed Work -- 7.3.1 Tools Used -- 7.3.2 Algorithm of the Proposed System -- 7.3.2.1 HOG-Based Individual Counting -- 7.3.2.2 Haar-Based Individual Counting -- 7.3.2.3 Combination of HOG and Haar -- 7.3.2.4 AdaBoost Learning Technique -- 7.3.2.5 KLT Tracker -- 7.4 Experiments and Results -- 7.5 Conclusion and Scope of Future Work -- References -- Chapter 8 A Comparative Analysis of Different CNN Models for Spatial Domain Steganalysis -- 8.1 Introduction -- 8.2 General Framework -- 8.2.1 Dataset -- 8.2.2 Deep Learning CNN Models -- 8.2.2.1 XuNet.
8.2.2.2 Pretrained Networks -- 8.3 Experimental Results and Analysis -- 8.4 Conclusion and Discussion -- Acknowledgments -- References -- Chapter 9 Making Invisible Bluewater Visible Using Machine and Deep Learning Techniques-A Review -- 9.1 Introduction -- 9.1.1 Why is It Difficult to Measure Subsurface Groundwater? -- 9.1.2 What are High Level Tasks Involved in Groundwater Measurement? -- 9.2 Determination of Groundwater Potential (GWP) Parameters -- 9.2.1 Groundwater Potential (GWP) Parameters -- 9.2.2 Analysis of the Key GWP Parameters -- 9.3 GWP Determination: Methods and Techniques -- 9.4 GWP Output: Applications -- 9.5 GWP Research Gaps: Future Research Areas -- 9.6 Conclusion -- References -- Chapter 10 Fruit Leaf Classification Using Transfer Learning for Automation and Industrial Applications -- 10.1 Introduction -- 10.1.1 Overview of Fruit Leaf Classification and Its Relevance in Automation and Industrial Applications -- 10.1.2 Challenges of Building a Classification Model from Scratch -- 10.1.3 Introduction to Transfer Learning as a Solution -- 10.1.4 Overview of Popular Pre-Trained Models -- 10.1.4.1 Visual Geometry Group -- 10.1.4.2 Residual Network -- 10.1.4.3 Inception -- 10.2 Data Collection and Preprocessing -- 10.2.1 Importance of Data Collection and Preprocessing -- 10.2.2 Data Augmentation in Fruit Leaf Classification -- 10.2.3 Normalization and Resizing in Fruit Leaf Classification -- 10.3 Loading a Pre-Trained Model for Fruit Leaf Classification Using Transfer Learning -- 10.3.1 Code Examples for Implementing Transfer Learning Using TensorFlow -- 10.4 Training and Evaluation -- 10.4.1 Explanation of Training and Evaluation Process -- 10.4.2 Metrics for Measuring Model Performance -- 10.5 Applications in Automation and Industry -- 10.5.1 Benefits of Using Transfer Learning in Automation and Industrial Settings.
10.5.2 Case Studies of Fruit Leaf Classification in Industry Using Transfer Learning -- 10.6 Conclusion -- 10.7 Future Work -- References -- Chapter 11 Green AI: Carbon-Footprint Decoupling System -- 11.1 Introduction -- 11.2 CO2 Emissions in Sectors -- 11.3 Heating and Cooking Emissions -- 11.4 Automobile Systems Emission -- 11.5 Power Systems Emission -- 11.5.1 Map -- 11.6 Total CO2 Emission -- 11.6.1 Relationship Between Tables -- 11.6.2 Group by Clause -- 11.6.3 Offshore Wind Storage Integration Method -- 11.6.4 Offshore Floating Wind and Power Generation Technology (OFWPP) -- 11.6.5 Wind Power Plant for Storage Mixing -- 11.6.6 The Effect on the Environment when Using Battery Storage -- 11.7 Green AI With a Control Strategy of Carbon Emission -- 11.8 Green Software -- 11.9 Conclusion -- 11.10 Future Scope and Limitation -- References -- Chapter 12 Review of State-of-Art Techniques for Political Polarization from Social Media Network -- 12.1 Introduction -- 12.1.1 Social Media -- 12.2 Political Polarization -- 12.2.1 Identification of the Parties -- 12.2.2 Definition of Political Ideology -- 12.2.3 Voting Conduct (Definition) -- 12.2.4 Definition of Policy Positions -- 12.2.5 Definition of Affective Polarization -- 12.2.6 Identifiability of Parties (Definition) -- 12.2.7 Definition of Political Ideology -- 12.2.8 Definition of Voting Behavior -- 12.2.9 Policy Positions (Definition) -- 12.2.10 Party Sorting -- 12.2.11 Affective Polarization (Definition) -- 12.3 State-of-the-Art Techniques -- 12.3.1 Word Embedding (WE) -- 12.3.2 Customary Models -- 12.3.3 Models of Deep Neural Networks (DNN) -- 12.3.4 Single and Hybrid ML Techniques -- 12.3.4.1 Single Methods -- 12.3.4.2 Hybrid Approaches -- 12.3.5 Multitask Learning (V) -- 12.3.5.1 Learning-Related Problem -- 12.3.5.2 Multi-Task Learning MTL -- 12.3.5.3 Architectures for Multiple Tasks.
12.3.5.4 Two MTL Deep Learning Methods -- 12.3.6 Techniques for Deep Learning -- 12.4 Literature Survey -- 12.5 Conclusion -- References -- Chapter 13 Collaborative Design and Case Analysis of Mobile Shopping Apps: A Deep Learning Approach -- 13.1 Introduction -- 13.1.1 Basic Rules in Shopping App Interaction Design -- 13.1.1.1 User-Centered Design Rules -- 13.1.2 Visual Interface Consistency -- 13.2 Personalized Interaction Design Framework for Mobile Shopping -- 13.2.1 Modelized Interaction Information Framework -- 13.2.2 Interactive Design Path Analysis -- 13.2.3 Optimization Design in the Page System -- 13.3 Case Analysis -- 13.4 Conclusions -- References -- Chapter 14 Exploring the Potential of Machine Learning and Deep Learning for COVID-19 Detection -- 14.1 Introduction -- 14.2 Supervised Learning Techniques -- 14.3 Unsupervised Learning Techniques -- 14.4 Deep Learning Techniques -- 14.5 Reinforcement Learning Techniques -- 14.6 Comparison of Machine Learning and Deep Learning Techniques -- 14.7 Challenges and Limitations -- 14.8 Conclusion and Future Directions -- References -- Index -- EULA.
Record Nr. UNINA-9910869199103321
Rathore Pramod Singh  
Newark : , : John Wiley & Sons, Incorporated, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Intelligent Green Technologies for Sustainable Smart Cities
Intelligent Green Technologies for Sustainable Smart Cities
Autore Tripathi Suman Lata
Pubbl/distr/stampa Newark : , : John Wiley & Sons, Incorporated, , 2022
Descrizione fisica 1 online resource (369 pages)
Altri autori (Persone) GanguliSouvik
KumarAbhishek
MagradzeTengiz
Collana Advances in Cyber Security Ser.
Soggetto genere / forma Electronic books.
ISBN 1-119-81609-2
1-119-81610-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910590099503321
Tripathi Suman Lata  
Newark : , : John Wiley & Sons, Incorporated, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Internet of Things Use Cases for the Healthcare Industry [[electronic resource] /] / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan
Internet of Things Use Cases for the Healthcare Industry [[electronic resource] /] / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (XII, 296 p. 79 illus., 59 illus. in color.)
Disciplina 610.28563
Soggetto topico Computer communication systems
Computer engineering
Internet of things
Embedded computer systems
Health informatics
Input-output equipment (Computers)
Computer Communication Networks
Cyber-physical systems, IoT
Health Informatics
Input/Output and Data Communications
ISBN 3-030-37526-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto AI in Health Sector -- Real-Time Smart Healthcare Model using IoT -- A Fog Based Approach for Real-Time Analytics of IoT-Enabled Healthcare -- Applications of IoT in Indoor Air Quality Monitoring Systems -- CloudIoT for Smart Healthcare: Architecture, Issues and Challenges -- Impact of IoT on the Healthcare Producers: Epitomizing Pharmaceutical Drug Discovery Process -- Cyber-Security Threats in Medical Devices -- Smart Healthcare Use Cases and Applications -- IoT Use Cases and Applications -- Internet of Things for Ambient Assisted Living - An Overview -- Smart Health care Applications and Real Time Analytics through Edge Computing -- The Role of Blockchain for Medical Electronics Security -- Clinical Data Analysis using IoT Data Analytics Platforms -- Internet of Things - Tools and Technologies in Healthcare -- Clinical data analysis using IoT -- Security Issues in IoT and Healthcare Devices.
Record Nr. UNISA-996465460403316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Internet of Things Use Cases for the Healthcare Industry / / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan
Internet of Things Use Cases for the Healthcare Industry / / edited by Pethuru Raj, Jyotir Moy Chatterjee, Abhishek Kumar, B. Balamurugan
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (XII, 296 p. 79 illus., 59 illus. in color.)
Disciplina 610.28563
Soggetto topico Computer communication systems
Computer engineering
Internet of things
Embedded computer systems
Health informatics
Input-output equipment (Computers)
Computer Communication Networks
Cyber-physical systems, IoT
Health Informatics
Input/Output and Data Communications
ISBN 3-030-37526-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto AI in Health Sector -- Real-Time Smart Healthcare Model using IoT -- A Fog Based Approach for Real-Time Analytics of IoT-Enabled Healthcare -- Applications of IoT in Indoor Air Quality Monitoring Systems -- CloudIoT for Smart Healthcare: Architecture, Issues and Challenges -- Impact of IoT on the Healthcare Producers: Epitomizing Pharmaceutical Drug Discovery Process -- Cyber-Security Threats in Medical Devices -- Smart Healthcare Use Cases and Applications -- IoT Use Cases and Applications -- Internet of Things for Ambient Assisted Living - An Overview -- Smart Health care Applications and Real Time Analytics through Edge Computing -- The Role of Blockchain for Medical Electronics Security -- Clinical Data Analysis using IoT Data Analytics Platforms -- Internet of Things - Tools and Technologies in Healthcare -- Clinical data analysis using IoT -- Security Issues in IoT and Healthcare Devices.
Record Nr. UNINA-9910410049303321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Machine learning techniques for VLSI chip design / / edited by Abhishek Kumar, Suman Lata Tripathi, and K. Srinivasa Rao
Machine learning techniques for VLSI chip design / / edited by Abhishek Kumar, Suman Lata Tripathi, and K. Srinivasa Rao
Pubbl/distr/stampa Hoboken, NJ ; Beverly, MA : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2023]
Descrizione fisica 1 online resource (239 pages)
Disciplina 330
Soggetto topico Integrated circuits - Very large scale integration - Design - Data processing
Machine learning
ISBN 1-119-91049-8
1-119-91048-X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 Applications of VLSI Design in Artificial Intelligence and Machine Learning -- 1.1 Introduction -- 1.2 Artificial Intelligence -- 1.3 Artificial Intelligence & -- VLSI (AI and VLSI) -- 1.4 Applications of AI -- 1.5 Machine Learning -- 1.6 Applications of ML -- 1.6.1 Role of ML in Manufacturing Process -- 1.6.2 Reducing Maintenance Costs and Improving Reliability -- 1.6.3 Enhancing New Design -- 1.7 Role of ML in Mask Synthesis -- 1.8 Applications in Physical Design -- 1.8.1 Lithography Hotspot Detection -- 1.8.2 Pattern Matching Approach -- 1.9 Improving Analysis Correlation -- 1.10 Role of ML in Data Path Placement -- 1.11 Role of ML on Route Ability Prediction -- 1.12 Conclusion -- References -- Chapter 2 Design of an Accelerated Squarer Architecture Based on Yavadunam Sutra for Machine Learning -- 2.1 Introduction -- 2.2 Methods and Methodology -- 2.2.1 Design of an n-Bit Squaring Circuit Based on (n-1)-Bit Squaring Circuit Architecture -- 2.2.1.1 Architecture for Case 1: A < -- B -- 2.2.1.2 Architecture for Case 2: A > -- B -- 2.2.1.3 Architecture for Case 3: A = B -- 2.3 Results and Discussion -- 2.4 Conclusion -- References -- Chapter 3 Machine Learning-Based VLSI Test and Verification -- 3.1 Introduction -- 3.2 The VLSI Testing Process -- 3.2.1 Off-Chip Testing -- 3.2.2 On-Chip Testing -- 3.2.3 Combinational Circuit Testing -- 3.2.3.1 Fault Model -- 3.2.3.2 Path Sensitizing -- 3.2.4 Sequential Circuit Testing -- 3.2.4.1 Scan Path Test -- 3.2.4.2 Built-In-Self Test (BIST) -- 3.2.4.3 Boundary Scan Test (BST) -- 3.2.5 The Advantages of VLSI Testing -- 3.3 Machine Learning's Advantages in VLSI Design -- 3.3.1 Ease in the Verification Process -- 3.3.2 Time-Saving -- 3.3.3 3Ps (Power, Performance, Price).
3.4 Electronic Design Automation (EDA) -- 3.4.1 System-Level Design -- 3.4.2 Logic Synthesis and Physical Design -- 3.4.3 Test, Diagnosis, and Validation -- 3.5 Verification -- 3.6 Challenges -- 3.7 Conclusion -- References -- Chapter 4 IoT-Based Smart Home Security Alert System for Continuous Supervision -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Results and Discussions -- 4.3.1 Raspberry Pi-3 B+Module -- 4.3.2 Pi Camera -- 4.3.3 Relay -- 4.3.4 Power Source -- 4.3.5 Sensors -- 4.3.5.1 IR & -- Ultrasonic Sensor -- 4.3.5.2 Gas Sensor -- 4.3.5.3 Fire Sensor -- 4.3.5.4 GSM Module -- 4.3.5.5 Buzzer -- 4.3.5.6 Cloud -- 4.3.5.7 Mobile -- 4.4 Conclusions -- References -- Chapter 5 A Detailed Roadmap from Conventional-MOSFET to Nanowire-MOSFET -- 5.1 Introduction -- 5.2 Scaling Challenges Beyond 100nm Node -- 5.3 Alternate Concepts in MOFSETs -- 5.4 Thin-Body Field-Effect Transistors -- 5.4.1 Single-Gate Ultrathin-Body Field-Effect Transistor -- 5.4.2 Multiple-Gate Ultrathin-Body Field-Effect Transistor -- 5.5 Fin-FET Devices -- 5.6 GAA Nanowire-MOSFETS -- 5.7 Conclusion -- References -- Chapter 6 Gate All Around MOSFETs-A Futuristic Approach -- 6.1 Introduction -- 6.1.1 Semiconductor Technology: History -- 6.2 Importance of Scaling in CMOS Technology -- 6.2.1 Scaling Rules -- 6.2.2 The End of Planar Scaling -- 6.2.3 Enhance Power Efficiency -- 6.2.4 Scaling Challenges -- 6.2.4.1 Poly Silicon Depletion Effect -- 6.2.4.2 Quantum Effect -- 6.2.4.3 Gate Tunneling -- 6.2.5 Horizontal Scaling Challenges -- 6.2.5.1 Threshold Voltage Roll-Off -- 6.2.5.2 Drain Induce Barrier Lowering (DIBL) -- 6.2.5.3 Trap Charge Carrier -- 6.2.5.4 Mobility Degradation -- 6.3 Remedies of Scaling Challenges -- 6.3.1 By Channel Engineering (Horizontal) -- 6.3.1.1 Shallow S/D Junction -- 6.3.1.2 Multi-Material Gate -- 6.3.2 By Gate Engineering (Vertical).
6.3.2.1 High-K Dielectric -- 6.3.2.2 Metal Gate -- 6.3.2.3 Multiple Gate -- 6.4 Role of High-K in CMOS Miniaturization -- 6.5 Current Mosfet Technologies -- 6.6 Conclusion -- References -- Chapter 7 Investigation of Diabetic Retinopathy Level Based on Convolution Neural Network Using Fundus Images -- 7.1 Introduction -- 7.2 The Proposed Methodology -- 7.3 Dataset Description and Feature Extraction -- 7.3.1 Depiction of Datasets -- 7.3.2 Preprocessing -- 7.3.3 Detection of Blood Vessels -- 7.3.4 Microaneurysm Detection -- 7.4 Results and Discussions -- 7.5 Conclusions -- References -- Chapter 8 Anti-Theft Technology of Museum Cultural Relics Using RFID Technology -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Software Implementation -- 8.4 Components -- 8.4.1 Arduino UNO -- 8.4.2 EM18 Reader Module -- 8.4.3 RFID Tag -- 8.4.4 LCD Display -- 8.4.5 Sensors -- 8.4.5.1 Fire Sensor -- 8.4.5.2 IR Sensor -- 8.4.6 Relay -- 8.5 Working Principle -- 8.5.1 Working Principle -- 8.6 Results and Discussions -- 8.7 Conclusions -- References -- Chapter 9 Smart Irrigation System Using Machine Learning Techniques -- 9.1 Introduction -- 9.2 Hardware Module -- 9.2.1 Soil Moisture Sensor -- 9.2.2 LM35-Temperature Sensor -- 9.2.3 POT Resistor -- 9.2.4 BC-547 Transistor -- 9.2.5 Sounder -- 9.2.6 LCD 16x2 -- 9.2.7 Relay -- 9.2.8 Push Button -- 9.2.9 LED -- 9.2.10 Motor -- 9.3 Software Module -- 9.3.1 Proteus Tool -- 9.3.2 Arduino Based Prototyping -- 9.4 Machine Learning (Ml) Into Irrigation -- 9.5 Conclusion -- References -- Chapter 10 Design of Smart Wheelchair with Health Monitoring System -- 10.1 Introduction -- 10.2 Proposed Methodology -- 10.3 The Proposed System -- 10.4 Results and Discussions -- 10.5 Conclusions -- References -- Chapter 11 Design and Analysis of Anti-Poaching Alert System for Red Sandalwood Safety -- 11.1 Introduction.
11.2 Various Existing Proposed Anti-Poaching Systems -- 11.3 System Framework and Construction -- 11.4 Results and Discussions -- 11.5 Conclusion and Future Scope -- References -- Chapter 12 Tumor Detection Using Morphological Image Segmentation with DSP Processor TMS320C6748 -- 12.1 Introduction -- 12.2 Image Processing -- 12.2.1 Image Acquisition -- 12.2.2 Image Segmentation Method -- 12.3 TMS320C6748 DSP Processor -- 12.4 Code Composer Studio -- 12.5 Morphological Image Segmentation -- 12.5.1 Optimization -- 12.6 Results and Discussions -- 12.7 Conclusions -- References -- Chapter 13 Design Challenges for Machine/Deep Learning Algorithms -- 13.1 Introduction -- 13.2 Design Challenges of Machine Learning -- 13.2.1 Data of Low Quality -- 13.2.2 Training Data Underfitting -- 13.2.3 Training Data Overfitting -- 13.2.4 Insufficient Training Data -- 13.2.5 Uncommon Training Data -- 13.2.6 Machine Learning Is a Time-Consuming Process -- 13.2.7 Unwanted Features -- 13.2.8 Implementation is Taking Longer Than Expected -- 13.2.9 Flaws When Data Grows -- 13.2.10 The Model's Offline Learning and Deployment -- 13.2.11 Bad Recommendations -- 13.2.12 Abuse of Talent -- 13.2.13 Implementation -- 13.2.14 Assumption are Made in the Wrong Way -- 13.2.15 Infrastructure Deficiency -- 13.2.16 When Data Grows, Algorithms Become Obsolete -- 13.2.17 Skilled Resources are Not Available -- 13.2.18 Separation of Customers -- 13.2.19 Complexity -- 13.2.20 Results Take Time -- 13.2.21 Maintenance -- 13.2.22 Drift in Ideas -- 13.2.23 Bias in Data -- 13.2.24 Error Probability -- 13.2.25 Inability to Explain -- 13.3 Commonly Used Algorithms in Machine Learning -- 13.3.1 Algorithms for Supervised Learning -- 13.3.2 Algorithms for Unsupervised Learning -- 13.3.3 Algorithm for Reinforcement Learning -- 13.4 Applications of Machine Learning -- 13.4.1 Image Recognition.
13.4.2 Speech Recognition -- 13.4.3 Traffic Prediction -- 13.4.4 Product Recommendations -- 13.4.5 Email Spam and Malware Filtering -- 13.5 Conclusion -- References -- About the Editors -- Index -- EULA.
Record Nr. UNINA-9910830227103321
Hoboken, NJ ; Beverly, MA : , : John Wiley & Sons, Inc. : , : Scrivener Publishing LLC, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui