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E-grāmata: Big data management in Sensing: Applications in AI and IoT

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  • Formāts: 286 pages
  • Izdošanas datums: 01-Sep-2022
  • Izdevniecība: River Publishers
  • ISBN-13: 9781000797435
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Big data management in Sensing: Applications in AI and IoTPalielināt
  • Formāts: 286 pages
  • Izdošanas datums: 01-Sep-2022
  • Izdevniecība: River Publishers
  • ISBN-13: 9781000797435
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The book is centrally focused on human computer interaction and how sensors withinsmall and wide groups of nano-robots employ deep learning for applications in industry. It covers a wide array of topics that are useful for researchers and students to gain knowledge about AI and sensors in nanobots. Furthermore, the book explores deep learning approaches to enhance the accuracy of AI systems applied in medical robotics for surgical techniques. Secondly, it explores bio-nano-robotics, which is a field in nano-robotics, that deals with automatic intelligence handling, self-assembly and replication, information processing and programmability.

The book is centrally focused on human computer Interaction and how sensors within small and wide groups of Nano-robots employ Deep Learning for applications in industry. It covers a wide array of topics that are useful for researchers and students to gain knowledge about AI and sensors in nanobots. Furthermore, the book explores Deep Learning approaches to enhance the accuracy of AI systems applied in medical robotics for surgical techniques. Secondly, we plan to explore bio-nano-robotics, which is a field in nano-robotics, that deals with automatic intelligence handling, self-assembly and replication, information processing and programmability.



The book is centrally focused on human computer Interaction and how sensors within small and wide groups of Nano-robots employ Deep Learning for applications in industry.

Preface xv
List of Figures xvii
List of Tables xxi
List of Contributors xxiii
List of Abbreviations xxvii
1 Classification of Histopathological Variants of Oral Squamous Cell Carcinoma Using Convolutional Neural Networks 1(14)
P. Archana
T. Megala
D. Udaya
S. Prabavathy
1.1 Introduction
2(2)
1.2 Convolutional Neural Networks
4(3)
1.2.1 Convolutional Layer
5(1)
1.2.2 Pooling Layer
5(1)
1.2.3 Fully Connected Layers
5(1)
1.2.4 Receptive Field
5(1)
1.2.5 Weights
6(1)
1.2.6 ReLU Layer
6(1)
1.2.7 Softmax Layer
6(1)
1.2.8 Dropout
6(1)
1.2.9 Steps Involved in Convolutional Neural Network
7(1)
1.3 Proposed Convolutional Neural Network
7(5)
1.3.1 Performance Evaluation for CNN Models
8(2)
1.3.2 Comparative Result Analysis
10(2)
1.4 Conclusion
12(1)
References
12(3)
2 Voice Recognition Using Natural Language Processing 15(10)
J. Pradeep
K. Vijayakumar
M. Harikrishnan
2.1 Introduction
15(2)
2.2 Proposed System
17(2)
2.2.1 Automatic Speech Recognition
17(1)
2.2.2 Auto-detect Language
18(1)
2.2.3 Syntactic Analysis
18(1)
2.2.4 Semantic Analysis
18(1)
2.2.5 Pragmatic Analysis
19(1)
2.3 Experimental Results
19(3)
2.4 Conclusion
22(1)
References
22(3)
3 Detection of Tuberculosis Using Computer-Aided Diagnosis System 25(22)
Murali Krishna Puttagunta
S. Ravi
A. Anbarasi
3.1 Introduction
26(2)
3.2 Pre-Processing
28(1)
3.3 Segmentation
28(2)
3.3.1 Rule-Based Algorithm
28(1)
3.3.2 Pixel Classification
29(1)
3.3.3 Deformable Models
29(1)
3.3.4 Hybrid Methods
30(1)
3.4 Feature Extraction
30(3)
3.4.1 Histogram Features
30(1)
3.4.2 Shape Descriptor Histogram
31(1)
3.4.3 Curvature Descriptor
31(1)
3.4.4 Local Binary Pattern (LBP)
31(1)
3.4.5 Histogram of Gradients
32(1)
3.4.6 Gabor Features
32(1)
3.5 Classification
33(1)
3.6 Discussion
34(3)
3.7 Conclusion
37(1)
References
38(9)
4 Forecasting Time Series Data Using ARIMA and Facebook Prophet Models 47(14)
S. Sivaramakrishnan
Terrance Frederick Fernandez
R.G. Babukarthik
S. Premalatha
4.1 Introduction
48(2)
4.2 Arima Model
50(5)
4.2.1 Data Analysis Using ARIMA Model
51(4)
4.3 Data Analysis Using Facebook Prophet Model
55(2)
4.4 Conculsion
57(1)
References
57(4)
5 A Novel Technique for User Decision Prediction and Assistance Using Machine Learning and NLP: A Model to Transform the E-commerce System 61(16)
V. Vivek
T.R. Mahesh
C. Saravanan
K. Vinay Kumar
5.1 Introduction
62(2)
5.2 Related Work
64(4)
5.3 Research Methodology
68(4)
5.4 Experimental Results
72(2)
5.5 Conclusion and Future Scope
74(1)
References
75(2)
6 Machine Learning-Based Intelligent Video Analytics Design Using Depth Intra Coding 77(10)
Kumbala Pradeep Reddy
Sarangam Kodati
Thotakura Veeranna
G. Ravi
6.1 Introduction
78(4)
6.1.1 Object Detection
80(1)
6.1.2 Deep Learning
80(1)
6.1.3 Geometric Depth Modeling
80(1)
6.1.3.1 Plane fitting
80(1)
6.1.4 Depth Coding Based on Geometric Primitives
81(1)
6.2 Video Analytics Design Using Depth Intra Coding
82(1)
6.3 Results
83(2)
6.4 Conclusion
85(1)
References
85(2)
7 A Novel Approach for Automatic Brain Tumor Detection Using Machine Learning Algorithms 87(16)
G. Sindhu Madhuri
T.R. Mahesh
V. Vivek
7.1 Introduction
88(2)
7.1.1 Medical Imaging
89(1)
7.2 Image Processing Approach-Detection of Brain Tumor From MRI Images
90(4)
7.3 Machine Learning Approach-Detection of Brain Tumor From MRI Images
94(4)
7.4 Nano-Robotic Approach-Detection of Brain Tumor From Mn Images
98(1)
References
99(4)
8 A Swarm-Based Feature Extraction and Weight Optimization in Neural Network for Classification on Speaker Recognition 103(12)
G. Raja
P. Salini
M. Pradeep
Terrance Frederick Fernandez
8.1 Introduction
104(1)
8.1.1 Swarm-based Feature Extraction Merits
104(1)
8.1.2 Objectives of Our
Chapter
105(1)
8.2 State of Art
105(2)
8.2.1 Mel Frequency Cepstral Coefficients (MFCC)
106(1)
8.2.2 Swarm Intelligence (SI)
106(1)
8.2.3 Text-independent Speaker Identification
106(1)
8.2.4 Voice Activity Detection (VAD)
107(1)
8.3 Differential Evolution Technique (DE)
107(1)
8.4 Survey on Swarm Intelligence
107(1)
8.5 Our Framework and Metrics
108(2)
8.6 Results and Discussion
110(2)
References
112(3)
9 Fault Tolerance-Based Attack Detection Using Ensemble Classifier Machine Learning with IOT Security 115(34)
A. Arulmurugan
R. Kaviarasan
Saiyed Faiayaz Waris
9.1 Introduction
116(2)
9.2 Background
118(2)
9.2.1 IoT Security Attacks
118(6)
9.2.1.1 Perception Layer Attacks
118(1)
9.2.1.2 Network Layer Attacks
119(1)
9.2.1.3 Routing Attacks
119(1)
9.3 Deep Learning and IoT Security
120(3)
9.4 Deep Learning and Big Data Technologies for IoT Security
123(1)
9.5 Cloud Framework for Profound Learning, Enormous Information Advances, and IoT Security
124(2)
9.5.1 Related Works
124(2)
9.6 Motivation of the Proposed Methodology
126(1)
9.7 Research Methodology
126(14)
9.7.1 Dimensionality Reduction
128(1)
9.7.2 Independent Component Analysis
129(1)
9.7.3 Principal Component Analysis
130(1)
9.7.4 Cloud Architecture
131(1)
9.7.5 Encryption Decryption Using OTP
131(4)
9.7.6 OTP Algorithm
135(1)
9.7.7 Ensemble Classifier SVM, Random Forest Classification
136(3)
9.7.8 Random Forest
139(1)
9.8 Performance Metrics
140(1)
9.9 Dataset Description
141(4)
9.10 Conclusion
145(1)
References
146(3)
10 Design a Novel IoT-Based Agriculture Automation Using Machine Learning 149(10)
G. Ravi
Kumbala Pradeep Reddy
M. Mohan Rao
Sarangam Kodati
J. Praveen Kumar
10.1 Introduction
150(1)
10.2 Literature Survey
151(2)
10.3 Novel IoT-Based Agriculture Automation Using Machine Learning
153(3)
10.4 Conclusion
156(1)
References
156(3)
11 Building a Smart Healthcare System Using Internet of Things and Machine Learning 159(20)
Shruti Kute
Amit Kumar Tyagi
Rohit Sahoo
Shaveta Malik
11.1 Smart Healthcare-An Introduction
160(1)
11.2 Background Study
161(1)
11.3 Motivation of This Work
162(1)
11.4 Internet of Things-Enabled Safe Smart Hospital Cabin Door Knocker
162(2)
11.5 Smart Healthcare System Communication Protocol
164(1)
11.6 IoT-Cloud Based Smart Healthcare Data Collection System
165(1)
11.7 Use of Machine Learning in Different Fields of Medical Science
166(1)
11.8 Illness Identification/Diagnosis
167(2)
11.8.1 Discovery of Drug & Manufacturing
167(1)
11.8.2 Diagnosis of Medical Imaging
168(1)
11.8.3 Clinical Trial
168(1)
11.8.4 Epidemic Outbreak Prediction
168(1)
11.8.5 Robotic Surgery
168(1)
11.8.6 Smart Health Record
169(1)
11.9 Challenge's Faced Towards 5G With lot and Machine Learning Technique
169(2)
11.9.1 5G and IoT Empower More Assault Vectors
169(1)
11.9.2 Smarter Bots Can Likewise Misuse These Assault Vectors
170(1)
11.10 Future Possibility of Smart Healthcare With Internet of Things
171(2)
11.11 Conclusion and Future Scope
173(1)
References
174(5)
12 Research Issues and Future Research Directions Toward Smart Healthcare Using Internet of Things and Machine Learning 179(22)
Shruti Kute
Amit Kumar Tyagi
Meghna Manoj Nair
12.1 Introduction
180(1)
12.2 Background Work
180(5)
12.3 Healthcare and Internet of Things
185(1)
12.4 Internet of Things-Based Healthcare Solutions
185(1)
12.4.1 Clinical Care
186(1)
12.4.2 Distant Checking
186(1)
12.5 Machine Learning-Based Healthcare
186(3)
12.5.1 Future Model of Healthcare-based IoT and Machine Learning
187(2)
12.6 Wearable System for Smart Healthcare
189(1)
12.7 Communication Standards
190(1)
12.8 Challenges in Healthcare Adoption with IoT and Machine Learning
191(1)
12.9 Improving Adoption of Healthcare System with IoT and Machine Learning
192(3)
12.9.1 Proof-based Consideration
192(1)
12.9.2 Self-learning and Personal Growth
193(1)
12.9.3 Normalization
194(1)
12.9.4 Protection and Security
194(1)
12.9.5 Intelligent Announcing and Representation
195(1)
12.10 Proposed Solution Based on IOT and Machine Learning for Smart Healthcare Systems
195(3)
12.11 Conclusion
198(1)
References
199(2)
13 A Novel Adaptive Authentication Scheme for Securing Medical Information Stored in Clouds 201(14)
N. Moganarangan
N. Palanivel
S. Balaji
13.1 Introduction
202(2)
13.2 Adaptive Authentication Scheme
204(1)
13.3 Information Storage/Update
205(3)
13.4 Integrity Check
208(1)
13.5 Performance Analysis
209(3)
13.5.1 Process Delay
209(1)
13.5.2 Integrity Check Bytes
210(1)
13.5.3 Overhead
210(2)
13.6 Conclusion
212(1)
References
212(3)
14 E-Tree MSI Query Learning Analytics on Secured Big Data Streams 215(12)
B. Balamurugan
S. Jegadeeswari
14.1 Introduction
216(1)
14.2 Literature Review
217(1)
14.3 Proposed Framework-Secured Framework for Balancing Load Factor Using Ensemble Tree Classification
218(4)
14.3.1 Fast Predictive Look-ahead Scheduling Approach
220(1)
14.3.2 Parallel Ensemble Tree Classification (PETC)
221(1)
14.3.3 Bilinear Quadrilateral Mapping
222(1)
14.4 Conclusion
222(1)
References
223(4)
15 Lethal Vulnerability of Robotics in Industrial Sectors 227(12)
R.G. Babukarthik
Terrance Frederick Fernandez
Sivaramakrishnan
Aiswariya Milan
15.1 Introduction
228(1)
15.1.1 Robotics' Impact on Manufacturing Industries
228(1)
15.2 Robotics and Innovation
228(3)
15.2.1 Data Collection
229(1)
15.2.2 Walking Robots
229(1)
15.2.3 Various Robot Names and Dimensions
230(1)
15.3 Robot Service in Hotels
231(3)
15.3.1 Study 1A
233(1)
15.3.2 Study 1B
233(1)
15.4 Cyber Security Attacks on Robotic Platforms
234(1)
15.5 Conclusion
235(1)
References
236(3)
16 Smart IoT Assistant for Government Schemes and Policies Using Natural Language Processing 239(16)
J. Pradeep
K. Manojkiran
V.P. Gopi
B. Jayakumar
16.1 Introduction
240(1)
16.2 Literature Survey
240(3)
16.3 Proposed Smart System
243(4)
16.3.1 Data Extraction
244(1)
16.3.2 Data Processing
244(1)
16.3.3 Sending SMS
245(1)
16.3.4 Language Translation
245(1)
16.3.5 Text-To-Speech
245(2)
16.3.5.1 Input text
246(1)
16.3.5.2 Text analysis
246(1)
16.3.5.3 Phonetic analysis
246(1)
16.3.5.4 Speech database
246(1)
16.3.5.5 Concatenation & Waveform generation
247(1)
16.3.5.6 Synthesized speech
247(1)
16.4 Methodology
247(3)
16.4.1 Input Text Data
247(1)
16.4.2 URL Data Extraction
248(1)
16.4.3 Image to Text Conversion
248(1)
16.4.4 Extract Text from PDF
248(1)
16.4.5 SMS Update
249(1)
16.4.6 GSM
249(1)
16.4.7 Language Selection
249(1)
16.4.8 Text-To-Speech
249(1)
16.4.9 GUI
250(1)
16.5 Experimental Results
250(2)
16.6 Conclusion
252(1)
References
252(3)
Index 255(2)
About the Editors 257
Renny Fernandez, Terrance Frederick Fernandez
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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