Big Data Analytics for Human Computer Interactions a New Era of Computation 1st Edition by Kuldeep Singh Kaswan ISBN 9815079948 9789815079944 by Kaswan, Kuldeep Singh;baliyan, Anupam;dhatterwal, Jagjit Singh; instant download after payment.

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ISBN 10: 9815079948 
ISBN 13: 9789815079944
Author: Kuldeep Singh Kaswan

Big Data is playing a vital role in HCI projects across a range of industries: healthcare, cybersecurity, forensics, education, business organizations, and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Working on HCI projects requires specific skill sets to implement IT solutions. Big Data Analytics for Human-Computer Interactions: A New Era of Computation is a comprehensive guide that discusses the evolution of Big Data in Human Computer Interaction from promise to reality. This book provides an introduction to Big Data and HCI, followed by an overview of the state-of-the-art algorithms for processing big data, Subsequent chapters also explain the characteristics, applications, opportunities and challenges of big data systems, by describing theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in designing HIC systems. The book also presents solutions for analyzing complex patterns in user data and improving productivity. Readers will be able to understand the technology that drives big data solutions in HCI projects and understand its capacity in transforming an organization.The book also helps the reader to understand HCI system design and explains how to evaluate an application portfolio that can be used when selecting pilot projects. This book is a resource for researchers, students, and professionals interested in the fields of HCI, artificial intelligence, data analytics, and computer engineering.

Big Data Analytics for Human Computer Interactions a New Era of Computation 1st Table of contents:

1. INTRODUCTION
2. WHAT IS BIG DATA?
3. MEANING OF BIG DATA
4. HISTORY OF BIG DATA
5. Ancient History of Data
6. C 18,000 BCE
7. C 2400 BCE
8. 300 BC – 48 AD
9. C 100 – 200 AD
10. The Emergence of Statistics
11. 1663
12. 1865
13. 1880
14. The Early Days of Modern Data Storage
15. 1928
16. 1944
17. The Beginnings of Business Intelligence
18. 1958
19. 1962
20. 1964
21. Large Data Centers Start
22. 1965
23. 1970
24. 1976
25. 1989
26. The Emergence of the Internet
27. 1991
28. 1996
29. 1997
30. Big Data Early Ideas
31. 1999
32. 2000
33. 2001
34. Web 2.0 Enhances volume of Data
35. 2005
36. Nowadays, the Word 'Big Data' is being Used
37. 2007
38. 2008
39. 2009
40. 2010
41. 2011
42. 2014
43. BIG PART AND DATA PART MORE CRITICAL?
44. MODERNIZATION OF BIG DATA
45. BIG DATA UTILIZATION
46. INNOVATION OF BIG DATA
47. Extensibility and Scalability of Data
48. CHALLENGES OF BIG DATA
49. PRIVACY ISSUE IN BIG DATA
50. WHY BIG DATA IS IMPORTANT
51. THE STRUCTURE OF BIG DATA
52. Structuring and Analysis of Big Data
53. ENHANCEMENT OF QUALITY OF BIG DATA
54. HOW DATA VALUE DELIVER IN BIG DATA
55. Waves of Big Data
56. Web Services in Big Data
57. KNOWLEDGE FILTERING IN BIG DATA
58. BIG DATA VS. TRADITIONAL DATA
59. THE NEED FOR STANDARD TRANSITION PARAMETERS
60. Enhancing Big Data Standards
61. BIG DATA STRATEGY
62. SURROUNDING VIEW WEB DATA
63. Up to Date Four Quadrant View
64. What is Missing in Big Data
65. How Big Data Influences Businesses?
66. What Kind of Data should be Collected?
67. WEB-BASED DATA
68. Privacy of Big Data
69. Analysis of Customer Relationship Management (CRM)
70. Online Information on Web Data
71. Shopping Behaviors
72. TO FULFIL CUSTOMER'S NEED
73. Customer Chose Suitable Path
74. Research Behaviors
75. Feedback Behaviors
76. WEB INFORMATION COLLECTING CLIENT INFORMATION
77. BEHAVIOR CURVE
78. Attentiveness in Modeling
79. Response Modeling
80. Customer Segmentation
81. CURRENT DEMOGRAPHICS ARISE FROM WEB DATA
82. Assessing Advertising Results
83. Online Services
84. CONCLUSION
85. Human-Computer Interface Introduction
86. INTRODUCTION
87. Objective of HCI
88. HISTORY
89. PRINCIPLES OF DISPLAY DESIGN
90. Perceptual Principles
91. Mental Model Principles
92. Principles based on Attention
93. Memory Principles
94. ROOTS OF HCI IN INDIA
95. GUIDELINES IN HCI
96. Schneiderman’s Eight Golden Rules
97. NORMAN'S SEVEN PRINCIPLES
98. PRINCIPLE EVALUATION HEURISTIC
99. Nielsen's Ten Heuristic Principles
100. INTERFACE DESIGN GUIDELINES
101. Interaction Protocol
102. Displaying Information
103. Data Entry
104. INTERACTIVE SYSTEM DESIGN
105. User Involvement in Usability Engineering
106. Objectives of Usability Engineering
107. Function Usability
108. Usability Study
109. Usability Testing
110. Acceptance Testing
111. Software Tools
112. HCI AND SOFTWARE ENGINEERING
113. The Waterfall Method
114. Interactive System Design
115. PROTOTYPING OF INTERFACE DESIGN
116. USER-CENTERED DESIGN (UCD)
117. Limitation of UCD
118. Designing of GUI and Aesthetics
119. HCI in Indian Industries
120. HCI Analogy
121. Method of Interactive Devices
122. DESIGN PROCESS & TASK ANALYSIS
123. Design of HCI
124. Design Methodologies
125. Design Participation
126. ANALYSIS OF TASK
127. What is a TASK?
128. Hierarchical Task Analysis
129. Analysis Techniques
130. Task Models
131. Characteristics of Task Models of Engineering Model
132. CONCUR TASK TREE (CTT)
133. Dialog Design
134. Dialog Representation
135. Basic of Formalism
136. Dialogue's Progression State Transition Network (STN)
137. State Maps
138. Model of Petri Nets
139. VISUAL VISION THINKING
140. DIRECT INTERFACE DESIGN PROGRAMMING
141. Problems with Direct Manipulation
142. Presentation of Item in Sequence
143. Menu Layout
144. Form Fill-in Dialog Boxes
145. INFORMATION SEARCH AND VISUALIZATION
146. Database Query
147. Search of Multimedia Documents
148. Advanced Filtering
149. Media Associations
150. Object Action Interface Model for Web Development
151. OBJECT-ORIENTED PROGRAMMING
152. Objects
153. Encapsulation Techniques
154. Public Interface
155. Entity of Class
156. Inheritance
157. Polymorphism
158. Object-Oriented Modeling of User Interface Design
159. CONCLUSION
160. HCI Learning From Cognitive Web
161. INTRODUCTION
162. The Basic Sense Reality
163. Algorithm Indexing
164. Brain and Google
165. Google Knowledge Base
166. TRANSMISSION
167. Innovative Computational Telecommunication
168. Clicks on Different Web Pages
169. Appropriate Advertising
170. Turing to Develop Computer Simulation
171. Voice Recognition Software
172. Better Connection to the Internet
173. VISION
174. Learning Through Perception
175. Attributes Classification
176. Principles
177. Collaborative Filtering Through Association Rules
178. Grouping Algorithm
179. Latent Features on E-commerce Websites
180. Improve Knowledge Discovery
181. Cognitive Approach
182. CONNECT WITH SOFTWARE
183. Improvement in Boolean Algebra
184. Use Semantic Web Interaction with Machine
185. Informal Mapping
186. Description of Logics
187. Monitoring Stages
188. Collective Reasoning to Solve Complex Problem
189. INDICATOR
190. Computing Statistical Forecasting Information
191. Computational Equations
192. Predictive Analytics
193. Bayes Classification
194. Memory Sequence Modelling
195. Brain Predication
196. Network Science for Web Production
197. CORRECT PREDICTABLE KNOWLEDGE
198. Automobile Route
199. Monitoring Feedback Control
200. Navigation Sensor
201. Flocks and Swarms
202. Swarm Methods
203. Ants at Work
204. Genetic Algorithm
205. Intelligent Systems
206. CONCLUSION
207. Thinking Tool Based HCI
208. SCENARIOS
209. Strategic Planning
210. Morphological Analysis
211. Design in CRT
212. FRAMEWORK OF COGNITIVE THINKING
213. Develop Thinking Method
214. Resources
215. Element of Building Block
216. Controlling Capabilities
217. Strategic Ambitions
218. Planning Strategy
219. OPTIMIZATION PROFIT ORIENTED ORGANIZATION
220. Space for Socio-cognitive-cyber-physical Effect
221. Cryptographic
222. Networks Operations
223. Design Numerous Nodes
224. Identify Detected Networks
225. Logical Activation Functions
226. Deny Access and Improve Capabilities to a Network
227. Contro Effective Connectivity
228. Isolate a Network
229. Penetrate Harm Information
230. Destroy an Existing Network
231. Restructuring Connection Surgery
232. Hide Network within Other Networks
233. CONCLUSION
234. Big Data Decision Computations To HCI
235. RED PARTNERING BASIC COMPUTING COMPONENTS
236. From Traditional Problems to Computer Networking
237. CRT Example
238. ACTIVITY OF CRT
239. Purpose and Aim of CRT
240. Hypothesis Formulation
241. Analytic Mind Factors
242. Cause-Effect Brain Storming Relationship
243. ADVANCE SEARCH AND OPTIMIZATION
244. Blind vs. Knowledge-Based Classical Optimization Technique
245. Negotiation-Based Optimization
246. SIMULATION BRAIN CRT
247. Resolution, Abstraction, and Fidelity used to Build Good Decision
248. DATA ANALYSIS AND MINING TECHNIQUES
249. C4.5 Classification Tree
250. BIG DATA USE FOR COGNITION DECISION
251. The 6 V’s Big Data Characteristics
252. Architectures for Big Data Storage
253. Real-Time Operations
254. GDL Data Fusion Architecture
255. COMPUTATIONAL RETEAMING SYSTEMS BIG-DATA-TO-DECISIONS
256. Computational Reteaming System Preliminary Forms
257. Sophisticated Computer-related-teaming Systems Progressive Development
258. Advanced Forms of Computational-Red Teaming-Systems
259. Shadow CRT Machine
260. CONCLUSION
261. Relationship Between Big Data, NLP, And Cognitive Computing
262. INTRODUCTION
263. THE MAJOR ROLE PLAY NLP IN A COGNITIVE SYSTEM
264. The Importance of Cognition System
265. Connecting Words for Communication
266. Identification of Language and Tokenization
267. Phonology Speech Recognition
268. Morphology
269. Lexical Analysis
270. Syntax and Syntactic Analysis
271. Construction Grammars
272. Discourse Analysis for Intelligent Computing
273. Pragmatics
274. Techniques for Resolving Structural Ambiguity
275. Importance of Hidden Markov Models
276. Word-Sense Disambiguation (WSD)
277. SEMANTIC WEB
278. THE USE OF SPEECH RECOGNITION CAPABILITIES TO ADDRESS BUSINESS CHALLENGES
279. Enhancing the Shopping Experience
280. Leveraging the Connected World of Internet of Things
281. Voice of the Customer
282. Fraud Detection
283. DEALING WITH HUMAN-GENERATED DATA
284. DEFINING BIG DATA
285. Volume, Variety, Velocity, and Veracity
286. THE ARCHITECTURAL FOUNDATION FOR BIG DATA
287. The Physical Foundation for Big Data
288. Security Infrastructure
289. Operational Databases
290. Role of Structured and Unstructured Data
291. Data Services and Tools
292. ANALYTICAL DATA WAREHOUSES
293. Big Data Analytics
294. HADOOP TECHNOLOGY
295. DATA IN MOTION AND STREAMING DATA
296. Analyzing Dark Data
297. INTEGRATION OF BIG DATA WITH TRADITIONAL DATA
298. CONCLUSION
299. Electronic Automation of Smart Computing
300. INTRODUCTION
301. MENTAL ABILITY OF SMART COMPUTING
302. BUILDING THE CORPUS
303. Protection of Hazardous Data
304. BRINGING KNOWLEDGE INFORMATION INTO THE COGNITIVE SYSTEM
305. Managing Internal and External Data Sources
306. Image Segmentation in Cognitive Approach
307. Analytics Services
308. COMPUTER ALGORITHMS
309. Finding Patterns in Data
310. Optimization Method
311. Reinforcement Learning
312. Inferential Algorithm
313. EVIDENCE BASED REASONING
314. Cognitive Assumptions
315. Hypothesis Query Scoring
316. PRESENTATION CYCLE AND VISUALIZATION SERVICES
317. INFRASTRUCTURE
318. CONCLUSION
319. Representation of Knowledge in Taxonomies and Ontologies and their Application in Advance Analysis t
320. INTRODUCTION
321. REPRESENTING KNOWLEDGE
322. Developing a Cognitive System
323. DEFINING TAXONOMIES, AND ONTOLOGIES FRAMEWORK
324. TECHNIQUES OF REPRESENT KNOWLEDGE
325. Controlling Multiple Views of Knowledge
326. MODELS FOR KNOWLEDGE REPRESENTATION
327. Taxonomies Description
328. Ontologies Represent Knowledge
329. Other Methods of Knowledge Representation
330. Simple Trees
331. The Semantic Web
332. The Importance of Persistence and State
333. IMPLEMENTATION CONSIDERATIONS
334. ADVANCED ANALYTICS COGNITIVE COMPUTING
335. CAPABILITIES IN ADVANCED ANALYTICS
336. The Relationship between Statistics, Data Mining, and Machine Learning
337. Using Machine Learning in the Analytics Process
338. Supervised Learning
339. Unsupervised Learning
340. Predictive Analytics
341. Business Value of Predictive Analytics
342. Text Analytics in Cognitive System
343. Business Value of Text Analytics
344. Image Analytics
345. Speech Analytics
346. Using Advanced Analytics
347. Building Value with In-memory Capabilities
348. Effective Open-Source Tools on Advanced Analytics
349. CONCLUSION
350. Innovation HCI Knowledge
351. SOCIAL MEDIA PLATFORMS
352. EMPIRICAL ANALYSIS
353. IMPACT ON OPINION MINING
354. Impacts on Opinion Mining Process
355. EVALUATION EFFICIENCY OF SHAP D2 ALGORITHM
356. Semantic Compression
357. Source Document (Fragment)
358. Subject Document (Fragment)
359. Comparison Between Original and Subject Document after Semantic Compression
360. INTELLIGENT TECHNOLOGY USE IN EDUCATION DATA MINNIG
361. Displaying Learning: Past, Present, and Future
362. DETECTION IN INTERACTIVE MULTIMEDIA ENVIRONMENTS
363. Feature Extraction
364. Classification of Deception
365. CART Decision Tree
366. HEURISTIC METHOD
367. INTENT RECOGNITION USING NEURAL NETWORKS AND KALMAN FILTERS
368. Feature Calculation
369. Neural Network-Based Model
370. Kalman Filter Based Model
371. Evaluation Criteria
372. HCI EMPOWERED MINING FOR CROSS-DOMAIN KNOWLEDGE DISCOVERY
373. Main System Functionality
374. AN INTERACTIVE COURSE ANALYZER FOR IMPROVING LEARNING STYLES SUPPORT LEVEL
375. Course Analyzing Mechanism
376. Availability and Frequency Factors
377. Sequence Factor
378. Interactive Course Analyzer
379. DIGITAL ARCHIVES: SEMANTIC SEARCH AND RETRIEVAL
380. The Rationale SARA User Interface
381. User Experience Considerations
382. Design and Technical Challenges
383. A MODEL-BASED APPROACH
384. The Robust Data Quality Analysis
385. RANDOM FORESTS FOR FEATURE SELECTION IN NON-INVASIVE BRAIN-COMPUTER INTERFACING
386. CONCLUSION
387. HCI: An Intelligent Learning Environment
388. OPTIMIZING CLASSROOM ENVIRONMENT TO SUPPORT TECHNOLOGY-ENHANCED LEARNING
389. Research Tools
390. A SMART PROBLEM-SOLVING ENVIRONMENT
391. A Smart Constructivist Learning Environment
392. Architectural Approach
393. Information Extraction
394. QUERY GENERATION
395. SMART LEARNING COMMUNITIES
396. The Concept of ‘Smart’ Learning Environments
397. Smart – But for Whom?
398. SMART LEARNING THEORY
399. WHY IS IT SMART TO COLLABORATE?
400. WHY WE NEED SLES?
401. Generating Benefits through the Community
402. Drivers and Design Principles for SLEs
403. INTELLIGENT OPEN-END KNOWLEDGE MANAGEMENT THAT SUPPORTS COGNITIVE AND METACOGNITIVE PROCESSES FOR LE
404. Learning Environment
405. MEASURING COGNITION AND METACOGNITION
406. THE CONCEPT OF ENCYCLOPEDIAS FROM THE PERSPECTIVE OF ITS PARTICIPANTS IN K-12 CLASSES
407. Framework and Procedures
408. OPEN-ENDED QUERIES
409. DEVELOPING ADAPTIVE LEARNING SYSTEMS
410. System Implementation
411. Learning Content Module
412. ADAPTIVE PRESENTATION MODULE
413. ADAPTIVE CONTENT MODULE
414. LEARNING MODULE
415. AUTOMATING THE E-LEARNING
416. Automating the Design of Personalized Learning Scenarios
417. Learning Simulation Framework
418. CONCLUSION
419. Data Visualisation and Data Analytics in HCI
420. ASSESSMENT OF LIDAR POINT CLOUD OPTIMIZED VISUALIZATION, BASED ON THE VISUAL PERCEPTIONS
421. LOD Management
422. LiDAR Data Organization
423. Simplification of the Scene
424. VISUALIZATION OF THE ATTRIBUTES OF THE BIOLOGICAL CELLS
425. INTUITIVE MULTIMEDIA TRANSFORMATIONS FOR TIME-ORIENTED DATA SYMBOLISATION
426. Methods for Data Simplification
427. Time-Centered Algorithms for Pattern Finding
428. Samples for Application of Interactive Visual Interfaces in KDD
429. Interactive Visual Data Simplification
430. The User Interface
431. Usage Scenario
432. ORGANIZING DOCUMENTS TO SUPPORT ACTIVITIES
433. Analysis of Document
434. Workspace Personalization
435. Organization inside a Folder
436. File Location and Identification
437. Temporary Files
438. Design
439. CONCLUSION
440. HCI with Big Data Analytics
441. INTRODUCTION
442. Big Data and Its Market Value
443. Big Data in Healthcare
444. Cloud Computing with Big Data Analytics
445. Layers of Big Data
446. Hospitals and Healthcare Institutes
447. Government and Public Sector Unit
448. Social Networking
449. Computing Platforms
450. Nature and Natural Processes
451. Solving Big Data Storage Challenges with Private Cloud
452. Solving Big Data Computational Platform
453. Solving Big Data Storage Challenges with Hybrid Cloud
454. Internet of Things
455. HCI
456. Human-Computer Interaction Models
457. 2.1.1. Unimodality HCI System
458. Multimodal HCI System
459. COGNITIVE ENGINEERING
460. HCI IN MOBILE DEVICES
461. OPERATING SYSTEMS FOR MOBILE DEVICES
462. CHALLENGES OF HCI IN MOBILE DEVICES
463. Hardware Challenges
464. Software Challenges
465. HCI WITH BIG DATA
466. Data Visualization and Human Perception
467. HCI Architecture
468. HUMAN INTERACTION WITH MACHINES AND COMPUTERS
469. Audio-Based HCI
470. Visual-Based HCI
471. Sensor-Based HCI
472. CONCLUSION
473. REFERENCES
474. Subject Index

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