2 AI technologies, tools, and industrial use cases
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Shaik Mohammad Rafee
Abstract
Artificial intelligence (AI) technology refers to various technologies and tools used in the development of systems that can carry out tasks that normally require human intelligence. AI systems can process large datasets and communicate in natural languages, taking decisions, recognizing objects, detecting anomalies, and managing data to provide personalized recommendations. AI technologies can be used in almost any field, including healthcare, finance, e-commerce, marketing, robotics, Internet of things, and more. Popular AI technologies and tools include computer vision, natural language processing (NLP), machine learning (ML), deep learning (DL), sentiment analysis, voice recognition, and robotics. Computer vision is used to identify objects in pictures or videos. NLP is used to understand language. ML is used for data modeling and to find patterns in data. DL techniques are used to process large amounts of data and automate decisions. Sentiment analysis is used to analyze customer feedback. Voice recognition is used to recognize and understand spoken words. Robotics technology is used to build and program robots. Businesses are increasingly using AI technologies and tools to improve customer experience, increase efficiency, automate tasks, increase scalability, and provide personalized recommendations and services.
Abstract
Artificial intelligence (AI) technology refers to various technologies and tools used in the development of systems that can carry out tasks that normally require human intelligence. AI systems can process large datasets and communicate in natural languages, taking decisions, recognizing objects, detecting anomalies, and managing data to provide personalized recommendations. AI technologies can be used in almost any field, including healthcare, finance, e-commerce, marketing, robotics, Internet of things, and more. Popular AI technologies and tools include computer vision, natural language processing (NLP), machine learning (ML), deep learning (DL), sentiment analysis, voice recognition, and robotics. Computer vision is used to identify objects in pictures or videos. NLP is used to understand language. ML is used for data modeling and to find patterns in data. DL techniques are used to process large amounts of data and automate decisions. Sentiment analysis is used to analyze customer feedback. Voice recognition is used to recognize and understand spoken words. Robotics technology is used to build and program robots. Businesses are increasingly using AI technologies and tools to improve customer experience, increase efficiency, automate tasks, increase scalability, and provide personalized recommendations and services.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- List of authors IX
- About the editors XIII
- 1 Introduction to artificial intelligence 1
- 2 AI technologies, tools, and industrial use cases 21
- 3 Classification and regression algorithms 53
- 4 Clustering and association algorithm 87
- 5 Reinforcement learning 109
- 6 Evaluation of AI model performance 125
- 7 Methods of cross-validation and bootstrapping 145
- 8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies 167
- 9 AI: issues, concerns, and ethical considerations 189
- 10 The future with AI and AI in action 213
- 11 A survey of AI in industry: from basic concepts to industrial and business applications 233
- 12 The intelligent implications of artificial intelligence-driven decision-making in business management 251
- 13 An innovative analysis of AI-powered automation techniques for business management 269
- 14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications 287
- 15 Utilizing AI technologies to enhance e-commerce business operations 309
- 16 Exploring the potential of artificial intelligence in wireless sensor networks 331
- 17 Exploring artificial intelligence techniques for enhanced sentiment analysis through data mining 345
- 18 Exploring the potential of artificial intelligence for automated sentiment 361
- 19 A novel blockchain-based artificial intelligence application for healthcare automation 373
- 20 Enhancing industrial efficiency with AI-enabled blockchain-based solutions 387
- Index 401
