14 The smart and secured AI-powered strategies for optimizing processes in multi-vendor business applications
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B. Girimurugan
Abstract
The development of artificial intelligence (AI)-powered processes in multivendor business applications has created significant opportunities to optimize resources, streamline operations, and reduce costs. However, deploying AI in multi-vendor business applications presents a unique set of challenges, including the potential for data privacy and security issues. To ensure the success of AI-enabled processes, organizations should implement best practices for proper data protection and secure AI-powered strategies. With the proper implementation of data security protocols and usage strategies, organizations can harness the potential of AI-powered processes in multivendor business applications to drive greater efficiency and cost savings. This includes creating greater process efficiency, reducing manual labor costs, and improving customer experiences. This will also enable organizations to remain competitive in the rapidly changing business landscape.
Abstract
The development of artificial intelligence (AI)-powered processes in multivendor business applications has created significant opportunities to optimize resources, streamline operations, and reduce costs. However, deploying AI in multi-vendor business applications presents a unique set of challenges, including the potential for data privacy and security issues. To ensure the success of AI-enabled processes, organizations should implement best practices for proper data protection and secure AI-powered strategies. With the proper implementation of data security protocols and usage strategies, organizations can harness the potential of AI-powered processes in multivendor business applications to drive greater efficiency and cost savings. This includes creating greater process efficiency, reducing manual labor costs, and improving customer experiences. This will also enable organizations to remain competitive in the rapidly changing business landscape.
Chapters in this book
- 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
Chapters in this book
- 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
