Generative AI Supply Chain Research Topics

Generative AI in Supply Chain research topics is one of the most recently used topics, it is a class of Artificial Intelligence. It has many applications and its complex to differentiate it with human – generated content. Then in this research we provide the explanations and the contents related Generative AI:

1. Define Generative AI

Initially we look over the definition for generative AI; it defines a class of artificial intelligence systems which have the capacity to create content like images, text or even music separately. These frameworks employ deep learning methods, such as RNNs (Recurrent Neural Networks) and GANs (Generative Adversarial Networks), to make novel data which is frequently similar from human – created content. It has many applications like Image Synthesis, Advanced Content Generation and Natural Language Generation.

2. What is Generative AI?

Next to the definition we see the deep description of our proposed research; Generative Artificial Intelligence is the expanded form of Generative AI, it is one of the classifications of AI models designed to produce novel and original content. These frameworks use Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs) to generate data, like images, audio, text or any other forms of information, which is complicated to differentiate from human-generated content. It has applications in different fields like image synthesis, art, natural language generation and more.

3. Where Generative AI used?

After the seep description we converse where to employ Generative Adversarial Network. It identifies the applications in different fields and is utilized for Natural Language Generation in content generations and chatbots. In the universe of media and art, it assists in creative content generation, image synthesis and music composition. Moreover it also plays a part in finance, healthcare and autonomous vehicles, providing medical imaging to risk estimation and traffic forecasting. Its flexibility generates a precious apparatus in many driving creation, automation and industries.

4. Why Generative AI technology Proposed? , Previous technology issues

Generative AI technology is proposed in this research, where the previous healthcare supply chain handles difficult in demand prediction due to procurement inadequacies, while transportation cost evaluations lack in accuracy. Ethereum blockchain transactions obtain unstable gas costs, tackled in private setups by utilizing consensus method for cost reduction and constant, the major issues are transportation cost estimation problem, Gas costs on Ethereum Blockchains and Demand forecasting and inventory management challenges are some of the difficulties in the previous technologies.

5. Algorithms / protocols

Dijkstra’s algorithm, Proof of Authority, Conditional Generative Adversarial Networks (CGANs) with Krill Herd Optimization (KHO) and Q-learning with Genetic algorithms are the methods to be used for our proposed research on Generative AI technology.

6. Comparative study / Analysis

For our proposed research we compare various methods to gain the corresponding findings. The methods that we compared are as follows

• Dijkstra’s algorithm and Conditional Generative Adversarial Networks with Krill Herd Optimization (CGANs – KHO) for optimized inventory management, increased transparency through blockchain, precise demand forecasting and effective product delivery.
• For encouraging belief and clarity, a private blockchain with a Proof of Authority (PoA) consensus mechanism was presented.
• To decrease the rate of execution, operations and energy consumptions, Q – learning with Genetic Algorithm is utilized.

7. Simulation results / parameters

Our proposed work is compared with the performance metrics like Quality of Medical Supplies, Demand and Inventory levels with time and the Route with time and the Transaction with Transparency are the metrics that are evaluated to find the best findings for our research.

8. Dataset LINKS / Important URL

In this research we proposed a Generative AI technology, where it generates new data that is frequently similar from human-created content. Here we provide some links that are related to our proposed strategy that are useful when we go through it.

• https://www.sciencedirect.com/science/article/pii/S2444569X22001111
• https://link.springer.com/article/10.1007/s10462-023-10559-4
• https://www.tandfonline.com/doi/abs/10.1080/00207543.2020.1715505
• https://www.mdpi.com/2076-3417/12/14/7088

9. Generative AI Applications

The proposed technique utilizes the applications that extend an extensive spectrum, from natural language generation for chatbots and contents to image synthesis and artistic generations. This technology also identifies the use in autonomous vehicles, healthcare and finance, assisting in traffic simulation, medical imaging and predictive modeling. Their adaptive fuels create multiple domains.

10. Topology for Generative AI

We employ the healthcare supply chain management characters data-driven predicting, blockchain integration and optimization are the topology for our proposed supply chain management based Generative AI technology.

11. Environment in Generative AI

Let’s converse about the environment that will be used for Generative AI research. It creates a dynamic and complex logistic landscape, including differing demands, multiple facilities and the need for safe and clear supply chain management. This topology combines machine learning methods and for secure networks offers a strong protection mechanism.

12. Simulation tools

Now we see the simulation tool or software requirements that are needed for the proposed Generative AI technology. The Python 3.11.3.2 tool is utilized to implement our research and Python programming language is utilized. Then the NS3 development tool is employed for this research. The operating system that used to execute our work is Windows 10 – (64 – bit).

13. Results

Generative AI technology is proposed in this research and it has the capacity to create content, like images, text or even music individually. This technology is compared with different methods and then is contrasted with various performance metrics to get the accurate possible findings. Then the research is implemented by using the tool NS3 simulator.

Generative AI Supply Chain Research Ideas:

Below are the research topics that are related to Generative Artificial Intelligence, we offer these topics to go through the contents or descriptions of our proposed research.

1. Engineers’ Perspectives on the Use of Generative Artificial Intelligence Tools in the Workplace
2. Investigating the Impact of Generative Artificial Intelligence on Brainstorming: A Preliminary Study
3. WIP: Using Generative AI to Assist in Individual Performance Feedback for Engineering Student Teams
4. Multimodal Image Synthesis and Editing: The Generative AI Era
5. Generative AI-Empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
6. Enhancing Text Classification Models with Generative AI-aided Data Augmentation
7. Can China Catch Up to the United States in Generative Artificial Intelligence?
8. Can Generative AI Eliminate Speech Harms? A Study on Detection of Abusive and Hate Speech during the COVID-19 Pandemic
9. Generative AI for Software Practitioners
10. Effectiveness of Generative Artificial Intelligence for Scientific Content Analysis
11. Factors Influencing the Adoption of Generative AI for Art Designing Among Chinese Generation Z: A Structural Equation Modeling Approach
12. Generative AI Enables EEG Data Augmentation for Alzheimer’s Disease Detection Via Diffusion Model
13. Generative AI for Industrial Applications: Synthetic Dataset
14. Editorial: What Have Large-Language Models and Generative Al Got to Do With Artificial Life?
15. YOLO-Based Semantic Communication With Generative AI-Aided Resource Allocation for Digital Twins Construction
16. Generative AI: Here to stay, but for good?
17. Generative AI for analysis and identification of Medicare improper payments by provider type and HCPC code
18. Originality and the future of copyright in an age of generative AI
19. What If Ethics Got in the Way of Generative AI?
20. Generative AI and the Call for Brevity
21. Can Architecture Knowledge Guide Software Development With Generative AI?
22. Generative Artificial Intelligence and Remote Sensing: A perspective on the past and the future [Perspectives]
23. Impact of COVID-19 on mental health in the US with generative AI
24. Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators
25. Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI
26. May the force of text data analysis be with you: Unleashing the power of generative AI for social psychology research
27. ChatGPT and the rise of generative AI: Threat to academic integrity?
28. Task-interdependencies between Generative AI and Workers
29. Beyond ChatGPT: Multimodal generative AI for L2 writers
30. The false positives and false negatives of generative AI detection tools in education and academic research: The case of ChatGPT
31. Data-driven Learning Meets Generative AI: Introducing the Framework of Metacognitive Resource Use
32. Generative AI for performance-based design of engineered cementitious composite
33. Reconceptualizing ChatGPT and generative AI as a student-driven innovation in higher education
34. Generative AI and deceptive news consumption
35. Integrating generative AI in knowledge building
36. The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation
37. Generative AI tools and assessment: Guidelines of the world’s top-ranking universities
38. Are both generative AI and ChatGPT game changers for 21st-Century operations and supply chain excellence?
39. ChatGPT: The transformative influence of generative AI on science and healthcare
40. Using ChatGPT and other forms of generative AI in systematic reviews: Challenges and opportunities
41. Generative Artificial Intelligence (AI) and Medical Ethics: A Symbiotic Dance for the Future
42. Prompt engineering when using generative AI in nursing education
43. Preserving ethics and integrity of scientific writing and reviewing after the advent of generative AI and AI-assisted technologies
44. Generative AI and the end of corpus-assisted data-driven learning? Not so fast!
45. Generative AI: Same same but different?
46. EP06.01-01 Concordance Between Generative AI GPT-4 and NCCN Guidelines: Biomarker-Driven Treatment Strategies in NSCLC
47. Literature review in the generative AI era – how to make a compelling contribution
48. Fostering Support for Pediatric Surgery by Generative AI
49. Embracing generative AI in health care
50. An Honeur Framework for Generating Computer Understandable Cohort Definitions from Clinical Trial Protocols in Multiple Myeloma through Generative AI for Comparing with Real-World Data