Artificial Intelligence

Table of contents

1. Preface
2. Machine learning (ML)
   1. Omobayo Ayokunle Esan, Munienge Mbodila, Patrick Mukeninay Madimba Detection of lesions in breast image using median filtering and convolutional neural networks
      1. 1 Introduction
      2. 2 Background
      3. 3 Method
      4. 4 Experimental evaluations and results
      5. 5 Conclusion
   2. Martin Roa-Villescas, Jin-Guo Liu, Patrick W. A. Wijnings, Sander Stuijk, Henk Corporaal Pushing the boundaries of probabilistic inference through message contraction optimization
      1. 1 Introduction
      2. 2 Inference in junction trees
      3. 3 Message contraction optimization
      4. 4 A metaprogramming-based inference framework
      5. 5 Experimental evaluation
      6. 6 Discussion
      7. 7 Conclusion
   3. Darshan Nayak, Abhijot Bedi, David Degbor, Shelley Zhang, Eugene Chabot Facilitating cooperative missions through information sharing in heterogeneous agent teams
      1. 1 Introduction
      2. 2 RCRS agent rescue testbed and sample agents
      3. 3 Role of communication in decision-making
      4. 4 Communication among heterogeneous agents
      5. 5 Communication among homogeneous agents
      6. 6 Experimentation setup
      7. 7 Results
      8. 8 Conclusion and future work
   4. Sait Alp, Taymaz Akan, Mohammad Alfrad Nobel Bhuiyan Transferring knowledge: CNNs in Martian surface image classification
      1. 1 Introduction
      2. 2 Proposed approach
      3. 3 Experimental results and discussion
      4. 4 Conclusion
      5. Acknowledgment
   5. Alireza Bagheri Rajeoni, Breanna Pederson, Ali Firooz, Hamed Abdollahi, Andrew K. Smith, Daniel G. Clair, Susan M. Lessner, Homayoun Valafar Vascular system segmentation using deep learning
      1. 1 Introduction
      2. 2 Background and methods
      3. 3 Results
      4. 4 Discussion and future work
      5. 5 Conclusion
   6. Afsaneh Shams, Kyle Becker, Drew Becker, Soheyla Amirian, Khaled Rasheed Evolutionary CNN-based architectures with attention mechanisms for enhanced image classification
      1. 1 Introduction
      2. 2 Related work
      3. 3 Methodology
      4. 4 Dataset
      5. 5 Experiments
      6. 6 Conclusion and future work
3. Convolutional neural network (CNN)
   1. Md Mahmudur Rahman, Bikesh Regmi Multi-label concept detection in imaging entities of biomedical literature leveraging deep learning-based classification and object detection
      1. 1 Introduction
      2. 2 Related work
      3. 3 Multi-label classification of concepts
      4. 4 Concept detection as region-of-interests (ROIs)
      5. 5 Experiments
      6. 6 Result analysis
      7. 7 Conclusion
   2. Beilei Zhu, Chandrasekar Vuppalapati Revolutionizing supply chain dynamics: deep meta-learning and multi-task learning for enhanced predictive insights
      1. 1 Introduction
      2. 2 Enhancing adaptability in supply chain management: Integrating mechanistic and probabilistic meta-learning approaches
      3. 3 Meta-learning algorithms for addressing data dynamics in supply chain management
      4. 4 Black box approach
      5. 5 Optimization-based meta-learning
      6. 6 Optimize based meta-learning implementation and results for supply chain regression use case
      7. 7 A deep dive into supply chain product classification: meta-learning in master data management
      8. 8 Conclusion
   3. Cory Davis, Patrick Stockton, Eugene B. John, Zachary Susskind, Lizy K. John Characterization of Neuro-Symbolic AI and Graph Convolutional Network workloads
      1. 1 Introduction
      2. 2 Model overview
      3. 3 Methodology
      4. 4 Results
      5. 5 Analysis
      6. 6 Conclusion
      7. Appendix
   4. Nikhila Vintha, Devinder Kaur Multivariant time series prediction using variants of LSTM deep neural networks
      1. 1 Introduction
      2. 2 Data preprocessing techniques
      3. 3 Recurrent neural networks
      4. 4 Methodology and implementation
      5. 5 Optimizers and model accuracy
      6. 6 Conclusion and future work
   5. Anthony C. Brunson, Ryan D. Clendening, Richard Dill, Brett J. Borghetti, Brett Smolenski, Darren Haddad, Douglas D. Hodson Cellphone-based sUAS range estimation: a deep-learning classification and regression approach
      1. 1 Introduction
      2. 2 Related works
      3. 3 Methodology
      4. 4 Results
      5. 5 Conclusion
   6. B. Chandra, Kushal Pal Singh, Prem Kalra, Rajiv Narang Automatic diagnosis of 12-lead ECG using DINOv2
      1. 1 Introduction
      2. 2 Related work
      3. 3 Vision Transformer (ViT) and DINOv2
      4. 4 Methodology
      5. 5 Results and analysis
      6. 6 Conclusion & future scopeConclusion & future scope
4. Large language model (LLM)
   1. Sean Choi, Jinyoung Jo Leveraging linguistic features to improve machine learning models for detecting ChatGPT usage on exams
      1. 1 Introduction
      2. 2 Background
      3. 3 Overview
      4. 4 Evaluations
      5. 5 Related works
      6. 6 Discussion and future works
      7. 7 Conclusion
   2. Michael Sandborn, Carlos Olea, Anwar Said, Mudassir Shabir, Peter Volgyesi, Xenofon Koutsoukos, Jules White Towards AI-augmented design space exploration pipelines for UAVs
      1. 1 Introduction
      2. 2 Related work
      3. 3 UAV design pipeline
      4. 4 Experimental results
      5. 5 Large language models in the design process
      6. 6 Conclusion
   3. Paula Lauren Improving subword embeddings in large language models using morphological information
      1. 1 Introduction
      2. 2 Related work
      3. 3 Analysis of GPT subword tokens
      4. 4 Proposed work
      5. 5 Result analysis
      6. 6 Conclusion
      7. 7 Future work
   4. Massoud Alibakhsh Swarm intelligence: a new software paradigm
      1. 1 Introduction: it is all about communication
      2. 2 A quick background: in the beginning, there were the paper forms, then came the computers
      3. 3 Two major paradigm shifts: first the GUI and then the Cloud
      4. 4 Email and the promise of panacea: the first wave
      5. 5 Enterprise social networks for business: the second wave
      6. 6 Workflow-based communication: the 3rd wave – OMIO [patents pending]
      7. 7 A new software design approach for AI as a platform
      8. 8 Self-aware intelligent objects
      9. 9 Integration, deep integration, and AI fusion: meaningful integration with AI in one step
      10. 10 A quick review of the mechanics
      11. 11 Migrating to the new LLM AI platform
      12. 12 What LLMs are not suited for
      13. 13 A new kind of Operating System: introduction to OMIO OS
      14. 14 Conclusion
      15. 15 Project OMADEUS
      16. Disclosure
   5. Xiaowei Xu, Bi T. Foua, Xingqiao Wang, Vivek Gunasekaran, John R. Talburt Leveraging large language models for efficient representation learning for entity resolution
      1. 1 Introduction
      2. 2 Related work
      3. 3 Methodology
      4. 4 Experimental results
      5. 5 Conclusion and future work
      6. 6 Appendix
   6. Xiaowei Xu, Bi Foua, Xingqiao Wang, Vivek Gunasekaran, Jonathan White, John Talburt TOAA: Train once, apply anywhere
      1. 1 Introduction
      2. 2 Related work
      3. 3 Methodology
      4. 4 Experimental results
      5. 5 Conclusion and future work
      6. 6 Appendix
5. Index