Hybrid Intelligence World’s Post

In this letter, the authors propose the use of a meta-learning based precoder optimization framework to directly optimize the #Rate-#Splitting #Multiple #Access (#RSMA) precoders with partial #Channel #State #Information #at #the #Transmitter (#CSIT). By exploiting the overfitting of the compact neural network to maximize the explicit #Average #Sum-#Rate (#ASR) expression, they effectively bypass the need for any other training data while minimizing the total running time. Numerical results reveal that the meta-learning based solution achieves similar ASR performance to conventional precoder optimization in medium-scale scenarios, and significantly outperforms sub-optimal low complexity precoder algorithms in the large-scale regime. ---- Rafael Cerna Loli, Bruno Clerckx More details can be found at this link: https://lnkd.in/eU6fZh_N

I am thrilled to share that our paper, "Heterogeneous Hypergraph Embedding for Node Classification in Dynamic Networks," has been accepted for publication in IEEE Transactions on Artificial Intelligence! A huge thank you to my supervisors, Prof. Jian Yang, A/Prof. Jia Wu, and especially Dr. Shan (Emma) XUE, for their invaluable guidance and support throughout this journey. This work focuses on advanced methods for node classification in dynamic networks using heterogeneous hypergraphs. Follow the online version for more details: https://lnkd.in/dctEZGRi #Hypergraph #Classification #DynamicNetworks #IEEE #AI

Zentropy for AI While we are working on using our zentropy theory for AI as shown in https://lnkd.in/eA9CuMiy, it was a pleasant surprise to learn that a group in computer science already published 3 papers using zentropy theory as part of their AI system. * K. Yuan, D. Miao, W. Pedrycz, W. Ding, H. Zhang, Ze-HFS: Zentropy-Based Uncertainty Measure for Heterogeneous Feature Selection and Knowledge Discovery, IEEE Trans. Knowl. Data Eng. 36 (2024) 7326–7339. https://lnkd.in/eQy-jhVy. * K. Yuan, D. Miao, Y. Yao, H. Zhang, X. Zhao, Feature Selection Using Zentropy-Based Uncertainty Measure, IEEE Trans. Fuzzy Syst. 32 (2024) 2246–2260. https://lnkd.in/eFRyWAkU. * C. Liu, B. Lin, D. Miao, A novel adaptive neighborhood rough sets based on sparrow search algorithm and feature selection, Inf. Sci. (Ny). 679 (2024) 121099. https://lnkd.in/ebiK9sEw.

I am excited to share that my first paper, titled "Designing a Haptic Boot for Space with Prompt Engineering: Process, Insights, and Implications," has been published in IEEE Access! I want to express my heartfelt gratitude to my co-authors Dr.Fatma Taher, Jose Berengueres ,Sana Khan and especially to Professor Mohammad Amin Kuhail for their invaluable support and guidance throughout. This experience has motivated me to pursue further research in the field of AI and Machine Learning. Link to the paper: https://lnkd.in/dHYpFs_s

My research paper, "A Comparative Analysis on AI-Driven Speech Protection Approaches," is now published in the IEEE Library! Key Highlights: Explores AI-driven techniques for moderating and protecting speech on digital platforms. Analyzes the balance between freedom of expression and ethical moderation. Evaluates the strengths and limitations of current AI models. Focuses on privacy-centric, secure AI solutions for user data. Provides actionable insights for building ethical and effective speech protection systems. Grateful to Prof. Vijayaraj Allwarsamy for his mentorship and Shai Kumar R for his collaboration. Read the full paper 👉 [https://lnkd.in/gkMQ3Jix]

Hi connections! 📚 Excited to share my recent publication in IEEE on object detection using YOLO! 🌟 🔍 Object detection is crucial in computer vision, and our study used YOLO to achieve significant advancements. We improved accuracy and enhanced efficiency, achieving "reducing inference time by 30% compared to previous state-of-the-art methods". 🔬 Published in IEEE [ICDSNS-2023], this project was a collaboration with [#RLakshmiVenkatesh ], supported by #Dr D Balakishnan. 📈 Our findings highlight breakthroughs in real-time object detection. This research marks a milestone in advancing both accuracy and efficiency in computer vision applications. 🙏 Grateful for the opportunity and looking forward to further advancements in the field! #ObjectDetection #ComputerVision #AI #Research #IEEE #YOLO #MachineLearning

I'm happy to share that our latest research paper, 'Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies', has been accepted by IEEE Transactions on Intelligent Transportation Systems! In this study, we tackle the ever-growing challenge of urban traffic congestion by introducing a novel traffic signal control strategy. Unlike conventional methods, our approach leverages a centralized critic-decentralized actor framework, enabling optimized traffic flow even with varying control intervals. This innovation enhances both stability and efficiency in real-world scenarios, significantly reducing congestion while maintaining safety and performance. #TrafficManagement #ReinforcementLearning #SmartCities #ITS #AI

Evolving wireless network monitoring with AI Did you know the next wave of intelligent radio communication systems can classify network traffic directly from spectrum data? Our latest research takes a further step towards practical, efficient network monitoring in complex environments like shared spectrum. 🔗 Explore our findings in detail here https://lnkd.in/eAD-ryiw In this paper, we've developed a novel method that integrates Multi-task Learning with advanced Neural Network optimization, making it feasible to deploy these technologies even on devices with limited resources. The benefits? Not only does our model achieve high accuracy, but it also runs fast and conserves energy on diverse and constrained edge computing platforms. #6G #wirelessnetworks #AI

Researcher, professor, author: three of many titles held by Walid Saad 🤩 Dr. Saad, #VTInnovationCampus next-G wireless lead, and his colleagues (Christina Chaccour (VT alum), Christo K Thomas (Innovation Campus researcher), and Merouane Debbah) have just published their new book, "Foundations of Semantic Communication Networks". The Wiley-IEEE Press publication explores the evolving field of semantic communications, a topic that could shape networks beyond 6G and serve as a stepping stone toward AGI-native systems. Learn more about the book here 🔗 https://lnkd.in/eW-RvDjd #TheCenterofNext

This paper provides an #overview of #processing #steps and #evaluation #methods for #channel #charting and proposes a novel dissimilarity metric that takes into account #angular-#domain #information as well as a novel #deep #learning-#based #metric. Furthermore, the authors suggest a method to fuse dissimilarity metrics such that both the time at which channels were measured as well as similarities in channel state information can be taken into consideration while learning a #channel #chart. By applying both classical and deep learning-based manifold learning to a dataset containing #sub-#6 GHz distributed #massive #MIMO #channel #measurements, they show that their metrics outperform previously proposed dissimilarity measures. ----Phillip Stephan, Florian E., @Stephan Ten Brink More details can be found at this link: https://lnkd.in/gZtvsxF3