In this paper, the aythors propose a series of #Resource #Allocation (#RA) strategic algorithms harnessing the Transfer Learning, #Growth-#Share (#GS) matrix, #Game #Theory (#GT), and service priorities to tailor the aforementioned trade-off. This endeavour renders the network more intelligent, self-sufficient, and resilient. Furthermore, they have seamlessly integrated Device-to-Device communication scenarios into their proposed algorithms, enhancing #Spectrum #Efficiency (#SE) and network capacity. The proposed integration aims to strengthen overall system performance and accommodate the evolving demands of future wireless networks. Their primary contribution lies in the development of the #GS-#GT-#based #Optimal #PathFinder (#GS-#GTOPF) algorithm to identify optimal paths based on SE using Deep Neural Networks. Thereafter, they formulate an enhanced version of it by integrating #service #priorities (#GS-#GTOPF-#SP). This refinement has been further advanced by reducing the #Computational #Time (#CT), resulting in #GS-#GTOPF-#SP-#rCT. Further improvement is achieved by introducing the angle criterion (#GS-#GTOPF-#SP-#rCT- #θ). ---- Vivek Pathak, Chethan R, Rahul Jashvantbhai Pandya, Sridhar Iyer, Vimal Bhatia More details can be found at this link: https://lnkd.in/etxP5fsd
Shannon Wireless’ Post
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Found this super interesting article that explains an exciting breakthrough in materials science! The Graph Networks for Materials Exploration (GNoME) AI tool has unveiled 2.2 million new crystals, including 380,000 stable materials for futuristic technologies like superconductors and advanced batteries. This demonstrates the incredible potential of AI to revolutionize materials discovery—making science fiction a reality! Click this article if you want to learn more! #ML https://lnkd.in/ewbcuXyj
Millions of new materials discovered with deep learning
deepmind.google
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The world of AI is abuzz with new architectures, and the Kolmogorov-Arnold Network (KAN) is one to watch! Inspired by physics, KANs offer a unique approach with the potential to revolutionize how AI models learn and process information. #LLM #Transformers #NeuralNetworks Read my latest article below
Unveiling the Kolmogorov-Arnold Network (KAN): A Paradigm Shift in Artificial Intelligence?
link.medium.com
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This paper proposed a deep learning approach to detect data and system anomalies using high-resolution #continuous #point-#on-#wave (#CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed which combines local detections to form more reliable detections. ---- Kursat Mestav, Xinyi Wang, Lang Tong More details can be found at this link: https://lnkd.in/efHs-Myf
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems
ieeexplore.ieee.org
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Every neural network needs at least one activation function to make accurate predictions. Choosing the right one can result in a precise, high-performance network consistently delivering the desired results. #DataScience #ArtificialIntelligence https://hubs.ly/Q026QMTy0
How to Choose the Right Activation Function for Neural Networks
opendatascience.com
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This paper dives deeper into #SNN, a promising data-efficient ML technique. Furthermore, to improve data efficiency, an extension of the SNN framework is proposed by applying a memory-based continual learning algorithm, Experience Replay. Empirical evaluations are conducted using several datasets, including a dataset from real live mobile networks, to illustrate the potential of SNNs and to highlight the contribution to sustainable AI for mobile networks. The findings show that in this context, SNNs can outperform standard classification neural networks, under both the standard multi-class classification case and the continual learning case, while trained on significantly less data. ---- Khalid El Yaacoub, Oscar Stenhammar, Selim Ickin, Konstantinos Vandikas More details can be found at this link: https://lnkd.in/ebkAKpQQ
Continual Learning With Siamese Neural Networks for Sustainable Network Management
ieeexplore.ieee.org
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Operations Research | Training and Consulting | Data Scientist | Analytics | Mathematical Modeling | Predictive Modeling | DSS | Reinforcement Learning
🚀 Excited to share my latest article on deep learning! 🧠✨ Title: Deep Learning-Based Patch-Wise Illumination Estimation for Enhanced #Multi_Exposure_Fusion In this article, I delve into the world of #DeepLearning and its application in #imageprocessing . The focus is on improving multi-exposure fusion through innovative patch-wise illumination estimation techniques. 👉 Check out the full article https://lnkd.in/e-PdsVkz.
Deep Learning-Based Patch-Wise Illumination Estimation for Enhanced Multi-Exposure Fusion
ieeexplore.ieee.org
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#JournalPaperAccepted RISING Lab’s recent work “Guarding Deep Learning Systems with Boosted Evasion Attack Detection and Model Update” has been accepted in IEEE Internet of Things Journal. Thanks to the lab members Xiangru Chen, Dipal Halder and Kazi Mejbaul Islam Deep learning systems are vulnerable to evasion attacks, where input data is manipulated to mislead the system intentionally. Existing detection methods are computationally heavy and unsuitable for real-time use on resource-limited devices. This paper introduces GERALT, an innovative infrastructure designed to enhance evasion attack detection for real-time execution on edge devices. It improves evasion attack detection efficiency on edge devices by over 3x compared to standard accelerators like Eyeriss. Congratulations!! #ieee #iotjournal #deeplearningsystem #evasionattack #detection #geralt #risinglab #uf
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In the dynamic landscape of technology, the convergence of deep learning and computer vision has ushered in a transformative era for real-time number plate detection and recognition. #Aksha - Smart Surveillance System, our comprehensive solution goes beyond mere detection. It not only recognizes license plates but also reads and interprets their contents. The system generates detailed reports, empowering you with valuable insights into the monitored activities. At the core of our solution is the utilization of a number plate dataset, through which we harness the power of YOLOv7 to achieve unparalleled precision in Number Plate Detection.🚛🚧 With a keen focus on detail, our technology employs advanced image processing techniques to ensure optimal performance, particularly under controlled lighting conditions with predictable license plates. The result is a seamless extraction of text from the license plates, enhancing the overall efficiency of the recognition process. Elevate your surveillance capabilities with our innovative approach, staying at the forefront of Object Detection, Computer Vision, and Smart Surveillance technologies.🔎 #ObjectDetection #ComputerVision #SmartSurveillance #NumberPlateRecognition #TechnologyInnovation
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The three key elements of artificial intelligence: data, computing power, and algorithm. #AI #AIGC #GenerativeAI #semiconductor
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Hypothalamus Artificial Intelligence, HAI, is a knowledge-intensive company in advanced and complex mathematical modelling Its main research focuses on the integration of Traditional Artificial Intelligence, mainly supported by artificial neural nets (ANN), and the machine learning algorithms, based on support vector machines (SVM), with the Large-Scale Mathematical Programing principles. This is aimed at producing artificial brains, that is DIGITAL TRANSFORMATION DRIVEN BY AUTONOMOUS AND INTELLIGENT ALGORITHMS IN REAL TIME, in accordance with the era of INDUSTRY 4.0.
HAI - HYPOTHALAMUS Artificial Intelligence. Last Pitch
https://www.youtube.com/
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