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Science

Smarter Networks: AI’s Role in 6G Energy Efficiency

22.11.2025 by qfx

The evolving dynamics inherent in 6G systems significantly influence energy efficiency, demanding a holistic consideration of these interdependencies for optimized performance.

This review explores how artificial intelligence is being leveraged to create more sustainable and adaptable next-generation wireless communication systems.

Categories Science

Seeing Through the Smoke: AI’s Next Step in Wildfire Detection

21.11.2025 by qfx

The research demonstrates a method for augmenting smoke image datasets by leveraging real smoke imagery and corresponding masks to generate more realistic synthetic data, effectively minimizing the discrepancy between simulated and real-world conditions-a crucial step for robust computer vision applications.

New research explores how synthetic data and advanced machine learning can overcome the challenges of identifying wildfires in real-world imagery.

Categories Science

Unmasking Monero: A New Approach to Blockchain Forensics

21.11.2025 by qfx

The articulation of relational topology through ART-graphs provides a framework for understanding system evolution as a process of constrained decay, where interconnectedness defines the pathways of inevitable change.

Researchers have developed a novel graph-based framework to analyze transaction patterns in the privacy-focused cryptocurrency Monero, offering a path to detect illicit activity without breaking its core anonymity features.

Categories Science

Seeing Clearly: AI Sheds Light on Diabetic Retinopathy

21.11.2025 by qfx

A new deep learning framework combines advanced image analysis with explainable AI to improve both the accuracy and clinical understanding of diabetic retinopathy detection.

Categories Science

Decoding the Market: Information Theory’s Edge

21.11.2025 by qfx

Information-theoretic measures applied to S&P 500 returns from 2000 to 2025 reveal that Shannon entropy spikes during periods of market uncertainty-notably the 2008-2009 financial crisis and the COVID-19 pandemic-while Kullback-Leibler divergence identifies significant distributional shifts exceeding $ \mu + 2\sigma $ during crises, and normalized mutual information, typically below 0.05 during stable periods, surges during major market disruptions, collectively suggesting these measures can effectively characterize and quantify market regime changes.

A new framework leverages the principles of information theory to quantify market efficiency and refine financial risk management.

Categories Science

Accounting’s AI Future: Charting a New Research Course

21.11.2025 by qfx

As artificial intelligence reshapes the business world, this article provides a critical framework for accounting researchers to navigate the opportunities and challenges of this rapidly evolving landscape.

Categories Science

Can Machines Beat Chance in Options Trading?

21.11.2025 by qfx

The multilayer perceptron, despite achieving ever-increasing training accuracy, succumbed to overfitting-a widening gulf between learned patterns and real-world performance-ultimately yielding a final test accuracy that mirrored the performance of a completely uninformed baseline, confirming the model’s failure to generalize beyond memorization of the training data.

New research casts doubt on the ability of machine learning to consistently predict short-term binary option movements.

Categories Science

Trading Smarter: AI-Powered Forex Forecasting

21.11.2025 by qfx

New research demonstrates how combining artificial intelligence with both technical and fundamental data can significantly improve currency market predictions.

Categories Science

Seeing Through the Weeds: AI-Powered Precision Farming

21.11.2025 by qfx

A novel deep learning framework combines the strengths of convolutional, transformer, and graph neural networks to dramatically improve weed detection in agricultural settings.

Categories Science

Seeing, Hearing, and Feeling Depression: A Multimodal Approach

21.11.2025 by qfx

A novel framework detects depression by initially processing audio, video, and visual saliency data through parallel convolutional and Bi-LSTM networks to generate $64 \times 64$-dimensional embeddings, subsequently modeling intermodal relationships with a Graph Convolutional Network incorporating a Multi-frequency filter bank module, and ultimately combining these cross-modal features with the original unimodal representations for classification.

Researchers are combining data from eye movements, facial expressions, and speech to improve the accuracy of depression detection.

Categories Science
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