Tootfinder

Bayesian Topological Convolutional Neural Nets
Sarah Harkins Dayton, Hayden Everett, Ioannis Schizas, David L. Boothe Jr., Vasileios Maroulas
https://arxiv.org/abs/2510.11704 ht…

Bayesian Topological Convolutional Neural Nets
Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce overconfident predictions, and frequently lack the ability to quantify the uncertainty of their predictions. To address these concerns, we propose a new Bayesian topological CNN that promotes a novel interplay between topology-aware learning and Bayesian sampling. Specifically, it utilizes information from important ma…

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Ahsan Farabi, Muhaiminul Rashid Shad, Israt Khandaker
https://arxiv.org/abs/2510.00572

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD. To address these issues, we propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. The architecture is further optimized using the Squirrel Search Algorithm (SSA), e…

COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
Boyang Chen, Mohd Tasleem Khan, George Goussetis, Mathini Sellathurai, Yuan Ding, Jo\~ao F. C. Mota
https://arxiv.org/abs/2510.03516

COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
Convolutional Neural Networks (CNNs) are highly effective for computer vision and pattern recognition tasks; however, their computational intensity and reliance on hardware such as FPGAs pose challenges for deployment on low-power edge devices. In this work, we present COMET, a framework of CNN designs that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approach formulates CNN inference with OBC representati…

Vision-Based CNN Prediction of Sunspot Numbers from SDO/HMI Images
Fabian C. Quintero-Pareja, Diederik A. Montano-Burbano, Santiago Quintero-Pareja, D. Sierra-Porta
https://arxiv.org/abs/2510.03473

Vision-Based CNN Prediction of Sunspot Numbers from SDO/HMI Images
Sunspot numbers constitute the longest and most widely used record of solar activity, with direct implications for space weather forecasting and heliophysical research. Traditional sunspot counting relies on visual inspection or algorithmic feature detection, both of which are limited by subjectivity, image quality, and methodological inconsistencies. Recent advances in deep learning, particularly convolutional neural networks (CNNs), enable the direct use of solar imagery for automated predict…

Explicit modelling of subject dependency in BCI decoding
Michele Romani, Francesco Paissan, Andrea Foss\`a, Elisabetta Farella
https://arxiv.org/abs/2509.23247 https://

Explicit modelling of subject dependency in BCI decoding
Brain-Computer Interfaces (BCIs) suffer from high inter-subject variability and limited labeled data, often requiring lengthy calibration phases. In this work, we present an end-to-end approach that explicitly models the subject dependency using lightweight convolutional neural networks (CNNs) conditioned on the subject's identity. Our method integrates hyperparameter optimization strategies that prioritize class imbalance and evaluates two conditioning mechanisms to adapt pre-trained models to…

Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
Derek Jiu, Kiran Nijjer, Nishant Chinta, Ryan Bui, Ben Liu, Kevin Zhu
https://arxiv.org/abs/2509.25265

Evaluating the Impact of Radiographic Noise on Chest X-ray Semantic Segmentation and Disease Classification Using a Scalable Noise Injection Framework
Deep learning models are increasingly used for radiographic analysis, but their reliability is challenged by the stochastic noise inherent in clinical imaging. A systematic, cross-task understanding of how different noise types impact these models is lacking. Here, we evaluate the robustness of state-of-the-art convolutional neural networks (CNNs) to simulated quantum (Poisson) and electronic (Gaussian) noise in two key chest X-ray tasks: semantic segmentation and pulmonary disease classificati…