Tootfinder

Modeling blazar broadband emission with convolutional neural networks -- III. proton synchrotron and hybrid models
N. Sahakyan, D. B\'egu\'e, A. Casotto, H. Dereli-B\'egu\'e, V. Vardanyan, M. Khachatryan, P. Giommi, A. Pe'er
https://arxiv.org/abs/2506.23885

Modeling blazar broadband emission with convolutional neural networks -- III. proton synchrotron and hybrid models
Modeling the broadband emission of blazars has become increasingly challenging with the advent of multimessenger observations. Building upon previous successes in applying convolutional neural networks (CNNs) to leptonic emission scenarios, we present an efficient CNN-based approach for modeling blazar emission under proton synchrotron and hybrid lepto-hadronic frameworks. Our CNN is trained on extensive numerical simulations generated by SOPRANO, which span a comprehensive parameter space acco…

Subgrid BoostCNN: Efficient Boosting of Convolutional Networks via Gradient-Guided Feature Selection
Biyi Fang, Jean Utke, Truong Vo, Diego Klabjan
https://arxiv.org/abs/2507.22842

Subgrid BoostCNN: Efficient Boosting of Convolutional Networks via Gradient-Guided Feature Selection
Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep architectures. However, the large number of layers and millions of parameters often make CNNs computationally expensive to train, requiring extensive time and manual tuning to discover optimal architectures. In this paper, we introduce a novel framework for boosting CNN performance that integrates dynamic feature selection w…

AirCANS: CFD 2D Mesh Optimisation-based Airfoil Classification and Assessment using Neural Networks
Lushun Fan, Yuqin Xia, Jun Li, Karl Jenkins
https://arxiv.org/abs/2506.22662

AirCANS: CFD 2D Mesh Optimisation-based Airfoil Classification and Assessment using Neural Networks
This study explores the possibilities of automating the loading, classification and assessment of Computational Fluid Dynamics (CFD) mesh data by Convolutional Neural Networks (CNNs). The research aim is finding a feasible way to quickly make classification and assessment on airfoil mesh data. For this purpose, this study designed a new framework named CFD-based airfoil Classification and Assessment Network (AirCANS) for CFD mesh data which including the data loader and improved the CNN structu…

Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification
Kunal Kawadkar
https://arxiv.org/abs/2507.21156 https://

Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification
The emergence of Vision Transformers (ViTs) has revolutionized computer vision, yet their effectiveness compared to traditional Convolutional Neural Networks (CNNs) in medical imaging remains under-explored. This study presents a comprehensive comparative analysis of CNN and ViT architectures across three critical medical imaging tasks: chest X-ray pneumonia detection, brain tumor classification, and skin cancer melanoma detection. We evaluated four state-of-the-art models - ResNet-50, Efficien…

Autonomous UAV Navigation for Search and Rescue Missions Using Computer Vision and Convolutional Neural Networks
Luka \v{S}iktar, Branimir \'Caran, Bojan \v{S}ekoranja, Marko \v{S}vaco
https://arxiv.org/abs/2507.18160

Autonomous UAV Navigation for Search and Rescue Missions Using Computer Vision and Convolutional Neural Networks
In this paper, we present a subsystem, using Unmanned Aerial Vehicles (UAV), for search and rescue missions, focusing on people detection, face recognition and tracking of identified individuals. The proposed solution integrates a UAV with ROS2 framework, that utilizes multiple convolutional neural networks (CNN) for search missions. System identification and PD controller deployment are performed for autonomous UAV navigation. The ROS2 environment utilizes the YOLOv11 and YOLOv11-pose CNNs for…

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation
Hayat Ullah, Syed Muhammad Talha Zaidi, Arslan Munir
https://arxiv.org/abs/2507.20996

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation
Convolutional neural networks (CNNs) excel in computer vision but are susceptible to adversarial attacks, crafted perturbations designed to mislead predictions. Despite advances in adversarial training, a gap persists between model accuracy and robustness. To mitigate this issue, in this paper, we present a multi-teacher adversarial robustness distillation using an adaptive learning strategy. Specifically, our proposed method first trained multiple clones of a baseline CNN model using an advers…

Improving Deep Learning-based Respiratory Sound Analysis with Frequency Selection and Attention Mechanism
Nouhaila Fraihi, Ouassim Karrakchou, Mounir Ghogho
https://arxiv.org/abs/2507.20052

Improving Deep Learning-based Respiratory Sound Analysis with Frequency Selection and Attention Mechanism
Accurate classification of respiratory sounds requires deep learning models that effectively capture fine-grained acoustic features and long-range temporal dependencies. Convolutional Neural Networks (CNNs) are well-suited for extracting local time-frequency patterns but are limited in modeling global context. In contrast, transformer-based models can capture long-range dependencies, albeit with higher computational demands. To address these limitations, we propose a compact CNN-Temporal Self-A…

Modeling Habitat Shifts: Integrating Convolutional Neural Networks and Tabular Data for Species Migration Prediction
Emir Durakovic, Min-Hong Shih
https://arxiv.org/abs/2507.10993

Modeling Habitat Shifts: Integrating Convolutional Neural Networks and Tabular Data for Species Migration Prediction
Due to climate-induced changes, many habitats are experiencing range shifts away from their traditional geographic locations (Piguet, 2011). We propose a solution to accurately model whether bird species are present in a specific habitat through the combination of Convolutional Neural Networks (CNNs) (O'Shea, 2015) and tabular data. Our approach makes use of satellite imagery and environmental features (e.g., temperature, precipitation, elevation) to predict bird presence across various climate…

CNN-based Surface Temperature Forecasts with Ensemble Numerical Weather Prediction over Medium-range Forecast Periods
Takuya Inoue (Meteorological Research Institute, Tsukuba, Japan), Takuya Kawabata (Meteorological Research Institute, Tsukuba, Japan)
https://arxiv.org/abs/2507.18937

CNN-based Surface Temperature Forecasts with Ensemble Numerical Weather Prediction over Medium-range Forecast Periods
This study proposes a method that integrates convolutional neural networks (CNNs) with ensemble numerical weather prediction (NWP) models, enabling surface temperature forecasting at lead times beyond the short-range (five-day) forecast period. Owing to limited computational resources, operational medium-range temperature forecasts typically rely on low-resolution NWP models, which are prone to systematic and random errors. To resolve these limitations, the proposed method first reduces systema…

Efficient Column-Wise N:M Pruning on RISC-V CPU
Chi-Wei Chu, Ding-Yong Hong, Jan-Jan Wu
https://arxiv.org/abs/2507.17301 https://arxiv.org/pdf/2507.17301…

Efficient Column-Wise N:M Pruning on RISC-V CPU
In deep learning frameworks, weight pruning is a widely used technique for improving computational efficiency by reducing the size of large models. This is especially critical for convolutional operators, which often act as performance bottlenecks in convolutional neural networks (CNNs). However, the effectiveness of pruning heavily depends on how it is implemented, as different methods can significantly impact both computational performance and memory footprint. In this work, we propose a colu…

Interpretable Classification of Levantine Ceramic Thin Sections via Neural Networks
Sara Capriotti, Alessio Devoto, Simone Scardapane, Silvano Mignardi, Laura Medeghini
https://arxiv.org/abs/2506.12250

Interpretable Classification of Levantine Ceramic Thin Sections via Neural Networks
Classification of ceramic thin sections is fundamental for understanding ancient pottery production techniques, provenance, and trade networks. Although effective, traditional petrographic analysis is time-consuming. This study explores the application of deep learning models, specifically Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), as complementary tools to support the classification of Levantine ceramics based on their petrographic fabrics. A dataset of 1,424 thin sec…

This https://arxiv.org/abs/2505.13017 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Optimal Scalogram for Computational Complexity Reduction in Acoustic Recognition Using Deep Learning
The Continuous Wavelet Transform (CWT) is an effective tool for feature extraction in acoustic recognition using Convolutional Neural Networks (CNNs), particularly when applied to non-stationary audio. However, its high computational cost poses a significant challenge, often leading researchers to prefer alternative methods such as the Short-Time Fourier Transform (STFT). To address this issue, this paper proposes a method to reduce the computational complexity of CWT by optimizing the length o…

NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs
Khuram Naveed, Bruna Neves de Freitas, Ruben Pauwels
https://arxiv.org/abs/2506.19387

NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs
Convolutional denoising autoencoders (DAEs) are powerful tools for image restoration. However, they inherit a key limitation of convolutional neural networks (CNNs): they tend to recover low-frequency features, such as smooth regions, more effectively than high-frequency details. This leads to the loss of fine details, which is particularly problematic in dental radiographs where preserving subtle anatomical structures is crucial. While self-attention mechanisms can help mitigate this issue by …

Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architectures
Amir Hossein Baradaran
https://arxiv.org/abs/2506.15043 https://

Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architectures
Advancements in the defense industry are paramount for ensuring the safety and security of nations, providing robust protection against emerging threats. Among these threats, hypersonic missiles pose a significant challenge due to their extreme speeds and maneuverability, making accurate trajectory prediction a critical necessity for effective countermeasures. This paper addresses this challenge by employing a novel hybrid deep learning approach, integrating Convolutional Neural Networks (CNNs)…

HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD
Hanwen Liu, Yuhe Huang, Yifeng Gong, Yanjie Zhai, Jiaxuan Lu
https://arxiv.org/abs/2507.12133

HyDRA: A Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD
Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fingerprint Identification (RFFI) offers a non-cryptographic solution by exploiting hardware-induced signal distortions. This paper proposes HyDRA, a Hybrid Dual-mode RF Architecture that integrates an optimized Variational Mode Decomposition (VMD) with a novel architecture based on the fusion of Convolutional Neural Networks (CNNs), Transformers, and Ma…

Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention
Yu-Chao Hsu, Kuan-Cheng Chen, Tai-Yue Li, Nan-Yow Chen
https://arxiv.org/abs/2507.11217

Quantum Adaptive Excitation Network with Variational Quantum Circuits for Channel Attention
In this work, we introduce the Quantum Adaptive Excitation Network (QAE-Net), a hybrid quantum-classical framework designed to enhance channel attention mechanisms in Convolutional Neural Networks (CNNs). QAE-Net replaces the classical excitation block of Squeeze-and-Excitation modules with a shallow Variational Quantum Circuit (VQC), leveraging quantum superposition and entanglement to capture higher-order inter-channel dependencies that are challenging to model with purely classical approache…

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
Mana Mohammadi, Amirhesam Jafari Rad, Ashkan Behrouzi
https://arxiv.org/abs/2506.15364

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature extraction. Accurate and timely stroke detection is critical for effective treatment and improved patient outcomes in neuroimaging. While Convolutional Neural Networks (CNNs) are widely used for medical image analysis, their computational complexity often hinders deployment in re…

DPUV4E: High-Throughput DPU Architecture Design for CNN on Versal ACAP
Guoyu Li (AMD), Pengbo Zheng (AMD), Jian Weng (AMD), Enshan Yang (AMD)
https://arxiv.org/abs/2506.11441

DPUV4E: High-Throughput DPU Architecture Design for CNN on Versal ACAP
Convolutional Neural Networks (CNNs) remain prevalent in computer vision applications, and FPGAs, known for their flexibility and energy efficiency, have become essential components in heterogeneous acceleration systems. However, traditional FPGAs face challenges in balancing performance and versatility due to limited on-chip resources. AMD's Versal ACAP architecture, tailored for AI applications, incorporates AI Engines (AIEs) to deliver high computational power. Nevertheless, the platform suf…

Feature Complementation Architecture for Visual Place Recognition
Weiwei Wang, Meijia Wang, Haoyi Wang, Wenqiang Guo, Jiapan Guo, Changming Sun, Lingkun Ma, Weichuan Zhang
https://arxiv.org/abs/2506.12401

Feature Complementation Architecture for Visual Place Recognition
Visual place recognition (VPR) plays a crucial role in robotic localization and navigation. The key challenge lies in constructing feature representations that are robust to environmental changes. Existing methods typically adopt convolutional neural networks (CNNs) or vision Transformers (ViTs) as feature extractors. However, these architectures excel in different aspects -- CNNs are effective at capturing local details. At the same time, ViTs are better suited for modeling global context, mak…

DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
A. Ruzza, G. Lodato, G. P. Rosotti, P. J. Armitage
https://arxiv.org/abs/2506.11200

DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
Dust substructures in protoplanetary discs can be signatures of embedded young planets whose detection and characterisation would provide a better understanding of planet formation. Traditional techniques used to link substructures' morphology to the properties of putative embedded planets present several limitations that the use of deep learning methods has partly overcome. In our previous work, we developed DBNets, a tool exploiting an ensemble of Convolutional Neural Networks (CNNs) to estim…

Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshes
Saqib Nazir (UNICAEN), Olivier L\'ezoray (UNICAEN), S\'ebastien Bougleux (UNICAEN)
https://arxiv.org/abs/2507.05304

Self-Attention Based Multi-Scale Graph Auto-Encoder Network of 3D Meshes
3D meshes are fundamental data representations for capturing complex geometric shapes in computer vision and graphics applications. While Convolutional Neural Networks (CNNs) have excelled in structured data like images, extending them to irregular 3D meshes is challenging due to the non-Euclidean nature of the data. Graph Convolutional Networks (GCNs) offer a solution by applying convolutions to graph-structured data, but many existing methods rely on isotropic filters or spectral decompositio…

Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion
Lamiaa H. Zain, Hossam H. Ammar, Raafat E. Shalaby
https://arxiv.org/abs/2507.08112

Imitation Learning for Obstacle Avoidance Using End-to-End CNN-Based Sensor Fusion
Obstacle avoidance is crucial for mobile robots' navigation in both known and unknown environments. This research designs, trains, and tests two custom Convolutional Neural Networks (CNNs), using color and depth images from a depth camera as inputs. Both networks adopt sensor fusion to produce an output: the mobile robot's angular velocity, which serves as the robot's steering command. A newly obtained visual dataset for navigation was collected in diverse environments with varying lighting con…

DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
Hayat Ullah, Muhammad Ali Shafique, Abbas Khan, Arslan Munir
https://arxiv.org/abs/2507.12426

DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
The landscape of video recognition has evolved significantly, shifting from traditional Convolutional Neural Networks (CNNs) to Transformer-based architectures for improved accuracy. While 3D CNNs have been effective at capturing spatiotemporal dynamics, recent Transformer models leverage self-attention to model long-range spatial and temporal dependencies. Despite achieving state-of-the-art performance on major benchmarks, Transformers remain computationally expensive, particularly with dense …

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Ahmad Mustafa, Reza Rastegar, Ghassan AlRegib
https://arxiv.org/abs/2506.19687 …

ReCoGNet: Recurrent Context-Guided Network for 3D MRI Prostate Segmentation
Prostate gland segmentation from T2-weighted MRI is a critical yet challenging task in clinical prostate cancer assessment. While deep learning-based methods have significantly advanced automated segmentation, most conventional approaches-particularly 2D convolutional neural networks (CNNs)-fail to leverage inter-slice anatomical continuity, limiting their accuracy and robustness. Fully 3D models offer improved spatial coherence but require large amounts of annotated data, which is often imprac…

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Daniya Najiha Abdul Kareem, Abdul Hannan, Mubashir Noman, Jean Lahoud, Mustansar Fiaz, Hisham Cholakkal
https://arxiv.org/abs/2506.12208

InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreove…

Classification of Histopathology Slides with Persistence Homology Convolutions
Shrunal Pothagoni, Benjamin Schweinhart
https://arxiv.org/abs/2507.14378 htt…

Classification of Histopathology Slides with Persistence Homology Convolutions
Convolutional neural networks (CNNs) are a standard tool for computer vision tasks such as image classification. However, typical model architectures may result in the loss of topological information. In specific domains such as histopathology, topology is an important descriptor that can be used to distinguish between disease-indicating tissue by analyzing the shape characteristics of cells. Current literature suggests that reintroducing topological information using persistent homology can im…

Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
Martin Sondermann, Pinar Bisgin, Niklas Tschorn, Anja Burmann, Christoph M. Friedrich
https://arxiv.org/abs/2507.07058

Comparative Analysis of CNN and Transformer Architectures with Heart Cycle Normalization for Automated Phonocardiogram Classification
The automated classification of phonocardiogram (PCG) recordings represents a substantial advancement in cardiovascular diagnostics. This paper presents a systematic comparison of four distinct models for heart murmur detection: two specialized convolutional neural networks (CNNs) and two zero-shot universal audio transformers (BEATs), evaluated using fixed-length and heart cycle normalization approaches. Utilizing the PhysioNet2022 dataset, a custom heart cycle normalization method tailored to…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
Ahmed Farooq
https://arxiv.org/abs/2507.08766 https://…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that b…

Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy
El Arbi Belfarsi, Henry Flores, Maria Valero
https://arxiv.org/abs/2506.13819 https://

Reliable Noninvasive Glucose Sensing via CNN-Based Spectroscopy
In this study, we present a dual-modal AI framework based on short-wave infrared (SWIR) spectroscopy. The first modality employs a multi-wavelength SWIR imaging system coupled with convolutional neural networks (CNNs) to capture spatial features linked to glucose absorption. The second modality uses a compact photodiode voltage sensor and machine learning regressors (e.g., random forest) on normalized optical signals. Both approaches were evaluated on synthetic blood phantoms and skin-mimicking…

evMLP: An Efficient Event-Driven MLP Architecture for Vision
Zhentan Zheng
https://arxiv.org/abs/2507.01927 https://arxiv.org/pdf/250…

evMLP: An Efficient Event-Driven MLP Architecture for Vision
Deep neural networks have achieved remarkable results in computer vision tasks. In the early days, Convolutional Neural Networks (CNNs) were the mainstream architecture. In recent years, Vision Transformers (ViTs) have become increasingly popular. In addition, exploring applications of multi-layer perceptrons (MLPs) has provided new perspectives for research into vision model architectures. In this paper, we present evMLP accompanied by a simple event-driven local update mechanism. The proposed…

DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
Usman Muhammad, Jorma Laaksonen
https://arxiv.org/abs/2506.05041 https:…

DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
2D convolutional neural networks (CNNs) have attracted significant attention for hyperspectral image super-resolution tasks. However, a key limitation is their reliance on local neighborhoods, which leads to a lack of global contextual understanding. Moreover, band correlation and data scarcity continue to limit their performance. To mitigate these issues, we introduce DACN, a dual-attention convolutional network for hyperspectral image super-resolution. Specifically, the model first employs au…

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Buddhi Wijenayake, Athulya Ratnayake, Praveen Sumanasekara, Nichula Wasalathilaka, Mathivathanan Piratheepan, Roshan Godaliyadda, Mervyn Ekanayake, Vijitha Herath
https://arxiv.org/abs/2507.11523

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Remote sensing change detection is vital for monitoring environmental and urban transformations but faces challenges like manual feature extraction and sensitivity to noise. Traditional methods and early deep learning models, such as convolutional neural networks (CNNs), struggle to capture long-range dependencies and global context essential for accurate change detection in complex scenes. While Transformer-based models mitigate these issues, their computational complexity limits their applica…

This https://arxiv.org/abs/2505.23503 has been replaced.
link: https://scholar.google.com/scholar?q=a

Can Large Language Models Challenge CNNS in Medical Image Analysis?
This study presents a multimodal AI framework designed for precisely classifying medical diagnostic images. Utilizing publicly available datasets, the proposed system compares the strengths of convolutional neural networks (CNNs) and different large language models (LLMs). This in-depth comparative analysis highlights key differences in diagnostic performance, execution efficiency, and environmental impacts. Model evaluation was based on accuracy, F1-score, average execution time, average energ…