Tootfinder

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
Fabian C. Pe\~na, Steffen Herbold
https://arxiv.org/abs/2506.08581

Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification
This work evaluates Sentence-BERT for a multi-label code comment classification task seeking to maximize the classification performance while controlling efficiency constraints during inference. Using a dataset of 13,216 labeled comment sentences, Sentence-BERT models are fine-tuned and combined with different classification heads to recognize comment types. While larger models outperform smaller ones in terms of F1, the latter offer outstanding efficiency, both in runtime and GFLOPS. As result…

This https://arxiv.org/abs/2506.06155 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

A Novel Large-scale Crop Dataset and Dual-stream Transformer Method for Fine-grained Hierarchical Crop Classification from Integrated Hyperspectral EnMAP Data and Multispectral Sentinel-2 Time Series
Fine-grained crop classification is crucial for precision agriculture and food security monitoring. It requires simultaneous capture of both phenological dynamics (obtained from multi-temporal satellite data like Sentinel-2) and subtle spectral variations (demanding nanometer-scale spectral resolution from hyperspectral imagery). Research combining these two modalities remains scarce currently due to challenges in hyperspectral data acquisition and crop types annotation costs. To address these …

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Zvonimir Bujanovi\'c, Matija Kazalicki, Domagoj Vlah
https://arxiv.org/abs/2506.07967

Improving elliptic curve rank classification using multi-value and learned Mestre-Nagao sums
Determining the rank of an elliptic curve E/Q remains a central challenge in number theory. Heuristics such as Mestre--Nagao sums are widely used to estimate ranks, but there is considerable room for improving their predictive power. This paper introduces two novel methods for enhancing rank classification using Mestre--Nagao sums. First, we propose a ``multi-value'' approach that simultaneously uses two distinct sums, S_0 and S_5, evaluated over multiple ranges. This multi-sum perspective sign…

Class conditional conformal prediction for multiple inputs by p-value aggregation
Jean-Baptiste Fermanian (IMAG, IROKO), Mohamed Hebiri (LAMA), Joseph Salmon (IMAG, IROKO)
https://arxiv.org/abs/2507.07150

Class conditional conformal prediction for multiple inputs by p-value aggregation
Conformal prediction methods are statistical tools designed to quantify uncertainty and generate predictive sets with guaranteed coverage probabilities. This work introduces an innovative refinement to these methods for classification tasks, specifically tailored for scenarios where multiple observations (multi-inputs) of a single instance are available at prediction time. Our approach is particularly motivated by applications in citizen science, where multiple images of the same plant or anima…

Anti-Interference Diffractive Deep Neural Networks for Multi-Object Recognition
Zhiqi Huang, Yufei Liu, Nan Zhang, Zian Zhang, Qiming Liao, Cong He, Shendong Liu, Youhai Liu, Hongtao Wang, Xingdu Qiao, Joel K. W. Yang, Yan Zhang, Lingling Huang, Yongtian Wang
https://arxiv.org/abs/2507.06978…

Anti-Interference Diffractive Deep Neural Networks for Multi-Object Recognition
Optical neural networks (ONNs) are emerging as a promising neuromorphic computing paradigm for object recognition, offering unprecedented advantages in light-speed computation, ultra-low power consumption, and inherent parallelism. However, most of ONNs are only capable of performing simple object classification tasks. These tasks are typically constrained to single-object scenarios, which limits their practical applications in multi-object recognition tasks. Here, we propose an anti-interferen…

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound
Minglang Chen, Jie He, Caixu Xu, Bocheng Liang, Shengli Li, Guannan He, Xiongjie Tao
https://arxiv.org/abs/2506.06054

FPDANet: A Multi-Section Classification Model for Intelligent Screening of Fetal Ultrasound
ResNet has been widely used in image classification tasks due to its ability to model the residual dependence of constant mappings for linear computation. However, the ResNet method adopts a unidirectional transfer of features and lacks an effective method to correlate contextual information, which is not effective in classifying fetal ultrasound images in the classification task, and fetal ultrasound images have problems such as low contrast, high similarity, and high noise. Therefore, we prop…

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Bijay Gurung, David T. Hoffmann, Thomas Brox
https://arxiv.org/abs/2507.07985

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Contrastive vision-language models like CLIP are used for a large variety of applications, such as zero-shot classification or as vision encoder for multi-modal models. Despite their popularity, their representations show major limitations. For instance, CLIP models learn bag-of-words representations and, as a consequence, fail to distinguish whether an image is of "a yellow submarine and a blue bus" or "a blue submarine and a yellow bus". Previous attempts to fix this issue added hard negative…

Ranking-based Fusion Algorithms for Extreme Multi-label Text Classification (XMTC)
Celso Fran\c{c}a, Gestefane Rabbi, Thiago Salles, Washington Cunha, Leonardo Rocha, Marcos Andr\'e Gon\c{c}alves
https://arxiv.org/abs/2507.03761

Ranking-based Fusion Algorithms for Extreme Multi-label Text Classification (XMTC)
In the context of Extreme Multi-label Text Classification (XMTC), where labels are assigned to text instances from a large label space, the long-tail distribution of labels presents a significant challenge. Labels can be broadly categorized into frequent, high-coverage \textbf{head labels} and infrequent, low-coverage \textbf{tail labels}, complicating the task of balancing effectiveness across all labels. To address this, combining predictions from multiple retrieval methods, such as sparse re…

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Payel Bhattacharjee, Fengwei Tian, Ravi Tandon, Joseph Lo, Heidi Hanson, Geoffrey Rubin, Nirav Merchant, John Gounley
https://arxiv.org/abs/2506.04450

Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Purpose: This study proposes a framework for fine-tuning large language models (LLMs) with differential privacy (DP) to perform multi-abnormality classification on radiology report text. By injecting calibrated noise during fine-tuning, the framework seeks to mitigate the privacy risks associated with sensitive patient data and protect against data leakage while maintaining classification performance. Materials and Methods: We used 50,232 radiology reports from the publicly available MIMIC-CXR …

OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction
Pablo Alonso-Jim\'enez, Pedro Ramoneda, R. Oguz Araz, Andrea Poltronieri, Dmitry Bogdanov
https://arxiv.org/abs/2507.03482

OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction
Developing open-source foundation models is essential for advancing research in music audio understanding and ensuring access to powerful, multipurpose representations for music information retrieval. We present OMAR-RQ, a model trained with self-supervision via masked token classification methodologies using a large-scale dataset with over 330,000 hours of music audio. We experiment with different input features and quantization options, and achieve state-of-the-art performance in music taggin…

Dynamic Frequency Feature Fusion Network for Multi-Source Remote Sensing Data Classification
Yikang Zhao, Feng Gao, Xuepeng Jin, Junyu Dong, Qian Du
https://arxiv.org/abs/2507.04510

Dynamic Frequency Feature Fusion Network for Multi-Source Remote Sensing Data Classification
Multi-source data classification is a critical yet challenging task for remote sensing image interpretation. Existing methods lack adaptability to diverse land cover types when modeling frequency domain features. To this end, we propose a Dynamic Frequency Feature Fusion Network (DFFNet) for hyperspectral image (HSI) and Synthetic Aperture Radar (SAR) / Light Detection and Ranging (LiDAR) data joint classification. Specifically, we design a dynamic filter block to dynamically learn the filter k…

Towards a unified framework for programming paradigms: A systematic review of classification formalisms and methodological foundations
Mikel Vandeloise
https://arxiv.org/abs/2508.00534

Towards a unified framework for programming paradigms: A systematic review of classification formalisms and methodological foundations
The rise of multi-paradigm languages challenges traditional classification methods, leading to practical software engineering issues like interoperability defects. This systematic literature review (SLR) maps the formal foundations of programming paradigms. Our objective is twofold: (1) to assess the state of the art of classification formalisms and their limitations, and (2) to identify the conceptual primitives and mathematical frameworks for a more powerful, reconstructive approach. Based …

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
Binbin Xu, G\'erard Dray
https://arxiv.org/abs/2507.03122 h…

Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings
This study investigates the feasibility and performance of federated learning (FL) for multi-label ICD code classification using clinical notes from the MIMIC-IV dataset. Unlike previous approaches that rely on centralized training or fine-tuned large language models, we propose a lightweight and scalable pipeline combining frozen text embeddings with simple multilayer perceptron (MLP) classifiers. This design offers a privacy-preserving and deployment-efficient alternative for clinical NLP app…

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Simon Baur, Wojciech Samek, Jackie Ma
https://arxiv.org/abs/2508.04457 https://…

Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neural networks to quantify predictive, epistemic, and aleatoric uncertainties using an information-theoretical approach in synthetic or well defined data settings like natural image classification, its applicability to real life medical diagnosis tasks remains underexplored. In this study, we provide an extensive uncerta…

Enhancing Customer Service Chatbots with Context-Aware NLU through Selective Attention and Multi-task Learning
Subhadip Nandi, Neeraj Agrawal, Anshika Singh, Priyanka Bhatt
https://arxiv.org/abs/2506.01781

Enhancing Customer Service Chatbots with Context-Aware NLU through Selective Attention and Multi-task Learning
Customer service chatbots are conversational systems aimed at addressing customer queries, often by directing them to automated workflows. A crucial aspect of this process is the classification of the customer's intent. Presently, most intent classification models for customer care utilise only customer query for intent prediction. This may result in low-accuracy models, which cannot handle ambiguous queries. An ambiguous query like "I didn't receive my package" could indicate a delayed order, …

Replaced article(s) found for q-bio.GN. https://arxiv.org/list/q-bio.GN/new
[1/1]:
- Classification of autoimmune diseases from Peripheral blood TCR repertoires by multimodal multi-i...
Ruihao Zhang, Fei Ye, Dandan Meng, Yixuan Huang, Maochen, Xiao Liu

Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems
Domenico Pomarico, Roberto Cilli, Alfonso Monaco, Loredana Bellantuono, Marianna La Rocca, Tommaso Maggipinto, Giuseppe Magnifico, Marlis Ontivero Ortega, Ester Pantaleo, Sabina Tangaro, Sebastiano Stramaglia, Roberto Bellotti, Nicola Amoroso
https://

Transfer entropy and O-information to detect grokking in tensor network multi-class classification problems
Quantum-enhanced machine learning, encompassing both quantum algorithms and quantum-inspired classical methods such as tensor networks, offers promising tools for extracting structure from complex, high-dimensional data. In this work, we study the training dynamics of Matrix Product State (MPS) classifiers applied to three-class problems, using both fashion MNIST and hyper-spectral satellite imagery as representative datasets. We investigate the phenomenon of grokking, where generalization emer…

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
Pralay Kumar Das, Nayana Majumdar, Supratik Mukhopadhyay
https://arxiv.org/abs/2506.02506

Classification of Hoyle State Decay Branches in Active Target Time Projection Chamber using Neural Network
A multi-class convolutional neural network (CNN) model has been developed using Keras deep learning library in Python for image-based classification of $^{12}$C Hoyle state decay branches from tracking information, recorded by Saha Active Target Time Projection Chamber, SAT-TPC (currently under development). The nuclear events, produced by the 30 MeV $α$-particle beam in the SAT-TPC, filled with Ar + CO$_2$ (90:10) gas mixture at atmospheric pressure, have been considered for training and vali…

SketchMind: A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches
Ehsan Latif, Zirak Khan, Xiaoming Zhai
https://arxiv.org/abs/2507.22904 https://

SketchMind: A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches
Scientific sketches (e.g., models) offer a powerful lens into students' conceptual understanding, yet AI-powered automated assessment of such free-form, visually diverse artifacts remains a critical challenge. Existing solutions often treat sketch evaluation as either an image classification task or monolithic vision-language models, which lack interpretability, pedagogical alignment, and adaptability across cognitive levels. To address these limitations, we present SketchMind, a cognitively gr…

Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification
Hang Guo, Qing Zhang, Zixuan Gao, Siyuan Yang, Shulin Peng, Xiang Tao, Ting Yu, Yan Wang, Qingli Li
https://arxiv.org/abs/2508.03277

Efficient Multi-Slide Visual-Language Feature Fusion for Placental Disease Classification
Accurate prediction of placental diseases via whole slide images (WSIs) is critical for preventing severe maternal and fetal complications. However, WSI analysis presents significant computational challenges due to the massive data volume. Existing WSI classification methods encounter critical limitations: (1) inadequate patch selection strategies that either compromise performance or fail to sufficiently reduce computational demands, and (2) the loss of global histological context resulting fr…

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
MD Shaikh Rahman, Feiroz Humayara, Syed Maudud E Rabbi, Muhammad Mahbubur Rashid
https://arxiv.org/abs/2508.04790

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
Content-based mammographic image retrieval systems require exact BIRADS categorical matching across five distinct classes, presenting significantly greater complexity than binary classification tasks commonly addressed in literature. Current medical image retrieval studies suffer from methodological limitations including inadequate sample sizes, improper data splitting, and insufficient statistical validation that hinder clinical translation. We developed a comprehensive evaluation framework sy…

A Multi-Stage Hybrid CNN-Transformer Network for Automated Pediatric Lung Sound Classification
Samiul Based Shuvo, Taufiq Hasan
https://arxiv.org/abs/2507.20408 https://

A Multi-Stage Hybrid CNN-Transformer Network for Automated Pediatric Lung Sound Classification
Automated analysis of lung sound auscultation is essential for monitoring respiratory health, especially in regions facing a shortage of skilled healthcare workers. While respiratory sound classification has been widely studied in adults, its ap plication in pediatric populations, particularly in children aged <6 years, remains an underexplored area. The developmental changes in pediatric lungs considerably alter the acoustic proper ties of respiratory sounds, necessitating specialized classifi…

Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Ming Huang, Zehan Li, Yan Hu, Wanjing Wang, Andrew Wen, Scott Lane, Salih Selek, Lokesh Shahani, Rodrigo Machado-Vieira, Jair Soares, Hua Xu, Hongfang Liu
https://arxiv.org/abs/2507.17009

Multi-Label Classification with Generative AI Models in Healthcare: A Case Study of Suicidality and Risk Factors
Suicide remains a pressing global health crisis, with over 720,000 deaths annually and millions more affected by suicide ideation (SI) and suicide attempts (SA). Early identification of suicidality-related factors (SrFs), including SI, SA, exposure to suicide (ES), and non-suicidal self-injury (NSSI), is critical for timely intervention. While prior studies have applied AI to detect SrFs in clinical notes, most treat suicidality as a binary classification task, overlooking the complexity of coo…

CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification
Black Sun (Delia), Die (Delia), Hu
https://arxiv.org/abs/2507.22205 htt…

CTG-Insight: A Multi-Agent Interpretable LLM Framework for Cardiotocography Analysis and Classification
Remote fetal monitoring technologies are becoming increasingly common. Yet, most current systems offer limited interpretability, leaving expectant parents with raw cardiotocography (CTG) data that is difficult to understand. In this work, we present CTG-Insight, a multi-agent LLM system that provides structured interpretations of fetal heart rate (FHR) and uterine contraction (UC) signals. Drawing from established medical guidelines, CTG-Insight decomposes each CTG trace into five medically def…

Discovery of Disease Relationships via Transcriptomic Signature Analysis Powered by Agentic AI
Ke Chen, Haohan Wang
https://arxiv.org/abs/2508.04742 https://

Discovery of Disease Relationships via Transcriptomic Signature Analysis Powered by Agentic AI
Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for discovering disease relationships by analyzing over 1300 disease-condition pairs using GenoMAS, a fully automated agentic AI system. Beyond identifying robust gene-level overlaps, we develop a novel pathway-based similarity framework that integrates multi-database enrichment analysis to quantify functional convergence…

Multi-agent Auditory Scene Analysis
Caleb Rascon, Luis Gato-Diaz, Eduardo Garc\'ia-Alarc\'on
https://arxiv.org/abs/2507.02755 https://

Multi-agent Auditory Scene Analysis
Auditory scene analysis (ASA) aims to retrieve information from the acoustic environment, by carrying out three main tasks: sound source location, separation, and classification. These tasks are traditionally executed with a linear data flow, where the sound sources are first located; then, using their location, each source is separated into its own audio stream; from each of which, information is extracted that is relevant to the application scenario (audio event detection, speaker identificat…

Preliminary Investigation into Uncertainty-Aware Attack Stage Classification
Alessandro Gaudenzi, Lorenzo Nodari, Lance Kaplan, Alessandra Russo, Murat Sensoy, Federico Cerutti
https://arxiv.org/abs/2508.00368

Preliminary Investigation into Uncertainty-Aware Attack Stage Classification
Advanced Persistent Threats (APTs) represent a significant challenge in cybersecurity due to their prolonged, multi-stage nature and the sophistication of their operators. Traditional detection systems typically focus on identifying malicious activity in binary terms (benign or malicious) without accounting for the progression of an attack. However, effective response strategies depend on accurate inference of the attack's current stage, as countermeasures must be tailored to whether an adversa…

M$^3$AD: Multi-task Multi-gate Mixture of Experts for Alzheimer's Disease Diagnosis with Conversion Pattern Modeling
Yufeng Jiang, Hexiao Ding, Hongzhao Chen, Jing Lan, Xinzhi Teng, Gerald W. Y. Cheng, Zongxi Li, Haoran Xie, Jung Sun Yoo, Jing Cai
https://arxiv.org/abs/2508.01819

M$^3$AD: Multi-task Multi-gate Mixture of Experts for Alzheimer's Disease Diagnosis with Conversion Pattern Modeling
Alzheimer's disease (AD) progression follows a complex continuum from normal cognition (NC) through mild cognitive impairment (MCI) to dementia, yet most deep learning approaches oversimplify this into discrete classification tasks. This study introduces M$^3$AD, a novel multi-task multi-gate mixture of experts framework that jointly addresses diagnostic classification and cognitive transition modeling using structural MRI. We incorporate three key innovations: (1) an open-source T1-weighted sM…

Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum
Nirmal Gaud, Surej Mouli, Preeti Katiyar, Vaduguru Venkata Ramya
https://arxiv.org/abs/2508.04293

Comparative Analysis of Novel NIRMAL Optimizer Against Adam and SGD with Momentum
This study proposes NIRMAL (Novel Integrated Robust Multi-Adaptation Learning), a novel optimization algorithm that combines multiple strategies inspired by the movements of the chess piece. These strategies include gradient descent, momentum, stochastic perturbations, adaptive learning rates, and non-linear transformations. We carefully evaluated NIRMAL against two widely used and successful optimizers, Adam and SGD with Momentum, on four benchmark image classification datasets: MNIST, Fashion…

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
Sinuo Cheng, Ruyi Zhou, Wenhao Feng, Huaiguang Yang, Haibo Gao, Zongquan Deng, Liang Ding
https://arxiv.org/abs/2506.16703

VLM-Empowered Multi-Mode System for Efficient and Safe Planetary Navigation
The increasingly complex and diverse planetary exploration environment requires more adaptable and flexible rover navigation strategy. In this study, we propose a VLM-empowered multi-mode system to achieve efficient while safe autonomous navigation for planetary rovers. Vision-Language Model (VLM) is used to parse scene information by image inputs to achieve a human-level understanding of terrain complexity. Based on the complexity classification, the system switches to the most suitable naviga…

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
Utku Demir, Yalin E. Sagduyu, Tugba Erpek, Hossein Jafari, Sastry Kompella, Mengran Xue
https://arxiv.org/abs/2505.23706

Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats
In connected and autonomous vehicles, machine learning for safety message classification has become critical for detecting malicious or anomalous behavior. However, conventional approaches that rely on centralized data collection or purely local training face limitations due to the large scale, high mobility, and heterogeneous data distributions inherent in inter-vehicle networks. To overcome these challenges, this paper explores Distributed Federated Learning (DFL), whereby vehicles collaborat…

Jet Reconstruction with Mamba Networks in Collider Events
Jinmian Li, Peng Li, Bingwei Long, Rao Zhang
https://arxiv.org/abs/2506.18336 https://

Jet Reconstruction with Mamba Networks in Collider Events
We introduce a novel end-to-end framework for jet reconstruction in high-energy collider events, leveraging the efficiency and long-range modeling capabilities of the Mamba architecture. Our model unifies instance segmentation, classification, and kinematic regression into a single multi-task learning system, enabling a sophisticated multi-level reconstruction that simultaneously identifies primary heavy jets ($t$, $H$, $W/Z$) and their constituent sub-jets. To facilitate supervised learning fo…

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Dennis Jacob, Chong Xiang, Prateek Mittal
https://arxiv.org/abs/2505.24703

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophically impair performance. The physically realizable nature of these attacks calls for certifiable defenses, which feature provable guarantees on robustness. While certifiable defenses have been successfully applied to single-label classification, limited work has been done for multi-label classification. In this work, we…

Identification and photometric classification of extragalactic transients in the Vera C. Rubin Observatory's Data Preview 1
James Freeburn, Igor Andreoni, Kaylee M. de Soto, Cristina Andrade, Akash Anumarlapudi, Tyler Barna, Jonathan Carney, Sushant Sharma Chaudhary, Michael W. Coughlin, Felipe Fontinele Nunes, Sarah Teague, Mickael Rigault, V. Ashley Villar

Identification and photometric classification of extragalactic transients in the Vera C. Rubin Observatory's Data Preview 1
The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a ComCam observing campaign between November and December 2024 with multi-band imaging of seven fields, covering roughly 0.4 square degree each, provides a first glimpse into the data products that will become available once the Legacy Survey of Space and Time begins. In…

Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
Sompote Youwai, David Phim, Vianne Gayl Murcia, Rianne Clair Onas
https://arxiv.org/abs/2506.13811

Investigating the Potential of Large Language Model-Based Router Multi-Agent Architectures for Foundation Design Automation: A Task Classification and Expert Selection Study
This study investigates router-based multi-agent systems for automating foundation design calculations through intelligent task classification and expert selection. Three approaches were evaluated: single-agent processing, multi-agent designer-checker architecture, and router-based expert selection. Performance assessment utilized baseline models including DeepSeek R1, ChatGPT 4 Turbo, Grok 3, and Gemini 2.5 Pro across shallow foundation and pile design scenarios. The router-based configuration…

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
B. Paroli, F. Borghi, M. A. C. Potenza, P. Milani
https://arxiv.org/abs/2506.19642

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
Threshold logic gates (TLGs) have been proposed as artificial counterparts of biological neurons with classification capabilities based on a linear predictor function combining a set of weights with the feature vector. The linearity of TLGs limits their classification capabilities requiring the use of networks for the accomplishment of complex tasks. A generalization of the TLG model called receptron, characterized by input-dependent weight functions allows for a significant enhancement of clas…

Whilter: A Whisper-based Data Filter for "In-the-Wild" Speech Corpora Using Utterance-level Multi-Task Classification
William Ravenscroft, George Close, Kit Bower-Morris, Jamie Stacey, Dmitry Sityaev, Kris Y. Hong
https://arxiv.org/abs/2507.21642

Whilter: A Whisper-based Data Filter for "In-the-Wild" Speech Corpora Using Utterance-level Multi-Task Classification
Large-scale in-the-wild speech datasets have become more prevalent in recent years due to increased interest in models that can learn useful features from unlabelled data for tasks such as speech recognition or synthesis. These datasets often contain undesirable features, such as multiple speakers, non-target languages, and music, which may impact model learning. The Whilter model is proposed as a multitask solution to identify these undesirable samples. Whilter uses a Whisper encoder with an a…

High-Dimensional Data Classification in Concentric Coordinates
Alice Williams, Boris Kovalerchuk
https://arxiv.org/abs/2507.18450 https://arxiv.org/pdf/250…

High-Dimensional Data Classification in Concentric Coordinates
The visualization of multi-dimensional data with interpretable methods remains limited by capabilities for both high-dimensional lossless visualizations that do not suffer from occlusion and that are computationally capable by parameterized visualization. This paper proposes a low to high dimensional data supporting framework using lossless Concentric Coordinates that are a more compact generalization of Parallel Coordinates along with former Circular Coordinates. These are forms of the General…

At this year's Berlin Buzzwords, Viola Rädle & Raphael Franke will present gamma_flow, an open-source Python package for real-time spectral data analysis.
Learn more: https://2025.berlinbuzzwords.de/session/gamma_flow-deno…

Beyond Binary Moderation: Identifying Fine-Grained Sexist and Misogynistic Behavior on GitHub with Large Language Models
Tanni Dev, Sayma Sultana, Amiangshu Bosu
https://arxiv.org/abs/2507.20358

Beyond Binary Moderation: Identifying Fine-Grained Sexist and Misogynistic Behavior on GitHub with Large Language Models
Background: Sexist and misogynistic behavior significantly hinders inclusion in technical communities like GitHub, causing developers, especially minorities, to leave due to subtle biases and microaggressions. Current moderation tools primarily rely on keyword filtering or binary classifiers, limiting their ability to detect nuanced harm effectively. Aims: This study introduces a fine-grained, multi-class classification framework that leverages instruction-tuned Large Language Models (LLMs) t…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification
Chengliang Liu, Jie Wen, Yong Xu, Bob Zhang, Liqiang Nie, Min Zhang

Radar and Acoustic Sensor Fusion using a Transformer Encoder for Robust Drone Detection and Classification
Gevindu Ganganath, Pasindu Sankalpa, Samal Punsara, Demitha Pasindu, Chamira U. S. Edussooriya, Ranga Rodrigo, Udaya S. K. P. Miriya Thanthrige
https://arxiv.org/abs/2507.19785

Radar and Acoustic Sensor Fusion using a Transformer Encoder for Robust Drone Detection and Classification
The use of drones in a wide range of applications is steadily increasing. However, this has also raised critical security concerns such as unauthorized drone intrusions into restricted zones. Therefore, robust and accurate drone detection and classification mechanisms are required despite significant challenges due to small size of drones, low-altitude flight, and environmental noise. In this letter, we propose a multi-modal approach combining radar and acoustic sensing for detecting and classi…

REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification
Hongzhao Chen, Hexiao Ding, Yufeng Jiang, Jing Lan, Ka Chun Li, Gerald W. Y. Cheng, Sam Ng, Chi Lai Ho, Jing Cai, Liang-ting Lin, Jung Sun Yoo
https://arxiv.org/abs/2508.02104

REACT-KD: Region-Aware Cross-modal Topological Knowledge Distillation for Interpretable Medical Image Classification
Reliable and interpretable tumor classification from clinical imaging remains a core challenge due to heterogeneous modality quality, limited annotations, and the lack of structured anatomical guidance. We introduce REACT-KD, a Region-Aware Cross-modal Topological Knowledge Distillation framework that transfers rich supervision from high-fidelity multi-modal sources into a lightweight CT-based student model. The framework uses a dual teacher design: one branch captures structure-function relati…

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
Urchade Zaratiana, Gil Pasternak, Oliver Boyd, George Hurn-Maloney, Ash Lewis
https://arxiv.org/abs/2507.18546

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built pretrained transformer encoder architecture, GLiNER2 maintains…

Mammo-Clustering: Context Clustering based Multi-view Tri Level Information Fusion for Lesion Location and Classification in Mammography
Shilong Yang, Chulong Zhang, Xiaokun Liang, Qi Zang, Juan Yu, Liang Zeng, Xiao Luo, Yexuan Xing, Xin Pan, Qi Li, Linlin Shen, Yaoqin Xie
https://arxiv.org/abs/2507.18642

Mammo-Clustering: Context Clustering based Multi-view Tri Level Information Fusion for Lesion Location and Classification in Mammography
Breast cancer is a significant global health issue, and the diagnosis of breast cancer through imaging remains challenging. Mammography images are characterized by extremely high resolution, while lesions often occupy only a small portion of the image. Down-sampling in neural networks can easily lead to the loss of microcalcifications or subtle structures. To tackle these challenges, we propose a Context Clustering based triple information fusion framework. First, in comparison to CNNs or trans…

Frabjous: Deep Learning Fast Radio Burst Morphologies
Ajay Kumar, Ashish A. Mahabal, Shriharsh P. Tendulkar
https://arxiv.org/abs/2507.14854 https://

Frabjous: Deep Learning Fast Radio Burst Morphologies
The increasing field of view of radio telescopes and improved data processing capabilities have led to a surge in the detection of Fast Radio Bursts (FRBs). The discovery rate of FRBs is already a few per day and is expected to increase rapidly with new surveys coming online. The growing number of events necessitates prioritized follow-up due to limited multi-wavelength resources, requiring rapid and automated classification. In this study, we introduce Frabjous, a deep learning framework for a…

AuralNet: Hierarchical Attention-based 3D Binaural Localization of Overlapping Speakers
Linya Fu, Yu Liu, Zhijie Liu, Zedong Yang, Zhong-Qiu Wang, Youfu Li, He Kong
https://arxiv.org/abs/2506.02773

AuralNet: Hierarchical Attention-based 3D Binaural Localization of Overlapping Speakers
We propose AuralNet, a novel 3D multi-source binaural sound source localization approach that localizes overlapping sources in both azimuth and elevation without prior knowledge of the number of sources. AuralNet employs a gated coarse-tofine architecture, combining a coarse classification stage with a fine-grained regression stage, allowing for flexible spatial resolution through sector partitioning. The model incorporates a multi-head self-attention mechanism to capture spatial cues in binaur…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/8]:
- Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation
Long Shi, Chuanqing Tang, Huangyi Deng, Cai Xu, Lei Xing, Badong Chen

Entanglement Classification in the Graph States: The generalization to $n$-Qubits States using the Entanglement Matrix
Sameer Sharma
https://arxiv.org/abs/2507.11458

Entanglement Classification in the Graph States: The generalization to $n$-Qubits States using the Entanglement Matrix
Graph states represent a significant class of multi-partite entangled quantum states with applications in quantum error correction, quantum communication, and quantum computation. In this work, we introduce a novel formalism called the Entanglement Matrix for quantifying and classifying entanglement in n-qubit graph states. Leveraging concepts from graph theory and quantum information, we develop a systematic approach to analyze entanglement by identifying primary and secondary midpoints in gra…

CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays
Rajesh Madhipati, Sheethal Bhat, Lukas Buess, Andreas Maier
https://arxiv.org/abs/2507.19398

CXR-CML: Improved zero-shot classification of long-tailed multi-label diseases in Chest X-Rays
Chest radiography (CXR) plays a crucial role in the diagnosis of various diseases. However, the inherent class imbalance in the distribution of clinical findings presents a significant challenge for current self-supervised deep learning models. These models often fail to accurately classify long-tailed classes. Current Vision-Language models such as Contrastive Language Image Pre-training (CLIP) models effectively model the manifold distribution of the latent space, enabling high zero-shot clas…

Joint Feature and Output Distillation for Low-complexity Acoustic Scene Classification
Haowen Li, Ziyi Yang, Mou Wang, Ee-Leng Tan, Junwei Yeow, Santi Peksi, Woon-Seng Gan
https://arxiv.org/abs/2507.19557

Joint Feature and Output Distillation for Low-complexity Acoustic Scene Classification
This report presents a dual-level knowledge distillation framework with multi-teacher guidance for low-complexity acoustic scene classification (ASC) in DCASE2025 Task 1. We propose a distillation strategy that jointly transfers both soft logits and intermediate feature representations. Specifically, we pre-trained PaSST and CP-ResNet models as teacher models. Logits from teachers are averaged to generate soft targets, while one CP-ResNet is selected for feature-level distillation. This enables…

Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
Thai T. Vu, John Le
https://arxiv.org/abs/2507.15145 https:/…

Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
This paper proposes a communication-efficient, event-triggered inference framework for cooperative edge AI systems comprising multiple user devices and edge servers. Building upon dual-threshold early-exit strategies for rare-event detection, the proposed approach extends classical single-device inference to a distributed, multi-device setting while incorporating proportional fairness constraints across users. A joint optimization framework is formulated to maximize classification utility under…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new/
[1/1]:
Tile Classification Based Viewport Prediction with Multi-modal Fusion Transformer

Rethinking Dataset Discovery with DataScout
Rachel Lin, Bhavya Chopra, Wenjing Lin, Shreya Shankar, Madelon Hulsebos, Aditya G. Parameswaran
https://arxiv.org/abs/2507.18971 htt…

Rethinking Dataset Discovery with DataScout
Dataset Search -- the process of finding appropriate datasets for a given task -- remains a critical yet under-explored challenge in data science workflows. Assessing dataset suitability for a task (e.g., training a classification model) is a multi-pronged affair that involves understanding: data characteristics (e.g. granularity, attributes, size), semantics (e.g., data semantics, creation goals), and relevance to the task at hand. Present-day dataset search interfaces are restrictive -- users…

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Ziheng Zhao, Lisong Dai, Ya Zhang, Yanfeng Wang, Weidi Xie
https://arxiv.org/abs/2506.03238

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing …

Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
Yongjie Si, Yanxiong Li, Jiaxin Tan, Qianhua He, Il-Youp Kwak
https://arxiv.org/abs/2506.18406

Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier
In the task of Few-shot Class-incremental Audio Classification (FCAC), training samples of each base class are required to be abundant to train model. However, it is not easy to collect abundant training samples for many base classes due to data scarcity and high collection cost. We discuss a more realistic issue, Fully FCAC (FFCAC), in which training samples of both base and incremental classes are only a few. Furthermore, we propose a FFCAC method using a model which is decoupled into a multi…

All Optical Classification Surpasses Cascaded Diffractive Networks through Dual Wavelength Differential Modulation within a Single Layer Architecture
Haoyu Wang, Yanmin Zhu, Tong Fu
https://arxiv.org/abs/2507.17374

All Optical Classification Surpasses Cascaded Diffractive Networks through Dual Wavelength Differential Modulation within a Single Layer Architecture
Diffractive deep neural networks (D2NNs), which perform computation using light instead of electrons, offer a promising pathway toward accelerating artificial intelligence by leveraging the inherent advantages of optics in speed, parallelism, and energy efficiency. However, conventional multi-layer D2NNs suffer from inter-layer misalignments that significantly increase system complexity and degrade performance, particularly under visible-light operation where optical alignment is highly sensiti…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[2/2]:
- VGS-ATD: Robust Distributed Learning for Multi-Label Medical Image Classification Under Heterogen...
Zehui Zhao, Laith Alzubaidi, Haider A. Alwzwazy, Jinglan Zhang, Yuantong Gu

Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision
KMA Solaiman, Bharat Bhargava
https://arxiv.org/abs/2506.20070 https://…

Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision
Existing multi-media retrieval models either rely on creating a common subspace with modality-specific representation models or require schema mapping among modalities to measure similarities among multi-media data. Our goal is to avoid the annotation overhead incurred from considering retrieval as a supervised classification task and re-use the pretrained encoders in large language models and vision tasks. We propose "FemmIR", a framework to retrieve multimodal results relevant to information …

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, Chih-Chung Hsu
https://arxiv.org/abs/2507.19858

Taming Domain Shift in Multi-source CT-Scan Classification via Input-Space Standardization
Multi-source CT-scan classification suffers from domain shifts that impair cross-source generalization. While preprocessing pipelines combining Spatial-Slice Feature Learning (SSFL++) and Kernel-Density-based Slice Sampling (KDS) have shown empirical success, the mechanisms underlying their domain robustness remain underexplored. This study analyzes how this input-space standardization manages the trade-off between local discriminability and cross-source generalization. The SSFL++ and KDS pipel…

ChatMyopia: An AI Agent for Pre-consultation Education in Primary Eye Care Settings
Yue Wu, Xiaolan Chen, Weiyi Zhang, Shunming Liu, Wing Man Rita Sum, Xinyuan Wu, Xianwen Shang, Chea-su Kee, Mingguang He, Danli Shi
https://arxiv.org/abs/2507.19498

ChatMyopia: An AI Agent for Pre-consultation Education in Primary Eye Care Settings
Large language models (LLMs) show promise for tailored healthcare communication but face challenges in interpretability and multi-task integration particularly for domain-specific needs like myopia, and their real-world effectiveness as patient education tools has yet to be demonstrated. Here, we introduce ChatMyopia, an LLM-based AI agent designed to address text and image-based inquiries related to myopia. To achieve this, ChatMyopia integrates an image classification tool and a retrieval-aug…

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…

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
Mohammed Guhdar, Ramadhan J. Mstafa, Abdulhakeem O. Mohammed
https://arxiv.org/abs/2507.12645

A Novel Data Augmentation Strategy for Robust Deep Learning Classification of Biomedical Time-Series Data: Application to ECG and EEG Analysis
The increasing need for accurate and unified analysis of diverse biological signals, such as ECG and EEG, is paramount for comprehensive patient assessment, especially in synchronous monitoring. Despite advances in multi-sensor fusion, a critical gap remains in developing unified architectures that effectively process and extract features from fundamentally different physiological signals. Another challenge is the inherent class imbalance in many biomedical datasets, often causing biased perfor…

Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose, Vernon Lawhern, Nicholas R Waytowich, Yongcan Cao
https://arxiv.org/abs/2506.09183

Multi-Task Reward Learning from Human Ratings
Reinforcement learning from human feeback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we lever…

DeSamba: Decoupled Spectral Adaptive Framework for 3D Multi-Sequence MRI Lesion Classification
Dezhen Wang, Sheng Miao, Rongxin Chai, Jiufa Cui
https://arxiv.org/abs/2507.15487

DeSamba: Decoupled Spectral Adaptive Framework for 3D Multi-Sequence MRI Lesion Classification
Magnetic Resonance Imaging (MRI) sequences provide rich spatial and frequency domain information, which is crucial for accurate lesion classification in medical imaging. However, effectively integrating multi-sequence MRI data for robust 3D lesion classification remains a challenge. In this paper, we propose DeSamba (Decoupled Spectral Adaptive Network and Mamba-Based Model), a novel framework designed to extract decoupled representations and adaptively fuse spatial and spectral features for le…

PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection
Wenhao Li, Selvakumar Manickam, Yung-wey Chong, Shankar Karuppayah
https://arxiv.org/abs/2506.15656 …

PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection
Phishing websites continue to pose a significant cybersecurity threat, often leveraging deceptive structures, brand impersonation, and social engineering tactics to evade detection. While recent advances in large language models (LLMs) have enabled improved phishing detection through contextual understanding, most existing approaches rely on single-agent classification facing the risks of hallucination and lack interpretability or robustness. To address these limitations, we propose PhishDebate…

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Tianlong Ai, Tianzhu Liu, Haochen Jiang, Yanfeng Gu
https://arxiv.org/abs/2507.08741

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Hierarchical land cover and land use (LCLU) classification aims to assign pixel-wise labels with multiple levels of semantic granularity to remote sensing (RS) imagery. However, existing deep learning-based methods face two major challenges: 1) They predominantly adopt a flat classification paradigm, which limits their ability to generate end-to-end multi-granularity hierarchical predictions aligned with tree-structured hierarchies used in practice. 2) Most cross-domain studies focus on perform…

Classification of Multi-Parametric Body MRI Series Using Deep Learning
Boah Kim, Tejas Sudharshan Mathai, Kimberly Helm, Peter A. Pinto, Ronald M. Summers
https://arxiv.org/abs/2506.15182

Classification of Multi-Parametric Body MRI Series Using Deep Learning
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional technologist errors. To address this, we present a deep learning-based classification model to classify 8 different body mpMRI series types so that radiologists read the exams efficiently. Using mpMRI data from various institutions, multiple deep lea…

LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification
Fengxiao Tang, Huan Li, Ming Zhao, Zongzong Wu, Shisong Peng, Tao Yin
https://arxiv.org/abs/2507.11310

LRCTI: A Large Language Model-Based Framework for Multi-Step Evidence Retrieval and Reasoning in Cyber Threat Intelligence Credibility Verification
Verifying the credibility of Cyber Threat Intelligence (CTI) is essential for reliable cybersecurity defense. However, traditional approaches typically treat this task as a static classification problem, relying on handcrafted features or isolated deep learning models. These methods often lack the robustness needed to handle incomplete, heterogeneous, or noisy intelligence, and they provide limited transparency in decision-making-factors that reduce their effectiveness in real-world threat envi…

Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography
Farnoush Bayatmakou, Reza Taleei, Nicole Simone, Arash Mohammadi
https://arxiv.org/abs/2507.17662

Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography
Breast cancer (BC) remains one of the leading causes of cancer-related mortality among women, despite recent advances in Computer-Aided Diagnosis (CAD) systems. Accurate and efficient interpretation of multi-view mammograms is essential for early detection, driving a surge of interest in Artificial Intelligence (AI)-powered CAD models. While state-of-the-art multi-view mammogram classification models are largely based on Transformer architectures, their computational complexity scales quadratic…

NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification
Wajih Hassan Raza, Aamir Bader Shah, Yu Wen, Yidan Shen, Juan Diego Martinez Lemus, Mya Caryn Schiess, Timothy Michael Ellmore, Renjie Hu, Xin Fu
https://arxiv.org/abs/2506.14970…

NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification
The integration of multi-modal Magnetic Resonance Imaging (MRI) and clinical data holds great promise for enhancing the diagnosis of neurological disorders (NDs) in real-world clinical settings. Deep Learning (DL) has recently emerged as a powerful tool for extracting meaningful patterns from medical data to aid in diagnosis. However, existing DL approaches struggle to effectively leverage multi-modal MRI and clinical data, leading to suboptimal performance. To address this challenge, we util…

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…

Improving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
Nerma Kadric, Amila Akagic, Medina Kapo
https://arxiv.org/abs/2507.12961

Improving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
Pigmented skin lesions represent localized areas of increased melanin and can indicate serious conditions like melanoma, a major contributor to skin cancer mortality. The MedMNIST v2 dataset, inspired by MNIST, was recently introduced to advance research in biomedical imaging and includes DermaMNIST, a dataset for classifying pigmented lesions based on the HAM10000 dataset. This study assesses ResNet-50 and EfficientNetV2L models for multi-class classification using DermaMNIST, employing transf…