Papers Multi-class Classification
“Multi-class Classification” 태그가 달린 논문 1,009편 · 필터 해제
Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes
Kolmogorov-Arnold Networks (KAN) offer universal function approximation using univariate spline compositions without nonlinear activations. In this work, we integrate Error-Correcting Output Codes (ECOC) into the KAN fra…
Classificationimage-classificationImage ClassificationKolmogorov-Arnold Networks+2In-Context Learning for Label-Efficient Cancer Image Classification in Oncology
The application of AI in oncology has been limited by its reliance on large, annotated datasets and the need for retraining models for domain-specific diagnostic tasks. Taking heed of these limitations, we investigated i…
Binary ClassificationDiagnosticimage-classificationImage Classification+2TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning
Hybrid quantum-classical neural networks (HQCNNs) represent a promising frontier in machine learning, leveraging the complementary strengths of both models. In this work, we propose the development of TunnElQNN, a non-se…
Multi-class ClassificationLight Weight CNN for classification of Brain Tumors from MRI Images
This study presents a convolutional neural network (CNN)-based approach for the multi-class classification of brain tumors using magnetic resonance imaging (MRI) scans. We utilize a publicly available dataset containing …
Data AugmentationDiagnosticMulti-class ClassificationFinancial Data Analysis with Robust Federated Logistic Regression
In this study, we focus on the analysis of financial data in a federated setting, wherein data is distributed across multiple clients or locations, and the raw data never leaves the local devices. Our primary focus is no…
Federated LearningMulti-class ClassificationregressionThyroidEffi 1.0: A Cost-Effective System for High-Performance Multi-Class Thyroid Carcinoma Classification
Background: Automated classification of thyroid fine needle aspiration biopsy (FNAB) images faces challenges in limited data, inter-observer variability, and computational cost. Efficient, interpretable models are crucia…
CPUDiagnosticimage-classificationImage Classification+1Multi-output Classification Framework and Frequency Layer Normalization for Compound Fault Diagnosis in Motor
This work introduces a multi-output classification (MOC) framework designed for domain adaptation in fault diagnosis, particularly under partially labeled (PL) target domain scenarios and compound fault conditions in rot…
ClassificationDiagnosticDomain AdaptationFault Diagnosis+1Quantum neural networks facilitating quantum state classification
The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised…
ClassificationMulti-class ClassificationTabKAN: Advancing Tabular Data Analysis using Kolmogorov-Arnold Network
Tabular data analysis presents unique challenges due to its heterogeneous feature types, missing values, and complex interactions. While traditional machine learning methods, such as gradient boosting, often outperform d…
BenchmarkingDeep LearningKolmogorov-Arnold NetworksMissing Values+2PRACH Preamble Detection as a Multi-Class Classification Problem: A Machine Learning Approach Using SVM
This study addresses the preamble detection problem in the Random Access procedure of LTE/5G networks by formulating it as a multi-class classification task and evaluating the effectiveness of machine learning techniques…
Multi-class ClassificationLeveraging Embedding Techniques in Multimodal Machine Learning for Mental Illness Assessment
The increasing global prevalence of mental disorders, such as depression and PTSD, requires objective and scalable diagnostic tools. Traditional clinical assessments often face limitations in accessibility, objectivity, …
ChunkingDiagnosticLarge Language ModelMulti-class Classification+1Measuring Online Hate on 4chan using Pre-trained Deep Learning Models
Online hate speech can harmfully impact individuals and groups, specifically on non-moderated platforms such as 4chan where users can post anonymous content. This work focuses on analysing and measuring the prevalence of…
Multi-class ClassificationSKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection
The rapid advancement of large language models (LLMs) has introduced new challenges in distinguishing human-written text from AI-generated content. In this work, we explored a pipelined approach for AI-generated text det…
Binary ClassificationClassificationMulti-class ClassificationText DetectionDeep Learning Approaches for Blood Disease Diagnosis Across Hematopoietic Lineages
We present a foundation modeling framework that leverages deep learning to uncover latent genetic signatures across the hematopoietic hierarchy. Our approach trains a fully connected autoencoder on multipotent progenitor…
Binary ClassificationClassificationDeep LearningMulti-class ClassificationiNatAg: Multi-Class Classification Models Enabled by a Large-Scale Benchmark Dataset with 4.7M Images of 2,959 Crop and Weed Species
Accurate identification of crop and weed species is critical for precision agriculture and sustainable farming. However, it remains a challenging task due to a variety of factors -- a high degree of visual similarity amo…
Multi-class ClassificationLabel-free pathological subtyping of non-small cell lung cancer using deep classification and virtual immunohistochemical staining
The differentiation between pathological subtypes of non-small cell lung cancer (NSCLC) is an essential step in guiding treatment options and prognosis. However, current clinical practice relies on multi-step staining an…
DiagnosticLung Cancer DiagnosisMulti-class ClassificationPrognosisQuantum Complex-Valued Self-Attention Model
Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete mechanisms. To address this limitation, …
modelMulti-class ClassificationQuantum Machine LearningAutomated diagnosis of lung diseases using vision transformer: a comparative study on chest x-ray classification
Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-re…
Binary ClassificationClassificationDiagnosticMulti-class Classification+2Probabilistic Quantum SVM Training on Ising Machine
Quantum computing holds significant potential to accelerate machine learning algorithms, especially in solving optimization problems like those encountered in Support Vector Machine (SVM) training. However, current QUBO-…
Binary ClassificationMulti-class ClassificationMulti-output Classification for Compound Fault Diagnosis in Motor under Partially Labeled Target Domain
This study presents a novel multi-output classification (MOC) framework designed for domain adaptation in fault diagnosis, addressing challenges posed by partially labeled (PL) target domain dataset and coexisting faults…
DiagnosticDomain AdaptationFault DiagnosisMulti-class Classification