Multiclass classification ≈ Multiclass classification
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A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks Open
Intrusion detection plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. In this paper, we explore how to model an intrusion detection system based on de…
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Robust Loss Functions under Label Noise for Deep Neural Networks Open
In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learnin…
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Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning Open
We address the challenging problem of whole slide image (WSI) classification. WSIs have very high resolutions and usually lack localized annotations. WSI classification can be cast as a multiple instance learning (MIL) problem when only sl…
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Modeling the Detection of Textual Cyberbullying Open
The scourge of cyberbullying has assumed alarming proportions with an ever-increasing number of adolescents admitting to having dealt with it either as a victim or as a bystander. Anonymity and the lack of meaningful supervision in the ele…
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Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection Open
Network intrusion detection systems (NIDSs) provide a better solution to network security than other traditional network defense technologies, such as firewall systems. The success of NIDS is highly dependent on the performance of the algo…
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Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning Open
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generi…
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Boosting methods for multi-class imbalanced data classification: an experimental review Open
Since canonical machine learning algorithms assume that the dataset has equal number of samples in each class, binary classification became a very challenging task to discriminate the minority class samples efficiently in imbalanced datase…
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Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks Open
The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cybe…
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Superior skin cancer classification by the combination of human and artificial intelligence Open
Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binar…
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Real-Time Detection of Traffic From Twitter Stream Analysis Open
Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event d…
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Multimodal skin lesion classification using deep learning Open
While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies have generally considered only a single clinical/macroscopic image and output a binary decision. In this work, we ha…
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Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification Open
Applied datasets can vary from a few hundred to thousands of samples in typical quantitative structure-activity/property (QSAR/QSPR) relationships and classification. However, the size of the datasets and the train/test split ratios can gr…
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A decision support system for multimodal brain tumor classification using deep learning Open
Multiclass classification of brain tumors is an important area of research in the field of medical imaging. Since accuracy is crucial in the classification, a number of techniques are introduced by computer vision researchers; however, the…
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Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization Open
Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated a…
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Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches Open
The existing deep learning-based Personal Protective Equipment (PPE) detectors can only detect limited types of PPE and their performance needs to be improved, particularly for their deployment on real construction sites. This paper introd…
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Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem Open
The current paper presents a novel method for reducing a multiclass confusion matrix into a 2×2 version enabling the exploitation of the relevant performance metrics and methods such as the receiver operating characteristic and area under …
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μVulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection Open
Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able t…
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A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter Open
Sentiment analysis and opinion mining in social networks present nowadays a hot topic of research. However, most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from so…
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Robust Loss Functions under Label Noise for Deep Neural Networks Open
In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learnin…
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CNN Based Multiclass Brain Tumor Detection Using Medical Imaging Open
Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wr…
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Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization Open
Logistic Regression (LR) has been widely used in statistics for many years, and has received extensive study in machine learning community recently due to its close relations to Support Vector Machines (SVM) and AdaBoost. In this paper, we…
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Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine Open
The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancem…
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AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer Open
Artificial intelligence (AI) applications in oncology have been developed rapidly with reported successes in recent years. This work aims to evaluate the performance of deep convolutional neural network (CNN) algorithms for the classificat…
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Deep Learning Applied for Histological Diagnosis of Breast Cancer Open
Deep learning, as one of the currently most popular computer science research trends, improves neural networks, which has more and deeper layers allowing higher abstraction levels and more accurate data analysis. Although deep convolutiona…
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Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics Open
Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. The prediction of toxic vs. non-toxic molecules is important…
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An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging Open
Alzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient br…
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Cervical cancer detection in pap smear whole slide images using convNet with transfer learning and progressive resizing Open
Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant con…
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Supervised Machine Learning Based Multi-Task Artificial Intelligence Classification of Retinopathies Open
Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists ma…
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Multi-class sentiment analysis on twitter: Classification performance and challenges Open
Sentiment analysis refers to the automatic collection, aggregation, and classification of data collected online into different emotion classes. While most of the work related to sentiment analysis of texts focuses on the binary and ternary…
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Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study Open
The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical …