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  • Open Access

    ARTICLE

    A Novel Framework for Learning and Classifying the Imbalanced Multi-Label Data

    P. K. A. Chitra1, S. Appavu alias Balamurugan2, S. Geetha3, Seifedine Kadry4,5,6, Jungeun Kim7,*, Keejun Han8

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1367-1385, 2024, DOI:10.32604/csse.2023.034373 - 13 September 2024

    Abstract A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l21 norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed… More >

  • Open Access

    ARTICLE

    Multi-Label Image Classification Based on Object Detection and Dynamic Graph Convolutional Networks

    Xiaoyu Liu, Yong Hu*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4413-4432, 2024, DOI:10.32604/cmc.2024.053938 - 12 September 2024

    Abstract Multi-label image classification is recognized as an important task within the field of computer vision, a discipline that has experienced a significant escalation in research endeavors in recent years. The widespread adoption of convolutional neural networks (CNNs) has catalyzed the remarkable success of architectures such as ResNet-101 within the domain of image classification. However, in multi-label image classification tasks, it is crucial to consider the correlation between labels. In order to improve the accuracy and performance of multi-label classification and fully combine visual and semantic features, many existing studies use graph convolutional networks (GCN) for… More >

  • Open Access

    ARTICLE

    Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

    Jieren Cheng1,2, Xiaolong Chen1,*, Wenghang Xu3, Shuai Hua3, Zhu Tang1, Victor S. Sheng4

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1779-1793, 2023, DOI:10.32604/cmc.2023.042980 - 29 November 2023

    Abstract In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic… More >

  • Open Access

    ARTICLE

    Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

    Asifa Mehmood Qureshi1, Nouf Abdullah Almujally2, Saud S. Alotaibi3, Mohammed Hamad Alatiyyah4, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3707-3725, 2023, DOI:10.32604/cmc.2023.040738 - 08 October 2023

    Abstract Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob… More >

  • Open Access

    ARTICLE

    Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

    Ali Haider Khan1,2,*, Hassaan Malik2, Wajeeha Khalil3, Sayyid Kamran Hussain4, Tayyaba Anees5, Muzammil Hussain2

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 133-150, 2023, DOI:10.32604/cmc.2023.039518 - 08 June 2023

    Abstract To prevent irreversible damage to one’s eyesight, ocular diseases (ODs) need to be recognized and treated immediately. Color fundus imaging (CFI) is a screening technology that is both effective and economical. According to CFIs, the early stages of the disease are characterized by a paucity of observable symptoms, which necessitates the prompt creation of automated and robust diagnostic algorithms. The traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of eyes. In addition, they usually only target… More >

  • Open Access

    ARTICLE

    A Novel Metadata Based Multi-Label Document Classification Technique

    Naseer Ahmed Sajid1, Munir Ahmad1, Atta-ur Rahman2,*, Gohar Zaman3, Mohammed Salih Ahmed4, Nehad Ibrahim2, Mohammed Imran B. Ahmed4, Gomathi Krishnasamy6, Reem Alzaher2, Mariam Alkharraa2, Dania AlKhulaifi2, Maryam AlQahtani2, Asiya A. Salam6, Linah Saraireh5, Mohammed Gollapalli6, Rashad Ahmed7

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2195-2214, 2023, DOI:10.32604/csse.2023.033844 - 09 February 2023

    Abstract From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To… More >

  • Open Access

    ARTICLE

    Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

    K. Anuratha1,*, M. Parvathy2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 3005-3021, 2023, DOI:10.32604/csse.2023.031553 - 21 December 2022

    Abstract The COVID-19 pandemic has become one of the severe diseases in recent years. As it majorly affects the common livelihood of people across the universe, it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the outbreak. The public opinions are been shared enormously in microblogging media like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics, sports, entertainment etc., This work presents a combination of Intensity Based… More >

  • Open Access

    ARTICLE

    ENSOCOM: Ensemble of Multi-Output Neural Network’s Components for Multi-Label Classification

    Khudran M. Alzhrani*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5459-5479, 2022, DOI:10.32604/cmc.2022.028512 - 21 April 2022

    Abstract Multitasking and multioutput neural networks models jointly learn related classification tasks from a shared structure. Hard parameters sharing is a multitasking approach that shares hidden layers between multiple task-specific outputs. The output layers’ weights are essential in transforming aggregated neurons outputs into tasks labels. This paper redirects the multioutput network research to prove that the ensemble of output layers prediction can improve network performance in classifying multi-label classification tasks. The network’s output layers initialized with different weights simulate multiple semi-independent classifiers that can make non-identical label sets predictions for the same instance. The ensemble of… More >

  • Open Access

    ARTICLE

    A Novel Semi-Supervised Multi-Label Twin Support Vector Machine

    Qing Ai1,2,*, Yude Kang1, Anna Wang2

    Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 205-220, 2021, DOI:10.32604/iasc.2021.013357 - 07 January 2021

    Abstract Multi-label learning is a meaningful supervised learning task in which each sample may belong to multiple labels simultaneously. Due to this characteristic, multi-label learning is more complicated and more difficult than multi-class classification learning. The multi-label twin support vector machine (MLTSVM) [], which is an effective multi-label learning algorithm based on the twin support vector machine (TSVM), has been widely studied because of its good classification performance. To obtain good generalization performance, the MLTSVM often needs a large number of labelled samples. In practical engineering problems, it is very time consuming and difficult to obtain… More >

  • Open Access

    ARTICLE

    Soft Computing Based Evolutionary Multi-Label Classification

    Rubina Aslam1,*, Manzoor Illahi Tamimy1, Waqar Aslam2

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1233-1249, 2020, DOI:10.32604/iasc.2020.013086 - 24 December 2020

    Abstract Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classification lies at the core of Machine Learning and Multi-label Classification (MLC) is the closest to real-life problems related to heuristics. It is a type of classification problem where multiple labels or classes can be assigned to more than one instance simultaneously. The level of complexity in MLC is increased by factors such as data imbalance, high dimensionality, label correlations, and noise. Conventional MLC techniques such as ensembles-based approaches, Multi-label Stacking,… More >

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