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  • Open Access

    ARTICLE

    An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

    Romany F. Mansour1,*, Shaha Al-Otaibi2, Amal Al-Rasheed2, Hanan Aljuaid3, Irina V. Pustokhina4, Denis A. Pustokhin5

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2843-2858, 2021, DOI:10.32604/cmc.2021.016626

    Abstract Big data streams started becoming ubiquitous in recent years, thanks to rapid generation of massive volumes of data by different applications. It is challenging to apply existing data mining tools and techniques directly in these big data streams. At the same time, streaming data from several applications results in two major problems such as class imbalance and concept drift. The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection (MOMBD-CDD) method on High-Dimensional Streaming Data. The presented MOMBD-CDD model has different operational stages such as pre-processing, CDD, and classification. MOMBD-CDD model overcomes class… More >

  • Open Access

    ARTICLE

    Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions

    Minh Thanh Vo1, Anh H. Vo2, Trang Nguyen3, Rohit Sharma4, Tuong Le2,5,*

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 521-535, 2021, DOI:10.32604/cmc.2021.015645

    Abstract In recent years, the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age. Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting. However, the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs. This causes a reduction in the predictability and performance of traditional machine learning models. We therefore present an efficient framework that uses an oversampling technique called FJD-OT… More >

  • Open Access

    ARTICLE

    Mixed Re-Sampled Class-Imbalanced Semi-Supervised Learning for Skin Lesion Classification

    Ye Tian1, Liguo Zhang1,2, Linshan Shen1,*, Guisheng Yin1, Lei Chen3

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 195-211, 2021, DOI:10.32604/iasc.2021.016314

    Abstract Skin cancer is one of the most common types of cancer in the world, melanoma is considered to be the deadliest type among other skin cancers. Quite recently, automated skin lesion classification in dermoscopy images has become a hot and challenging research topic due to its essential way to improve diagnostic performance, thus reducing melanoma deaths. Convolution Neural Networks (CNNs) are at the heart of this promising performance among a variety of supervised classification techniques. However, these successes rely heavily on large amounts of class-balanced clearly labeled samples, which are expensive to obtain for skin lesion classification in the real… More >

  • Open Access

    ARTICLE

    MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

    Yuqing Yang1, 3, Peng Fu2, *, Xiaojiang Yang1, 4, Hong Hong5, Dequn Zhou1

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2413-2423, 2020, DOI:10.32604/cmc.2020.011881

    Abstract Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better… More >

  • Open Access

    ARTICLE

    Study on Multi-Label Classification of Medical Dispute Documents

    Baili Zhang1, 2, 3, *, Shan Zhou1, Le Yang1, Jianhua Lv1, 2, Mingjun Zhong4

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1975-1986, 2020, DOI:10.32604/cmc.2020.010914

    Abstract The Internet of Medical Things (IoMT) will come to be of great importance in the mediation of medical disputes, as it is emerging as the core of intelligent medical treatment. First, IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution. Second, IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff. This information includes recommendation of similar historical cases, guidance for medical treatment, alerting of hired dispute profiteers etc. The multi-label classification of medical dispute documents (MDDs) plays an important role as a front-end process… More >

  • Open Access

    ARTICLE

    Distant Supervised Relation Extraction with Cost-Sensitive Loss

    Daojian Zeng1,2, Yao Xiao1,2, Jin Wang2,*, Yuan Dai1,2, Arun Kumar Sangaiah3

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1251-1261, 2019, DOI:10.32604/cmc.2019.06100

    Abstract Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which… More >

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