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  • Open Access

    ARTICLE

    An Online Fake Review Detection Approach Using Famous Machine Learning Algorithms

    Asma Hassan Alshehri*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2767-2786, 2024, DOI:10.32604/cmc.2023.046838

    Abstract Online review platforms are becoming increasingly popular, encouraging dishonest merchants and service providers to deceive customers by creating fake reviews for their goods or services. Using Sybil accounts, bot farms, and real account purchases, immoral actors demonize rivals and advertise their goods. Most academic and industry efforts have been aimed at detecting fake/fraudulent product or service evaluations for years. The primary hurdle to identifying fraudulent reviews is the lack of a reliable means to distinguish fraudulent reviews from real ones. This paper adopts a semi-supervised machine learning method to detect fake reviews on any website, More >

  • Open Access

    ARTICLE

    A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

    Bishrul Haq1, Sher Muhammad Daudpota1, Ali Shariq Imran2, Zenun Kastrati3,*, Waheed Noor4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 115-137, 2023, DOI:10.32604/cmc.2023.040638

    Abstract The Internet has become one of the significant sources for sharing information and expressing users’ opinions about products and their interests with the associated aspects. It is essential to learn about product reviews; however, to react to such reviews, extracting aspects of the entity to which these reviews belong is equally important. Aspect-based Sentiment Analysis (ABSA) refers to aspects extracted from an opinionated text. The literature proposes different approaches for ABSA; however, most research is focused on supervised approaches, which require labeled datasets with manual sentiment polarity labeling and aspect tagging. This study proposes a… More >

  • Open Access

    ARTICLE

    Cross-Domain TSK Fuzzy System Based on Semi-Supervised Learning for Epilepsy Classification

    Zaihe Cheng1, Yuwen Tao2, Xiaoqing Gu3, Yizhang Jiang2, Pengjiang Qian2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1613-1633, 2023, DOI:10.32604/cmes.2023.027708

    Abstract Through semi-supervised learning and knowledge inheritance, a novel Takagi-Sugeno-Kang (TSK) fuzzy system framework is proposed for epilepsy data classification in this study. The new method is based on the maximum mean discrepancy (MMD) method and TSK fuzzy system, as a basic model for the classification of epilepsy data. First, for medical data, the interpretability of TSK fuzzy systems can ensure that the prediction results are traceable and safe. Second, in view of the deviation in the data distribution between the real source domain and the target domain, MMD is used to measure the distance between… More >

  • Open Access

    ARTICLE

    Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

    Qi Liu1,2, Jing Li1,2,*, Xianmin Wang1,*, Wenpeng Zhao1

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1753-1771, 2023, DOI:10.32604/iasc.2023.039600

    Abstract Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further… More >

  • Open Access

    ARTICLE

    XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

    Yuna Han1, Hangbae Chang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 221-237, 2023, DOI:10.32604/cmc.2023.039463

    Abstract Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous… More >

  • Open Access

    ARTICLE

    Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping

    Xiong Xu1, Chun Zhou2,*, Chenggang Wang1, Xiaoyan Zhang2, Hua Meng2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656

    Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this… More >

  • Open Access

    ARTICLE

    Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

    Pham Huy Thong1,2,3, Florentin Smarandache4, Phung The Huan5, Tran Manh Tuan6, Tran Thi Ngan6,*, Vu Duc Thai5, Nguyen Long Giang2, Le Hoang Son3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1981-1997, 2023, DOI:10.32604/csse.2023.035692

    Abstract Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data… More >

  • Open Access

    ARTICLE

    A Method for Classification and Evaluation of Pilot’s Mental States Based on CNN

    Qianlei Wang1,2,3,*, Zaijun Wang3, Renhe Xiong4, Xingbin Liao1,2, Xiaojun Tan5

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1999-2020, 2023, DOI:10.32604/csse.2023.034183

    Abstract How to accurately recognize the mental state of pilots is a focus in civil aviation safety. The mental state of pilots is closely related to their cognitive ability in piloting. Whether the cognitive ability meets the standard is related to flight safety. However, the pilot's working state is unique, which increases the difficulty of analyzing the pilot's mental state. In this work, we proposed a Convolutional Neural Network (CNN) that merges attention to classify the mental state of pilots through electroencephalography (EEG). Considering the individual differences in EEG, semi-supervised learning based on improved K-Means is… More >

  • Open Access

    ARTICLE

    Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

    Ibrar Amin1, Saima Hassan1, Samir Brahim Belhaouari2,*, Muhammad Hamza Azam3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6335-6349, 2023, DOI:10.32604/cmc.2023.033860

    Abstract Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic More >

  • Open Access

    ARTICLE

    Using Informative Score for Instance Selection Strategy in Semi-Supervised Sentiment Classification

    Vivian Lee Lay Shan, Gan Keng Hoon*, Tan Tien Ping, Rosni Abdullah

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4801-4818, 2023, DOI:10.32604/cmc.2023.033752

    Abstract Sentiment classification is a useful tool to classify reviews about sentiments and attitudes towards a product or service. Existing studies heavily rely on sentiment classification methods that require fully annotated inputs. However, there is limited labelled text available, making the acquirement process of the fully annotated input costly and labour-intensive. Lately, semi-supervised methods emerge as they require only partially labelled input but perform comparably to supervised methods. Nevertheless, some works reported that the performance of the semi-supervised model degraded after adding unlabelled instances into training. Literature also shows that not all unlabelled instances are equally… More >

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