Muhammad Usman Ghani Khan¹, Abid Mehmood², Mourad Elhadef², Shehzad Ashraf Chaudhry^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2225-2249, 2023, DOI:10.32604/cmc.2023.038303

Abstract The proliferation of deluding data such as fake news and phony audits on news web journals, online publications, and internet business apps has been aided by the availability of the web, cell phones, and social media. Individuals can quickly fabricate comments and news on social media. The most difficult challenge is determining which news is real or fake. Accordingly, tracking down programmed techniques to recognize fake news online is imperative. With an emphasis on false news, this study presents the evolution of artificial intelligence techniques for detecting spurious social media content. This study shows past, current, and possible methods that… More >

Ahmad Alassaf^*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2773-2789, 2023, DOI:10.32604/csse.2023.035899

Abstract Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare. Deep Learning (DL) models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models. On the other hand, skin lesion-based segregation and disintegration procedures play an essential role in earlier skin cancer detection. However, artefacts, an unclear boundary, poor contrast, and different lesion sizes make detection difficult. To address the issues in skin lesion diagnosis, this study creates the UDLS-DDOA model, an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder (UDLS) optimized by Dynamic Differential Annealed Optimization (DDOA). Pre-processing, segregation,… More >

Jingyao Liu^1,2, Qinghe Feng⁴, Jiashi Zhao^2,3, Yu Miao^2,3, Wei He², Weili Shi^2,3, Zhengang Jiang^2,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2649-2665, 2023, DOI:10.32604/cmc.2023.038891

Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction… More >

Owais Muhammad¹, Hong Jiang^1,*, Mushtaq Muhammad Umer¹, Bilal Muhammad², Naeem Muhammad Ahtsam³

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3193-3212, 2023, DOI:10.32604/iasc.2023.041232

Abstract To meet the high-performance requirements of fifth-generation (5G) and sixth-generation (6G) wireless networks, in particular, ultra-reliable and low-latency communication (URLLC) is considered to be one of the most important communication scenarios in a wireless network. In this paper, we consider the effects of the Rician fading channel on the performance of cooperative device-to-device (D2D) communication with URLLC. For better performance, we maximize and examine the system’s minimal rate of D2D communication. Due to the interference in D2D communication, the problem of maximizing the minimum rate becomes non-convex and difficult to solve. To solve this problem, a learning-to-optimize-based algorithm is proposed… More >

Zaihe Cheng¹, Yuwen Tao², Xiaoqing Gu³, Yizhang Jiang², Pengjiang Qian^2,*

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 dierent data distributions. The objective… More >

Qi Liu^1,2, Jing Li^1,2,*, Xianmin Wang^1,*, 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 facilitating the classifier to distinguish… More >

Yuna Han¹, Hangbae Chang^2,*

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 detection model that dynamically trains… More >

Muhammad Asfand-e-Yar^1,*, Qadeer Hashir^1,*, Syed Hassan Tanvir¹, Wajeeha Khalil²

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2921-2938, 2023, DOI:10.32604/cmc.2023.034741

Abstract The growth of the internet and technology has had a significant effect on social interactions. False information has become an important research topic due to the massive amount of misinformed content on social networks. It is very easy for any user to spread misinformation through the media. Therefore, misinformation is a problem for professionals, organizers, and societies. Hence, it is essential to observe the credibility and validity of the News articles being shared on social media. The core challenge is to distinguish the difference between accurate and false information. Recent studies focus on News article content, such as News titles… More >

Qianlei Wang^1,2,3,*, Zaijun Wang³, Renhe Xiong⁴, Xingbin Liao^1,2, 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 used in the model training… More >

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 useful; thus identifying the informative… More >