Wen Jiang^1,2, Mingshu Zhang^1,2,*, Xu'an Wang^1,3, Wei Bin^1,2, Xiong Zhang^1,2, Kelan Ren^1,2, Facheng Yan^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2161-2179, 2024, DOI:10.32604/cmc.2024.053762 - 15 August 2024

Abstract With the rapid spread of Internet information and the spread of fake news, the detection of fake news becomes more and more important. Traditional detection methods often rely on a single emotional or semantic feature to identify fake news, but these methods have limitations when dealing with news in specific domains. In order to solve the problem of weak feature correlation between data from different domains, a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed. This method makes full use of the attention mechanism, grasps the correlation between different… More >

Glody Muka^1,*, Patrick Mukala^1,2,*

Journal on Artificial Intelligence, Vol.6, pp. 211-223, 2024, DOI:10.32604/jai.2024.049685 - 07 August 2024

Abstract Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors’ markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models… More >

Fangfang Shan^1,2,*, Mengyao Liu^1,2, Menghan Zhang^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1521-1542, 2024, DOI:10.32604/cmc.2024.053937 - 18 July 2024

Abstract Social media has become increasingly significant in modern society, but it has also turned into a breeding ground for the propagation of misleading information, potentially causing a detrimental impact on public opinion and daily life. Compared to pure text content, multmodal content significantly increases the visibility and share ability of posts. This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection. To effectively address the critical challenge of accurately detecting fake news on social media, this paper proposes a fake… More >

ZHAOYING ZHU^1,#, YANJIA HU^2,#, FENG YE², HAIBO TENG², GUOLIANG YOU¹, YUNHUI ZENG², MENG TIAN², JIANGUO XU², JIN LI², ZHIYONG LIU², HAO LIU^2,*, NIANDONG ZHENG^1,*

Oncology Research, Vol.32, No.7, pp. 1173-1184, 2024, DOI:10.32604/or.2024.042456 - 20 June 2024

Abstract Background: Inhibitor of NF-κB kinase-interacting protein (IKIP) is known to promote proliferation of glioblastoma (GBM) cells, but how it affects migration and invasion by those cells is unclear. Methods: We compared levels of IKIP between glioma tissues and normal brain tissue in clinical samples and public databases. We examined the effects of IKIP overexpression and knockdown on the migration and invasion of GBM using transwell and wound healing assays, and we compared the transcriptomes under these different conditions to identify the molecular mechanisms involved. Results: Based on data from our clinical samples and from public databases, More >

Yin Ni¹, Wu Zeng^2,*, Peng Xia¹, Guang Stanley Yang³, Ruochen Tan⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5295-5312, 2024, DOI:10.32604/cmc.2024.049911 - 20 June 2024

Abstract Deepfake-generated fake faces, commonly utilized in identity-related activities such as political propaganda, celebrity impersonations, evidence forgery, and familiar fraud, pose new societal threats. Although current deepfake generators strive for high realism in visual effects, they do not replicate biometric signals indicative of cardiac activity. Addressing this gap, many researchers have developed detection methods focusing on biometric characteristics. These methods utilize classification networks to analyze both temporal and spectral domain features of the remote photoplethysmography (rPPG) signal, resulting in high detection accuracy. However, in the spectral analysis, existing approaches often only consider the power spectral density… More >

Dmitry Gura^1,2, Bo Dong^3,*, Duaa Mehiar⁴, Nidal Al Said⁵

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1995-2014, 2024, DOI:10.32604/cmc.2024.048238 - 15 May 2024

Abstract The motivation for this study is that the quality of deep fakes is constantly improving, which leads to the need to develop new methods for their detection. The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection, which is then used as input to the CNN. The customized Convolutional Neural Network method is the date augmented-based CNN model to generate ‘fake data’ or ‘fake images’. This study was carried out using Python and its libraries. We used 242 films from the dataset gathered by the Deep Fake… More >

Fangfang Shan^1,2,*, Huifang Sun^1,2, Mengyi Wang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 581-605, 2024, DOI:10.32604/cmc.2024.046202 - 25 April 2024

Abstract As social networks become increasingly complex, contemporary fake news often includes textual descriptions of events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely to create a misleading perception among users. While early research primarily focused on text-based features for fake news detection mechanisms, there has been relatively limited exploration of learning shared representations in multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal model for detecting fake news, which relies on similarity reasoning and adversarial networks. The model employs Bidirectional Encoder Representation from Transformers… More >

Lifang Fu¹, Huanxin Peng^2,*, Changjin Ma², Yuhan Liu²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053 - 26 March 2024

Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is… More >

XINFENG ZHANG^1,2,#, SHUANG LI^2,#, MEIRU SONG^1,2, YUE CHEN³, LIANGZHENG CHANG³, ZHERUI LIU⁴, HONGYUAN DAI³, YUTAO WANG⁴, GANGQI YANG³, YUN JIANG^5,6,*, YINYING LU^1,2,*

Oncology Research, Vol.32, No.4, pp. 679-690, 2024, DOI:10.32604/or.2024.046231 - 20 March 2024

Abstract Liver cancer is a prevalent malignant cancer, ranking third in terms of mortality rate. Metastasis and recurrence primarily contribute to the high mortality rate of liver cancer. Hepatocellular carcinoma (HCC) has low expression of focal adhesion kinase (FAK), which increases the risk of metastasis and recurrence. Nevertheless, the efficacy of FAK phosphorylation inhibitors is currently limited. Thus, investigating the mechanisms by which FAK affects HCC metastasis to develop targeted therapies for FAK may present a novel strategy to inhibit HCC metastasis. This study examined the correlation between FAK expression and the prognosis of HCC. Additionally,… More >

Asma Hassan Alshehri^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2767-2786, 2024, DOI:10.32604/cmc.2023.046838 - 27 February 2024

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 >