Raed Alotaibi^1,*, Muhammad Atta Othman Ahmed², Omar Reyad^3,4,*, Nahla Fathy Omran⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076156 - 09 April 2026

Abstract The widespread use of social media has made assessing users’ tastes and preferences increasingly complex and important. At the same time, the rapid dissemination of misinformation on these platforms poses a critical challenge, driving significant efforts to develop effective detection methods. This study offers a comprehensive analysis leveraging advanced Machine Learning (ML) techniques to classify news articles as fake or true, contributing to discourse on media integrity and combating misinformation. The suggested method employed a diverse dataset encompassing a wide range of topics. The method evaluates the performance of five ML models: Artificial Neural Networks… More >

Chun-Shiang Lin^1,2,#, Ta-Wen Hsu^3,4,#, Hsiang-Lin Lee^5,6,*, Shao-Hsuan Kao^1,7,*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.074231 - 23 March 2026

Abstract Objectives: Cholecystokinin A receptor (CCKAR) has been linked to poor prognosis in colon cancer patients, but the role of CCKAR in colon cancer cell invasiveness and the underlying mechanisms remain elusive. This study aimed to explore the effect of CCKAR on the invasive potential of colon cancer cells. Methods: Different human colon cancer cell lines were used. Gene expression was evaluated by reverse transcription polymerase chain reaction (RT-PCR) and quantitative real-time RT-PCR (qPCR), while protein expression and phosphorylation were assessed by Western blotting. Cell motility and invasiveness were examined through wound healing and invasion assays,… More > Graphic Abstract

Sara Tehsin^1,*, Inzamam Mashood Nasir¹, Wiem Abdelbaki², Fadwa Alrowais³, Reham Abualhamayel⁴, Abdulsamad Ebrahim Yahya⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076514 - 12 March 2026

Abstract The rapid proliferation of multimodal misinformation on social media demands detection frameworks that are not only accurate but also robust to noise, adversarial manipulation, and semantic inconsistency between modalities. Existing multimodal fake news detection approaches often rely on deterministic fusion strategies, which limits their ability to model uncertainty and complex cross-modal dependencies. To address these challenges, we propose Q-ALIGNer, a quantum-inspired multimodal framework that integrates classical feature extraction with quantum state encoding, learnable cross-modal entanglement, and robustness-aware training objectives. The proposed framework adopts quantum formalism as a representational abstraction, enabling probabilistic modeling of multimodal alignment… More >

Yuechuan Zhang^1,2, Mingshu Zhang^1,2,*, Bin Wei^1,2, Hongyu Jin^1,2, Yaxuan Wang^1,2

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.073996 - 12 March 2026

Abstract Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise, inter-modal imbalance, computational bottlenecks, and semantic ambiguity. To address these issues, we propose SparseMoE-MFN, a novel unified framework that integrates sparse attention with a sparse-activated Mixture-of-Experts (MoE) architecture. This framework aims to enhance the efficiency, inferential depth, and interpretability of multimodal fake news detection. SparseMoE-MFN leverages LLaVA-v1.6-Mistral-7B-HF for efficient visual encoding and Qwen/Qwen2-7B for text processing. The sparse attention module adaptively filters irrelevant tokens and focuses on key regions, reducing computational costs and noise. The sparse MoE module dynamically… More >

Haoyang Xu^*

Journal on Artificial Intelligence, Vol.8, pp. 153-167, 2026, DOI:10.32604/jai.2026.078014 - 11 March 2026

Abstract This paper conducted a comprehensive study on the robustness of three widely used DFD deep learning models—namely, ResNet50, FreqNet, and Xception v1—to controlled perturbation attacks and frequency masking across a range of 12 different distortions. The study was performed on 254,166 ForenSynth test images, characterizing the distribution of FSI-drop values derived from over 3.05 million paired predictions. The distribution of FSI-drop values is sharply peaked around zero: 99.7% of the samples exhibit |Δ| < 0.1, and the maximum |Δ| ≈ 1.5 × 10⁻³, indicating high baseline stability. In terms of perturbation-wise comparison, Gaussian blur dominates, yielding… More >

Md. Ahsan Habib¹, Md. Anwar Hussen Wadud², M. F. Mridha^3,*, Md. Jakir Hossen^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.070235 - 10 February 2026

Abstract The problem of fake news detection (FND) is becoming increasingly important in the field of natural language processing (NLP) because of the rapid dissemination of misleading information on the web. Large language models (LLMs) such as GPT-4. Zero excels in natural language understanding tasks but can still struggle to distinguish between fact and fiction, particularly when applied in the wild. However, a key challenge of existing FND methods is that they only consider unimodal data (e.g., images), while more detailed multimodal data (e.g., user behaviour, temporal dynamics) is neglected, and the latter is crucial for… More >

Xiaoyu Zhang^1,#, RenFei Zong^1,#, Yan Sun¹, Nan Chen², Kunyao Zhu¹, Hang Tong¹, Tinghao Li¹, Junlong Zhu¹, Zijia Qin¹, Linfeng Wu¹, Aimin Wang¹, Weiyang He^1,*

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.072084 - 19 January 2026

Abstract Objective: While cisplatin-based chemotherapy is pivotal for advanced bladder cancer, acquired resistance remains a major obstacle. This study investigates key molecular drivers of this resistance and potential reversal strategies. Methods: We established GC (Gemcitabine and Cisplatin)-resistant T24-R and UC3-R cell lines from T24 and UM-UC-3 (UC3) cells. Transcriptomic and proteomic analyses identified differentially expressed molecules. Apoptosis and cell viability were assessed by flow cytometry and CCK-8 (Cell Counting Kit-8) assays, while RT-qPCR (Reverse Transcription Quantitative Polymerase Chain Reaction) and Western blot analyzed gene and protein expression. Immunofluorescence evaluated FAK (Focal Adhesion Kinase) phosphorylation, and a… More >

Ahmed Abdel-Wahab¹, Mohammad Alkhatib^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

Hamza Murad Khan¹, Shakila Basheer², Mohammad Tabrez Quasim³, Raja`a Al-Naimi⁴, Vijaykumar Varadarajan⁵, Anwar Khan^1,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069327 - 10 November 2025

Abstract With the increasing growth of online news, fake electronic news detection has become one of the most important paradigms of modern research. Traditional electronic news detection techniques are generally based on contextual understanding, sequential dependencies, and/or data imbalance. This makes distinction between genuine and fabricated news a challenging task. To address this problem, we propose a novel hybrid architecture, T5-SA-LSTM, which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attention-enhanced (SA) Long Short-Term Memory (LSTM). The LSTM is trained using the Adam optimizer, which provides faster and more stable convergence compared… More >

Pengpeng Yang^1,2,*, Chen Zhou¹, Dasara Shullani², Lanxi Liu¹, Daniele Baracchi²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2487-2526, 2025, DOI:10.32604/cmc.2025.069129 - 23 September 2025

Abstract Images and videos play an increasingly vital role in daily life and are widely utilized as key evidentiary sources in judicial investigations and forensic analysis. Simultaneously, advancements in image and video processing technologies have facilitated the widespread availability of powerful editing tools, such as Deepfakes, enabling anyone to easily create manipulated or fake visual content, which poses an enormous threat to social security and public trust. To verify the authenticity and integrity of images and videos, numerous approaches have been proposed, which are primarily based on content analysis and their effectiveness is susceptible to interference… More >