Tao Yi^1,3, Xingshu Chen^1,2,*, Mingdong Yang³, Qindong Li¹, Yi Zhu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 929-955, 2024, DOI:10.32604/cmes.2024.048793

Abstract Due to the rapid evolution of Advanced Persistent Threats (APTs) attacks, the emergence of new and rare attack samples, and even those never seen before, make it challenging for traditional rule-based detection methods to extract universal rules for effective detection. With the progress in techniques such as transfer learning and meta-learning, few-shot network attack detection has progressed. However, challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning, difficulties in capturing rich information from original flow in the case of insufficient samples, and the… More >

Xuelong Wu¹, Junsheng Wang^1,*, Zhongyao Li¹, Yisheng Miao¹, Chengpeng Xue¹, Yuling Lang², Decai Kong², Xiaoying Ma², Haibao Qiao²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3703-3720, 2024, DOI:10.32604/cmc.2024.047027

Abstract X-ray Computed Tomography (XCT) enables non-destructive acquisition of the internal structure of materials, and image segmentation plays a crucial role in analyzing material XCT images. This paper proposes an image segmentation method based on the Segment Anything model (SAM). We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering, histogram equalization, and gamma correction. Subsequently, SAM was fine-tuned to adapt to the task of material XCT image segmentation, resulting in Material-SAM. We compared the performance of threshold segmentation, SAM, U-Net model, and Material-SAM. Our method achieved 88.45% Class Pixel Accuracy… More >

Rabia Abid¹, Muhammad Rizwan², Abdulatif Alabdulatif^3,*, Abdullah Alnajim⁴, Meznah Alamro⁵, Mourade Azrour⁶

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3413-3429, 2024, DOI:10.32604/cmc.2024.046880

Abstract Explainable Artificial Intelligence (XAI) has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning (ML) and Deep Learning (DL) based algorithms. In this paper, we chose e-healthcare systems for efficient decision-making and data classification, especially in data security, data handling, diagnostics, laboratories, and decision-making. Federated Machine Learning (FML) is a new and advanced technology that helps to maintain privacy for Personal Health Records (PHR) and handle a large amount of medical data effectively. In this context, XAI, along with FML, increases efficiency and improves the security of e-healthcare systems. The… More >

Ekanayake Mudiyanselage Chulabhaya Lankanatha Ekanayake^1,2, Abubakar Sulaiman Gezawa^3,*, Yunqi Lei¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 2941-2965, 2024, DOI:10.32604/cmc.2024.046155

Abstract Video description generates natural language sentences that describe the subject, verb, and objects of the targeted Video. The video description has been used to help visually impaired people to understand the content. It is also playing an essential role in devolving human-robot interaction. The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping. Deep learning is changing the shape of computer vision (CV) technologies and natural language processing (NLP). There are hundreds of deep learning models, datasets, and evaluations that can improve the gaps in current research. This article… More >

Xinqiao Liu^*, Linxin Zhang, Xinran Zhang

International Journal of Mental Health Promotion, Vol.26, No.2, pp. 105-116, 2024, DOI:10.32604/ijmhp.2023.042359

Abstract Under the effects of COVID-19 and a number of ongoing lockdown tactics, anxiety symptoms and poor sleep quality have become common mental health issues among college freshmen and are intimately related to their emotional adaptation. To explore this connection, this study gathered data from a sample of 256 freshmen enrolled in an elite university in China in September 2022. The association between sleep quality, anxiety symptoms, and emotional adaptation was clarified using correlation analysis. Additionally, the mediating function of anxiety symptoms between sleep quality and emotional adaptation was investigated using a structural equation model. The results reveal that: (1) Chinese… More >

Bin Luo^1,2,3, Liangguo Chen^1,2,3, Shuhua Ruan^1,2,3,*, Yonggang Luo^2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1731-1754, 2024, DOI:10.32604/cmc.2023.045739

Abstract Considering the stealthiness and persistence of Advanced Persistent Threats (APTs), system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host. Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks, and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection, which requires lots of manual efforts to locate attack entities. This paper proposes an APT-exploited process detection approach called ThreatSniffer, which constructs the benign provenance graph from attack-free audit logs, fits normal system entity… More >

Mustafa AlRifaee¹, Sally Almanasra^2,*, Adnan Hnaif³, Ahmad Althunibat³, Mohammad Abdallah³, Thamer Alrawashdeh³

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1591-1609, 2024, DOI:10.32604/cmc.2023.043520

Abstract In standard iris recognition systems, a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture, look-and-stare constraints, and a close distance requirement to the capture device. When these conditions are relaxed, the system’s performance significantly deteriorates due to segmentation and feature extraction problems. Herein, a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments. First, the algorithm scans the whole iris image in the Hue Saturation Value (HSV) color space for local maxima to detect the sclera region. The image… More >

Kwok Tai Chui^1,*, Brij B. Gupta^2,3,4,5,6,*, Varsha Arya^7,8,9, Miguel Torres-Ruiz¹⁰

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1363-1379, 2024, DOI:10.32604/cmc.2023.046762

Abstract The visions of Industry 4.0 and 5.0 have reinforced the industrial environment. They have also made artificial intelligence incorporated as a major facilitator. Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure, and thus timely maintenance can ensure safe operations. Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model, which typically involves two datasets. In response to the availability of multiple datasets, this paper proposes using selective and adaptive incremental transfer learning (SA-ITL), which fuses three… More >

Ou Ye, Xinli Wei, Zhenhua Yu^*, Yan Fu, Ying Yang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1071-1093, 2024, DOI:10.32604/cmc.2023.046418

Abstract In the video captioning methods based on an encoder-decoder, limited visual features are extracted by an encoder, and a natural sentence of the video content is generated using a decoder. However, this kind of method is dependent on a single video input source and few visual labels, and there is a problem with semantic alignment between video contents and generated natural sentences, which are not suitable for accurately comprehending and describing the video contents. To address this issue, this paper proposes a video captioning method by semantic topic-guided generation. First, a 3D convolutional neural network is utilized to extract the… More >

Yasmin Alaa Hassan^1,*, Abdul Monem S. Rahma²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1423-1442, 2024, DOI:10.32604/cmc.2023.046149

Abstract Video watermarking plays a crucial role in protecting intellectual property rights and ensuring content authenticity. This study delves into the integration of Galois Field (GF) multiplication tables, especially GF(2⁴), and their interaction with distinct irreducible polynomials. The primary aim is to enhance watermarking techniques for achieving imperceptibility, robustness, and efficient execution time. The research employs scene selection and adaptive thresholding techniques to streamline the watermarking process. Scene selection is used strategically to embed watermarks in the most vital frames of the video, while adaptive thresholding methods ensure that the watermarking process adheres to imperceptibility criteria, maintaining the video’s visual quality.… More >