Arar Al Tawil^1,*, Laiali Almazaydeh², Doaa Qawasmeh³, Baraah Qawasmeh⁴, Mohammad Alshinwan^1,5, Khaled Elleithy⁶

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3395-3412, 2024, DOI:10.32604/cmc.2024.057279 - 18 November 2024

Abstract Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information, a practice known as phishing. This study utilizes three distinct methodologies, Term Frequency-Inverse Document Frequency, Word2Vec, and Bidirectional Encoder Representations from Transformers, to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks. The study uses feature extraction methods to assess the performance of Logistic Regression, Decision Tree, Random Forest, and Multilayer Perceptron algorithms. The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron (Precision: 0.98, Recall: 0.98, F1-score: 0.98, Accuracy: 0.98). Word2Vec’s More >

Mohamed A. Mahdi¹, Suliman Mohamed Fati^2,*, Mohamed A.G. Hazber¹, Shahanawaj Ahamad³, Sawsan A. Saad⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1651-1671, 2024, DOI:10.32604/cmes.2024.052291 - 27 September 2024

Abstract Cyberbullying, a critical concern for digital safety, necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces. To tackle this challenge, our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers (BERT) base model (cased), originally pretrained in English. This model is uniquely adapted to recognize the intricate nuances of Arabic online communication, a key aspect often overlooked in conventional cyberbullying detection methods. Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media (SM) tweets showing a notable… More >

Wenquan Sun^1,2, Jia Liu^1,2,*, Lifeng Chen^1,2, Weina Dong^1,2, Fuqiang Di^1,2

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4031-4046, 2024, DOI:10.32604/cmc.2024.052745 - 12 September 2024

Abstract Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos (NeRV). While explicit methods exist for accurately embedding ownership or copyright information in video data, the nascent NeRV framework has yet to address this issue comprehensively. In response, this paper introduces MarkINeRV, a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV, which models the embedding and extraction of watermarks as a pair of… More >

Ayesha Khaliq¹, Salman Afsar Awan¹, Fahad Ahmad^2,*, Muhammad Azam Zia¹, Muhammad Zafar Iqbal³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3221-3242, 2024, DOI:10.32604/cmc.2024.053488 - 15 August 2024

Abstract The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity. Current approaches in Extractive Text Summarization (ETS) leverage the modeling of inter-sentence relationships, a task of paramount importance in producing coherent summaries. This study introduces an innovative model that integrates Graph Attention Networks (GATs) with Transformer-based Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA), further enhanced by Term Frequency-Inverse Document Frequency (TF-IDF) values, to improve sentence selection by capturing comprehensive topical information. Our… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918 - 26 March 2024

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

Guilhem Paillard-Brunet^*, Audrey Couillet

Psycho-Oncologie, Vol.18, No.1, pp. 23-31, 2024, DOI:10.32604/po.2023.047888 - 25 March 2024

Abstract Cette étude qualitative visait à recueillir chez les professionnels d’un Centre de Lutte Contre le Cancer les attentes et les réticences qu’ils pouvaient avoir vis-à-vis des interventions de pair-aidance. Des entretiens individuels semi-structurés ont été menés auprès de 12 professionnels issus de professions différentes. Une retranscription intégrale des entretiens puis une analyse thématique de leur contenu ont été conduites. L’analyse des données a permis de faire émerger trois thèmes principaux quant aux attentes exprimées par les soignants : le besoin d’un accompagnement plus soutenant des patients, rompre leur isolement dans la maladie et enrichir le More >

Yajing Ma^1,2,3, Gulila Altenbek^1,2,3,*, Yingxia Yu¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2023.045486 - 30 January 2024

Abstract Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event… More >

Akmalbek Abdusalomov¹, Alpamis Kutlimuratov², Rashid Nasimov³, Taeg Keun Whangbo^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2915-2933, 2023, DOI:10.32604/cmc.2023.044466 - 26 December 2023

Abstract The performance of a speech emotion recognition (SER) system is heavily influenced by the efficacy of its feature extraction techniques. The study was designed to advance the field of SER by optimizing feature extraction techniques, specifically through the incorporation of high-resolution Mel-spectrograms and the expedited calculation of Mel Frequency Cepstral Coefficients (MFCC). This initiative aimed to refine the system’s accuracy by identifying and mitigating the shortcomings commonly found in current approaches. Ultimately, the primary objective was to elevate both the intricacy and effectiveness of our SER model, with a focus on augmenting its proficiency in… More >

Somin Park¹, Mpabulungi Mark¹, Bogyung Park², Hyunki Hong^1,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1009-1030, 2023, DOI:10.32604/cmc.2023.041332 - 31 October 2023

Abstract Speech emotion recognition is essential for frictionless human-machine interaction, where machines respond to human instructions with context-aware actions. The properties of individuals’ voices vary with culture, language, gender, and personality. These variations in speaker-specific properties may hamper the performance of standard representations in downstream tasks such as speech emotion recognition (SER). This study demonstrates the significance of speaker-specific speech characteristics and how considering them can be leveraged to improve the performance of SER models. In the proposed approach, two wav2vec-based modules (a speaker-identification network and an emotion classification network) are trained with the Arcface loss.… More >

Brigitta Munkácsi^1,*, Enikő Felszeghy¹, Flóra Kenyhercz², Beáta Erika Nagy¹

International Journal of Mental Health Promotion, Vol.25, No.8, pp. 961-969, 2023, DOI:10.32604/ijmhp.2023.027319 - 06 July 2023

Abstract The most common comorbid psychiatric disorders in children with type 1 diabetes mellitus (T1DM) are depression, anxiety and behavioral disorders. Patients with comorbid psychopathology are less capable of psychically adjusting to the new life situation resulting from T1DM, which may negatively affect glycemic control and adherence related to the treatment. We aimed to investigate the association between mental health and type 1 diabetes including illness representation. 115 children and adolescents with T1DM were recruited through the outpatient clinic in Debrecen, Hungary. Measures: PRISM-D, Child Depression Inventory (CDI), Cantril Ladder and Self-Rated Health, Glycosylaeted haemoglobin (HbA1C) More > Graphic Abstract