Poluru Eswaraiah¹, Uddagiri Sirisha^2,*, Shaik Abdul Nabi³, Revathi Durgam⁴, Pallavi Malavath⁵, Gilakara Muni Nagamani⁶

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.078511 - 15 June 2026

Abstract The growing volume of digital text complicates the extraction of relevant information from unstructured data. Transformer models such as BERT, ALBERT, and RoBERTa are powerful, but they may face challenges in hyperparameter optimization and adaptation to new domains. To address this issue, a hybrid ensemble BERT model is suggested, optimized using the Walrus Optimization Algorithm (WaOA). The framework applies PCA to reduce dimensionality, ontology normalization, and K-means clustering to improve semantic comprehension. Experimental results on the SQuAD 2.0 and MS MARCO datasets show that the proposed model outperforms the baseline models. WaOA (Weighted Average of More >

Shuvodeep De^1,*, Agnivo Gosai^2,#, Karun Thankachan^3,#, Ramadan A. ZeinEldin⁴, Abdulaziz T. Almaktoom⁵, Mustafa Bayram⁶, Ali Wagdy Mohamed^7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080601 - 27 May 2026

Abstract Sentiment analysis (SA) has evolved from a niche text-classification task into a central problem in natural language processing, spanning multiple domains, modalities, and languages. This survey provides a comprehensive review of sentiment analysis methods from their origins in lexicon-based approaches through classical machine learning, deep learning architectures, pre-trained transformers, and the current era of large language models (LLMs). We formalize the SA problem across multiple granularity levels (document, sentence, and aspect) and present a taxonomy that encompasses classification, regression, aspect-based sentiment analysis (ABSA), emotion detection, and stance detection tasks across diverse domains including movie reviews,… More >

Ezz El-Din Hemdan¹, Haitham Elwahsh^2,3, Samah Alshathri^4,*, Amged Sayed^5,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079305 - 27 May 2026

Abstract Ensuring robust methods for maintaining high levels of medical data security is crucial in the Medical Internet of Things (IoT) for the protection of sensitive patient data during real-time transmission and analysis. Electroencephalography (EEG) signals in medical IoT systems are transmitted through cloud and edge networks, which create risks of cyber threats, unauthorized access, and data breaches. Consequently, there is an urgent need for efficient encryption methods to ensure the confidentiality of EEG signals during classification and prediction processes, as several state-of-the-art models either neglect security during classification or suffer from increased computational overhead that… More >

Ghadi Almaktoom, Suliman Aladhadh, Salim El Khediri^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.078774 - 27 May 2026

Abstract The rapid growth of phishing attempts in the enterprise could potentially lead to bankruptcy. The primary focus of the research is on detecting phishing attacks, with no interest in how the data is processed. Attackers use fraudulent methods to obtain valuable, confidential information, resulting in billions of dollars in financial losses for enterprises. In our review, we examined the methods used in phishing-detection studies. We concluded that the two main sections, centralized and decentralized methods, were the centralized ones, which aggregate data in a central server and thus violate data protection regulations, such as GDPR.… More >

Akmalbek Abdusalomov¹, Mukhriddin Mukhiddinov^2,3, Kamola Abdurashidova², Alpamis Kutlimuratov⁴, Avazjon Marakhimov⁵, Kuanishbay Seytnazarov⁶, Young-Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077057 - 08 May 2026

Abstract Human emotions are intricate and difficult to decipher through various modalities. Current methodologies frequently employ inflexible fusion strategies that do not consider the dynamic and context-sensitive characteristics of emotional expressions in both visual and textual mediums. This paper presents SYMPHONIA (Synchronizing Facial and Textual Modalities for Emotion Understanding), an innovative architecture engineered to capture and amalgamate emotional signals from facial expressions and language, attuned to contextual and modality interactions. There are two parts to SYMPHONIA: a Facial Emotion Branch that uses Vision Transformers and facial landmarks, and a Textual Emotion Branch that uses RoBERTa embeddings… More >

Amani Al-Ajlan, Lama Almelaifi, Remas Alharbi, Shahad Al-Hussain, Fay Alfarraj, Najwa Altwaijry, Isra Al-Turaiki^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.076992 - 08 May 2026

Abstract Phishing emails are an increasing threat to both individuals and organizations, demanding more sophisticated methods of detection beyond traditional blacklisting and heuristic techniques. One of the main challenges in phishing detection is the limited availability of high-quality phishing datasets. To address this issue, we use generative AI to create synthetic emails that help reduce class imbalance, improve model generalization, and overcome data scarcity. We employ a large language model, DeepSeek-7B-Chat, to generate realistic and context-aware phishing and non-phishing emails. Through prompt engineering and fine-tuning, the model produces diverse and modern phishing-style emails that strengthen phishing… More >

Daniil Kamakaev, Khaled Mahbub^*

Journal on Artificial Intelligence, Vol.8, pp. 183-202, 2026, DOI:10.32604/jai.2026.077823 - 15 April 2026

Abstract This paper presents an innovative system designed to automate the analysis of candidate interviews by integrating multiple analytical techniques into a single multimodal framework. This system combines text sentiment analysis, audio sentiment analysis, keyword extraction, and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction to evaluate candidate performance holistically. This system employs text sentiment analysis using VADER and transformer-based sentiment features (probability-based outputs), audio sentiment analysis with an SVM model trained on both IEMOCAP and MELD datasets, keyword extraction via KeyBERT, and audio feature extraction including MFCCs, delta MFCCs, pitch, and energy to evaluate candidate performance holistically. More >

Lan Xiong, Liang Wan^*, Jingxia Ren

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076986 - 09 April 2026

Abstract The semantic complexity of large-scale malicious payloads in modern network traffic severely limits the robustness and generalization of existing Intrusion Detection Systems (IDS). This limitation presents a major challenge to network security. This paper proposes a dual-branch intrusion detection method called CPS-IDS. This method fuses deep semantic features with statistical features. The first branch uses the DeBERTav2 module. It performs deep semantic modeling on the session payload. This branch also incorporates a Time Encoder. The Time Encoder models the temporal behavior of the packet arrival interval time series. A Cross-Attention mechanism achieves the joint modeling… More >

Yiming Yu¹, Yunfei Guo², Junchen Liu³, Yiping Sun⁴, Junliang Du^5,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074959 - 12 March 2026

Abstract Graph neural networks (GNNs) have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control- and data-flow graphs. Despite their effectiveness, most GNN-based vulnerability detectors operate as black boxes, making their decisions difficult to interpret and thus less suitable for critical security auditing. The information bottleneck (IB) principle provides a theoretical framework for isolating task-relevant graph components. However, existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics. To address these issues, we introduce ContractGIB, an interpretable graph information bottleneck framework for function-level vulnerability More >

Ichiro Kutsuna^1,2,*, Masanao Ikeya^2,3, Akane Fujii², Aiko Hoshino⁴, Kazuya Sakai¹

International Journal of Mental Health Promotion, Vol.28, No.2, 2026, DOI:10.32604/ijmhp.2025.074249 - 27 February 2026

Abstract Background: In mental health, recovery is emphasized, and qualitative analyses of service users’ narratives have accumulated; however, while qualitative approaches excel at capturing rich context and generating new concepts, they are limited in generalizability and feasible data volume. This study aimed to quantify the subjective life history narratives of users of psychiatric home-visit nursing using natural language processing (NLP) and to clarify the relationships between linguistic features and recovery-related indicators. Methods: We conducted audio-recorded and transcribed semi-structured interviews on daily life verbatim and collected self-report questionnaires (Recovery Assessment Scale [RAS]) and clinician ratings (Global Assessment of… More >