Expanding extensive and intricate social networks has required sophisticated community detection techniques. This study presents an innovative hybrid methodology that utilizes node space similarity and local knowledge to enhance community identification. Node space similarity is defined by integrating eigenvector centrality (EC), which quantifies a node’s influence inside the network, with proximity metrics, such as closeness, to evaluate the connectivity between nodes. This enables us to identify cohorts of individuals with analogous influence and connectivity. We use local knowledge by concentrating on these pivotal nodes' direct connections and attributes, allowing the technique to broaden community discovery (CD) outward effectively. Our five-phase methodology, grounded in an iterative seed expansion algorithm, commences with identifying highly central nodes and progressively develops communities by integrating nodes exhibiting high similarity and local connectivity. The method incorporates graph statistical inference and embedding features to improve accuracy and capture extensive network patterns. This integrated approach facilitates the precise and effective identification of communities within extensive social networks, exceeding the constraints of conventional techniques. This research attained a modularity of 95.05% on the DBLP dataset and 94.50% on the Amazon dataset. This study achieved a Normalized Mutual Information (NMI) of 91.80% on the DBLP dataset, 92.50% on the Amazon dataset, and 90.43% on the football dataset, demonstrating superior performance relative to previous methodologies. The findings indicate that the hybrid method outperforms other recognized methods in large-scale graphs, showcasing notable robustness and efficiency.

Feature selection in most black-box machine learning algorithms, such as BERT, is based on the cor-relations between features and the target variable rather than causal relationships in the dataset. This makes their predictive power and decisions questionable because of their potential bias. This paper presents novel BERT models that learn from causal variables in a clinical discharge dataset. The causal-directed acyclic Graphs (DAG) identify input variables for patients’ survival rate prediction and decisions. The core idea behind our model lies in the ability of the BERT-based model to learn from the causal DAG semi-synthetic dataset, enabling it to model the underlying causal structure accurately in-stead of the generic spurious correlations devoid of causation. The results from Causal DAG Conditional Independence Test (CIT) validation metrics showed that the conceptual assumptions of the causal DAG were supported, the Pearson correlation coefficient ranges between -1 and 1, the p-value was (>0.05), and the confidence interval of 95% and 25% were satisfied. We further mapped the semi-synthetic dataset that evolved from the Causal DAG to three BERT models. Two metrics, pre-diction accuracy, and AUC score, were used to compare the performance of the BERT models. The accuracy of the BERT models showed that the regular BERT has a performance of 96%, while Clinical-BERT performance was 90%, and Clinical-BERT-Discharge-summary was 92%. On the other hand, the AUC score for BERT was 79%, ClinicalBERT was 77%, while ClinicalBERT-discharge summary was 84%. Our experiments on the synthetic dataset for the patient’s survival rate from the causal DAG datasets demonstrate high predictive performance and explainable input variables for human under-standing to justify prediction.

Artificial intelligence can change many industries as a global phenomenon. Over the years, this transformation has supported Electronic Markets in reengineering the processes and activities that take place in traditional markets, focusing on improving transaction effectiveness and efficiency. While our dependence on intelligent machines continues to grow, the demand for more transparent and interpretable models equally grows. Thus, explanations for machine decisions and predictions are needed to justify their reliability, which requires greater interpretability and often elaborates the need to understand the algorithms' underlying mechanism. This paper, therefore, proposed models based on Decision Tree (DT), Long Short-Term Memory (LSTM), and an ensemble of the two aforementioned models for improving CLV accuracy, interpretability, and explainability of AI-based models in the multi-category electronic market. An open-source e-commerce Behavior Data from a multi-category store, previously used by similar studies on XAI and CLV, was used in this experiment, ensuring the robustness of the product prediction and explanations and fair comparison. From the results, the models from this study demonstrated remarkable performance in terms of minimal error rates of MAE, MSE, and RMSE, with LSTM outperforming the other models. Regarding explainability and interpretation, the begin_time is ranked as the most relevant feature in CLV prediction.

This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.

Phishing is a fraudulent activity wherein an attacker impersonates a trusted individual or organization to acquire sensitive information from an online user. Phishing websites have become a major cyber-security issue in the contemporary digital landscape. As online activities expand in e-commerce, banking, and social media, the hazards presented by these fraudulent websites have intensified. Deep learning-based Natural Language Processing (NLP) approaches offer an effective solution for detecting phishing URLs. However, deploying large models like BERT or RoBERTa for real-time detection poses computational challenges. This study proposes BERTPHIURL, a Teacher-Student Learning framework that leverages RoBERTa as the Teacher model and DistilRoBERTa as the Student model to improve phishing detection efficiency while reducing computational overhead. By applying knowledge distillation, the Student model learns from the Teacher, preserving high detection accuracy with significantly lower resource consumption. The proposed approach effectively captures contextual relevance and local features in malicious URL detection tasks. The experiments were conducted on a dataset exceeding 50,000 URLs to evaluate performance. Results indicate that BERTPHIURL achieves a 94.22% accuracy, outperforming existing phishing detection methods while maintaining efficiency suitable for real-time applications.

The research focuses on developing an Optimized Multiclass Intrusion Classifier (OMIC), an advanced framework for large-scale network intrusion detection in IoT environments. Traditional intrusion detection systems face significant challenges with increasing network complexity, attack sophistication, and the exponential growth of IoT devices, particularly in handling class imbalance, computational efficiency, and real-time processing of massive data volumes. OMIC introduces a novel ensemble approach combining LightGBM and XGBoost classifiers with a memory-optimized processing pipeline to address these limitations. The framework implements sophisticated data handling techniques, including dynamic chunk-based processing, adaptive sampling methods, and cost-sensitive learning to manage class imbalance. Experimental evaluation using the comprehensive CICIoT2023 dataset, comprising over 1 million records and 33 distinct attack types, demonstrates OMIC's exceptional performance with an overall accuracy of 99.26%. The framework achieves perfect precision, recall, and F1-scores for most DDoS and DoS attack categories, significantly outperforming traditional machine learning and deep learning approaches. While excelling in most attack categories, OMIC shows limitations in detecting certain web-based attacks and reconnaissance activities, suggesting areas for future enhancement. The framework's superior performance in handling large-scale data while maintaining high detection accuracy positions it as a significant advancement in IoT network security, offering practical solutions for real-world deployments.

Face spoofing detection is critical for biometric security systems to prevent unauthorized access. This study proposes a deep learning-based approach integrating FaceNet and DenseNet201 to enhance face spoofing detection performance. FaceNet generates identity-based embeddings, ensuring robust facial feature representation, while DenseNet201 extracts complementary texture-based features. These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. Ablation studies reveal that data augmentation does not always enhance performance, particularly on high-complexity datasets such as LCC-FASD, where augmentation increases the False Rejection Rate (FRR). Conversely, DenseNet201 benefits more from augmentation, while the proposed method performs best without augmentation. Comparative analysis with previous studies further confirms the superiority of the proposed approach in reducing error rates, particularly Half Total Error Rate (HTER), False Acceptance Rate (FAR), and FRR. These findings indicate that combining identity-based embeddings and texture-based feature extraction significantly improves spoofing detection and enhances model robustness across different attack scenarios. This study advances biometric security by introducing an efficient feature fusion strategy that strengthens deep learning-based spoof detection. Future research may explore further optimization strategies and evaluate the approach on more diverse datasets to enhance generalization.

This study aimed to predict and detect cyberattacks using hybrid machine-learning models. The CICIoT2023 dataset was utilized for attack prediction and detection, and model performance was evaluated separately by performing thirty-four class (33+1), eight class (7+1), and binary (1+1) classifications according to the types of attacks in the dataset. Voting and stacking hybrid machine learning models were employed in this study, with Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Random Forest (RF) algorithms selected as sub-models. Data preprocessing steps were applied to enhance model performance, and hyperparameter optimization was performed. As a result, this study achieved an accuracy of 98% in thirty-four class classifications, 88% in eight class classifications, and 99% in binary classifications with the Voting hybrid machine learning model. In contrast, the Stacking hybrid machine learning model reached an accuracy of 98% in both thirty-four class and eight class classifications and 99% in binary classifications. This study presents a significant innovation in the cybersecurity field by introducing an innovative approach to eliminating the disadvantages of single-model methods.

Project management is changing drastically due to the integration of artificial intelligence (AI) and quantum computing (QC), redefining traditional methods. This study explores Quantum AI (QAI) and AI-driven solutions to tackle enduring issues, including resource inefficiencies, schedule delays, and budget overruns. These technologies significantly enhance project outcomes by leveraging predictive analytics, dynamic scheduling, and high-dimensional optimization. A comparative analysis of prominent case studies, including the Crossrail Project, East Side Access, and the Montreal Olympics, highlights the superior performance of AI and QAI techniques compared to conventional methods. The study shows that QAI can cut delays by 60%, optimize resource allocation with 83% efficiency, and eliminate cost overruns by up to 40% using Monte Carlo simulations and Failure Mode Effects Analysis. These results demonstrate that quantum artificial intelligence is a ground-breaking tool for handling intricate, interconnected project settings. Additionally, this study emphasizes how QAI is scalable and applicable across industries, especially in fields that need real-time optimization and high-dimensional data processing. The proposed hybrid quantum-classical paradigm provides practical solutions and sets a benchmark for efficiency, scalability, and risk mitigation in project management.

Course recommendation aims to find suitable and attractive courses for students based on their needs, playing a significant role in the curricula-variable system. However, with the abundant available courses, students often face cognitive overload when selecting the most appropriate ones. This research proposes a course recommendation system called the Enhanced Hybrid Course Recommender to address this challenge. This system uses an ensemble learning approach to combine and leverage the power of multiple machine learning classifiers, including Random Forest, Naive Bayes, and Support Vector Machine. By utilizing TF-IDF vectorization for text data transformation and label encoding for target label compatibility, this experiment significantly enhances recommendation precision and relevance, easing students' decision-making process and improving the overall quality of course recommendations. A hybrid approach is applied to improve the recommendation quality by combining predictions from all three classifiers through weighted voting. This ensemble method improves overall robustness and accuracy. This approach not only mitigates the cognitive overload faced by students but also significantly improves the quality of recommendations. Our hybrid model represents a substantial advancement in personalized course recommendation technology by demonstrating superior performance across key evaluation metrics such as accuracy, precision, recall, F1-score, ARHR, and NDCG.