The rapid adoption of artificial intelligence (AI) in cybersecurity has created a double-edged sword: while AI enhances threat detection and response capabilities, it also creates new vulnerabilities through adversarial attacks and raises serious ethical concerns regarding fairness, accountability, and transparency. Current approaches often view technical security and ethical governance as separate challenges, and produce isolated solutions that are inadequate against emerging AI-driven threats. This paper proposes a new integrated ethical-security framework (IESF) that bridges this gap by incorporating ethical governance directly into AI cybersecurity operations. The framework offers a multi-layered architecture that combines technical threat analysis with real-time ethical impact assessments, dynamic human-AI collaboration protocols, and comprehensive accountability mechanisms. The results show 95.7% detection accuracy (11.4% improvement), 74.4% reduction in false positives, and 71.1% lower success rate for adversarial attacks. Ethically, this framework achieved 98% bias detection, 96.5% privacy protection, and 99.8% audit trail completeness. Operationally, the system reduced human analyst workload by 78.5% while improving response times by 79%, demonstrating that ethical integration enhances rather than hinders security effectiveness. This research provides both a practical framework for responsible AI cybersecurity and empirical evidence that integrating ethical considerations with technical security produces synergistic benefits. The findings show that the IESF provides a viable path for organizations to harness the defensive potential of AI while ensuring alignment with human values, providing a significant advance toward reliable and effective cybersecurity in the age of AI.

To give accurate and relevant answers in Visual Question Answering (VQA), you need to know how to use both visible and textual modes. Most fusion models used today are either based on static integration or don't do a good job of capturing fine-grained semantic interactions, which makes them bad at thinking. This paper presents an Explainable Adaptive Fusion and Multi-Hop Attention (EAF-MA) framework that fixes these problems by limiting the contributions of multimodal features and making the results easier to understand. The model suggests adding an Adaptive Fusion Layer that would change the importance of visual and textual features based on the context of the question, along with a Multi-Hop Attention system that would allow for iterative reasoning for complicated queries. An Explainability Module also sends out visual and verbal attention traces, which makes decision-making clearer. EAF-MA does better than the best fusion and transformer-based models in terms of accuracy, robustness, and explainability, as shown by tests on standard datasets like VQA v2, GQA, and CLEVR. This framework sets up a fair, easy-to-understand, and fast way to use multimodal reasoning in VQA problems.

Online reviews are becoming the most important way for buyers to determine whether to buy a product or use an online service. Spammers have been promoting bogus reviews on the internet more and more to trick customers into buying things, with the goal of promoting businesses, products, and services to boost sales and marketing, frequently at the expense of quality and after-sales care. This study analyzes a comprehensive feature engineering approach for detecting fraudulent reviews by employing linguistic, statistical, semantic, and behavioral features. A number of machine learning classifiers have been trained, verified, and tested, including ensemble models on benchmark datasets viz. “Fake Reviews Dataset” and “Yelp Labeled Dataset”. Focus has been on how the engineering features affect the classification performance. Experimental results demonstrate that models with numerous attributes outperform baseline techniques significantly. The mentioned two datasets have been used for testing to make the results more reliable. The ensemble model is the best performer on the leaderboard, with F1-score of 89.10% for the Fake reviews dataset and 85.72% for the Yelp labeled dataset. The ranking of the top 20 features that help find false reviews for each dataset has been stressed to show how important feature engineering is for finding phony reviews. The results have been emphasized through implication of spam score calculations leading to revised star rating of product and online service reviews, and the predictions were consolidated with Explainable AI (XAI) assimilation enhancing model interpretation. These consequences provide substantial information regarding the primary indicators of fraudulent reviews.

This publication shows the use of the quantum search algorithm by Grover to search unstructured data with a 5-qubit model in Qiskit. It is quadratically more likely to detect the correct state than in classical methods; the algorithm is more efficient in search. Simulated implementation demonstrates practically perfect amplification of the target state, whereas real IBM quantum hardware reveals the reality of practical issues of noise and gate errors. The results strengthen the possibility of quantum search methods on large unstructured data, and the possibility to reduce errors and scale protocols to better circuit designs.

This research investigates the reliability and availability of a multi-state k-out-of-n system composed of three interconnected subsystems: main power supply units, backup generators, and uninterruptible power supply units. Each subsystem operates under specific failure and repair protocols, including warm and cold standby arrangements. State transitions are modeled using stochastic processes, and the Sumudu transform technique is employed to solve the complex differential equations governing the system's behavior. Fermatean fuzzy sets are incorporated to effectively manage uncertainty. Numerical illustrations evaluate availability and reliability under various scenarios, demonstrating the influence of failure rates, maintenance delays, and repair personnel availability on system performance. The fuzzy approach provides a broader range of reliability and availability estimates compared to traditional Markov methods. The integration of advanced mathematical tools and Fermatean fuzzy logic enhances decision-making, maintenance planning, and cost optimization, enabling the design of robust, efficient, and cost-effective systems in complex engineering environments with prevalent uncertainties.

This paper proposes a robust multimodal emotion detection framework that leverages both visual and textual components of meme content using modality-specific deep learning pipelines and swarm intelligence-based optimization. The proposed architecture is divided into two distinct processing segments: one dedicated to visual content using a Convolutional Neural Network (CNN) with Firefly Algorithm-based hyper-parameter tuning, and the other focused on textual data processed through TF-IDF vectorization and Firefly-driven feature selection. The final emotion label is derived using a rule-based fusion strategy that combines predictions from both modalities. Experimental evaluations conducted on the Facebook Hateful Meme dataset demonstrate the superior performance of the proposed method over existing state-of-the-art techniques. The model achieves improvements of up to 8.3% in F1-score and 6.7% in accuracy compared to Abdullah et al. and Hamza et al., highlighting the significance of optimization in multimodal feature processing and decision fusion. This approach offers a lightweight yet interpretable solution for real-world meme analysis in applications involving hate speech detection, sentiment analysis, and affective computing.