National College of Business Administration and Economics

In rapidly growing cities, the move toward Autonomous Electric Vehicles (AEVs) is challenging the current Energy Management Systems (EMS). The goal in smart cities is to reduce emissions and improve efficiency by optimizing vehicular energy; however, it remains challenging to address real-time decisions, complex AI, and extensive computing requirements for this task. Although AI and optimization are regularly used, they cannot be trusted in safety-related situations due to issues with complexity, scalability, and lack of clarity in their actions. To achieve transparent, smart energy systems in future transportation, it is crucial to address these issues. This research proposes an Explainable Quantum AI (XQAI) model that combines the computational capabilities of Quantum Machine Learning (QML) with the interpretability of Explainable AI (XAI). With QML, dealing with complex vehicular data is more efficient, and the model uses Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to ensure transparency and interpretability in the model’s decision-making process. This proposed model is developed using data from real cities, encompassing a wide range of features, to predict vehicular energy consumption across various trip types accurately and to provide insight into the reasons behind these predictions. According to simulation results, the proposed XQAI model is effective, as the Hybrid Classical–Quantum Regressor shows superior prediction performance with an R2 score of 0.8439. Furthermore, using LIME revealed a confidence score of 0.95, further establishing its credibility, interpretability, and reliability. The results demonstrate that the model meets the needs for scalable, understandable, and regulated vehicular energy forecasting in smart cities.

Objective:To systematically evaluate transfer learning (TL) models for multiclass ocular disease diagnosis and assess their reliability using explainable artificial intelligence (AI). Methods:Eight pretrained convolutional neural network (CNN) models were evaluated on a public dataset covering cataract, diabetic retinopathy, glaucoma, and normal classes under a unified protocol. Performance was measured using accuracy, precision, recall, and F1-score. Grad-CAM, LIME, and SHAP were used for interpretability, and the Friedman test assessed performance consistency. Results:Several models achieved near-perfect performance for diabetic retinopathy. DenseNet121 and XceptionNet performed best for cataract detection, while glaucoma showed consistently weaker results, indicating the need for segmentation-based approaches. Despite similar accuracy, explainability revealed substantial differences in model attention. EfficientNetB3 produced the most clinically meaningful visual explanations. Conclusions:Accuracy alone is insufficient for trustworthy medical AI. Explainable AI is essential for model selection. EfficientNetB3 offers the best balance between performance and interpretability, and glaucoma diagnosis requires more advanced, segmentation-aware pipelines.

This study redefines human-robot collaboration (HRC) by introducing theomorphic robots—robotic systems embedded with cultural and symbolic design elements—and enhancing them through AI-driven and cloud-enabled solutions that prioritize ethical and privacy-preserving principles. It explores how symbolic robot design, rooted in emotional and cultural significance, combined with adaptive intelligence supported by cloud computing, can improve trust, emotional engagement, and collaboration efficiency across diverse sectors. The feasibility and benefits of theomorphic and anthropomorphic robots are examined in sensitive application domains, including healthcare, education, and service industries, where emotionally intelligent HRCs enhance user experience and operational performance. A comprehensive literature review, supported by multicultural case studies, demonstrates how symbolic design reduces the uncanny valley effect, fosters emotional resonance, and increases the acceptance of robots in socially sensitive and ethically complex environments. AI technologies enable behavior prediction, real-time adaptation, and affective interaction, which are further amplified through scalable, cloud-based training, inference, and secure data offloading architectures. This study also addresses critical ethical challenges such as cultural appropriation, emotional manipulation, and privacy risks, particularly in cloud-enabled HRC deployments. Encryption, multi-objective optimization, and privacy-aware AI techniques are discussed as mechanisms for mitigating these risks. Aligned with ethical AI governance frameworks, the findings offer a conceptual and technical foundation for the next generation of culturally aware, explainable, emotionally intelligent, and privacy-resilient collaborative robots.

The rapid proliferation of Internet of Things (IoT) devices has exponentially expanded the cyber-attack surface, rendering traditional centralized Intrusion Detection Systems (IDS) inefficient due to critical data privacy concerns and bandwidth constraints. While recent advancements like Self-Attention Deep Neural Networks (SA-DNN) have achieved high detection accuracy, their reliance on centralized data aggregation exposes sensitive user information and lacks the adaptability required for dynamic threat landscapes. To address these gaps, this paper proposes Fed-Trans-RL, a novel privacy-preserving framework that integrates Federated Learning (FL), Transformer Encoders, and Deep Reinforcement Learning (DRL). Crucially, to ensure robustness against both known and zero-day threats, we design a Universal IDS Pipeline featuring two distinct modes: a Supervised mode utilizing lightweight Transformer Encoders to classify known attack patterns, and an Unsupervised mode employing Deep Autoencoders and Isolation Forests to identify anomalies in wild, unlabeled traffic. We decentralize the detection process using Learnable Feature Gating (LFG) directly on IoT edge devices and utilize a DRL agent with Proximal Policy Optimization (PPO) at the aggregation server to dynamically optimize client selection based on real-time network states. Experimental validation on four heterogeneous datasets BOT-IoT, N-BAIOT, IoT-23, and TAN-IOT demonstrates that Fed-Trans-RL achieves detection accuracy comparable to centralized baselines (up to 99.5% on N-BAIOT) while reducing communication rounds by approximately 31%. These results confirm that the proposed framework successfully bridges the gap between high-precision security, operational efficiency, and strict privacy preservation for next-generation IoT networks.

Background:People who have Alzheimer's disease (AD) experience a progressive decline in their neurological function, which leads to mental deterioration and diminished memory abilities, and altered behaviors that affect both patients and their care providers severely. Diagnosis of the disease at an early stage and with precision helps ensure appropriate intervention strategies. Objectives:Modern artificial intelligence (AI) technology is promising in medical use for imaging and diagnostic work, specifically involving AD detection and classification. This study aims to develop and evaluate an explainable transformer-based framework that leverages Bidirectional-Encoder representations from Image Transformers (BEiT) to automatically classify AD stages from magnetic resonance imaging (MRI) brain scans. Method:The proposed framework employs BEiT as a feature extractor on a dataset of 8511 MRI brain images categorized into three diagnostic groups (mild, moderate, and no impairment). Class imbalance is addressed through a Wasserstein generative adversarial network with gradient penalty-based oversampling strategy that generates synthetic MRI images for minority classes, and these images are combined with the original scans to form a balanced training set. Results:The experiments showed outstanding accuracy levels reaching 96%, while the F1-scores indicated 0.94, 1.00, and 0.95 for mild, moderate, and no AD group classifications. Performance evaluation metrics from the study demonstrate strong outcomes with a mean absolute error reaching 0.0727 and Cohen's kappa equaling 0.9451, while Matthews correlation coefficient reached 0.9455 and Hamming loss remained at 0.0365.