Gehad Abdullah Amran

Conversational recommender systems (CRS) facilitate natural language interactions for more effective item suggestions. While these systems show promise, they face challenges in effectively utilizing and integrating informative data with conversation history through semantic fusion. In this study we present an innovative framework for extracting social information from conversational datasets by inferring ratings and constructing user-item interaction and user-user relationship graphs. We introduce a social information sensitive semantic fusion (SISSF) method that employs contrastive learning (CL) to bridge the semantic gap between generated social information and conversation history. We evaluated the framework on two public datasets (ReDial and INSPIRED) using both automatic and human evaluation metrics. Our SISSF framework demonstrated significant improvements over baseline models across all metrics. For the ReDial dataset, SISSF achieved superior performance in recommendation tasks (R@1: 0.062, R@50: 0.437) and conversational quality metrics (Distinct-2: 4.223, Distinct-3: 5.595, Distinct-4: 6.155). Human evaluation showed marked improvement in both fluency (1.81) and informativeness (1.63). We observed similar performance gains on the INSPIRED dataset, with notable improvements in recommendation accuracy (R@1: 0.046, R@10: 0.129, R@50: 0.269) and response diversity (Distinct-2: 2.061, Distinct-3: 4.293, Distinct-4: 6.242). The experimental results consistently validate the effectiveness of our approach in both recommendation and conversational tasks. These findings suggest that incorporating social context through CL can significantly improve the personalization and relevance of recommendations in conversational systems.

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel Multi-Objective Adaptive Learning Framework for UAV-Assisted Mobile Edge Computing (MEC) tailored for dynamic IoT environments. The framework integrates Multi-Objective Reinforcement Learning (MORL), Model Predictive Control (MPC), Adaptive Particle Swarm Optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted MEC, including multi-objective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework’s efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1,890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations.

In response to the urgent need for rapid COVID-19 screening, there has been a growing demand for efficient alternatives to the resource-intensive RT-PCR tests. One promising solution is the development of an intelligent, contact-free automated system for quick preliminary COVID-19 detection. This study introduces the COVID-19 Multi-Layer Ensemble framework (C19-MLE), designed to enhance the accuracy of COVID-19 detection. The approach begins with a 2D convolutional neural network (CNN) combined with a variation autoencoder for precise classification of cough sounds. Additionally, a UNet-based encoder-decoder architecture is used for segmenting chest X-ray images. These segmented images are then classified using two models, ResNet-50 and Inception V3, and their results are combined using an ensemble learning technique. This first-layer ensemble achieves an impressive accuracy of 98.5% in classifying chest X-rays. Meanwhile, the proposed 2D CNN model for cough classification achieves an accuracy of 97.79%. The second-layer ensemble, which fuses the results of both chest X-ray and cough classifications using a meta-classifier with a hard prediction and weighted sum-rule technique, achieves a remarkable overall accuracy of 99.89%. The C19-MLE framework demonstrates the powerful synergy between cough audio signals and chest X-ray images, providing a highly accurate method for preliminary and post-screening COVID-19 diagnosis. The high accuracy of this model highlights its potential as a crucial tool for early disease detection and prevention, especially in settings where resources are limited.

The rapid proliferation of Coronavirus Disease 2019 (COVID-19) has resulted in a substantial escalation of suspected and confirmed cases globally, posing a significant threat to public health, economic stability, and international relations This underscores the urgent need for alternative diagnostic strategies that leverage artificial intelligence to enhance pandemic response efficacy. This research endeavors to augment predictive accuracy through the Multi-Objective Advanced Genetic Algorithm (MoAGA), which automatically generates classification rules from datasets. To this end, the Multi-Objective Single Genetic Algorithm (MoSGA) was enhanced by incorporating a distributed population strategy alongside an optimized migration procedure. Furthermore, the proposed method was refined through the integration of two fuzzy control systems, which dynamically adjust crossover and mutation rates, culminating in the development of the Multi-Objective Fuzzy Genetic Algorithm (MoFGA). This innovative approach optimizes both population diversity and fitness. The methodology's efficacy was validated against COVID-19-related datasets, with performance assessed through metrics such as prediction accuracy, maximum fitness, and average fitness. Results indicated that the MoAGA achieved a prediction accuracy of 99.30%, with a maximum fitness of 0.88 and an average fitness of 0.86. This novel methodology demonstrates a superior ability to uncover rule sets with markedly higher predictive precision than existing techniques, enhancing the potential for optimal global solutions.