Accurate and reliable traffic prediction is crucial for intelligent transportation systems (ITS), enabling proactive traffic management, route optimization, and reduced congestion. This paper presents a novel hybrid deep learning architecture that leverages the strengths of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs) enhanced with attention mechanisms for improved spatio-temporal traffic prediction. The CNNs extract spatial features from traffic data, the RNNs model the temporal dependencies, and the GNNs capture the intricate relationships within the road network. Attention mechanisms are integrated to dynamically weigh the importance of different spatial and temporal features. The proposed model is evaluated on a real-world traffic dataset, demonstrating superior performance compared to state-of-the-art methods in terms of prediction accuracy, particularly during peak hours and under varying traffic conditions. The results highlight the effectiveness of the hybrid architecture and attention mechanisms in capturing complex spatio-temporal dependencies inherent in traffic flow, contributing to more efficient and responsive ITS.
The proliferation of Internet of Things (IoT) devices has created a vast attack surface, making IoT networks increasingly vulnerable to various cyber threats. Traditional intrusion detection systems (IDS) often struggle to effectively identify complex and evolving attack patterns in these dynamic environments. This paper proposes a novel hybrid attention-guided deep learning framework for enhanced intrusion detection in IoT networks. The framework integrates convolutional neural networks (CNNs) for feature extraction, recurrent neural networks (RNNs) with attention mechanisms for temporal dependency modeling, and a deep neural network (DNN) for classification. The attention mechanism allows the model to focus on the most relevant features during the detection process, improving accuracy and reducing false positives. The performance of the proposed framework is evaluated using the NSL-KDD dataset, demonstrating its superiority over existing state-of-the-art IDS approaches in terms of detection accuracy, precision, recall, and F1-score. The results highlight the effectiveness of the hybrid attention-guided deep learning model in securing IoT networks against sophisticated cyberattacks.
Sentiment analysis, the computational task of identifying and categorizing opinions expressed in text, has seen significant advancements with deep learning. However, its effectiveness is often hampered by the reliance on textual data alone, neglecting the rich information conveyed through other modalities like images and videos prevalent in social media. Moreover, existing approaches often lack the capacity to effectively capture the contextual nuances inherent in multimodal data. This paper introduces a novel Context-Aware Attentive Deep Learning (CAADL) framework for enhanced sentiment analysis in multimodal social media data. CAADL leverages deep learning models with attention mechanisms to extract salient features from both textual and visual modalities. Furthermore, it incorporates contextual information by employing a hierarchical attention network that models inter-modal and intra-modal relationships. The framework is trained and evaluated on a large-scale multimodal sentiment analysis dataset. Experimental results demonstrate that CAADL significantly outperforms state-of-the-art baselines in terms of accuracy, F1-score, and precision, highlighting the importance of context awareness and attention mechanisms in multimodal sentiment analysis. The proposed framework provides a robust and effective solution for understanding and interpreting sentiments expressed in the complex and dynamic landscape of social media.
Dynamic resource allocation in cloud computing environments is a complex optimization problem, characterized by high dimensionality, non-linearity, and stochastic workload patterns. Traditional methods often struggle to adapt effectively to these challenges, leading to suboptimal resource utilization and performance degradation. This paper proposes a novel approach that synergistically integrates Graph Neural Networks (GNNs) and Reinforcement Learning (RL) to address these limitations. We leverage GNNs to learn rich representations of the cloud infrastructure topology and resource dependencies, enabling a more informed and context-aware decision-making process. These representations are then fed into an RL agent, which learns an optimal policy for dynamic resource allocation through interaction with the cloud environment. We evaluate our approach in a simulated cloud environment, demonstrating significant improvements in resource utilization, task completion time, and overall system performance compared to existing state-of-the-art methods. Our findings highlight the potential of GNN-RL integration for enhancing dynamic resource allocation and improving the efficiency and scalability of cloud computing infrastructure.
The proliferation of Internet of Things (IoT) devices has created a vast and vulnerable attack surface, making robust intrusion detection systems (IDS) paramount. Traditional centralized IDS solutions face scalability and privacy challenges in the distributed IoT environment. This paper proposes a novel hybrid deep learning framework that integrates federated learning and attention mechanisms for enhanced intrusion detection in IoT networks. The framework leverages the decentralized nature of federated learning to train a global model collaboratively across IoT devices without sharing sensitive data. Attention mechanisms are incorporated within the deep learning architecture to focus on the most relevant features for accurate anomaly detection. We implement and evaluate the proposed framework using a benchmark IoT intrusion detection dataset, demonstrating significant improvements in detection accuracy, reduced communication overhead, and enhanced privacy compared to existing state-of-the-art approaches. The results showcase the potential of this hybrid approach for building resilient and privacy-preserving security solutions for the rapidly expanding IoT landscape.
Cardiac arrhythmias pose a significant threat to global health, necessitating accurate and timely diagnosis for effective treatment. This research investigates the application of hybrid feature selection techniques combined with ensemble learning methods to improve the predictive accuracy of cardiac arrhythmia classification. We propose a novel approach that integrates filter-based (Information Gain) and wrapper-based (Genetic Algorithm) feature selection to identify the most relevant electrocardiogram (ECG) features. These selected features are then utilized to train various ensemble models, including Random Forest, Gradient Boosting Machines (GBM), and XGBoost. The performance of these models is evaluated using a comprehensive dataset of ECG recordings, and the results demonstrate a significant improvement in classification accuracy, precision, recall, and F1-score compared to traditional machine learning approaches. The proposed methodology offers a robust and efficient solution for cardiac arrhythmia prediction, potentially aiding clinicians in early diagnosis and personalized treatment planning.