Metaheuristic algorithms, celebrated for their efficacy in solving complex optimization problems, often rely on manually tuned parameters. The performance of these algorithms is highly sensitive to these parameters, and suboptimal settings can lead to premature convergence or inefficient exploration of the search space. This paper introduces a novel framework for dynamically adapting metaheuristic algorithm parameters using reinforcement learning (RL). Specifically, we employ Q-learning to train an agent that learns to adjust parameters of the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) during the optimization process. The state space is defined by the current search progress and solution quality, while the action space consists of discrete parameter adjustments. The reward function is designed to incentivize exploration and exploitation based on the algorithm's performance. We evaluate the proposed framework on a suite of benchmark optimization problems, demonstrating significant improvements in solution quality, convergence speed, and robustness compared to static parameter settings and other adaptive approaches. The results indicate that RL-driven parameter adaptation offers a promising avenue for enhancing the global search capabilities of metaheuristic algorithms.
This paper explores the synergistic integration of deep learning techniques and knowledge graphs for enhancing clinical diagnosis and personalized treatment prediction. We address the limitations of traditional clinical decision support systems by leveraging the power of deep learning to extract intricate patterns from heterogeneous clinical data, while simultaneously utilizing knowledge graphs to represent and reason over complex biomedical relationships. We propose a novel framework that combines Graph Neural Networks (GNNs) with Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture both structured and unstructured data representations. The framework is evaluated on a large-scale clinical dataset, demonstrating significant improvements in diagnostic accuracy and treatment outcome prediction compared to state-of-the-art methods. Furthermore, we investigate the interpretability of the proposed model, providing insights into the key factors influencing diagnostic and treatment decisions. The results highlight the potential of this integrated approach to revolutionize healthcare by providing clinicians with more accurate, personalized, and explainable decision support tools.
The analysis of high-dimensional biomedical datasets presents significant challenges due to the curse of dimensionality, leading to increased computational complexity and reduced classification accuracy. Feature selection, a crucial preprocessing step, aims to identify a subset of relevant features, thereby mitigating these issues. This paper proposes an Adaptive Hybrid Metaheuristic Optimization Framework (AHMOF) for feature selection and classification in high-dimensional biomedical datasets. AHMOF synergistically integrates the strengths of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) with an adaptive control mechanism to dynamically adjust the balance between exploration and exploitation. The framework employs a novel fitness function that considers both classification accuracy and the number of selected features. Experimental results on several benchmark biomedical datasets demonstrate that AHMOF consistently outperforms traditional feature selection methods and standalone metaheuristic algorithms in terms of classification accuracy, feature subset size, and computational efficiency. The adaptive nature of AHMOF allows it to effectively navigate the complex search space, leading to robust and generalizable feature subsets for improved biomedical data analysis.
Accurate time series forecasting is crucial for optimizing complex industrial processes, enabling proactive decision-making and minimizing operational costs. Traditional statistical methods often struggle to capture the intricate non-linear dependencies and long-range dependencies inherent in such processes. This paper proposes a novel hybrid deep learning framework that combines the strengths of Long Short-Term Memory (LSTM) networks and Transformer architectures to enhance time series forecasting accuracy in complex industrial settings. The framework leverages LSTM networks for capturing local temporal patterns and Transformer networks for modeling long-range dependencies and contextual information. Furthermore, we incorporate a feature engineering module to extract relevant features from raw sensor data, improving the model's ability to learn complex relationships. We evaluate the proposed framework on a real-world industrial dataset and demonstrate its superior performance compared to state-of-the-art time series forecasting models. The results highlight the effectiveness of the hybrid approach in capturing both short-term and long-term dependencies, leading to significant improvements in forecasting accuracy and enabling more effective process optimization.
This paper introduces a novel hybrid deep learning architecture designed to enhance sentiment analysis of multimodal social media data. Social media sentiment is often expressed through a combination of textual, visual, and sometimes auditory content, necessitating approaches that can effectively integrate and interpret these diverse modalities. Our architecture leverages contextual embeddings derived from pre-trained language models like BERT and RoBERTa for textual analysis, alongside convolutional neural networks (CNNs) for visual feature extraction. Crucially, we incorporate attention mechanisms to dynamically weight the importance of different textual and visual features, allowing the model to focus on the most salient information for sentiment prediction. Furthermore, we introduce a fusion module that combines the modality-specific representations using a gated mechanism, enabling adaptive control over the contribution of each modality. The proposed architecture is evaluated on a benchmark multimodal sentiment analysis dataset, demonstrating significant improvements in accuracy, F1-score, and area under the ROC curve (AUC) compared to state-of-the-art methods. The results highlight the effectiveness of our hybrid approach in capturing nuanced sentiment expressed through the complex interplay of textual and visual cues in social media. We also provide an ablation study to analyze the contribution of each component of the proposed architecture. The paper concludes with a discussion of limitations and directions for future research, including exploring the integration of audio data and addressing biases in multimodal sentiment datasets.