Industrial Control Systems (ICS) are increasingly vulnerable to cyberattacks, necessitating robust Intrusion Detection Systems (IDS). Traditional IDS approaches often struggle with the complexity and evolving nature of ICS threats. Deep learning (DL) models offer promising solutions, but their performance relies heavily on large, centralized datasets, which may be impractical or infeasible due to data privacy concerns and regulatory constraints. This paper proposes a novel hybrid deep learning approach for enhanced intrusion detection in ICS, leveraging federated learning (FL) to train models collaboratively across multiple ICS environments without sharing sensitive data. We develop a hybrid architecture that combines a Convolutional Neural Network (CNN) for feature extraction from raw network traffic data with a Recurrent Neural Network (RNN) for capturing temporal dependencies. The FL framework enables distributed training on local datasets within each ICS site, followed by secure aggregation of model updates on a central server. Experimental results on a benchmark ICS dataset demonstrate that our hybrid federated learning approach achieves superior detection accuracy and lower false alarm rates compared to traditional centralized DL models and conventional machine learning techniques, while preserving data privacy. The proposed method addresses critical security challenges in ICS environments, enabling proactive threat detection and improved overall system resilience.
The integration of Artificial Intelligence (AI) into healthcare holds immense potential for improving diagnostic accuracy, predicting disease progression, and personalizing treatment plans. This paper presents a novel hybrid deep learning approach that leverages both Electronic Health Records (EHRs) and medical imaging data to enhance predictive accuracy in healthcare applications. The proposed model integrates Convolutional Neural Networks (CNNs) for image analysis with Recurrent Neural Networks (RNNs) for sequential data processing from EHRs. We demonstrate the effectiveness of this hybrid architecture through experiments on a dataset comprising patient records and medical images, showing significant improvements in prediction accuracy compared to single-modality approaches and traditional machine learning models. The findings suggest that the synergistic combination of structured and unstructured data provides a more comprehensive patient representation, leading to more accurate and reliable predictive models for healthcare decision-making. We further explore the model's explainability and potential for clinical integration.
This research paper introduces a novel approach to predictive modeling of complex system dynamics by enhancing the Adaptive Neuro-Fuzzy Inference System (ANFIS) with metaheuristic optimization techniques. ANFIS, renowned for its ability to combine the learning capabilities of neural networks with the interpretability of fuzzy logic, often struggles with optimal parameter selection when applied to highly complex and non-linear systems. This study proposes a hybrid framework that leverages the strengths of both ANFIS and metaheuristic algorithms, specifically Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), to optimize the antecedent and consequent parameters of the fuzzy inference system. The proposed methodology is rigorously evaluated using benchmark datasets representing diverse complex systems, including time series forecasting, system identification, and chaotic system prediction. The results demonstrate that the metaheuristic-optimized ANFIS significantly outperforms traditional ANFIS and other established predictive modeling techniques in terms of accuracy, robustness, and generalization ability. This research contributes to the advancement of intelligent systems by providing a powerful and versatile tool for analyzing and predicting the behavior of complex systems across various domains.
Federated learning (FL) enables collaborative model training across decentralized devices without direct data sharing, proving particularly beneficial for Internet of Things (IoT) applications where data privacy and bandwidth limitations are paramount. However, the heterogeneity of IoT devices and their data distributions poses significant challenges for traditional FL algorithms. This paper proposes an adaptive meta-learning framework for personalized federated learning (AMFL-P) designed to address these challenges in resource-constrained IoT environments. AMFL-P leverages meta-learning to learn a personalized initialization and adaptation strategy for each device, allowing for faster convergence and improved performance even with limited local data. The framework dynamically adjusts the meta-learning process based on device resources and data characteristics. We present a detailed methodology, including a novel adaptive weighting scheme for meta-gradient aggregation. Experimental results on a simulated IoT sensor dataset demonstrate that AMFL-P outperforms traditional FL and existing personalized FL approaches in terms of accuracy, convergence speed, and resource utilization. The findings highlight the potential of adaptive meta-learning to enhance the effectiveness of federated learning in practical IoT deployments.
Medical image segmentation is a crucial task in computer-aided diagnosis, enabling accurate localization and delineation of anatomical structures and pathological regions. However, deep learning-based segmentation methods typically require large amounts of annotated data, which are often scarce and expensive to acquire in the medical domain. Few-shot learning (FSL) offers a promising solution by enabling models to learn from limited labeled examples. This paper proposes an enhanced FSL framework for medical image segmentation that combines meta-learning with attention-guided feature augmentation. Specifically, we employ a Prototypical Network-based meta-learning architecture, which learns to extract task-specific prototypes from support sets. To address the challenge of limited data, we introduce an attention mechanism that focuses on salient image regions and guides feature augmentation, thereby enhancing the diversity and representativeness of the support set features. Experimental results on benchmark medical image segmentation datasets demonstrate that the proposed method significantly outperforms existing FSL approaches, achieving state-of-the-art performance with minimal labeled data. The proposed approach holds substantial promise for improving the efficiency and effectiveness of medical image analysis, particularly in scenarios with limited labeled data.
This paper investigates the application of context-aware federated learning (CAFL) to enhance predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments. The inherent challenges of IIoT, including data heterogeneity, privacy concerns, and resource constraints, limit the effectiveness of traditional centralized machine learning approaches for PdM. CAFL addresses these challenges by enabling collaborative model training across distributed edge devices without directly sharing raw data. We propose a novel CAFL framework that incorporates contextual information, such as operating conditions and environmental factors, to improve the accuracy and robustness of PdM models. The framework is evaluated using a simulated IIoT environment with diverse machine types and operating conditions. Experimental results demonstrate that CAFL significantly outperforms traditional federated learning and centralized learning approaches in terms of prediction accuracy, model generalizability, and data privacy preservation. The paper concludes by discussing the implications of CAFL for future IIoT applications and outlining potential avenues for further research.