Applied AI letters最新文献_第4页

Unveiling Marketing Competencies for MBA Graduates Through Text Mining and Machine Learning Applications 通过文本挖掘和机器学习应用揭示MBA毕业生的营销能力

Applied AI letters Pub Date : 2026-03-07 DOI: 10.1002/ail2.70020

Latha Arumugham, Mohanamani Palanisamy, Maria Tresita Paul Vincent, Md Billal Hossain

{"title":"Unveiling Marketing Competencies for MBA Graduates Through Text Mining and Machine Learning Applications","authors":"Latha Arumugham, Mohanamani Palanisamy, Maria Tresita Paul Vincent, Md Billal Hossain","doi":"10.1002/ail2.70020","DOIUrl":"https://doi.org/10.1002/ail2.70020","url":null,"abstract":"In today's dynamic business environment, marketing professionals are expected to demonstrate a balanced mix of conceptual knowledge, technical skills, and behavioral competencies while addressing their customers. This study analyses 2000 job postings from job web portal, collected between April and June 2023, to identify the competencies most demanded from master's in business administration (MBA) graduates specializing in marketing. Using a text mining approach, including systematic preprocessing, bigram extraction, and term frequency-inverse document frequency (TF-IDF) based term weighting, the extracted competencies were categorized into knowledge, skill, and behavioral based competencies. Findings of the study reveal strong emphasis by employers on marketing research, marketing management, and sales management as core knowledge-based competencies required. While selling ability, client relationship management, and digital marketing have evolved as key skill-based competencies. Time management, teamwork, and leadership evolved as critical behavioral attributes needed for an MBA graduate specializing in the marketing domain. These insights highlight the need for management curricula to integrate conceptual learning with practical skill development and structured behavioral guidance. The results also provide accreditation bodies with evidence-based competency trends for strengthening outcome-based frameworks and guiding industry partners in aligning recruitment, training, and academic collaboration efforts. By offering data driven competencies grounded in real job market demand, the study supports the development of more industry ready MBA marketing graduates.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Tuberculosis Detection in Chest Radiographs: A Hybrid Deep Learning Framework for Clinical Decision Support 胸片中的自动结核检测：用于临床决策支持的混合深度学习框架

Applied AI letters Pub Date : 2026-03-03 DOI: 10.1002/ail2.70022

Md. Tahmid Hossain, Hrittik Mural, Monowar Wadud Hridoy

{"title":"Automated Tuberculosis Detection in Chest Radiographs: A Hybrid Deep Learning Framework for Clinical Decision Support","authors":"Md. Tahmid Hossain, Hrittik Mural, Monowar Wadud Hridoy","doi":"10.1002/ail2.70022","DOIUrl":"https://doi.org/10.1002/ail2.70022","url":null,"abstract":"Tuberculosis (TB) remains a leading cause of death worldwide, particularly in resource-limited settings, where early detection is critical for controlling its spread. This study proposes a hybrid deep learning framework for the automated detection of TB from chest X-ray (CXR) images, integrating the strengths of three widely used Convolutional Neural Networks (CNNs): VGG16, ResNet50, and DenseNet121. The primary objective is to enhance the accuracy and reliability of TB diagnosis by combining the feature extraction capabilities of these models through a feature-level fusion strategy. The model was trained and evaluated on three publicly available datasets: the Montgomery County Chest X-ray dataset (138 CXR images), the Shenzhen Chest X-ray dataset (662 CXR images), and a third dataset containing 700 TB-positive and 3500 normal CXR images. Key performance metrics including accuracy, precision, recall, and F1-score were used to assess model performance. The hybrid model achieved a significant accuracy of 97.40%, with a precision of 0.96, recall of 0.96, and F1-score of 0.96, outperforming individual models such as VGG16 (93.00%), ResNet50 (95.90%), and DenseNet121 (96.30%). In comparison to existing methods, such as traditional manual interpretation and standalone deep learning models, the proposed hybrid model provides a more accurate, scalable, and robust solution for TB detection. The integration of complementary CNN architectures results in superior generalization, particularly in the face of dataset imbalances and varying image quality. This work demonstrates the potential of hybrid deep learning models to improve clinical decision support systems, offering a promising tool for rapid TB diagnosis, especially in resource-constrained healthcare settings.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Tuberculosis Detection in Chest Radiographs: A Hybrid Deep Learning Framework for Clinical Decision Support 胸片中的自动结核检测：用于临床决策支持的混合深度学习框架

Applied AI letters Pub Date : 2026-03-03 DOI: 10.1002/ail2.70022

Md. Tahmid Hossain, Hrittik Mural, Monowar Wadud Hridoy

{"title":"Automated Tuberculosis Detection in Chest Radiographs: A Hybrid Deep Learning Framework for Clinical Decision Support","authors":"Md. Tahmid Hossain, Hrittik Mural, Monowar Wadud Hridoy","doi":"10.1002/ail2.70022","DOIUrl":"https://doi.org/10.1002/ail2.70022","url":null,"abstract":"Tuberculosis (TB) remains a leading cause of death worldwide, particularly in resource-limited settings, where early detection is critical for controlling its spread. This study proposes a hybrid deep learning framework for the automated detection of TB from chest X-ray (CXR) images, integrating the strengths of three widely used Convolutional Neural Networks (CNNs): VGG16, ResNet50, and DenseNet121. The primary objective is to enhance the accuracy and reliability of TB diagnosis by combining the feature extraction capabilities of these models through a feature-level fusion strategy. The model was trained and evaluated on three publicly available datasets: the Montgomery County Chest X-ray dataset (138 CXR images), the Shenzhen Chest X-ray dataset (662 CXR images), and a third dataset containing 700 TB-positive and 3500 normal CXR images. Key performance metrics including accuracy, precision, recall, and F1-score were used to assess model performance. The hybrid model achieved a significant accuracy of 97.40%, with a precision of 0.96, recall of 0.96, and F1-score of 0.96, outperforming individual models such as VGG16 (93.00%), ResNet50 (95.90%), and DenseNet121 (96.30%). In comparison to existing methods, such as traditional manual interpretation and standalone deep learning models, the proposed hybrid model provides a more accurate, scalable, and robust solution for TB detection. The integration of complementary CNN architectures results in superior generalization, particularly in the face of dataset imbalances and varying image quality. This work demonstrates the potential of hybrid deep learning models to improve clinical decision support systems, offering a promising tool for rapid TB diagnosis, especially in resource-constrained healthcare settings.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-Model Feature Fusion for Robust Low-Resource Speech Recognition 基于模型间特征融合的鲁棒低资源语音识别

Applied AI letters Pub Date : 2026-03-03 DOI: 10.1002/ail2.70023

Ussen Kimanuka, Ciira wa Maina, Osman Büyük

{"title":"Inter-Model Feature Fusion for Robust Low-Resource Speech Recognition","authors":"Ussen Kimanuka, Ciira wa Maina, Osman Büyük","doi":"10.1002/ail2.70023","DOIUrl":"https://doi.org/10.1002/ail2.70023","url":null,"abstract":"Substantial improvements in automatic speech recognition performance have been realized through supervised fine-tuning after self-supervised pre-training of a speech foundation model. The large size of foundation models, along with their varied losses and objective functions, makes it impractical to obtain optimum results with these models, and fine-tuning each model independently for several downstream tasks is prohibitively expensive. The proposed methodology consists of three phases: feature extraction, feature fusion, and prediction. During feature extraction, several pre-trained models, each with varying losses and objective functions, are used to derive representations. Then, a designed co-attentional fusion mechanism is applied during feature fusion, enabling the network to adaptively weight different fusion operations to acquire common representations across models. Finally, a connectionist temporal classification (CTC) layer is used as a framework to generate transcription predictions. Moreover, the proposed self-supervised feature-fusion transformer block (SSF-FT), incorporating inter-model techniques, effectively captures both shared and distinctive information across all fused representations. We conducted an interpretability study in high-resource (English) and low-resource (Congolese) scenarios. In both settings, we observe that features performing well with shallow ensemble methods also perform well with attention-weighted soft mixing. Experimental results demonstrate that our approach offers complementary strengths to existing ensemble techniques, with particular improvements in acoustically challenging and low-resource scenarios.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-Model Feature Fusion for Robust Low-Resource Speech Recognition 基于模型间特征融合的鲁棒低资源语音识别

Applied AI letters Pub Date : 2026-03-03 DOI: 10.1002/ail2.70023

Ussen Kimanuka, Ciira wa Maina, Osman Büyük

{"title":"Inter-Model Feature Fusion for Robust Low-Resource Speech Recognition","authors":"Ussen Kimanuka, Ciira wa Maina, Osman Büyük","doi":"10.1002/ail2.70023","DOIUrl":"https://doi.org/10.1002/ail2.70023","url":null,"abstract":"Substantial improvements in automatic speech recognition performance have been realized through supervised fine-tuning after self-supervised pre-training of a speech foundation model. The large size of foundation models, along with their varied losses and objective functions, makes it impractical to obtain optimum results with these models, and fine-tuning each model independently for several downstream tasks is prohibitively expensive. The proposed methodology consists of three phases: feature extraction, feature fusion, and prediction. During feature extraction, several pre-trained models, each with varying losses and objective functions, are used to derive representations. Then, a designed co-attentional fusion mechanism is applied during feature fusion, enabling the network to adaptively weight different fusion operations to acquire common representations across models. Finally, a connectionist temporal classification (CTC) layer is used as a framework to generate transcription predictions. Moreover, the proposed self-supervised feature-fusion transformer block (SSF-FT), incorporating inter-model techniques, effectively captures both shared and distinctive information across all fused representations. We conducted an interpretability study in high-resource (English) and low-resource (Congolese) scenarios. In both settings, we observe that features performing well with shallow ensemble methods also perform well with attention-weighted soft mixing. Experimental results demonstrate that our approach offers complementary strengths to existing ensemble techniques, with particular improvements in acoustically challenging and low-resource scenarios.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Stable Edge-Aware GAN Approach for Data Augmentation and Privacy-Preserving High-Fidelity CT Synthesis 一种稳定的边缘感知GAN方法用于数据增强和保密性高保真CT合成

Applied AI letters Pub Date : 2026-02-08 DOI: 10.1002/ail2.70019

Raja Vavekanand, Muhammad Turab, Teerath Kumar

{"title":"A Stable Edge-Aware GAN Approach for Data Augmentation and Privacy-Preserving High-Fidelity CT Synthesis","authors":"Raja Vavekanand, Muhammad Turab, Teerath Kumar","doi":"10.1002/ail2.70019","DOIUrl":"https://doi.org/10.1002/ail2.70019","url":null,"abstract":"Generative adversarial networks (GANs) have shown remarkable potential in medical image synthesis but face persistent challenges in achieving diagnostic-grade quality, particularly in preserving anatomical edges and avoiding mode collapse. To address these limitations, we present a stable edge-aware GAN architecture featuring two key innovations: dynamically learnable bilateral kernels that adaptively enhance structural gradients during training, and a layered generator with interpolated skip connections to maintain spatial coherence. Our methodology leverages adversarial training with Wasserstein regularization on the CT Kidney Dataset (12,446 images), optimizing for both global fidelity and local precision. Comprehensive experiments demonstrate the model's superiority through quantitative metrics—achieving a 43% improvement in Fréchet Inception Distance (FID = 87 vs. DCGAN's 149, p < 0.01), 16% higher edge sharpness (Sobel gradient magnitude 45.2 ± 3.1 vs. 38.9 ± 4.2), and 0.82 ± 0.05 SSIM scores. Clinical validation by board-certified radiologists confirmed 89% diagnostic plausibility for synthetic images, with particular praise for tumor boundary delineation. The architecture also shows exceptional training stability, reducing loss fluctuations by 34% compared to conventional GANs while efficiently scaling to 128 × 128 resolution. These results establish a new benchmark for privacy-preserving medical data augmentation, offering immediate value for scenarios with limited annotated datasets. Future directions include extension to 3D volumetric synthesis and integration with diffusion models for multi-modal applications, potentially revolutionizing how healthcare institutions generate and share synthetic patient data without compromising privacy.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Framework for Stock Price Forecasting Using Metaheuristic Feature Selection Approaches and Transformer Models Enhanced by Temporal Embedding and Attention Pruning 基于元启发式特征选择方法和时间嵌入和注意剪枝增强的变压器模型的混合股价预测框架

Applied AI letters Pub Date : 2026-01-08 DOI: 10.1002/ail2.70018

Amirhossein Malakouti Semnani, Sohrab Kordrostami, Amirhossein Refahi Sheikhani, Mohammad Hossein Moattar

{"title":"A Hybrid Framework for Stock Price Forecasting Using Metaheuristic Feature Selection Approaches and Transformer Models Enhanced by Temporal Embedding and Attention Pruning","authors":"Amirhossein Malakouti Semnani, Sohrab Kordrostami, Amirhossein Refahi Sheikhani, Mohammad Hossein Moattar","doi":"10.1002/ail2.70018","DOIUrl":"https://doi.org/10.1002/ail2.70018","url":null,"abstract":"Accurately predicting stock prices remains a major challenge in financial analytics due to the complexity and noise inherent in market data. Feature selection plays a critical role in improving both computational efficiency and predictive performance. In this study, we introduce a novel hybrid framework that integrates metaheuristic feature-selection algorithms with an enhanced transformer-based prediction model fine-tuned using temporal embedding and adaptive attention pruning. We evaluate and compare the effectiveness of three nature-inspired metaheuristic algorithms: bat algorithm (BAT), gray wolf optimization (GWO), and beluga whale optimization (BWO) for selecting the most informative features from a time-series stock dataset. After feature selection, the optimal subsets are fed into our modified transformer equipped with temporal embeddings and adaptive attention pruning. Extensive experiments conducted on the Bharat Heavy Electricals Limited (BHEL) dataset show that the proposed hybrid framework outperforms traditional methods in terms of predictive accuracy. Among the evaluated approaches, the combination of BWO and the fine-tuned transformer achieves the best performance, yielding a Test RMSE of 0.0030 and a Test MAPE of 0.0108, demonstrating the superiority of BWO in identifying relevant features. This work provides a comprehensive comparative analysis of hybrid metaheuristic–deep learning models for stock price prediction and offers a foundation for integrating more explainable and scalable AI techniques into financial forecasting.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145986811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking IIoT Potential: A Systematic Review of AI Applications, Adoption Drivers, and Implementation Barriers 解锁工业物联网潜力：对人工智能应用、采用驱动因素和实施障碍的系统回顾

Applied AI letters Pub Date : 2026-01-08 DOI: 10.1002/ail2.70017

Tinashe Magara, Mampilo Phahlane

{"title":"Unlocking IIoT Potential: A Systematic Review of AI Applications, Adoption Drivers, and Implementation Barriers","authors":"Tinashe Magara, Mampilo Phahlane","doi":"10.1002/ail2.70017","DOIUrl":"https://doi.org/10.1002/ail2.70017","url":null,"abstract":"Artificial Intelligence (AI) is playing an increasingly vital role in the Industrial Internet of Things (IIoT), enabling predictive analytics, real-time monitoring, and autonomous operations across industries such as manufacturing, logistics, and energy. However, widespread adoption is hindered by technological, organizational, and infrastructural challenges. This paper examines the adoption, application, and challenges of AI–IIoT environments, focusing on implementation domains, adoption drivers, enabling technologies, and key barriers. We conducted a Systematic Literature Review (SLR using PRISMA). Peer-reviewed English-language journal articles published between 2018 and 2025 were sourced from ScienceDirect, Web of Science (WoS), Scopus, IEEE Xplore, Springer, Google Scholar, Elsevier, and Taylor & Francis. After applying inclusion criteria and screening procedures, 46 relevant journal articles were included for analysis. Key AI applications identified include predictive maintenance, anomaly detection, real-time monitoring, autonomous process control, and smart supply chains. Adoption is facilitated by external enablers 5G infrastructure, regulatory support, and internal factors, organizational readiness, and workforce skills. Challenges include data quality issues, cybersecurity risks, legacy system integration, and limited model scalability. Technologies such as edge computing, cloud platforms, and federated learning are instrumental in mitigating these challenges. While adoption is growing, significant barriers remain. AI has the potential to drive operational efficiency and innovation in IIoT, provided these constraints are addressed. This paper offers a comprehensive taxonomy of AI applications and proposes a framework of adoption factors, offering valuable insights for researchers, practitioners, and policymakers involved in AI-driven industrial transformation.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145963919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent Reinforcement Learning for Cyber Defence Transferability and Scalability 基于多智能体强化学习的网络防御可转移性和可扩展性

Applied AI letters Pub Date : 2025-12-22 DOI: 10.1002/ail2.70015

Andrew Thomas, Matthew Yates, Oliver Osborne

{"title":"Multi-Agent Reinforcement Learning for Cyber Defence Transferability and Scalability","authors":"Andrew Thomas, Matthew Yates, Oliver Osborne","doi":"10.1002/ail2.70015","DOIUrl":"https://doi.org/10.1002/ail2.70015","url":null,"abstract":"Reinforcement learning (RL) has shown to be effective for simple automated cyber defence (ACD) type tasks. However, there are limitations to these approaches that prevent them from being deployed onto real-world hardware. Trained RL policies will often have limited transferability across even small changes to the environment setup. Instability during training can prevent optimal learning, a problem that only increases as the environment scales and grows in complexity. This work looks at addressing these limitations with a zero-shot transfer approach based on multi-agent RL. This is achieved by partitioning the task into smaller network machine subtasks, where agents learn the solution to the local problem. These local agents are independent of the network scale and can therefore be transferred to larger networks by mapping the agents to machines in the new network. Initial experiments show that this transfer method is effective for direct application to a number of ACD tasks. It is also shown that its performance is robust to changes in network activity, attack scenario and reduces the effects of network scale on performance.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145846061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meta Reinforcement Learning for Automated Cyber Defence 用于自动网络防御的元强化学习

Applied AI letters Pub Date : 2025-12-09 DOI: 10.1002/ail2.70009

Andrew Thomas, Nick Tillyer

{"title":"Meta Reinforcement Learning for Automated Cyber Defence","authors":"Andrew Thomas, Nick Tillyer","doi":"10.1002/ail2.70009","DOIUrl":"https://doi.org/10.1002/ail2.70009","url":null,"abstract":"Reinforcement learning (RL) solutions have shown considerable promise for automating the defense of networks to cyber attacks. However, a limitation to their real world deployment is the sample efficiency and generalizability of RL agents. This means that even small changes to attack types require a new agent to be trained from scratch. Meta-learning for RL aims to improve the sample efficiency of training agents by encoding pre-training information that assists fast adaptation. This work focuses on two key meta-learning approaches, MAML and ML3, representing differing approaches to encoding meta learning knowledge. Both approaches are limited to sets of environments that use the same action and observation space. To overcome this, we also present an extension to ML3, Gen ML3, that removes this requirement by training the learned loss on the reward information only. Experiments have been conducted on a distribution of network setups based on the PrimAITE environment. All approaches demonstrated improvements in sample efficiency against a PPO baseline for a range of automated cyber defense (ACD) tasks. We also show effective meta-learning across network topologies with Gen ML3.","PeriodicalId":72253,"journal":{"name":"Applied AI letters","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ail2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0