A nomadic multi-agent based privacy metrics for e-health care: a deep learning approach

Dhasarathan, Chandramohan; Shanmugam, M.; Kumar, Manish; Tripathi, Diwakar; Khapre, Shailesh; Shankar, Achyut

doi:10.1007/s11042-023-15363-4

Cited by 21 publications

(5 citation statements)

References 49 publications

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“…With the gradual rise of deep learning technology, deep learning has been widely applied in various fields [10]. Credit loan default prediction tasks are one typical application.…”

Section: B Credit Default Prediction Methods Based On Deep Learningmentioning

confidence: 99%

Incorporating Feature Interactions and Contrastive Learning for Credit Prediction

Zhang,

Yu,

Zhou

et al. 2023

IEEE Access

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The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and weak supervisory signals. Addressing these limitations, we present a novel approach for credit default prediction that integrates feature interactions and contrastive learning. Specifically, we introduce second-order interactions atop standard linear models to achieve low-order feature interplay. Concurrently, the integration of deep neural networks and attention mechanisms facilitates the learning of concealed high-order features, thus enhancing the model's non-linear modeling capabilities and illuminating latent feature associations. Further, to ameliorate the issues of noise and diminished supervisory signals, we embed slight noise in feature embeddings for data augmentation and construct contrastive views, ultimately refining feature quality. To attest to the effectiveness of our approach, we conducted experiments on two real-world datasets, benchmarking against eight predictive methods including LR, XGBoost, and FiBiNET. The results unequivocally demonstrate the superior performance of our method across various metrics, underscoring its promise and excellence in the realm of credit risk assessment.

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“…With the gradual rise of deep learning technology, deep learning has been widely applied in various fields [10]. Credit loan default prediction tasks are one typical application.…”

Section: B Credit Default Prediction Methods Based On Deep Learningmentioning

confidence: 99%

Incorporating Feature Interactions and Contrastive Learning for Credit Prediction

Zhang,

Yu,

Zhou

et al. 2023

IEEE Access

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“…Furthermore, With the advancement of big data technology [ 23 ], some deep learning methods have been applied to multi-stage attack detection. Deep learning approaches can overcome some limitations of traditional shallow machine learning, capturing deep-seated features within the data [ 24 ], and enhancing detection performance [ 10 ]. Vinayakumar et al [ 25 ] introduced a deep learning framework for detecting zombie networks, which operates at the application layer of DNS services.…”

Section: Section 2: Related Workmentioning

confidence: 99%

“…Other researchers have used machine learning and artificial intelligence methods to mine the knowledge embedded in alert information and utilize this knowledge to detect, infer, and predict MSA. Algorithms employed include Hidden Markov Model (HMM), Support Vector Machine, Decision Trees, Bayesian Networks, and Deep Neural Networks, among others [5][6][7][8][9][10]. However, both of these approaches have their limitations.…”

Section: Section 1: Introductionmentioning

confidence: 99%

Anomaly based multi-stage attack detection method

Ma,

Hou,

Jin

et al. 2024

PLoS ONE

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Multi-stage attacks are one of the most critical security threats in the current cyberspace. To accurately identify multi-stage attacks, this paper proposes an anomaly-based multi-stage attack detection method. It constructs a Multi-Stage Profile (MSP) by modeling the stable system’s normal state to detect attack behaviors. Initially, the method employs Doc2Vec to vectorize alert messages generated by the intrusion detection systems (IDS), extracting profound inter-message correlations. Subsequently, Hidden Markov Models (HMM) are employed to model the normal system state, constructing an MSP, with relevant HMM parameters dynamically acquired via clustering algorithms. Finally, the detection of attacks is achieved by determining the anomaly threshold through the generation probability (GP). To evaluate the performance of the proposed method, experiments were conducted using three public datasets and compared with three advanced multi-stage attack detection methods. The experimental results demonstrate that our method achieves an accuracy of over 99% and precision of 100% in multi-stage attack detection. This confirms the effectiveness of our method in adapting to different attack scenarios and ultimately completing attack detection.

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“…Each agent's data is protected by privacy metrics in the system. Deep-learning is added to the multi-agent system to enhance diagnosis and therapy [16].…”

Section: Introductionmentioning

confidence: 99%

Classification of Paediatric Pneumonia Using Modified DenseNet-121 Deep-Learning Model

Arulananth,

Prakash,

Ayyasamy

et al. 2024

IEEE Access

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There is a substantial worldwide effect, both in terms of disease and death, that is caused by pediatric pneumonia, which is a disorder that affects children under the age of five. Even while Streptococcus pneumoniae is the most prevalent agent responsible for this sickness, it may also be brought on by other bacteria, viruses, or fungi. An efficient approach utilizing deep-learning methods to forecast pediatric pneumonia reliably using chest X-ray images has been developed. The current study presents an updated version of the DenseNet-121 deep-learning model developed for identifying scans of pediatric pneumonia. The batch normalization, maximum pooling, and dropout layers introduced into the standard model were done so to improve its accuracy. The activations of the preceding layers are scaled and normalized using batch normalization, leading to a mean value of zero and a variance of one. This helps to decrease internal variability during training, which in turn speeds up the training process, promotes model stability, and improves the model's overall capacity to generalize. Max pooling is a beneficial technique for cutting down on the number of model parameters, making the model more computationally effective. Meanwhile, dropout is a preventative measure against overfitting by decreasing the co-dependence of neurons. As a result, the network acquires more durable and adaptive features. Classifying instances of pediatric pneumonia with the help of the proposed model resulted in an exceptional accuracy rate of 97.03%.

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A nomadic multi-agent based privacy metrics for e-health care: a deep learning approach

Cited by 21 publications

References 49 publications

Incorporating Feature Interactions and Contrastive Learning for Credit Prediction

Incorporating Feature Interactions and Contrastive Learning for Credit Prediction

Anomaly based multi-stage attack detection method

Classification of Paediatric Pneumonia Using Modified DenseNet-121 Deep-Learning Model

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