Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Zhou, Feng; Hu, Silong; Xiaoli, Wan; Lu, Zhihui; Wu, Jie

doi:10.3390/electronics12122581

Cited by 10 publications

(4 citation statements)

References 53 publications

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“…FP means that the wrong prediction is a positive example. For example, FN indicates that the wrong prediction is a counterexample [2,3].…”

Section: Evaluation Metricsmentioning

confidence: 99%

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A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

Zhou,

Hu,

et al. 2024

Future Internet

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In the current field of disease risk prediction research, there are many methods of using servers for centralized computing to train and infer prediction models. However, this centralized computing method increases storage space, the load on network bandwidth, and the computing pressure on the central server. In this article, we design an image preprocessing method and propose a lightweight neural network model called Linge (Lightweight Neural Network Models for the Edge). We propose a distributed intelligent edge computing technology based on the federated learning algorithm for disease risk prediction. The intelligent edge computing method we proposed for disease risk prediction directly performs prediction model training and inference at the edge without increasing storage space. It also reduces the load on network bandwidth and reduces the computing pressure on the server. The lightweight neural network model we designed has only 7.63 MB of parameters and only takes up 155.28 MB of memory. In the experiment with the Linge model compared with the EfficientNetV2 model, the accuracy and precision increased by 2%, the recall rate increased by 1%, the specificity increased by 4%, the F1 score increased by 3%, and the AUC (Area Under the Curve) value increased by 2%.

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“…FP means that the wrong prediction is a positive example. For example, FN indicates that the wrong prediction is a counterexample [2,3].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Current research on disease risk prediction mainly uses physiological indicators and risk factors to predict disease risk [1], images to predict disease risk [2], and audio to predict disease risk [3]. These studies have achieved good results.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

Zhou,

Hu,

et al. 2024

Future Internet

Self Cite

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“…Notably, the realm of education emerges as a particularly promising domain in this era of innovation. This research stands at the forefront of these developments, actively contributing to the creation of a state-of-the-art teacher robot that seamlessly integrates [1], [2]. A primary and innovative focus of this endeavor lies in the infusion of emotional intelligence (EI) into robot teachers, achieved through the sophisticated analysis of facial expressions using advanced artificial intelligence techniques [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Sakhi,

Mansour,

Sekkaki

2024

Journal of Robotics and Control

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Recognizing the fundamental role of learners' emotions in the educational process, this study aims to enhance educational experiences by incorporating emotional intelligence (EI) into teacher robots through artificial intelligence and image processing technologies. The primary hurdle addressed is the inadequacy of conventional methods, particularly convolutional neural networks (CNNs) with pooling layers, in imbuing robots with emotional intelligence. To surmount this challenge, the research proposes an innovative solution—introducing a novel learning focal point (LFP) layer to replace pooling layers, resulting in significant enhancements in accuracy and other vital parameters. The distinctive contribution of this research lies in the creation and application of the LFP algorithm, providing a novel approach to emotion classification for teacher robots. The results showcase the LFP algorithm's superior performance compared to traditional CNN approaches. In conclusion, the study highlights the transformative impact of the LFP algorithm on the accuracy of classification models and, consequently, on emotionally intelligent teacher robots. This research contributes valuable insights to the convergence of artificial intelligence and education, with implications for future advancements in the field.

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“…For example, studies [5][6][7] examining the susceptibility of downstream models to attacks have confirmed that transfer learning can protect these downstream models from being easily attacked, thereby enhancing their robustness. Due to its practicality, transfer learning has attracted extensive attention in the field of computer vision [8][9][10][11][12], and has been applied in many task scenarios such as transportation, medical treatment [13], social media [14] and art [15][16][17][18]. Various works [19] have been proposed to explore the problem of what and how to transfer from the pre-trained models.…”

Section: Introductionmentioning

confidence: 99%

Discrepant Semantic Diffusion Boosts Transfer Learning Robustness

Gao,

Bai,

Zhao

et al. 2023

Electronics

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Transfer learning could improve the robustness and generalization of the model, reducing potential privacy and security risks. It operates by fine-tuning a pre-trained model on downstream datasets. This process not only enhances the model’s capacity to acquire generalizable features but also ensures an effective alignment between upstream and downstream knowledge domains. Transfer learning can effectively speed up the model convergence when adapting to novel tasks, thereby leading to the efficient conservation of both data and computational resources. However, existing methods often neglect the discrepant downstream–upstream connections. Instead, they rigidly preserve the upstream information without an adequate regularization of the downstream semantic discrepancy. Consequently, this results in weak generalization, issues with collapsed classification, and an overall inferior performance. The main reason lies in the collapsed downstream–upstream connection due to the mismatched semantic granularity. Therefore, we propose a discrepant semantic diffusion method for transfer learning, which could adjust the mismatched semantic granularity and alleviate the collapsed classification problem to improve the transfer learning performance. Specifically, the proposed framework consists of a Prior-Guided Diffusion for pre-training and a discrepant diffusion for fine-tuning. Firstly, the Prior-Guided Diffusion aims to empower the pre-trained model with the semantic-diffusion ability. This is achieved through a semantic prior, which consequently provides a more robust pre-trained model for downstream classification. Secondly, the discrepant diffusion focuses on encouraging semantic diffusion. Its design intends to avoid the unwanted semantic centralization, which often causes the collapsed classification. Furthermore, it is constrained by the semantic discrepancy, serving to elevate the downstream discrimination capabilities. Extensive experiments on eight prevalent downstream classification datasets confirm that our method can outperform a number of state-of-the-art approaches, especially for fine-grained datasets or datasets dissimilar to upstream data (e.g., 3.75% improvement for Cars dataset and 1.79% improvement for SUN dataset under the few-shot setting with 15% data). Furthermore, the experiments of data sparsity caused by privacy protection successfully validate our proposed method’s effectiveness in the field of artificial intelligence security.

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Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Cited by 10 publications

References 53 publications

A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

A Lightweight Neural Network Model for Disease Risk Prediction in Edge Intelligent Computing Architecture

Using Learning Focal Point Algorithm to Classify Emotional Intelligence

Discrepant Semantic Diffusion Boosts Transfer Learning Robustness

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