VESC: a new variational autoencoder based model for anomaly detection

Zhang, Chunkai; Wang, Xinyu; Zhang, Jiahua; Li, Shaocong; Zhang, Hanyu; Liu, Chuanyi; Han, Peiyi

doi:10.1007/s13042-022-01657-w

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…The method was tested on medical datasets from hospitals and it was found that LSTM was the best technique, since it modelled better the medical history. Finally, Zhang et al proposed a method for sequence-based recommendations using dynamic user-item interactions 35 . More specifically, a Long Short-Term Memory (LSTM) network was employed to capture sequential information and used collaborative filtering to suggest personalized meal plans.…”

Section: Related Workmentioning

confidence: 99%

AI nutrition recommendation using a deep generative model and ChatGPT

Papastratis,

Konstantinidis,

Daras

et al. 2024

Sci Rep

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In recent years, major advances in artificial intelligence (AI) have led to the development of powerful AI systems for use in the field of nutrition in order to enhance personalized dietary recommendations and improve overall health and well-being. However, the lack of guidelines from nutritional experts has raised questions on the accuracy and trustworthiness of the nutritional advice provided by such AI systems. This paper aims to address this issue by introducing a novel AI-based nutrition recommendation method that leverages the speed and explainability of a deep generative network and the use of novel sophisticated loss functions to align the network with established nutritional guidelines. The use of a variational autoencoder to robustly model the anthropometric measurements and medical condition of users in a descriptive latent space, as well as the use of an optimizer to adjust meal quantities based on users’ energy requirements enable the proposed method to generate highly accurate, nutritious and personalized weekly meal plans. Coupled with the ability of ChatGPT to provide an unparalleled pool of meals from various cuisines, the proposed method can achieve increased meal variety, accuracy and generalization capabilities. Extensive experiments on 3000 virtual user profiles and 84000 daily meal plans, as well as 1000 real profiles and 7000 daily meal plans, demonstrate the exceptional accuracy of the proposed diet recommendation method in generating weekly meal plans that are appropriate for the users in terms of energy intake and nutritional requirements, as well as the easiness with which it can be integrated into future diet recommendation systems.

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Section: Related Workmentioning

confidence: 99%

AI nutrition recommendation using a deep generative model and ChatGPT

Papastratis,

Konstantinidis,

Daras

et al. 2024

Sci Rep

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“…2) Electric Pole Maintenance: Maintenance and control of energy grids (and their assets) has become a widespread problem over the last decades due to the aging of components, the heterogeneity of the grids (e.g., multiple types of renewable sources), and the interlocking of the grids inside smart cities [15]. In energy grids, deep learning has found widespread use, ranging from optimal flow analysis [16] to anomaly detection [17] and energy forecasting [18]. Concerning electric poles in particular, a lot of attention has gone into ways of mapping them periodically, including manned [1] and unmanned [2] aerial flights, and remote sensing pipelines [3] (we refer to [2] for a more in-depth overview).…”

Section: Related Workmentioning

confidence: 99%

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

Devoto

Spinelli

Murabito

et al. 2023

IEEE Trans. Ind. Inf.

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In this article, we consider the task of reidentifying the same object in different photos taken from separate positions and angles during aerial reconnaissance, which is a crucial task for the maintenance and surveillance of critical large-scale infrastructure. To effectively hybridize deep neural networks with available domain expertise for a given scenario, we propose a customized pipeline, wherein a domain-dependent object detector is trained to extract the assets (i.e., subcomponents) present on the objects, and a siamese neural network learns to reidentify the objects, exploiting both visual features (i.e., the image crops corresponding to the assets) and the graphs describing the relations among their constituting assets. We describe a realworld application concerning the reidentification of electric poles in the Italian energy grid, showing our pipeline to significantly outperform siamese networks trained from visual information alone. We also provide a series of ablation studies of our framework to underline the effect of including topological asset information in the pipeline, learnable positional embeddings in the graphs, and the effect of different types of graph neural networks on the final accuracy.

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Multi-scale Feature Imitation for Unsupervised Anomaly Localization

Hu,

Lai

2024

Lecture Notes in Electrical Engineering

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VESC: a new variational autoencoder based model for anomaly detection

Cited by 6 publications

References 22 publications

AI nutrition recommendation using a deep generative model and ChatGPT

AI nutrition recommendation using a deep generative model and ChatGPT

Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks

Multi-scale Feature Imitation for Unsupervised Anomaly Localization

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