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Innumerable approaches of deep learning-based COVID-19 detection systems have been suggested by researchers in the recent past, due to their ability to process high-dimensional, complex data, leading to more accurate prediction of the COVID-19 ...

Recommender methods have been effectively used in both academic and industrial settings. However, the cold start problem with scarce prior information has become the barrier hindering recommender systems from gaining further improvements. To ...

In this work, Multimodal Autoencoder is proposed in which images are reconstructed using both image and text inputs, rather than just images. Two new loss terms are introduced: Image-Text-loss (L_{im − te}) that measures similarity between input image and ...

The intricate process of coronary in-stent restenosis (ISR) involves the interplay between different mediators, including platelet-derived growth factor, transforming growth factor-β, extracellular matrix, smooth muscle cells, ...

• Consideration of key influential factors of ISR: the continuum mechanics based constitutive framework incorporates critical factors such as platelet-derived growth factor, transforming growth factor- β , extracellular matrix, density of ...

Time-series human activity data obtained from sensor technologies facilitate various applications in industry and daily life, such as activity recognition, motion or fall detection, and health analysis. Recent research has shown that person re-...$\mathrm{}$$\mathrm{}$

Surveillance systems are essential in computer vision, with video anomaly detection (VAD) critical for real-world security. Conventional approaches anticipate abnormal frames using normal patterns learned with normal training data and determine ...

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• Patch anomaly generation enforces normality learning, in terms of appearance and motion.
• Anomaly distance learning enlarges the feature distance of normal and abnormal frames.
• Context-aware shortcut widens the quality gap between ...

Machine learning and deep learning (DL)-based forecasting models have shown excellent predictive performances, but they require a large amount of data for model construction. Insufficient data can be augmented using generative adversarial ...

The creation of binary and multi-classification models with the goal of accurately detecting and categorizing motor defects is important to study. This work explores how autoencoders can be used to apply self-supervised learning in industrial ...

At present, there are too many types and numbers of real estate features in the real estate market, and it is difficult to effectively recommend real estate to customers with more complicated needs. The datasets of real estate often encompass a ...

Security threats are becoming an increasingly relevant concern in cyber–physical systems. Cyber attacks on these systems are not only common today but also increasingly sophisticated and constantly evolving. One way to secure the system against ...

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• X-ray radiographs were used to identify apple and pear fruit with internal disorders.
• Classification was achieved by a fully convolutional autoencoder.
• We evaluated our model on both simulated and real data.
• Our model ...

A novel fully convolutional autoencoder (convAE) was introduced to analyze X-ray radiography images of ‘Braeburn’ apples and ‘Conference’ pears with and without disorders for online sorting purposes. The model was solely trained on either apple ...

Depression detection based on text analysis has emerged as a research hotspot. Existing research indicates that patients’ personalized characteristics are the primary factor contributing to differences in reported experiences, which poses ...

• Pioneered personalized modeling in text-based depression detection.
• Personalized models narrow the gap between generic symptoms and patient experiences.
• Defined key components of personalized information and proposed a novel ...

We implement deep learning for predicting bitcoin closing prices. Identifying two new determiners, we propose a novel LSTM Autoencoder using Mean Squared Error (MSE) loss which is regularized by False Nearest Neighbor (FNN) algorithm. The method ...

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and repetitive behaviors, with its exact etiology remaining unclear. Early and accurate diagnosis is critical for effective ...

One of the most serious security threats faced by the Internet today is multi-stage attacks. In response to this challenge, anomaly detection-based methods have been widely used to identify different stages of such attacks. However, current ...

Photorealistic style translation has gained significant attention in the field of computer vision and graphics due to its potential applications in many areas such as content generation, artistic expression and image editing. In this paper, we ...

Unsupervised feature selection (UFS) aims to identify a subset of the most informative features from high-dimensional data without labels. However, most existing UFS methods cannot adequately capture the intricate nonlinear relationships present ...

Since the convolutional operation pays too much attention to local information, resulting in the loss of global information and a decline in fusion quality. In order to ensure that the fused image fully captures the features of the entire scene, ...

• An end-to-end Multi-scale Global Feature Autoencoder is proposed to fuse images.
• Multi-scale global feature extraction module is proposed to extract global features.
• Adaptive embedded residual fusion module is proposed by embedded ...

The anomaly detection of aeroengines faces significant challenges, including high noise, complex parameter correlations, and imbalanced data. Current methods primarily rely on the reconstruction error of autoencoders to isolate anomalies. However,...

Characterization of the optimization problem is a crucial task in many recent optimization research topics (e.g., explainable algorithm performance assessment, and automated algorithm selection and configuration). The state-of-the-art approaches ...

• Representation learning is applied on each individual population of optimization process trajectory.
• The Opt2Vec representations are captured through the algorithm's behavior (its populations).
• The Opt2Vec representations are ...