Academic Articles & Research Papers

Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions

Abstract With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintain...

Intervention on modifiable lifestyle and physiological factors via variational autoencoder reveals changes in functional connectivity-mediated risk for Alzheimer's disease.

Collaborative Filtering Recommendation Algorithm Based on Semi-Autoencoder

To effectively use the user-item interaction history and auxiliary information in recommendation systems,this paper proposes an improved collaborative filtering recommendation algorithm.Based on semi-autoencoder,the features of auxiliary in...

Preventing Posterior Collapse with DVAE for Text Modeling

This paper introduces a novel variational autoencoder model termed DVAE to prevent posterior collapse in text modeling. DVAE employs a dual-path architecture within its decoder: path A and path B. Path A makes the direct input of text insta...

Low-Speed Bearing Fault Diagnosis Based on ArSSAE Model Using Acoustic Emission and Vibration Signals

The development of rolling element bearing fault diagnosis systems has attracted a great deal of attention due to bearing components having a high tendency toward unexpected failures. However, under low-speed operating conditions, the diagn...

Hybrid spatio-temporal transformer and variational autoencoder framework for advanced sports injury prediction and analysis

Sports injuries significantly affect sports performance and overall health, making using accurate and reliable prognostic models imperative. Conventional algorithms for injury prediction, e.g., random forest and XGBoost, generally cannot un...

Identifying multi-omics spatial domains via masked autoencoder and diffusion-based latent regularization with SpaMEDM.

A fault location method for secondary virtual circuits in smart substations based on the integration of GAE and GCN

Abstract The secondary virtual circuits in smart substations enable digital and networked information exchange between devices. However, their extensive logical connections, complex structure, and implicit interdependencies pose significant...

Deep Learning for Computer Vision: A Brief Review

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief ...

Assessing Wireless Sensing Potential With Large Intelligent Surfaces

Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention receiv...

Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection

Surveillance videos are crucial for crime prevention and public safety, yet the challenge of defining abnormal events hinders their effectiveness, limiting the applicability of supervised methods. This paper introduces an unsupervised end-t...

A Video Frame Prediction Method Based on Latent-Space Autoregressive Modeling

Video prediction is a fundamental task in computer vision with broad applications in intelligent robotics, autonomous driving, and related fields. However, existing methods often struggle to simultaneously model long-term temporal dependenc...

Overcomplete graph convolutional denoising autoencoder for noisy skeleton action recognition

Abstract Current skeleton‐based action recognition methods usually assume the input skeleton is complete and noise‐free. However, it is inevitable that the captured skeletons are incomplete due to occlusions or noisy due to changes in the e...

Evaluation of noise reduction methods in digital images: Classical filtering vs adaptive learning-based models

Digital images acquired through imaging systems often lose their original fidelity due to various types of noise introduced during acquisition, storage, or transmission, consequently reducing clarity and adversely affecting subsequent image...

Understanding the role of autoencoders for stiff dynamical systems using information theory

Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system....

Deep Learning in Microscopy Image Analysis: A Survey

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estim...

Comprehensive framework of machine learning and deep learning architectures with metaheuristic optimization for high-fidelity prediction of nanofluid specific heat capacity

Abstract Accurately predicting the specific heat capacity of nanofluids is critical for optimizing their performance in engineering and industrial applications. This study explores twelve machine learning and deep learning models using conv...

A tutorial survey of architectures, algorithms, and applications for deep learning

In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent develop...

Understanding Deep Learning Techniques for Image Segmentation

The machine learning community has been overwhelmed by a plethora of deep learning--based approaches. Many challenging computer vision tasks, such as detection, localization, recognition, and segmentation of objects in an unconstrained envi...