Academic Articles & Research Papers

<i>H²A²</i>Net: A Hybrid Convolution and Hybrid Resolution Network with Double Attention for Hyperspectral Image Classification

Deep learning (DL) has recently been a core ingredient in modern computer vision tasks, triggering a wave of revolutions in various fields. The hyperspectral image (HSI) classification task is no exception. A wide range of DL-based methods ...

An Integrated Method for River Water Level Recognition from Surveillance Images Using Convolution Neural Networks

Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This arti...

Deep learning, reinforcement learning, and world models

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and w...

A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks

Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that user...

A survey on deep learning and its applications

Synthetic Data Pretraining for Hyperspectral Image Super-Resolution

Large-scale self-supervised pretraining of deep learning models is known to be critical in several fields, such as language processing, where its has led to significant breakthroughs. Indeed, it is often more impactful than architectural de...

Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eli...

Statistical Characteristics of Nighttime Medium‐Scale Traveling Ionospheric Disturbances From 10‐Years of Airglow Observation by the Machine Learning Method

Abstract For the first time, we used the machine learning method to analyze the statistical occurrence and propagation characteristics of nighttime medium‐scale traveling ionospheric disturbances (MSTIDs) from October 2011 to December 2021 ...

A Numerical-Based Attention Method for Stock Market Prediction With Dual Information

Stock market prediction is of great importance for financial analysis. Traditionally, many studies only use the news or numerical data for the stock market prediction. In the recent years, in order to explore their complementary, some studi...

Machine learning in materials science

Abstract Traditional methods of discovering new materials, such as the empirical trial and error method and the density functional theory (DFT)‐based method, are unable to keep pace with the development of materials science today due to the...

Context Aware Evapotranspiration (ETs) for Saline Soils Reclamation

Accurate Evapotranspiration for saline soils (ETs) is important as well as challenging for the reclamation of saline soils through an effective leaching process. Evapotranspiration (ET) by FAO-56 Penman-Monteith standard method is complex, ...

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a...

Artificial Intelligence Adoption and Employee Productivity in Vietnam’s Technology Sector: Evidence From Telecommunications and IT Firms

This study investigates the impact of artificial intelligence (AI) adoption on employee productivity within Vietnam’s technology sector, with a particular focus on telecommunications and information technology firms. Using survey data colle...

AI-Driven Identification of Critical Dependencies in US-China Technology Supply Chains: Implications for Economic Security Policy

This research examines the critical dependencies within US-China technology supply chains through advanced artificial intelligence methodologies, addressing significant economic security implications in an era of strategic competition. The ...

Applying the radiated emission to the specific emitter identification

During the last years we have observed fast development of the electronic devices and electronic warfare systems (EW). One of the most principal functions of the ESM/ELINT system is gathering basic information from the entire electromagneti...

The impact of artificial intelligence on labor productivity

Abstract Recent evidence indicates an upsurge in artificial intelligence and robotics (AI) patenting activities in the latest years, suggesting that solutions based on AI technologies might have started to exert an effect on the economy. We...

IEEE Access Special Section Editorial: 5G and Beyond Mobile Wireless Communications Enabling Intelligent Mobility

Increasing urbanization is one major trend that shapes tomorrow&#x2019;s society; by 2050, more than 85&#x0025; of the developed world&#x2019;s population will live in a comparatively small number of ever-growing cities. Within such cities ...

Artificial Intelligence

Based on the principle of dialectic symmetry formulated by the philosophy of dialectic positivism, an interpretation of the concept of "artificial intelligence" is proposed. This principle assumes the existence of a hierarchy of information...

Egocentric Hand Activity Video Dataset and Bidirectional Motion-Priors for Hand Action Recognition

Recognizing tool-based hand activities from a first-person view is a critical yet challenging task in computer vision, due to the complexity of hand-object interactions and often subtle, ambiguous motion patterns. In real-world manufacturin...

Comparative Evaluation of Radio Network Planning for Different 5G-NR Channel Models on Urban Macro Environments in Quito City

The 5th-Generation New Radio (5G-NR) network have been widely deployed around the world in the frequency range 1/range 2 bands. Once specific frequency bands within these ranges can vary across different countries and regions due to regulat...