Arctic sea ice concentration plays a key role in the global ecosystem. However, accurate prediction of Arctic sea ice concentration remains a challenging task due to its inherent nonlinearity and complex spatiotemporal correlations. To address these...
This article aims to assess the effectiveness of state-of-the-art artificial neural network (ANN) models in time series analysis, specifically focusing on their application in prediction tasks of critical infrastructures (CIs). To accomplish this, sh...
Accurate vessel traffic flow prediction provides an important data basis for intelligent shipping management, including maritime traffic monitoring, navigational risk awareness, waterway organization, and emission-related assessment. Although recent...
Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0–2 h. Existing methods use radar echo maps and the Z–R relationship to directly predict future rainfall rates through dee...
The prediction of fire growth is crucial for effective firefighting and rescue operations. Recent advancements in vision-based techniques using RGB vision and infrared (IR) thermal imaging data, coupled with artificial intelligence and deep learning...
Deep neural network-based approaches have obtained remarkable progress in monaural speech enhancement. Nevertheless, current cutting-edge approaches remain vulnerable to complex acoustic scenarios. We propose a Symmetric Combined Convolution Network...
In the era of marine big data, making full use of multi-source satellite observations to accurately retrieve and predict the temperature structure of the ocean subsurface layer is very significant in advancing the understanding of oceanic processes a...
As a result of its important role in video surveillance, pedestrian attribute recognition has become an attractive facet of computer vision research. Because of the changes in viewpoints, illumination, resolution and occlusion, the task is very chall...
Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface obse...
Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. W...
The water and shadow areas in SAR images contain rich information for various applications, which cannot be extracted automatically and precisely at present. To handle this problem, a new framework called Multi-Resolution Dense Encoder and Decoder (M...
Introduction: Remote health monitoring plays a crucial role in telehealth services and the effective management of patients, which can be enhanced by vital sign prediction from facial videos. Facial videos are easily captured through various imaging...
Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths....
In this study, a compact smart-sensor tag is developed for estimating pork freshness. The smart sensor tag can be placed in areas where packaged meat is stored or displayed. Antennas and simulated models were developed to maximize the efficiency of r...
Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals....
Deep learning (DL) models are frequently employed to extract valuable features from heterogeneous and high-dimensional healthcare data, which are used to keep track of patient well-being via healthcare monitoring systems. Essentially, the training an...
Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection views required for reconstructing the cross-sectional image of an object. While SVCT significantly reduces X-ray radiation dose and speeds up scanning, insufficient...
Total Electron Content (TEC) is a key parameter for characterizing the state of the ionosphere, and its spatiotemporal variations can significantly affect satellite navigation, radio communication, and space weather monitoring. To address the pronoun...
A basic feature of El Niño is an abnormal increase in the surface temperature of the equatorial Pacific Ocean, which can throw ocean–atmosphere interactions out of balance, resulting in heavy rainfall and severe storms. This climate anom...
The maintenance of an uninterrupted electricity supply to meet demand is of paramount importance for maintaining the stable operation of an electrical power system. Machine learning and deep learning play a crucial role in maintaining that stable ope...
The accurate identification of channel-coding types plays a crucial role in wireless communication systems. The recognition of convolutional codes presents challenges, primarily due to their strong temporal dependencies, varying constraint lengths, a...
The trend of global Sea Surface Temperature (SST) has attracted widespread attention in several ocean-related fields such as global warming, marine environmental protection and marine biodiversity. Sea surface temperature is influenced by climate cha...
With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis d...
Mangroves are grown in intertidal zones along tropical and subtropical climate areas, which have many benefits for humans and ecosystems. The knowledge of mangrove conditions is essential to know the statuses of mangroves. Recently, satellite imagery...
Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involveme...
Common video-based object detectors exploit temporal contextual information to improve the performance of object detection. However, detecting objects under challenging conditions has not been thoroughly studied yet. In this paper, we focus on improv...
To meet the increasing sailing demand of the Northeast Passage of the Arctic, a daily prediction model of sea ice concentration (SIC) based on the convolutional long short-term memory network (ConvLSTM) algorithm was proposed in this study. Previousl...
An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecast...
Accurate ultra-short-term forecasting of photovoltaic (PV) power is critical for maintaining grid stability and facilitating renewable energy integration. Although convolutional neural networks have demonstrated strong performance in computer vision,...
Video prediction, which is the task of predicting future video frames based on past observations, remains a challenging problem because of the complexity and high dimensionality of spatiotemporal dynamics. To address the problems associated with spat...
Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for effective management and early warning of marine ecological systems. However, previous studies primarily focused on chlorophyll-a inversion and reconstructi...
SA-ConvLSTM is a recently proposed spatiotemporal model for total electron content (TEC) prediction, which effectively catches long-term temporal evolution and global-scale spatial correlations in TEC. However, its reliance on standard convolution li...
Nowadays, collaborative operations between Remotely Operated Vehicles (ROVs) face considerable challenges, particularly in leader–follower schemes. The underwater environment imposes limitations on acoustic modems, leading to reduced transmissi...
In power systems, the destruction of some important nodes may cause cascading faults. If the most important node in the power system can be found, the important node can be protected in advance, thereby avoiding a blackout accident. At present, the e...
Forecasting monthly precipitation in mountainous terrain poses challenges that push conventional deep learning approaches to their limits: convective processes operate locally while orographic effects span entire drainage basins. We compare three arc...
The ionospheric total electron content (TEC) has complex spatiotemporal variations, making its spatiotemporal prediction challenging. Capturing long-range spatial dependencies is of great significance for improving the spatiotemporal prediction accur...
Currently, three-dimensional convolutional neural networks (3DCNNs) are a popular approach in the field of human activity recognition. However, due to the variety of methods used for human activity recognition, we propose a new deep-learning model in...
Sea surface temperature (SST) prediction has received increasing attention in recent years due to its paramount importance in the various fields of oceanography. Existing studies have shown that neural networks are particularly effective in making ac...
Rapid urbanization has increasingly posed risks of inducing land subsidence in newly developed urban districts, posing growing threats to infrastructure safety. This study focuses on a selected rectangular area within the Shannan New District of Huai...
Soil moisture (SM) is a key variable in Earth system science that affects various hydrological and agricultural processes. Convolutional long short-term memory (Conv-LSTM) networks are widely used deep learning models for spatio-temporal SM predictio...
To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecast...
Ground deformation is a hazardous geological phenomenon. In this study, the small baseline subset (SBAS) with the coherence baseline interferometric technique was employed to derive historical ground deformation in Tianjin City, Northern China, betwe...
This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARI...
Agriculture is an important regional economic industry in Asian regions. Ensuring food security and stabilizing the food supply are a priority. In response to the frequent occurrence of natural disasters caused by global warming in recent years, the...
Precipitation is a key component of the water cycle. Different precipitation data sources have strengths and weaknesses. To combine these strengths and achieve accurate precipitation data, this study introduces a dual-layer neural network (D-ConvLSTM...
Optimizingthe cache hit rate in a multi-access edge computing (MEC) system is essential in increasing the utility of a system. A pivotal challenge within this context lies in predicting the popularity of a service. However, accurately predicting popu...
As an important marine environmental parameter, sound velocity greatly affects the sound propagation characteristics in the ocean. In marine surveying work, prompt and low-cost acquisition of accurate sound speed profiles (SSP) is of immense signific...
During the charging process of the electric vehicle (EV), a spontaneous combustion accident may occur due to overheating of the battery, causing personal danger and property damage. To address the charging safety of EVs, this paper proposes a new hyb...
As a flourishing basic transportation service in recent years, online car-hailing has made great achievements in metropolitan cities. Accurate spatiotemporal forecasting plays a significant role in the deployment of a network for online car-hailing d...
Accurate prediction results can provide an excellent reference value for the prevention of large-scale flight delays. Most of the currently available regression prediction algorithms use a single time series network to extract features, with less con...
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