Nowadays, wind power generation has become vital thanks to its advantages in cost, ecological friendliness, enormousness, and sustainability. However, the erratic and intermittent nature of this energy poses significant operational and management dif...
Employee attrition, which causes a significant loss for an organization, is the term used to describe the natural decline in the number of employees in an organization as a result of numerous unavoidable events. If a company can predict the likelihoo...
Traditional algorithms and single predictive models often face challenges such as limited prediction accuracy and insufficient modeling capabilities for complex time-series data in fault prediction tasks. To address these issues, this paper proposes...
Accurate prediction of carbon emissions is crucial for both providing effective carbon reduction guidance to the power grid sector and driving society-wide carbon emission reduction. Existing methods based on power flow calculation theory heavily rel...
Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past studies have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models. However, these studies do not ad...
The Transformer, a deep learning architecture, has shown exceptional adaptability across fields, including music information retrieval (MIR). Transformers excel at capturing global, long-range dependencies in sequences, which is valuable for tracking...
Surface electromyography (sEMG) offers a novel method in human–machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and no...
Accurate and reliable runoff forecasting is of great significance for hydropower station operation and watershed water resource allocation. However, various complex factors, such as climate conditions and human activities, constantly affect the forma...
Intrusion detection is essential to cybersecurity. However, the curse of dimensionality and class imbalance limit detection accuracy and impede the identification of rare attacks. To address these challenges, this paper proposes the high-dimensional...
Gait recognition using wearable sensor data is crucial for healthcare, rehabilitation, and monitoring neurological and musculoskeletal disorders. This study proposes a deep learning framework for gait classification using inertial measurements from f...
When recording conversations, there may be multiple people talking at once. While our human ears can filter out unwanted sounds, this can be challenging for automatic speech recognition (ASR) systems, leading to reduced accuracy. To address this issu...
This paper proposes a novel fault diagnosis framework that integrates the Osprey–Cauchy–Sparrow Search Algorithm (OCSSA) optimized Variational Mode Decomposition (VMD) with a Bidirectional Temporal Convolutional Network-Attention mechanis...
The accurate short-term forecasting (PV) of power is crucial for grid stability control, energy trading optimization, and renewable energy integration in smart grids. However, PV generation is extremely variable and non-linear due to environmental fl...
(1) Background: Ultra-short-term photovoltaic (PV) power prediction is crucial for optimizing grid scheduling and enhancing energy utilization efficiency. Existing prediction methods face challenges of missing data, noise interference, and insufficie...
Accurate short-term forecasting of photovoltaic (PV) output is critical to managing the variability of PV generation and ensuring reliable grid operation with high renewable integration. We propose an enhanced hybrid deep learning framework that comb...
Accurate prediction of electricity consumption is crucial for energy management and allocation. This study introduces a novel approach, named Attention-based Multivariate Temporal Convolutional Network (AMTCN), for electricity consumption forecasting...
There has been increased interest in recognizing the dynamic and flexible changes in shipborne multi-function radar (MFR) working modes. The working modes determine the distribution of pulse descriptor words (PDWs). However, building the mapping rela...
The market for eco-friendly batteries is increasing owing to population growth, environmental pollution, and energy crises. The widespread application of lithium-ion batteries necessitates their state of health (SOH) estimation, which is a popular an...
The increased risk of cardiovascular disease in people with spinal cord injuries motivates work to identify exercise options that improve health outcomes without causing risk of musculoskeletal injury. Handcycling is an exercise mode that may be bene...
In recent years, with the rapid development of the global demand and scale for deep underground space utilization, deep space has gradually transitioned from single-purpose uses such as underground transportation, civil defense, and commerce to a com...
Automatic modulation classification (AMC) under low signal-to-noise ratio (SNR) and complex channel conditions remains a significant challenge due to the trade-off between robustness and efficiency. This study proposes a lightweight temporal convolut...
Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintena...
Human activity recognition (HAR) is an important research area with a wide range of application scenarios, such as smart homes, healthcare, abnormal behavior detection, etc. Wearable sensors, computer vision, radar, and other technologies are commonl...
2 February 2026
Electroencephalography-based motor imagery (EEG-MI) classification is a cornerstone of Brain–Computer Interface (BCI) systems, enabling the identification of motor intentions by decoding neural patterns within EEG signals. However, conventional...
Eye-tracking technology enables communication for individuals with muscle control difficulties, making it a valuable assistive tool. Traditional systems rely on electrooculography (EOG) or infrared devices, which are accurate but costly and invasive....
The rapid advancement of machine learning techniques has led to their widespread application in various domains, including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a...
HVAC (Heating, Ventilation and Air Conditioning) system in buildings is a major component of energy consumption, and realizing high-precision energy consumption prediction is of great significance for intelligent building management. Aiming at the pr...
Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one ind...
Anomaly detection in time-series data is an integral part in the context of the Internet of Things (IoT). In particular, with the advent of sophisticated deep and machine learning-based techniques, this line of research has attracted many researchers...
The rapid evolution of modern vehicles into intelligent and interconnected systems presents new complexities in both functional safety and cybersecurity. In this context, ensuring the reliability and integrity of critical sensor data, such as wheel s...
Autism Spectrum Disorder (ASD) poses significant challenges in diagnosis due to its diverse symptomatology and the complexity of early detection. Atypical gait and gesture patterns, prominent behavioural markers of ASD, hold immense potential for fac...
Accurate dew intensity prediction is vital in multiple fields, such as agriculture, meteorology, industry, and transportation. This study addresses the cross-disciplinary demands for dew intensity prediction by proposing a hybrid deep learning model...
The performance of integrated Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) navigation often declines in complex urban environments due to frequent GNSS signal blockages. This poses a significant challenge for autonomou...
Accurate prediction of landslide-induced displacement is essential for the structural integrity and operational safety of buildings and infrastructure situated in geologically unstable regions. This study introduces a novel hybrid predictive framewor...
Phishing, especially brand impersonation attacks, is a critical cybersecurity threat that harms user trust and organization security. This paper establishes a lightweight model for real-time detection that relies on URL-only sequences, addressing lim...
This paper addresses the multi-trajectory prediction problem and a so-called Embedded-TCN-Transformer (EmbTCN-Transformer) model is designed by using the real-time historical trajectories in a formation. A temporal convolutional network (TCN) is util...
This study investigates on-edge seizure detection that aims to resolve two major constraints that hold the deployment of deep learning models in clinical settings at present. First, centralized training requires gathering and consolidating data acros...
Recent studies demonstrate that algorithmic music attracted global attention not only because of its amusement but also its considerable potential in the industry. Thus, the yield increased academic numbers spinning around on topics of algorithm musi...
The frequent incidents of password leakage have increased people’s attention and research on password security. Password guessing is an essential part of password cracking and password security research. The progression of deep learning technol...
Predicting the concentrations of air pollutants, particularly PM2.5, with accuracy and dependability is crucial for protecting human health and preserving a healthy natural environment. This research proposes a deep learning-based, robust prediction...
Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This st...
Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an un...
KPIs (Key Performance Indicators) in distributed systems may involve a variety of anomalies, which will lead to system failure and huge losses. Detecting KPI anomalies in the system is very important. This paper presents a time series anomaly detecti...
This study presents a novel methodology for multi-step Bitcoin (BTC) price prediction by combining advanced stacking-based architectures with temporal attention mechanisms. The proposed Temporal Attention-Enhanced Stacking Network (TAESN) integrates...
Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record i...
With reference to the trajectory-based operation (TBO) requirements proposed by the International Civil Aviation Organization (ICAO), this paper concentrates on the study of four-dimensional trajectory (4D Trajectory) prediction technology in busy te...
In response to the challenges posed by renewable energy integration, this study introduces a hybrid Attention-TCN-LSTM model for short-term photovoltaic (PV) power forecasting. The LSTM captures the sequence characteristics of PV output, which are th...
Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a frame...
Building energy consumption prediction has always played a significant role in assessing building energy efficiency, building commissioning, and detecting and diagnosing building system faults. With the progress of society and economic development, b...
Fault diagnosis is critical for ensuring the reliability of reciprocating pumps in industrial settings. However, challenges such as strong noise interference and unbalanced conditions of existing methods persist. To address these issues, this paper p...
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