We propose a Hybrid Parallel Temporal–Spatial CNN-LSTM (HPTS-CL) architecture for optimized indoor environment modeling in sports halls, addressing the computational and scalability challenges of high-resolution spatiotemporal data processing....
Global Navigation Satellite Systems (GNSS) have become essential tools in geomatics engineering for precise positioning, cadastral surveys, topographic mapping, and deformation monitoring. Recent advances integrate GNSS with emerging technologies suc...
Despite significant technological advancements in aviation safety systems, human-operator condition monitoring remains a critical challenge, with more than 75% of aircraft incidents stemming from attention-related perceptual failures. This study addr...
Intrusion detection systems (IDSs) are critical for securing modern networks, particularly in IoT and IIoT environments where traditional defenses such as firewalls and encryption are insufficient against evolving cyber threats. This paper proposes a...
This study addresses the performance of deep learning models for predicting human DNA sequence classification through an exploration of ideal feature representation, model architecture, and hyperparameter tuning. It contrasts traditional machine lear...
This study proposes an enhanced deep learning framework for accurate detection of P-peaks in noisy photoplethysmographic (PPG) signals, utilizing a hybrid architecture that integrates wavelet-based analysis with neural network components. The P-peak...
Spatiotemporal modeling and prediction of road network traffic flow are essential components of intelligent transport systems (ITS), aimed at effectively enhancing road service levels. Sustainable and reliable traffic management in smart cities requi...
In a global context where reducing the carbon footprint has become an urgent necessity, this article presents a hybrid CNN-LSTM prediction model to estimate CO2 emission rates of Paris road traffic using spatio-temporal data. Our hybrid prediction mo...
Accurate crop classification using satellite imagery is critical for agricultural monitoring, yield estimation, and land-use planning. However, this task remains challenging due to the spectral similarity among crops. Although crops differ in physiol...
The NASA Cyclone GNSS (CYGNSS) mission provides one Delay Doppler Map (DDM) per second along observational tracks. To account for spatiotemporal correlations within adjacent DDMs in a track, a deep hybrid CNN-LSTM model is proposed for wind speed pre...
The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic li...
Background: Deep learning (DL)-based medical image classification is becoming increasingly reliable, enabling physicians to make faster and more accurate decisions in diagnosis and treatment. A plethora of algorithms have been developed to classify a...
This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propos...
Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuato...
The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predict...
The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propos...
Carbon emission estimation for power systems is essential for identifying emission responsibilities and formulating effective mitigation measures. Current carbon emission prediction methods for power systems exhibit limited computational efficiency a...
This research introduces an enhanced fault detection approach, variational mode decomposition (VMD), for identifying open-circuit IGBT faults in the grid-side converter (GSC) of a doubly fed induction generator (DFIG) wind turbine system. VMD has man...
In order to address the challenge of accurately predicting nitrogen oxide (NOx) emission from diesel engines in transient operation using traditional neural network models, this study proposes a NOx emission forecasting model based on a hybrid neural...
This study presents a novel approach to passive human counting in indoor environments using Bluetooth Low Energy (BLE) signals and deep learning. The motivation behind this research is the need for non-intrusive, privacy-preserving occupancy monitori...
This study introduces a novel convolutional neural network (CNN) architecture, encompassing both single and multi-head designs, developed to identify a user’s locomotion activity while using a wearable lower limb robot. Our research involved 50...
This study is focused on developing a machine learning (ML) meta-model to predict the progression of a multiple steam generator tube rupture (MSGTR) accident in the APR1400 reactor. The accident was simulated using the thermal–hydraulic code RE...
This study presents a comprehensive comparative analysis of machine learning, deep learning, and optimization-based hybrid methods for malicious URL detection on the Malicious Phish dataset. For feature selection and model hyperparameter tuning, the...
The detection of deepfake images and videos is a critical concern in social communication due to the widespread utilization of deepfake techniques. The prevalence of these methods poses risks to trust and authenticity across various domains, emphasiz...
The rapid growth in the number of motor vehicles has exacerbated traffic congestion. The occurrence of congestion not only poses significant challenges for traffic management authorities but also severely impacts residents’ travel and daily rou...
The existing financial payment system has inherent credit card fraud problems that must be solved with strong and effective solutions. In this research, a combined deep learning model that incorporates a convolutional neural network (CNN), long-short...
The switching characteristics of GaN HEMT devices exhibit a very complex dynamic nonlinear behavior and multi-physics coupling characteristics, and traditional switching current models based on physical mechanisms have significant limitations. This a...
Deep Learning is a very active and important area for building Computer-Aided Diagnosis (CAD) applications. This work aims to present a hybrid model to classify lung ultrasound (LUS) videos captured by convex transducers to diagnose COVID-19. A Convo...
Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU...
Predicting the remaining useful life (RUL) is a pivotal step in ensuring the reliability of lithium-ion batteries (LIBs). In order to enhance the precision and stability of battery RUL prediction, this study introduces an innovative hybrid deep learn...
Accurate estimation of battery state of health (SOH) is critical to the efficient operation of energy storage battery systems. Furthermore, precise SOH estimation methods can significantly reduce resource waste by extending the battery service life a...
Many studies have explored divergent deep neural networks in human activity recognition (HAR) using a single accelerometer sensor. Multiple types of deep neural networks, such as convolutional neural networks (CNN), long short-term memory (LSTM), or...
Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the...
Breast cancer (BC) is a type of tumor that develops in the breast cells and is one of the most common cancers in women. Women are also at risk from BC, the second most life-threatening disease after lung cancer. The early diagnosis and classification...
Conventional power load forecasting frameworks face limitations in dynamic spatial topology capture and long-term dependency modeling. To address these issues, this study proposes a hybrid GAT-CNN-LSTM architecture for enhanced short-term power load...
Accurate forecasting of groundwater level dynamics poses a critical challenge for sustainable water management in arid regions. However, the strong spatiotemporal heterogeneity inherent in groundwater systems and their complex interactions between na...
Most of the established gait evaluation methods use inertial sensors mounted in the lower limb area (tibias, ankles, shoes). Such sensor placement gives good results in laboratory conditions but is hard to apply in everyday scenarios due to the senso...
One major factor influencing the development of eco-friendly policies and the implementation of climate change mitigation strategies is the accurate projection of CO2 emissions. Traditional statistical models face significant limitations in capturing...
Understanding customer attitudes has become a critical component of decision-making due to the growing influence of social media and e-commerce. Text-based opinions are the most structured, hence playing an important role in sentiment analysis. Most...
This study investigates non-contact respiratory pattern classification using Ultra-Wideband (UWB) radar sensors and deep learning. A CNN-LSTM hybrid architecture was developed combining spatial feature extraction through convolutional layers with tem...
We propose a hybrid CNN-LSTM architecture for energy-efficient spatiotemporal modeling in sports venue analytics, addressing the dual challenges of computational efficiency and prediction accuracy in dynamic environments. The proposed method integrat...
Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorolog...
Santa Ana winds are extreme meteorological events that strongly affect the U.S.–Mexico border region, often associated with droughts, high fire risk, and hydrological imbalance. Understanding the temporal behavior of key atmospheric variables d...
Ash content is an important production indicator of flotation performance, reflecting the current operating conditions of the flotation system and the recovery rate of clean coal. It also holds significant importance for the intelligent control of fl...
Significant challenges persist in diagnosing non-ischemic cardiomyopathies (NICMs) owing to early morphological overlap and subtle functional changes. While cardiac magnetic resonance (CMR) offers gold-standard structural assessment, current morpholo...
Traditional security detection methods struggle to identify zero-day attacks in Industrial Control Systems (ICSs), particularly within critical infrastructures (CIs) integrated with the Industrial Internet of Things (IIoT). These attacks exploit unkn...
Hybrid microgrids struggle to manage electricity due to renewable source, storage, and load demand variability. This paper proposes a centralized controller employing hybrid deep learning and evolutionary optimization to overcome these issues. Solar...
In the realm of foreign exchange (Forex) market predictions, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been commonly employed. However, these models often exhibit instability due to vulnerability to data perturbat...
An important question in planning and designing bike-sharing services is to support the user’s travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This s...
Accurate and reliable estimation of renewable energy generation is critical for modern power grid management, yet the inherent volatility and distinct physical drivers of multi-source renewables present significant modeling challenges. This paper pro...
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