Chemical mechanical polishing (CMP) has become one of the most important process stages in the fabrication of advanced integrated circuits (IC). The CMP pattern effect strongly influences the planarization of the chip surface morphology after CMP, de...
The accurate classification of cocoa pod ripeness is critical for optimizing harvest timing, improving post-harvest processing, and ensuring consistent quality in chocolate production. Traditional ripeness assessment methods are often subjective, lab...
Brain–computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (MI) serving as a key paradigm for decoding imagined movements. Efficient feature extraction fro...
Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certai...
Elastic optical networks (EONs) must allocate resources dynamically to accommodate heterogeneous, high-bandwidth demands. However, the continuous setup and teardown of connections with different bit rates can fragment the spectrum and lead to blockin...
The food industry increasingly relies on automated vision systems to ensure product quality, consistency, and safety. However, the visual classification of vegetables remains challenging due to high intra-class variability, illumination differences,...
This study introduces a Multi-Stage Automated Classification (MSTAC) system for COVID-19 chest X-ray (CXR) images, utilizing stacked Convolutional Neural Network (CNN) models. Suspected COVID-19 patients often undergo CXR imaging, making it valuable...
The rapid growth of digital cultural heritage repositories has increased the demand for reliable and interpretable methods for artwork image classification. This study investigates explainable artificial intelligence approaches for fine-grained artwo...
In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was t...
Accurate runoff prediction in complex slope catchments remains challenging due to terrain heterogeneity and dynamic rainfall interactions. This study conducts a systematic comparison between a physics-based Two-Dimensional Slope Hydrodynamic Model (T...
With the acceleration of economic globalization, China’s financial market has emerged as a vital force in the global financial system. The Baltic Dry Index (BDI) and China Container Freight Index (CCFI) serve as key indicators of the shipping s...
As an important kind of DNN (deep neural network), CNN (convolutional neural network) has made remarkable progress and been widely used in the vision and decision-making of autonomous robots. Nonetheless, in many scenarios, even a minor perturbation...
To improve software reliability, software defect prediction is used to find software bugs and prioritize testing efforts. Recently, some researchers introduced deep learning models, such as the deep belief network (DBN) and the state-of-the-art convo...
Pine trees are more vulnerable to diseases and pests than other trees, so prevention and management are necessary in advance. In this paper, two models of deep learning were mixed to quickly check whether or not to detect pine pests and to perform a...
Parkinson’s Disease (PD) is a progressive central nervous system disorder that is caused due to the neural degeneration mainly in the substantia nigra in the brain. It is responsible for the decline of various motor functions due to the loss of...
Speech Emotion Recognition (SER) technology helps computers understand human emotions in speech, which fills a critical niche in advancing human–computer interaction and mental health diagnostics. The primary objective of this study is to enhan...
Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good p...
Accurate prediction of a ship’s Estimated Time of Arrival (ETA) is of great significance for port scheduling, logistics management, and navigation safety. Traditional ETA prediction approaches often rely on manual experience for parameter tunin...
Due to its innovative structure and superior handling of long time series data with parallel input, the Transformer model has demonstrated a remarkable effectiveness. However, its application in lithium-ion battery degradation research requires a mas...
Power Quality Disturbances (PQDs) must be accurately detected and classified to preserve stability, efficiency, and power reliability in modern power systems as nonlinear loads and renewable energy sources become more common. Despite the excellent cl...
Deep learning for precipitation forecasting remains constrained by complex meteorological factors affecting accuracy. To address this issue, this paper proposes TransMambaCNN, which is a spatiotemporal transformer network fusing state-space models an...
Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variation...
Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares fou...
Industrial process control systems commonly exhibit features of time-varying behavior, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still...
The precise application of tightening torque is one of the important measures to ensure accurate bolt connection and improvement in product assembly quality. Currently, due to the limited assembly space and efficiency, a wrench without the function o...
Accurate prediction of landslide susceptibility is a key component of disaster risk reduction and early warning systems. Traditional landslide susceptibility prediction methods often face challenges in capturing complex nonlinear and spatio-temporal...
Accurate photovoltaic (PV) power forecasting is essential for reliable microgrid operation, efficient energy dispatch, and improved utilization of renewable energy resources. Existing forecasting methods often have limited capacity to represent the n...
Convolutional neural networks (CNNs) are widely used among the various deep learning techniques available because of their superior performance in the fields of computer vision and natural language processing. CNNs can effectively extract the localit...
In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to...
Soft continuum robots, inspired by the adaptability and agility of natural soft-bodied organisms like octopuses and elephant trunks, present a frontier in robotics research. However, exploiting their full potential necessitates precise modeling and c...
Various deep learning techniques have recently been developed in many fields due to the rapid advancement of technology and computing power. These techniques have been widely applied in finance for stock market prediction, portfolio optimization, ris...
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...
Intelligent recognition of ship motion states is a key technology for achieving smart maritime supervision and optimized port scheduling. To enhance both the modeling efficiency and recognition accuracy of AIS trajectory data, this paper proposes a s...
Natural gas wells operate under complex conditions with frequent environmental disturbances. Fault types vary significantly and often present weak signals, affecting both safety and efficiency. This paper proposes an intelligent fault-diagnosis metho...
Deep learning is an obvious method for the detection of disease, analyzing medical images and many researchers have looked into it. However, the performance of deep learning algorithms is frequently influenced by hyperparameter selection, the questio...
A cross-immunity model for influenza A(H3N2) based on convolutional neural networks (CNNs) was developed and validated under temporally structured conditions that mimic real-world forecasting. Antigenic distance was derived from hemagglutination inhi...
The large-scale production of edible mushrooms typically requires the use of greenhouses, as the greenhouse environment significantly affects the growth of edible mushrooms. It is crucial to effectively predict the temperature, humidity, and carbon d...
Sap flow is widely used to estimate the transpiration and water consumption of canopies and to manage water resources. In this paper, an improved time series prediction model was proposed by integrating three basic networks—CNN, GRU and BiLSTM&...
The Earth rotation parameters (ERPs), including polar motion (PMX and PMY) and universal time (UT1-UTC), play a central role in functions such as monitoring the Earth’s rotation and high-precision navigation and positioning. Variations in ERPs...
The ultrasonic guided lamb wave approach is an effective non-destructive testing (NDT) method used for detecting localized mechanical damage, corrosion, and welding defects in metallic pipelines. The signal processing of guided waves is often challen...
Wearable sensors-based gait recognition is an effective method to recognize people’s identity by recognizing the unique way they walk. Recently, the adoption of deep learning networks for gait recognition has achieved significant performance improvem...
The development of convolution neural networks (CNNs) has become a significant means to solve the problem of remote sensing scene image classification. However, well-performing CNNs generally have high complexity and are prone to overfitting. To hand...
The advent of machine learning (ML) has significantly transformed hydrology, particularly in the simulation of hydrological flows. However, ML techniques have not been employed to the same extent in stochastic hydrology. In applied sciences, the most...
Streamflow modelling is one of the most important elements for the management of water resources and flood control in the context of future climate change. With the advancement of numerical weather prediction and modern detection technologies, more a...
Predicting the price of Bitcoin is crucial, primarily because of the market’s rapid volatility and non-linear environment. For enhanced prediction of the price of Bitcoin, this research proposed a novel interpretable hybrid technique that combi...
The Tianjin Container Freight Index (TCI) is a key benchmark for container shipping prices in northern China and is strongly driven by macroeconomic conditions and trade fluctuations. This study proposes a PCC–CNN–GRU hybrid deep learning...
Recently, attention has been paid to the convolutional neural network (CNN) based synthetic aperture radar (SAR) target recognition method. Because of its advantages of automatic feature extraction and the preservation of translation invariance, the...
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...
A two-dimensional space turntable system has been used to ensure that the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) can track the Sun stably, and the prediction of the two-dimensional turntable trajectory is an important part of payload hea...
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....
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