The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed an...
There are inevitable multiphase flow problems in the process of subsea oil-gas acquisition and transportation, of which the two-phase flow involving gas and liquid is given much attention. The performance of pipelines and equipment in subsea systems...
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children and adolescents. A current area of interest is the association between ADHD and food consumption. The aim of this study was to determin...
Performance prediction is of significant importance. Previous mining of behaviour data was limited to machine learning models. Corresponding research has not made good use of the information of spatial location changes over time, in addition to discr...
Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway statio...
Background/Objectives: Normative perceptual segmentation facilitates event perception, comprehension, and memory. Given that native English listeners’ normative perceptual segmentation of English speech streams coexists with a highly selective...
The role of diet in the behavior of children has been controversial, but the association of several nutritional factors with childhood behavioral disorders has been continually suggested. We conducted a case-control study to identify dietary patterns...
Accurate identification of oil–water two-phase flow patterns is essential for ensuring the safety and operational efficiency of oil and gas extraction systems. While traditional methods using empirical models and sensor technologies have provid...
The two-phase flow in a microchannel consists of liquid–liquid and gas–liquid material components. The automatic recognition of flow patterns using deep learning approaches has been emerging. This study aimed to improve the recognition accuracy of fl...
Exhibit texts are commonly presented as labels or information panels near artworks to provide visitors with direct access to key exhibit information. Digital museums offer flexible and diverse ways to present artworks and communicate their cultural m...
City influence is a critical topic in regional studies, reflecting how cities draw attention and exert impact in various domains. Understanding city influence is essential for fostering sustainable urban growth. However, existing studies have failed...
Image steganography is often employed in information security and confidential communications, yet it typically faces challenges of imperceptibility and robustness during transmission. Meanwhile, insufficient attention has been paid to preserving the...
Accurate ship trajectory prediction is crucial for real-time vessel position tracking and maritime safety management. However, existing methods for ship trajectory prediction encounter significant challenges. They struggle to effectively extract long...
Attention-Deficit/Hyperactivity Disorder (ADHD) may be associated with alterations in eye movements, which in turn may reflect dysfunctions in executive functions and sensorimotor integration processes. This review analyzed the pattern of oculomotor...
Internet attention, as a reflection of the actual focus of the public, not only responds to potential tourism demand but also represents the overall perception and preference characteristics of tourists for a tourist destination. The study selected e...
To address the temporal jitter with low segmentation accuracy and the lack of high-precision transformations for specific object classes in video generation, we propose the fish body pattern sync-style network for ornamental fish videos. This network...
Repetitive head impacts (RHIs) in combat and contact sports are associated with long-term neuropsychological consequences. The present study explores episodic memory performance, with a focus on pattern separation, a memory process associated with hi...
To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large...
Symptoms of Attention Deficit Hyperactivity Disorder (ADHD) include excessive activity, difficulty sustaining attention, and inability to act in a reflective manner. Early diagnosis and treatment of ADHD is key but may be influenced by the observatio...
Increasing wind power penetration will profoundly impact a power system’s operating mechanism. It is necessary to study a control strategy so that wind farms can use energy storage to improve their controllability to the level of traditional un...
14 March 2023
The diagnostic criteria for attention deficit hyperactivity disorder (ADHD) reflect the behavioural and functional outcomes of cognitive processes. Historically they have been based on external observations and lack specificity: clinical cohorts of c...
This study investigates the impact of intelligible background speech on reading disruption utilising neuromarketing methodologies, specifically an eye-tracking webcam (Tobii Sticky) and AI eye-tracking software (Predict, v.1.0.). A cohort of 144 part...
With the development of data mining technology, educational data mining (EDM) has gained increasing amounts of attention. Research on massive open online courses (MOOCs) is an important area of EDM. Previous studies found that assignment-related beha...
Background: Preventive strategies, including adherence to specific dietary patterns, have received increasing attention as approaches to reduce cognitive decline and dementia risk. However, findings remain inconsistent, partly due to differences in d...
With the rapid expansion of the civil aviation industry, the surge in flight numbers has led to increasingly pronounced issues of air route congestion and flight conflicts. 4D trajectory prediction, by dynamically adjusting aircraft paths in real tim...
Understanding the tourism resource network attention is crucial for promoting sustainable tourism development. This study utilized multi-source data to assess tourism resource network attention in Western Hunan, with GIS spatial analysis and the Geod...
The near-future parking space availability is informative for the formulation of parking-related policy in urban areas. Plenty of studies have contributed to the spatial–temporal prediction for parking occupancy by considering the adjacency bet...
Scanpaths are composed of fixations and saccades. Viewing trends reflected by scanpaths play an important role in scientific studies like saccadic model evaluation and real-life applications like artistic design. Several scanpath synthesis methods ha...
Urban tourism network attention is important for measuring the competitiveness of the urban tourism industry, tourism attraction, and cultural soft power. In this study, we explored the spatiotemporal patterns and factors influencing network attentio...
Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to...
The squat is a multi-joint exercise widely used for everyday at-home fitness. Focusing on the fine-grained classification of squat motions, we propose a smartwatch-based wearable system that can recognize subtle motion differences. For data collectio...
Wave energy has emerged as a focal point in marine renewable energy research. Accurate prediction of wave power plays a pivotal role in enhancing power supply reliability. This paper introduces an innovative wave power prediction method that combines...
Literature reports superior performance when focusing one’s attention during a movement on environmental effects of that movement (external focus, EF) compared to focusing on the moving body (internal focus, IF). Nevertheless, IF instructions s...
Wafer defect pattern recognition can help engineers improve the production process of semiconductor chips. In real industrial scenarios, the recognition of mixed-type wafer defects is difficult and the production scale of semiconductor wafers is larg...
Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, the...
Accurate Short-Term Load Forecasting (STLF) is a critical task in managing and operating smart grids. Existing STLF methods primarily rely on mathematical modeling or neural networks, often struggle to effectively capture the correlations between inf...
Wafer map defect pattern classification supports quality monitoring in semiconductor manufacturing, but public benchmark datasets such as WM-811K exhibit extreme class imbalance, where majority classes can dominate standard metrics. This study aims t...
Human Action Recognition is a challenging task used in many applications. It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them. It mak...
Understanding users’ visual attention on websites is paramount to enhance the browsing experience, such as providing emergent information or dynamically adapting Web interfaces. Existing approaches to accomplish these challenges are generally b...
To address the limitations of insufficient multi-scale feature mining and rigid fusion strategies in existing remaining useful life (RUL) prediction methods, this paper proposes a novel approach based on a Reinforced Dynamic Degradation Evolution Mod...
Problem: In the deep image steganography field, the main challenge is to achieve a balance between visual image quality, reliably recovering the message, robustness, and interpretability, especially regarding image distortion because of noise, attack...
Facial emotion recognition has become increasingly important in healthcare, where understanding delicate cues like pain, discomfort, or unconsciousness can support more timely and responsive care. Yet, recognizing facial expressions in real-world set...
Predicting taxi-calling demands at the urban area level is vital to coordinate the supply–demand balance of the urban taxi system. Differing travel patterns, the impact of external data, and the expression of dynamic spatiotemporal demand depen...
Reliable state of health (SOH) estimation plays an important role in the safe and stable operation of lithium-ion battery energy storage systems. Nevertheless, the nonlinear degradation characteristics and complex aging behaviors of batteries hinder...
Offshore multilayer sandstone reservoirs are characterized by complex vertical alternating thick and thin layers, resulting in significant heterogeneity. Traditional productivity evaluation methods often fail to effectively represent the dynamic prod...
Attention-deficit/hyperactivity disorder (ADHD), a neuropsychiatric condition characterized by inattention, hyperactivity, and impulsivity, afflicts 5% of children worldwide. Each ADHD patient presents with individual cognitive and motivational pecul...
Quantitative forecasting and lifecycle pattern recognition of science and technology (S&T) innovation activities are essential for evidence-based technology management, yet modeling long-range dependencies in multivariate S&T time series whil...
Accurately predicting proton flux in the space radiation environment is crucial for satellite in-orbit management and space science research. This paper proposes a proton flux prediction method based on a hybrid neural network. This method is a predi...
The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss o...
Unmanned surface vehicle (USV)’s motion is represented by time-series data that exhibit highly nonlinear and non-stationary features, significantly influenced by environmental factors, such as wind speed and waves, when sailing on the sea. The...
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