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Customer feedback is pivotal in enhancing service quality and user satisfaction across digital platforms. However, traditional sentiment analysis methods often struggle with informal languages, contextual nuances, and as...

Accurate identification of plant-parasitic nematodes is fundamental to effective crop protection and the maintenance of soil ecosystem integrity. This study integrates morphological characterization with deep learning-ba...

Plastic waste is a significant environmental concern, constituting a major portion of the global waste stream. Improper disposal and accumulation have led to severe environmental challenges, including pollution, harm to ...

Abstract High-throughput analysis of EEG data has significantly contributed to understanding neural dynamics in Alzheimer’s disease diagnosis. However, the complexity and high dimensionality of EEG signals pose challen...

Fault Detection and Diagnosis (FDD) is crucial for ensuring safe, reliable, and energy-efficient operation of collaborative robots, especially with the growth of Industry 4.0. Industrial robots are nonlinear, complex, an...

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a n...

Abstract Alkaline water electrolysis represents a promising pathway for green hydrogen production, yet comprehensive multi-physics simulation remains computationally prohibitive for practical design optimization. This st...

Introduction. In recent days, electric vehicles, robotics and in many control system applications, permanent magnet synchronous motors (PMSMs) are widely utilized. Problem. Due to non-linear behavior of system, external ...

The increasing penetration of renewable energy sources (RES) reduces power system inertia, causing severe frequency excursions following disturbances. Conventional Automatic Generation Control (AGC) using fixed-gain cont...

Abstract The deep reinforcement learning techniques offers a novel approach to real-time control for hybrid power flow controllers. This paper introduces a bus voltage optimization adjustment strategy, utilizing a Markov...

ABSTRACT As a government‐regulated public service, traffic signal control (TSC) requires reliable and transparent decision‐making. However, existing deep reinforcement learning (DRL) methods, despite improvements in ...

Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and ge...

BackgroundNon-suicidal self-injury (NSSI) is common among adolescents and young adults and remains difficult to detect early using conventional approaches. machine learning (ML) has increasingly been applied to develop p...

Data-driven design approaches based on deep learning have been introduced in nanophotonics to reduce time-consuming iterative simulations, which have been a major challenge. Here, we report the first use of conditional d...

Cancer is the second-largest cause of death worldwide, constitute about one out of every six deaths. Diagnostic techniques have been developed for the early detection to determine benign and malignant ones and conduct ap...

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children’s normal life. Screening potential autistic children before professional diagnose is helpful to early detection and earl...

Abstract Individualized educational management cannot be avoided in meeting various learner needs in cases where teaching strategies are changed in line with the performance trends and preferences of students. Reinforcem...

Abstract With a growing number of institutions involved in the global education market, it has become increasingly challenging to verify the authenticity of academic documents and to match them between universities. The ...

ABSTRACT Graph contrastive learning (GCL) has emerged as a dominant paradigm for self‐supervised representation learning for attributed graph data. However, existing GCL methods heavily rely on empirical graph data aug...

Abstract The increase in vehicle ownership and urban population puts a strain on roads and increases traffic congestion within developed and developing nations. Congestion affects an individual’s time and increases an ...

Abstract To address the challenge of low adaptability in distribution network planning caused by significant regional differences in electricity consumption, this paper proposes a distribution network planning method bas...

Abstract Matching college students with appropriate innovative training projects is a challenging task that often relies on static assignment techniques, which overlook individual interests, skills, and learning styles. ...

Rich in multiple strategic rare metals, deep-sea manganese nodules constitute an essential mineral resource for balancing terrestrial resource supply and demand and facilitating the global energy transition. Accurate pre...

With the rapid advancement of online education, students’ sentiment feedback serves as a pivotal factor in enhancing course quality and refining pedagogical strategies. However, conventional sentiment analysis approach...