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Understanding the spatio-temporal structure and persistence of meteorological droughts is critical for climate risk assessment, particularly in regions with pronounced climatic gradients. Here, we present the first appli...

Spiking neural networks (SNNs) offer a biologically inspired, energy-efficient alternative to conventional artificial neural networks (ANNs). However, deep SNNs struggle to process complex EEG signals because their spike...

One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced cl...

Abstract Collaborative filtering and neural recommendation models have advanced substantially over the past decade, yet they frequently struggle to capture the fine-grained structure of user preferences and the rich, mul...

Over the past few years, there has been growing interest in using online methods for collecting data from large samples. However, only a few studies have administered online behavioral tasks to assess attention outside t...

This paper investigates the distributed state estimation problem for a class of continuous-time nonlinear systems over sensor networks. The nonlinearities considered are assumed to satisfy the Incremental Quadratic Const...

Graph-based text classification has become an effective paradigm for modeling corpus-level structure, particularly under limited supervision. However, most existing approaches rely on document–word representations...

This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. Th...

For the structural health monitoring of composite materials, data analysis technology must be very sophisticated, capable of detecting fault patterns that are multi-level and complicated. A comprehensive deep learning pa...

Abstract Satellite Image Classification is a process in remote sensing that aims at classifying pixels of satellite imagery into usable information of land use and land cover. This information is not only limited to seve...

Dynamic gesture recognition is a core technology for human–computer interaction, yet existing methods face prominent limitations in hand spatial topology modeling, long–short-term temporal feature fusion, and general...

Networks and especially network security, have recently become an area of essential concern in data communication due to the ever-present need for data security. Especially that data is to be exposed to the public and tr...

One of the deadliest diseases that can affect children and adults is a brain tumour. A brain tumour is the abnormal growth of cells in the brain or near it. Commonly, there are two types of tumours: malignant (cancerous)...

Language classification systems are used to classify spoken language from a particular phoneme sample and are usually the first step of many spoken language processing tasks, such as automatic speech recognition (ASR) sy...

Abstract Advances in AI have transformed the traditional Internet of Things (IoT) into smarter, more versatile next-generation devices. Enhanced connectivity among sensors, actuators, and appliances boosts data availabil...

A high accuracy of antenna beam pointing is essential for weather and cloud radars in order to precisely locate clouds and precipitation. It is also a critical requirement for estimating the horizontal wind field or retr...

Abstract Significant changes occur in brain structure and cognition during adolescence. Investigating their association can provide insight into brain-based cognitive development, yet previous studies are limited by narr...

Background Individual differences have been detected in individuals with opioid use disorders (OUD) in rehabilitation following protracted abstinence. Recent studies suggested that prediction models were effective for in...

Effective-medium approaches, such as the coherent potential approximation, are commonly used to describe the rigidity transition of disordered elastic networks. While successful in capturing the universal critical behavi...

Abstract While multi-access edge computing (MEC) enables flexible task offloading, the inherent dynamism and complexity of real-world environments, accompanied by various uncertainties, exacerbate the challenges in offlo...

We present analytical results on the exact tensor network representations and correlation functions of the first examples of 2D ground states with quantum phase transitions between area law and extensive entanglement ent...

Abstract Accurate prediction of terrestrial water cycle variables like soil moisture (SM) and evapotranspiration (ET) is crucial, yet existing Deep Learning (DL) models face limitations. Many adopt Single-Task Learning (...

Goal: To enable comfortable and non-invasive heart rhythm monitoring, this work aims to reconstruct electrocardiogram (ECG) signals from photoplethysmogram (PPG) signals, eliminating the need for multiple electrode attac...

Embodied and intersubjective experience is crucial to the social cognition processes on which interpersonal relationships are based. We assessed whether promoting these processes through participation in self-awareness a...