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Abstract Echo State Networks are recurrent neural networks that leverage a random reservoir’s dynamics, training only a simple readout layer. This approach is computationally efficient but limits network performance. B...

Abstract Graph Neural Networks (GNNs) are powerful tools for predicting chemical shifts in Nuclear Magnetic Resonance (NMR) spectroscopy. In this paper, we improve the state-of-the-art mean absolute error (MAE) on the Il...

ABSTRACT The growing use of neural networks in privacy‐sensitive applications necessitates architectures that inherently protect both data and model integrity. We present a model‐inversion‐resistant physical unclon...

Background For those with hearing loss, sign language (SL) is an essential communication tool. The most common and basic type of SL is American SL (ASL). ASL plays a vital role in various communicative contexts, includin...

Abstract This study uses the Levenberg–Marquardt strategy with feed forward neural networks (LMS-FNN) to inspect the Soret-Dufour effect on radiative hybrid nanofluid flow across a Riga plate with gyrotactic microorgan...

ABSTRACT Deep learning profoundly impacts various areas, such as face recognition and language translation. Owing to the increasingly high computational costs of training neural architectures, it is intractable to manual...

Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One o...

Reliable estimation of reference evapotranspiration (ET0) is essential for irrigation scheduling and agricultural water-resources management. Although the FAO-56 Penman–Monteith equation is widely accepted as the stand...

Abstract Pattern separation, the ability of a network to distinguish similar inputs by transforming them into distinct outputs, was postulated by the Marr-Albus theory to be realized by divergent feedforward excitatory c...

Abstract Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches. Traditional ex...

Airport planning depends to a large extent on the levels of activity that are anticipated. To plan the facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predic...

"Data-driven simulation of pedestrian dynamics is an incipient and promising approach for building reliable microscopic pedestrian models. We propose a methodology based on generalized regression neural networks, which d...

Abstract The last decade witnessed a great evolution on the fields of computer science, artificial intelligence, communication and data transmission. This evolution draw the attention of specialists to design a modern cr...

Post-traumatic stress disorder (PTSD) is a severe neuropsychiatric disorder characterised by re-experiencing, avoidance and hyperarousal. Memory abnormalities manifested as intrusive thoughts and prolonged distressful em...

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...

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...

Abstract Graph neural networks (GNNs) are widely used as surrogates for costly experiments and first-principles simulations to study the behavior of compounds at atomistic scale, and their architectural complexity is con...

Abstract Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in th...

The identification of suitable models for predicting daily water flow is important for planning and management of water storage in reservoirs of Argentina. Long-term prediction of water flow is crucial for regulating res...

After an earthquake, numerous buildings are destroyed, and the frequency of structural collapses increases significantly in regions lacking comprehensive geophysical surveys. Furthermore, the number of deaths increases d...

ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐...

Abstract Background Bacterial diseases include any illness caused by bacteria. Bacteria are tiny microorganisms that can only be seen with a microscope. Other types of microorganisms include viruses, fungi, and some para...

The rapid growth and increasing complexity of Internet of Things (IoT) networks require efficient real-time monitoring and anomaly detection mechanisms. Traditional machine learning approaches often struggle to handle th...

Abstract Alzheimer’s disease, a progressive neurodegenerative disorder, presents a growing global health challenge due to its increasing prevalence and lack of accessible early diagnostic methods. Even though it has en...