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Probabilistic analysis is an emerging field of structural engineering which is very significant in structures of great importance like dams, nuclear reactors etc. In this work a Neural Networks (NN) based Monte Carlo Sim...

To address the difficulty of blasthole detection during the charging phase of drill-and-blast tunnelling, which is aggravated by dust interference and insufficient illumination, this study proposes an intelligent blastho...

Abstract High-density, long-term stable decoding of whole-brain function is crucial for advancing basic neuroscience research and developing neural disorder therapies. However, two major challenges remain: the lack of sc...

Abstract Electric cable shovel (ECS) is a complex production equipment, which is widely utilized in open-pit mines. Rational valuations of load is the foundation for the development of intelligent or unmanned ECS, since ...

The integration of solar energy into modern power grids supports sustainability and energy efficiency but also introduces power quality (PQ) challenges such as harmonic distortion, voltage sag, swell, and fluctuations. I...

The rapid evolution of sustainability legislation, including the CSDDD and CSRD, has created a complex dual mandate for modern enterprises. Organizations are now required to provide granular transparency into their suppl...

Biofabricated scaffolds facilitate bona fide cellular interactions, cell type specification, and the formation of three-dimensional tissue architecture from human pluripotent stem cells (hPSCs). However, xenogenic biomat...

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 To address the limitations of existing oil-well production forecasting models, particularly suboptimal hyperparameter selection, difficulties in fine-tuning, insufficient global search capability, and slow conve...

Abstract Bone tissue engineering scaffolds must provide structural support while permitting fluid transport and maintaining safe hydrodynamic conditions for cell activity. Triply Periodic Minimal Surface (TPMS) architect...

Abstract Accurate prediction of bio-oil yield from pyrolysis of organic solid waste is still the main problem in the field of biomass valorization, mostly because of high variability in the composition and the heterogene...

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...

This study proposes and validates a conceptual framework using a sign language (SL) chatbot in the e-commerce domain to improve accessibility. Currently, the use of SL is a less-explored area in online platforms. It is d...

Car classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies h...

Drought is a slow-onset natural disaster that has far-reaching effects on agriculture, water security, and socio-economic systems, especially in climate-vulnerable countries such as Malaysia. It is imperative to predict ...

Free-space optical (FSO) communication using dual-polarization self-coherent (DP-SC) transceivers is a promising solution for achieving high data rates and enhanced spectral efficiency in the next optical wireless system...

Abstract Early and reliable smoke detection is critical for preventing fire-related disasters, safeguarding human lives, and ensuring infrastructure safety. Conventional sensor-based systems, however, are often constrain...

With the proposal of the national dual-carbon strategic goal, distributed photovoltaic (PV) ushers in new development opportunities, and the management of voltage quality for photovoltaic power stations becomes a key cha...

By introducing the artificial intelligence technology, on improved Nelder-Mead acceleration algorithm based on the neural network is proposed to obtain better optimization results. A back analysis method for construction...

Abstract The current study employs Bayesian-regularization optimizer technique with artificial neural networks (BROT-ANNs) to investigate the significance of local thermal non-equilibrium influences on wax + sand-bas...

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...

Abstract Engine tests are both costly and time consuming in developing a new internal combustion engine. Therefore, it is of great importance to predict engine characteristics with high accuracy using artificial intellig...

Tuberculosis (TB) continues to be one of the foremost public health issues in the world, and remains the second most salient communicable cause of death after COVID-19. In 2022, TB accounted for 10.6 million new infectio...

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...