Machine Learning and Safe and Inclusive Architecture for Fragile Users by Antonio Magarò, Adolfo F. L. Baratta

“…The contribution presents the first results of a research conducted in the Department of Architecture, Roma Tre University, aimed at testing Machine Learning algorithms for train Neural Networks in learning data from BIM, with the purpose of generating Augmented Reality contents. …”
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Consilience of Reductionism and Complexity Theory in Language Research: Adaptive Weight Model by Chao Zhang

“…This paper starts by discussing the adaptability of complex dynamic systems and combines cognitive processing model and artificial neural networks to construct and verify an adaptive weight model, showing that the study of reductionism is induction of high-weight elements and the study of complexity theory is a discussion of system complexity from adaptability, meaning that there is a good fit between the two frameworks. …”
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Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features by Samuel, Kakuba, Alwin, Poulose, Dong, Seog Han, Senior Member, Ieee

“…Spatial and temporal features have been extracted sequentially in deep learning-based models using convolutional neural networks (CNN) followed by recurrent neural networks (RNN) which may not only be weak at the detection of the separate spatial-temporal feature representations but also the semantic tendencies in speech. …”
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Spatial analysis of hyperspectral images for detecting adulteration levels in bon-sorkh (Allium jesdianum L.) seeds: Application of voting classifiers by Golshid Fathi, Seyed Ahmad Mireei, Mehrnoosh Jafari, Morteza Sadeghi, Hassan Karimmojeni, Majid Nazeri

“…After image preprocessing using median blur and bilateral filters, pixel-wise classification models were developed using artificial neural networks, random forest, and voting classifiers to detect pure bon-sorkh and shallot seeds. …”
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Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) by Dongling Wu, Hongxiang Zhang, Yiying Yang

“…In this study, the full convolutional neural networks FCN-8s, FCN-16s, and FCN-32s are applied to monitoring of concrete apparent cracks and according to the image characteristics of concrete cracks and experimental results. …”
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Adaptive Neural Control Approach for Switched Nonlinear Discrete-Time Systems With Actuator Faults and Input Dead Zone by Ymnah Alruwaily, Mohamed Kharrat

“…The complex structure of these systems, combined with actuator faults and dead-zone inputs, presents significant challenges for control and this problem is addressed by approximating the unknown functions of each subsystem using radial basis function neural networks. Under arbitrary switching signals, the suggested controller and adaptive laws ensure that all signals remain bounded and the system output tracks the reference signal with a small bounded tracking error. …”
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Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete by Miljan Kovačević, Marijana Hadzima-Nyarko, Predrag Petronijević, Tatijana Vasiljević, Miroslav Radomirović

“…This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and neural networks. The evaluation was based on their predictive accuracy. …”
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Funnel-Based Adaptive Neural Fault-Tolerant Control for Nonlinear Systems with Dead-Zone and Actuator Faults: Application to Rigid Robot Manipulator and Inverted Pendulum Systems by Ymnah Alruwaily, Mohamed Kharrat

“…To manage unknown nonlinear functions, radial basis function neural networks (RBFNN) are employed in designing an adaptive neural funnel fault-tolerant controller through the backstepping technique. …”
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A novel prediction model of grounding resistance based on long short-term memory by Xinghai Pu, Jing Zhang, Fei Wang, Shuai Xue

“…Furthermore, the study benchmarks the LSTM model’s performance against traditional Artificial Neural Networks, confirming the LSTM’s superior predictive accuracy regarding time-dependent changes in grounding resistance. …”
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Advancements in Smart Nondestructive Evaluation of Industrial Machines: A Comprehensive Review of Computer Vision and AI Techniques for Infrastructure Maintenance by Samira Mohammadi, Sasan Sattarpanah Karganroudi, Vahid Rahmanian

“…This review explores the current state of computer vision in NDE, discussing key techniques, applications across various infrastructure types, and the integration of deep learning models such as convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models. …”
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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles by Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

“…Experimental data collected from about 50 papers are analyzed by machine learning algorithms including linear regression, principal component analysis, Gaussian process regression, and artificial neural networks. The focus is to understand the effect of drug solubility, drug molecular weight, particle size, and pH-value of the release matrix/environment on drug release profiles. …”
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Prescribed Performance Neural Control of Strict-Feedback Systems via Disturbance Observers by Wei Xiang, Guangkui Xu, Fang Zhu, Chunzhi Yang

“…And radial basis function neural networks (RBFNNs) are used to approximate nonlinear functions, while second-order filters are employed to eliminate the “explosion-complexity” problem inherent in the existing method. …”
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From Net Topology to Synchronization in HR Neuron Grids by Stefano Cosenza, Paolo Crucitti, Luigi Fortuna, Mattia Frasca, Manuela La Rosa, Cecilia Stagni, Lisa Usai

“…We study it in Hindmarsh-Rose neural networks,with electrical and chemical synapses, where neurons are placed ona bi-dimensional lattice, folded on a torus, and the synapses areset according to several topologies. …”
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Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors by Jesús Enrique Sierra, Matilde Santos

“…In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. …”
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Predicting the Pullout Capacity of Small Ground Anchors Using Nonlinear Integrated Computing Techniques by Mosbeh R. Kaloop, Jong Wan Hu, Emad Elbeltagi

“…The results of the developed models are compared with previous studies that used artificial neural networks and least square support vector machine techniques for the same case study. …”
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Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon by Moumita Saha, Pabitra Mitra, Arun Chakraborty

“…Statistical schemes are mainly based on regression or neural networks. However, the variability of monsoon is significant over the years and a single model is often inadequate. …”
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Multi-frame blind deconvolution using X-ray microscope images of an in-plane rotating sample by Shinnosuke Kurimoto, Takato Inoue, Hitoshi Aoto, Toshiki Ito, Satsuki Ito, Yoshiki Kohmura, Makina Yabashi, Satoshi Matsuyama

“…Abstract We propose a multi-frame blind deconvolution method using an in-plane rotating sample optimized for X-ray microscopy, where the application of existing deconvolution methods is technically difficult. Untrained neural networks are employed as the reconstruction algorithm to enable robust reconstruction against stage motion errors caused by the in-plane rotation of samples. …”
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Evaluation of Novel AI Architectures for Uncertainty Estimation by Erik Pautsch, John Li, Silvio Rizzi, George K. Thiruvathukal, Maria Pantoja

“…Our research evaluates uncertainty in Convolutional Neural Networks (CNN) and Vision Transformers (ViT) using the MNIST and ImageNet-1K datasets. …”
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Seismic Damage Rapid Assessment of Road Networks considering Individual Road Damage State and Reliability of Road Networks in Emergency Conditions by Jinlong Liu, Hanxi Jia, Junqi Lin, Heng Hu

“…In addition, artificial neural networks are used to evaluate the damage state of an individual road based on the factors that are selected with higher importance. …”
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Maturation of the GABAergic Transmission in Normal and Pathologic Motoneurons by Anne-Emilie Allain, Hervé Le Corronc, Alain Delpy, William Cazenave, Pierre Meyrand, Pascal Legendre, Pascal Branchereau

“…In immature brain structures, GABA exerts depolarizing effects mostly contributing to the expression of spontaneous activities that are instructive for the construction of neural networks but GABA also acts as a potent trophic factor. …”
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