Spectral Bidirectional Reflectance Distribution Function Simplification by Shubham Chitnis, Aditya Sole, Sharat Chandran

“…Unlike tristimulus BRDF acquisition, this spectral approach has not, to our knowledge, been previously explored with neural networks. We demonstrate compelling results for diffuse, glossy, and goniochromatic materials.…”
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A Novel Output Prediction Method in Production Management Based on Parameter Evaluation Using DHNN by Jiantao Chang, Yuanying Qiu, Xianguang Kong

“…To overcome this difficulty, a dynamic-improved multiple linear regression model based on parameter evaluation using discrete Hopfield neural networks (DHNN) is presented. First, a traditional multiple linear regression model is established; this model takes the factors in production lifecycle (not only one phase of the production) into account, such as manufacturing resources, manufacturing process, and product rejection rate, so it makes the output prediction be more accurate. …”
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Adaptive Neural Control with Prespecified Tracking Accuracy for a Class of Switched Systems Subject to Input Delay by Xu Zhang, Jian Wu, Wu Ai, Jing Li

“…To achieve this aim, some nonnegative switching functions are introduced to replace the conventional Lyapunov function. In addition, neural networks are used to approximate the unknown simultaneous domination functions. …”
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A Fast Identification Method for Seismic Responses of Bridge Structures by Integrating Digital Signal Features and Deep Learning by Zhaoxu Lv, Youliang Ding, Junxiao Guo

“…The results show that the signal processing techniques can effectively extract the structural response features and reduce the overfitting phenomenon of neural networks, and the combination of signal processing techniques and deep learning techniques can recognize the seismic response of bridge structures with high accuracy and efficiency.…”
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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…The proposed method provided a new idea for the study of deep neural networks in the field of image denoising.…”
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Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review by Victoria Moglia, Owen Johnson, Gordon Cook, Marc de Kamps, Lesley Smith

“…The most common cancers predicted in the studies were colorectal (n = 9) and pancreatic cancer (n = 9). 16 studies used feature engineering to represent temporal data, with the most common features representing trends. 18 used deep learning models which take a direct sequential input, most commonly recurrent neural networks, but also including convolutional neural networks and transformers. …”
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Design of a Novel NNs Learning Tracking Controller for Robotic Manipulator with Joints Flexibility by Pengxiao Jia

“…The control scheme employs neural networks-based observers to estimate both motor velocity and link velocity. …”
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Comprehensive exploration of visual working memory mechanisms using large-scale behavioral experiment by Liqiang Huang

“…Despite its significantly reduced complexity (57 parameters versus 30,796), QCE-VWM outperforms neural networks in data fitting. The model provides an integrative framework for understanding human visual working memory, incorporating a dozen mechanisms—some directly adopted from previous studies, some modified, and others newly identified. …”
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Explainable analysis of infrared and visible light image fusion based on deep learning by Bo Yuan, Hongyu Sun, YinJing Guo, Qiang Liu, Xinghao Zhan

“…Firstly, a multimodal image fusion model was proposed based on the advantages of convolutional neural networks (CNN) for local context extraction and Transformer global attention mechanism. …”
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Attentional Phenotypes for the Analysis of Higher Mental Function by John Fossella, Michael I. Posner, Jin Fan, James M. Swanson, Donald W. Pfaff

“…First, brain imaging is used to specify a cognitive process “attention” in terms of the neural networks involved. Next, evidence is presented showing that the operation of each network involves a dominant neuromodulator. …”
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Determining university’s readiness to implement AI technologies for personalizing educational paths by V. S. Starostin, K. A. Arzhanova, D. V. Dolgopolov, A. D. Ditrikh

“…Methods of machine learning, predictive analytics, and modern generative neural networks allow to create recommendation services, with the help of which individual educational trajectories are formed by machine intelligence, simultaneously considering hundreds of parameters. …”
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Deep Learning Techniques for Peer-to-Peer Physical Systems Based on Communication Networks by Ajay. P, Nagaraj. B, Ruihang Huang

“…It discusses autoencoders, productive enemy networks, deep emotional networks, common neural networks, and long-term memory, all of which show promise in all aspects of a wireless communication network. …”
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Adaptive Differentiator-Based Predefined-Time Control for Nonlinear Systems Subject to Pure-Feedback Form and Unknown Disturbance by Man Yang, Qiang Zhang, Ke Xu, Ming Chen

“…Furthermore, the design difficulty from the uncertain nonlinear function is overcome by the excellent approximation performance of RBF neural networks (NNs). An adaptive predefined-time controller is designed by introducing the finite-time differentiator which is used to decrease the computational complexity problem appeared in the traditional backstepping control. …”
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Prediction of Ubiquitination Sites Using UbiNets by Sarthak Yadav, Manoj Gupta, Ankur Singh Bist

“…The paper proposes a new MLP architecture, named UbiNets, which is based on Densely Connected Convolutional Neural Networks (DenseNet). Computational machine learning techniques, such as Random Forest Classifier, Gradient Boosting Machines, and Multilayer Perceptrons (MLP), are taken for analysis. …”
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Contraction Mapping Theory and Approach to LMI-Based Stability Criteria of T-S Fuzzy Impulsive Time-Delays Integrodifferential Equations by Ruofeng Rao, Shouming Zhong

“…In this paper, Banach fixed point theorem is employed to derive LMI-based exponential stability of impulsive Takagi-Sugeno (T-S) fuzzy integrodifferential equations, originated from Cohen-Grossberg Neural Networks (CGNNs). As far as we know, Banach fixed point theorem is rarely employed to derive LMI criteria for T-S fuzzy CGNNs, and this inspires our present work. …”
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Optimization of Leaky Integrate-and-Fire Neuron Circuits Based on Nanoporous Graphene Memristors by Kannan Udaya Mohanan, Seyed Mehdi Sattari-Esfahlan, Eou-Sik Cho, Chang-Hyun Kim

“…However, existing neuron architectures still lack in area efficiency, especially considering the huge size of modern neural networks requiring millions of neurons. Here, we report on a compact leaky integrate and fire (LIF) neuron circuit based on graphene memristor device. …”
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A Fuzzy Inference System for the Conjunctive Use of Surface and Subsurface Water by Liang-Cheng Chang, Hone-Jay Chu, Yi-Wen Chen

“…Subsequently, water allocations in the surface water system are simulated by using linear programming techniques, and the responses of subsurface water system with respect to pumping are forecasted by using artificial neural networks. The operating rule for the water systems is that the more abundant water system supplies more water. …”
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Crack simulation for the cover of the landfill � A seismic design by Mehran Karimpour-Fard, Omer Mughieda, Filippo Berto, Nurmunira Muhammad

“…Rankine�s theory and the Phantom Node Method were used for the simulation length of the crack and the mechanism of the crack propagation in the nonlinear extended finite element method (NXFEM). Artificial Neural Networks (ANNs) based on Levenberg-Marquardt Algorithm and Abalone Rings Data Set mode were used to predict displacement in critical points of the model. …”
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Fractional-Order Memristor Emulator Circuits by C. Sánchez-López, V. H. Carbajal-Gómez, M. A. Carrasco-Aguilar, I. Carro-Pérez

“…Moreover, the FOM emulator circuits can be used for improving future applications such as cellular neural networks, modulators, sensors, chaotic systems, relaxation oscillators, nonvolatile memory devices, and programmable analog circuits.…”
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A Framework for the AI-based visualization and analysis of massive amounts of 4D tomography data for end users of beamlines by Steffen Kieß, Thomas Lang, Tomas Sauer, Andreas Michael Stock, Andrey Chernov, Yipeng Sun, Andreas K. Maier, Tomáš Faragó, Alexey Ershov, Gabriel Lefloch, Guilherme Silva, Tilo Baumbach, Simon Zabler, Astrid Hölzing, Kilian Dremel, Ali Riza Durmaz, Akhil Thomas, Ingo Manke, Nikolay Kardjilov, Tobias Arlt, Tak Ming Wong, Regine Willumeit-römer, Julian Moosmann, Berit Zeller-Plumhoff, Dieter Froning, Sven Simon

“…The framework enables the compensation of imaging artifacts, including the compression artifacts of the 4D dataset, through the integration of neural networks. The reduction of imaging artifacts can be performed at the imaging facility or at the user's home institution. …”
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