Deep learning and explainable AI for classification of potato leaf diseases by Sarah M. Alhammad, Doaa Sami Khafaga, Walaa M. El-hady, Farid M. Samy, Khalid M. Hosny

“…Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. …”
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Nonlinear memristive computational spectrometer by Xin Li, Jie Wang, Feilong Yu, Jin Chen, Xiaoshuang Chen, Wei Lu, Guanhai Li

“…Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. …”
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Classification of Animal Behaviour Using Deep Learning Models by M. Sowmya, M. Balasubramanian, K. Vaidehi

“…The proposed system detects animal behaviours in real time using deep learning-based models, namely, convolution neural network and transfer learning. Specifically, 2D-CNN, VGG16 and ResNet50 architectures have been used for classification. 2D-CNN, «VGG-16» and «ResNet50» have been trained on the video frames displaying a range of animal behaviours. …”
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PCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma by Caio L. B. Andrade, Marcos V. Ferreira, Brenno M. Alencar, Jorge L. S. B. Filho, Matheus A. Guimaraes, Iarley Porto Cruz Moraes, Tiago J. S. Lopes, Allan S. dos Santos, Mariane M. dos Santos, Maria I. C. S. e Silva, Izabela M. D. R. P. Rosa, Gilson C. de Carvalho, Herbert H. M. Santos, Márcia M. L. Santos, Roberto Meyer, Luciana M. P. B. Knop, Songeli M. Freire, Ricardo A. Rios, Tatiane N. Rios

“…We also share a Deep Neural Network model, as a benchmark, trained to identify and count plasma and non-plasma cells automatically. …”
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Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups by Yuanhui Fang, Yanmei Cui, Xianzhi Ao

“…We present in this study, based on the SDO/HMI SHARP data taken during the time interval 2010-2017, an automatic procedure for the recognition of the predefined magnetic types in sunspot groups utilizing a convolutional neural network (CNN) method. Three different models (A, B, and C) take magnetograms, continuum images, and the two-channel pictures as input, respectively. …”
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Scientific Article Writing: Will ChatGPT Help? by I. M. Zashikhina

“…The cases of writing academic papers using ChatGPT have led to a number of publications analyzing the pros and cons of using this neural network. In this paper, we investigate the possibility of using ChatGPT to write an introduction to a scientific paper on a topical issue of the Arctic governance. …”
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Machine learning-based prediction of soil organic matter via smartphone by Qingying Gao, Yi Chen, Hui Zhang, Jingjing Chen, Liang Wang

“…Random forest of classification (RFC), random forest of logical regression (RFLR), convolutional neural network (CNN) and MobileNet models are compared, which is better for SOM prediction. …”
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Predicting GPR Signals from Concrete Structures Using Artificial Intelligence-Based Method by Wael Zatar, Tu T. Nguyen, Hai Nguyen

“…A total of 288 rebar picks were used for training, validation, and testing the proposed Artificial Neural Network (ANN) model. Multiple ANN model configurations with a variation in learning algorithms and the number of nodes in the hidden layer were explored to obtain the optimal model for the nondestructive data. …”
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A Component Prediction Method for Flue Gas of Natural Gas Combustion Based on Nonlinear Partial Least Squares Method by Hui Cao, Xingyu Yan, Yaojiang Li, Yanxia Wang, Yan Zhou, Sanchun Yang

“…In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. …”
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ECT Image Recognition of Pipe Plugging Flow Patterns Based on Broad Learning System in Mining Filling by Xuebin Qin, ChenChen Ji, Yutong Shen, Pai Wang, Mingqiao Li, Junle Zhang

“…BLS is a feedforward neural network with few optimization parameters and fast training speed. …”
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Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network by Zhuqing Bi, Chenming Li, Xujie Li, Hongmin Gao

“…Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.…”
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An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud-Edge Collaborative Environment by Jiayong Zhong, Xiaofu Xiong

“…Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. …”
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A System for Robotic Extraction of Fasteners by Austin Clark, Musa K. Jouaneh

“…This study develops a system for extracting cross-recessed screws using a Deep Convolutional Neural Network (DCNN) for screw detection, integrated with industrial robot simulation software. …”
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Analysis of Feature Fusion Based on HIK SVM and Its Application for Pedestrian Detection by Song-Zhi Su, Shu-Yuan Chen

“…The proposed method combines the histogram of oriented gradient (HOG) and local binary pattern (LBP) features by a concatenated fusion method. Although neural network (NN) is an efficient tool for classification, the time complexity is heavy. …”
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Design and Realization of the Intelligent Design System for Tunnel Blasting in Mine Based on Database by Zhengyu Wu, Dayou Luo, Guan Chen

“…Based on the T-S fuzzy neural network model, the intelligent search rules of excavation blasting data are also constructed in the new system. …”
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Unveiling midcrustal seismic activity at the front of the Bolivian altiplano, Cochabamba region by Gonzalo Antonio Fernandez M, Benoit Derode, Laurent Bollinger, Bertrand Delouis, Mayra Nieto, Felipe Condori, Nathan Sarret, Jean Letort, Stephanie Godey, Mathilde Wimez, Teddy Griffiths, Walter Arce

“…This study highlights the initial 6-month seismic bulletin made by manual and automated deep-neural-network based seismic phase picking. We also test the network's ability to resolve focal mechanisms of moderate to small events with a combined inversion of waveforms and polarities. …”
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Comparative Analysis of Battery State of Charge Estimation Methods by Margal Ali, El Daoudi Soukaina, Khallouq Abdelmounaim, Karama Asma

“…This paper presents three methods for estimating SoC: the extended Kalman filter (EKF), the adaptive Luenberger observer (ALO), and a neural network model employing nonlinear auto-regressive with eXogenous inputs (NARX). …”
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Molecular Modeling of Antimalarial Agents by 3D-QSAR Study and Molecular Docking of Two Hybrids 4-Aminoquinoline-1,3,5-triazine and 4-Aminoquinoline-oxalamide Derivatives with the... by Hanine Hadni, Mohamed Mazigh, El’mbarki Charif, Asmae Bouayad, Menana Elhallaoui

“…The QSAR model tested with artificial neural network (ANN) method shows high performance towards its predictability. …”
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Deep-Learning-Based Bughole Detection for Concrete Surface Image by Gang Yao, Fujia Wei, Yang Yang, Yujia Sun

“…A deep convolutional neural network for detecting bugholes on concrete surfaces was developed, by adding the inception modules into the traditional convolution network structure to solve the problem of the relatively small size of input image (28 × 28 pixels) and the limited number of labeled examples in training set (less than 10 K). …”
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

“…In this research we propose a GPS-only data trajectory analysis and a novel scheme to convert GPS trajectory data to image-based data to train a custom Convolutional Neural Network (CNN) model. The empirical results with an extensive 5-fold cross-validation show that the proposed scheme identifies turn and not turn with more than 94% recall. …”
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