Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress) by Ryan M. McGranaghan, Jack Ziegler, Téo Bloch, Spencer Hatch, Enrico Camporeale, Kristina Lynch, Mathew Owens, Jesper Gjerloev, Binzheng Zhang, Susan Skone

“…The new total electron energy flux particle precipitation nowcast model, a neural network called PrecipNet, takes advantage of increased expressive power afforded by ML approaches to appropriately utilize diverse information from the solar wind and geomagnetic activity and, importantly, their time histories. …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…This paper presents a novel hybrid model of DL models combined a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for binary breast cancer classification on two datasets available at the Kaggle repository. …”
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Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques by Samitha Vidhanaarachchi, Janaka L. Wijekoon, W. A. Shanaka P. Abeysiriwardhana, Malitha Wijesundara

“…This paper presents a study conducted in Sri Lanka, demonstrating the effectiveness of employing transfer learning-based Convolutional Neural Network (CNN) and Mask Region-based-CNN (Mask R-CNN) to identify WCWLD and CCI at their early stages and to assess disease progression. …”
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Automated Defect Detection in Solar Cell Images Using Deep Learning Algorithms by Montaser Abdelsattar, Ahmed Abdelmoety, Mohamed A. Ismeil, Ahmed Emad-Eldeen

“…The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (ResNet), densely connected convolutional networks (DenseNet), visual geometry group (VGG), Inception, mobile network (MobileNet), Xception, SqueezeNet, and AlexNet—classify solar cells into defected and non-defective categories. …”
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The Significant Effects of Threshold Selection for Advancing Nitrogen Use Efficiency in Whole Genome of Bread Wheat by Mohammad Bahman Sadeqi, Agim Ballvora, Said Dadshani, Md. Nurealam Siddiqui, Mohammad Kamruzzaman, Ahossi Patrice Koua, Jens Léon

“…By incorporating the neural network algorithm, it is possible to improve the reliability of FDR threshold and increase the probability of identifying true genetic associations while minimizing the risk of false positives in GWAS results.…”
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Multi-dimensional oscillatory activity of mouse GnRH neurons in vivo by Su Young Han, Shel-Hwa Yeo, Jae-Chang Kim, Ziyue Zhou, Allan E Herbison

“…The gonadotropin-releasing hormone (GnRH) neurons represent the key output cells of the neural network controlling mammalian fertility. We used GCaMP fiber photometry to record the population activity of the GnRH neuron distal projections in the ventral arcuate nucleus where they merge before entering the median eminence to release GnRH into the portal vasculature. …”
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Embedded Rough-Neck Helmholtz Resonator Low-Frequency Acoustic Attenuator by Xianming Sun, Tao Yu, Lipeng Wang, Yunshu Lu, Changzheng Chen

“…A back-propagation (BP) neural network models and predicts how structural parameters impact the acoustic transmission coefficient, elucidating the effects of geometric variations. …”
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Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix by Maozhi Wang, Shu-Hua Chen, Jun Feng, Wenxi Xu, Daming Wang

“…The results for a case study to identify water, ice, snow, shadow, and other materials from Landsat 8 OLI data indicate that SF-CNM can identify the materials specified by the given samples successfully and accurately and that SF-CNM significantly outperforms those of spectral angle mapper algorithm, Mahalanobis classifier, maximum likelihood, and artificial neural network, and produces the performance similar to, even slightly better than that of support vector machine.…”
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Modeling of wave-induced drift based on stepwise parameter calibration by Kui Zhu, Xueyao Chen, Lin Mu, Lin Mu, Lin Mu, Dingfeng Yu, Dingfeng Yu, Runze Yu, Zhaolong Sun, Tong Zhou, Tong Zhou

“…A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. …”
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Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model by Gabriel A. A. Monteiro, Bruno A. A. Monteiro, Jefersson A. dos Santos, Alexander Wittemann

“…The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data. …”
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Data mining and analysis of adverse events of Vedolizumab based on the FAERS database by Qinyun Xu, Jing Zhang, Weihong Tang, Minhong Zhou, Xiaoling Zhang, Pu Yuan

“…Data from the second quarter of 2014 to the third quarter of 2023 were collected, employing various signal mining methods such as Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM). …”
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Identification and Characterization of Novel Perivascular Adventitial Cells in the Whole Mount Mesenteric Branch Artery Using Immunofluorescent Staining and Scanning Confocal Micro... by Chandra Somasundaram, Rahul K. Nath, Richard D. Bukoski, Debra I. Diz

“…In summary, CGRP, and NCAM-containing neural cells in the perivascular adventitia also express palladin and CaSR, and coexpress Gap-43 which may participate in response to stress/injury and vasodilator mechanisms as part of a perivascular sensory neural network.…”
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Regional Boundary Control of Traffic Network Based on MFD and FR-PID by Xin Yang, Juncheng Chen, Mantun Yan, Zhao He, Ziyan Qin, Jiandong Zhao

“…In order to effectively alleviate the traffic congestion of the regional road network, aiming at the problem of lack of accurate OD data of the road network, a regional boundary control method of the traffic network based on fuzzy RBF neural network PID (FR-PID) is proposed by combining the theory of macroscopic fundamental diagram (MFD). …”
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Indoor Positioning System in Learning Approach Experiments by Dodo Zaenal Abidin, Siti Nurmaini, Erwin, Errissya Rasywir, Yovi Pratama

“…The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. …”
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Examining the relationships between patients’ multimorbidity trajectories and prognostic outcomes after the initial hip fracture by Jessica Qiuhua Sheng, Da Xu, Paul Jen-Hwa Hu, Liang Li, Ting-Shuo Huang

“…We then leverage the discovered relationships to develop a cross-attention neural network method for estimating patients’ post-fracture prognoses and demonstrate its predictive utilities relative to several prevalent machine leaning methods. …”
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Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump by Wenjie Wang, Majeed Koranteng Osman, Ji Pei, Shouqi Yuan, Jian Cao, Fareed Konadu Osman

“…A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. …”
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Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…In this context, the widely used architecture Faster R-CNN (Region Convolutional Neural Network) faces catastrophic forgetting: the acquisition of new knowledge leads to the loss of previously learned information. …”
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Model-Oriented Training of Coordinators of the Decentralized Control System of Technological Facilities With Resource Interaction by Volodymyr M. Dubovoi, Maria S. Yukhimchuk, Viacheslav V. Kovtun, Krzysztof R. Grochla

“…Conducted experimental studies of the proposed method of training neural network coordinators, implemented on Python TensorFlow, showed greater effectiveness of Collaborative Federated Learning compared to independent training of coordinators or direct transfer of learning outcomes between coordinators.…”
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Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. …”
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