Opening the Black Box of the Radiation Belt Machine Learning Model by Donglai Ma, Jacob Bortnik, Xiangning Chu, Seth G. Claudepierre, Qianli Ma, Adam Kellerman

“…Abstract Many Machine Learning (ML) systems, especially deep neural networks, are fundamentally regarded as black boxes since it is difficult to fully grasp how they function once they have been trained. …”
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Forecasting outbound student mobility: A machine learning approach. by Stephanie Yang, Hsueh-Chih Chen, Wen-Ching Chen, Cheng-Hong Yang

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Deep representation learning using layer-wise VICReg losses by Joy Datta, Rawhatur Rabbi, Puja Saha, Aniqua Nusrat Zereen, M. Abdullah-Al-Wadud, Jia Uddin

“…In addition, we optimize weights for variance, invariance, and covariance terms of the loss function so that the model can capture higher-level semantic information optimally. …”
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Meta-Learning With Relation Embedding for Few-Shot Deepfake Detection by Xiaoyong Liu, Pengcheng Song, Pei Lu, Yanjun Wang

“…To tackle these issues, we introduce a few-shot deepfake detection approach based on meta-learning with relation embedding. Initially, we employ an embedding function to generate feature representations of the images. …”
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Calculation of hydrogen dispersion in cushion gases using machine learning by Ali Akbari, Mehdi Maleki, Yousef Kazemzadeh, Ali Ranjbar

“…This study addresses these challenges by integrating experimental data with advanced machine learning (ML) techniques to model hydrogen dispersion. …”
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Learning Energy-Efficient Transmitter Configurations for Massive MIMO Beamforming by Hamed Hojatian, Zoubeir Mlika, Jeremy Nadal, Jean-Francois Frigon, Francois Leduc-Primeau

“…Finally, towards obtaining a system that can be trained using in-the-field measurements, we investigate the ability of the model to be trained exclusively using imperfect channel state information (CSI), both for the input to the deep learning model and for the calculation of the loss function. …”
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Human motion similarity evaluation based on deep metric learning by Yidan Zhang, Lei Nie

“…More accurate and reliable human action similarity evaluation results were achieved by the loss function composed of three components, namely, cross-reconstruction loss, reconstruction loss and triplet loss. …”
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An advanced deep learning method for pepper diseases and pests detection by Xuewei Wang, Jun Liu, Qian Chen

“…Abstract Despite the significant progress in deep learning-based object detection, existing models struggle to perform optimally in complex agricultural environments. …”
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Development of time to event prediction models using federated learning by Rasmus Rask Kragh Jørgensen, Jonas Faartoft Jensen, Tarec El-Galaly, Martin Bøgsted, Rasmus Froberg Brøndum, Mikkel Runason Simonsen, Lasse Hjort Jakobsen

“…Alternatively, federated learning (FL) can be utilized to train models based on data located at multiple sites. …”
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Learning dual context aware POI representations for geographic mapping by Quan Qin, Tinghua Ai, Shishuo Xu, Yan Zhang, Weiming Huang, Mingyi Du, Songnian Li

“…For the type context of POIs, we propose a type hierarchical aggregation neural network architecture for DCA, and design a type infomax optimization objective following contrastive learning mechanism. The superiority of DCA is demonstrated in three geographic mapping tasks, including urban function mapping, region popularity mapping, and housing price mapping. …”
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Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients by A. Mayya, H. Solieman

“…The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. …”
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Advancing remote sensing of biocrusts with drone imagery and machine learning by Jana Stewart, Roxane J. Francis, David J. Eldridge, Richard T. Kingsford, Nathali Machado de Lima

“…Biocrusts are a major ground cover type in drylands, driving ecosystem function and contributing to biodiversity at large scales. …”
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Randomization-Driven Hybrid Deep Learning for Diabetic Retinopathy Detection by A. M. Mutawa, G. R. Hemalakshmi, N. B. Prakash, M. Murugappan

“…This study pioneers an innovative framework, using Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, combined with a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, to enhance the detection of DR. …”
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Interpolating numerically exact many-body wave functions for accelerated molecular dynamics by Yannic Rath, George H. Booth

“…This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. …”
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Calibration of Electron Microscopes Through Deep Learning and Bayesian Optimization by Jilles S. van Hulst, Roy A. C. van Zuijlen, Narges Javaheri, Maurits Diephuis, Duarte J. Antunes, W. P. M. H. Heemels

“…Specifically, we perform transfer learning on an adapted ResNet18 architecture using a large data set of simulated Ronchigrams. …”
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Same data, different results? Machine learning approaches in bioacoustics by Kaja Wierucka, Derek Murphy, Stuart K. Watson, Nikola Falk, Claudia Fichtel, Julian León, Stephan T. Leu, Peter M. Kappeler, Elodie F. Briefer, Marta B. Manser, Nikhil Phaniraj, Marina Scheumann, Judith M. Burkart

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Target informed client recruitment for efficient federated learning in healthcare by Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke, Bart De Moor

“…Abstract Background Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. …”
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Oral cannabidiol did not impair learning and memory in healthy adults by Hanna H. Gebregzi, Joanna S. Zeiger, Jeffrey P. Smith, Libby Stuyt, Luann Cullen, Jim Carsella, Daniel C. Rogers, Jordan Lafebre, Jennah Knalfec, Alfredo Vargas, Moussa M. Diawara

“…Abstract Background The effect of oral Cannabidiol (CBD) on interference during learning and memory (L&M) in healthy human volunteers has not been studied. …”
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Power Consumption Optimization of GPU Server With Offline Reinforcement Learning by Heechan Chung, Yeeun Im, Jongchan Park, Taeho Lee, Tae-Young Kim, Hyungjun Kim, Donghwan Lee

“…The reward function balances power efficiency with performance. …”
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Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines by Cheng Yin, Le Thanh Binh, Duong Tran Anh, Son T. Mai, Anh Le, Van-Hau Nguyen, Van-Chien Nguyen, Nguyen Xuan Tinh, Hitoshi Tanaka, Nguyen Trung Viet, Long D. Nguyen, Trung Q. Duong

“…Compared to the Empirical Orthogonal Function (EOF), the most common method used for predicting shoreline changes from cameras, we demonstrate that the SARIMA, NNAR and LSTM models outperform the EOF model significantly in terms of prediction accuracy. …”
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