Opening the AI Black Box: Distilling Machine-Learned Algorithms into Code by Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelić, Max Tegmark

“…Although this mission sounds extremely challenging, we show that it is not entirely impossible by presenting a proof-of-concept method, MIPS, that can synthesize programs based on the automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. …”
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Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm by Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci

“…This work is oriented toward the successful implementation of onboard prediction systems, which is essential for the future of space exploration. We present a simple and efficient machine learning regression algorithm that is able to forecast the energetic proton flux up to 1 hr ahead by exploiting features derived from the electron flux only. …”
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SparseBatch: Communication-efficient Federated Learning with Partially Homomorphic Encryption by Chong Wang, Jing Wang, Zheng Lou, Linghai Kong, WeiSong Tao, Yun Wang

“…Cross-silo federated learning (FL) enables collaborative model training among various organizations (e.g., financial or medical). …”
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Hierarchical Semi-Supervised Representation Learning for Cyber Physical Social Intelligence by Na Song, Jing Yang, Xuemei Fu, Xiangli Yang, Ying Xie, Shiping Wang

“…To tackle the challenges in CPSI, we present a multi-level feature learning framework for semi-supervised classification tasks. …”
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Transfer Learning for Distance Classification of Marine Vessels Using Underwater Sound by Decrop Wout, Deneudt Klaas, Parcerisas Clea, Schall Elena, Debusschere Elisabeth

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Recent advances in structural health diagnosis: a machine learning perspective by Yuequan Bao, Huabin Sun, Yang Xu, Xiaoshu Guan, Qiuyue Pan, Dawei Liu

“…For complex bridge structures, diagnosing structural health based on highly incomplete monitoring data presents an inherent high-dimensional problem. Machine learning methods are particularly well-suited for addressing these issues due to their capabilities in effective feature extraction, efficient optimization, and robust scalability. …”
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Comparative analysis of machine learning models for the detection of fraudulent banking transactions by Pedro María Preciado Martínez, Ricardo Francisco Reier Forradellas, Luis Miguel Garay Gallastegui, Sergio Luis Náñez Alonso

“…This research presents a comparative analysis of machine learning models for detecting fraudulent banking transactions, a growing problem in the digital financial sector. …”
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Using machine learning to classify temporomandibular disorders: a proof of concept by Fernanda Pretto Zatt, João Victor Cunha Cordeiro, Lauren Bohner, Beatriz Dulcineia Mendes de Souza, Victor Emanoel Armini Caldas, Ricardo Armini Caldas

“…Conclusion: The use of decision tree-based machine learning holds significant support potential for TMD classification.…”
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Navigating Unknowns: Deep Learning Robustness for Gravitational-wave Signal Reconstruction by Chayan Chatterjee, Karan Jani

“…We present a rapid and reliable deep-learning-based method for gravitational-wave (GW) signal reconstruction from elusive, generic binary black hole mergers in LIGO data. …”
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An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

“…In this paper, a method is presented that uses meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and gray wolf to find the optimal hyperparameters for the deep learning network and the intrusion detection system is created based on these hyperparameters. …”
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Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification by Neny Sulistianingsih, Galih Hendro Martono

“… The classification of edible versus poisonous mushrooms presents a critical challenge in the domains of applied biology and public health, particularly due to the serious implications of misidentification. …”
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Deep Learning Multi-Modal Melanoma Detection: Algorithm Development and Validation by Nithika Vivek, Karthik Ramesh

“…ObjectiveThis study aimed to present a novel multimodal deep learning-based technique to distinguish between melanoma and seborrheic keratosis. …”
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Research on Intelligent Detection and Segmentation of Rock Joints Based on Deep Learning by Lei Peng, Haibo Wang, Chun Zhou, Feng Hu, Xiaoyang Tian, Zhu Hongtai

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Tooth ARcademy: A mobile app for teaching and learning of oral histology. by Nazlee Sharmin, Hady Abdallah, Elias Jirgees, Ava K Chow

“…m-Learning is gaining popularity in health professional education; however, reports on mobile apps targeting didactic teaching and learning are scarce, particularly in the context of health professional courses such as histology. …”
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The Impact of the 5E Learning Model Improved with Concept Maps on Motivation by Lütfiye Varoğlu, Ayhan Yilmaz, Şenol Şen

“…The constructivist approach significantly impacts the improvement of students’ motivation. The present study aims to examine the use of the 5E learning model with concept maps to support students’ motivation and compare the 5E approach and the classical approach to teaching chemistry in terms of motivational dimensions. …”
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Educational aspects of the electronic learning process implementation in the conditions of the economic crisis by Savely Evgenievich Cherkezov, Elena Vladimirovna Efimova, Natalya Alexandrovna Rutta

“…Thus, the problem arises of choosing a software platform for a distance education system that can meet the criteria for organizing and implementing a high-quality and uninterrupted educational process at a university under conditions of distance learning. The article presents the experience of implementing e-learning in the conditions of isolation of students and teachers of Rostov State University of Economics (RINH). …”
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Comparative Analysis of Machine Learning Based Approaches for Face Detection and Recognition by Ratnesh Kumar Shukla, Arvind Kumar Tiwari

“…In this article, we are performing comprehensive and systemic studies to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. …”
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Mass Generation of Programming Learning Problems from Public Code Repositories by Oleg Sychev, Dmitry Shashkov

“…We present an automatic approach for generating learning problems for teaching introductory programming in different programming languages. …”
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Design of machine learning-based controllers for speed control of PMSM drive by Ashly Mary Tom, J. L. Febin Daya

“…Abstract This study presents machine learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. …”
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A variational deep-learning approach to modeling memory T cell dynamics. by Christiaan H van Dorp, Joshua I Gray, Daniel H Paik, Donna L Farber, Andrew J Yates

“…The ubiquity of deep phenotyping therefore presents a new challenge; how do we confront tractable and interpretable mathematical models with high-dimensional data? …”
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