Seismic first break picking based on multi-task learning by Zhongpo Zhang, Jing Yang

“…During the training process, we use different loss functions for different decoding branches, and guide the network fitting through joint loss. …”
Get full text

Learning from Octopuses: Cutting-Edge Developments and Future Directions by Jinjie Duan, Yuning Lei, Jie Fang, Qi Qi, Zhiming Zhan, Yuxiang Wu

“…This paper also introduces the concept of expected functional safety for the first time to explore the safe design of soft robots in failure or unknown situations. …”
Get full text

Low‐Frequency Reconstruction for Full Waveform Inversion by Unsupervised Learning by Ningcheng Cui, Tao Lei, Wei Zhang

“…Unlike supervised learning, CycleGAN allows the use of field data as input to train the network, which is more closely aligned with practical applications. …”
Get full text

Verbal Learning as a predictor of risks of accidents in elderly drivers by Adriana Machado Vasques, Wyllians Vendramini Borelli, Márcio Sarroglia Pinho, Mirna Wetters Portuguez

“…Objective: We aimed to measure main cognitive functions associated with a high number of traffic violations in different driving settings. …”
Get full text

Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems by Alireza Famili, Shihua Sun, Tolga Atalay, Angelos Stavrou

“…Moreover, the meta-training approach enables the learned policy to quickly adapt to diverse new environments. …”
Get full text

Bumblebee social learning outcomes correlate with their flower-facing behaviour by Yuyi Lu, Zhenwei Zhuo, Mark Roper, Lars Chittka, Cwyn Solvi, Fei Peng, Ying Zhou

“…Abstract Previous studies suggest that social learning in bumblebees can occur through second-order conditioning, with conspecifics functioning as first-order reinforcers. …”
Get full text

Deepmol: an automated machine and deep learning framework for computational chemistry by João Correia, João Capela, Miguel Rocha

“…DeepMol's predefined configurations and customizable objective functions make it accessible to users at all skill levels while enabling efficient and reproducible workflows. …”
Get full text

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC by Maliazurina B. Saad, Qasem Al-Tashi, Lingzhi Hong, Vivek Verma, Wentao Li, Daniel Boiarsky, Shenduo Li, Milena Petranovic, Carol C. Wu, Brett W. Carter, Girish S. Shroff, Tina Cascone, Xiuning Le, Yasir Y. Elamin, Mehmet Altan, Simon Heeke, Ajay Sheshadri, Joe Y. Chang, Percy P. Lee, Zhongxing Liao, Don L. Gibbons, Ara A. Vaporciyan, J. Jack Lee, Ignacio I. Wistuba, Cara Haymaker, Seyedali Mirjalili, David Jaffray, Justin F. Gainor, Yanyan Lou, Alessandro Di Federico, Federica Pecci, Mark Awad, Biagio Ricciuti, John V. Heymach, Natalie I. Vokes, Jianjun Zhang, Jia Wu

“…Benefit scores are calculated using five distinct functions derived from 28 genomic and 6 clinical features. …”
Get full text

Few-Shot Learning With Prototypical Networks for Improved Memory Forensics by Muhammad Fahad Malik, Ammara Gul, Ayesha Saadia, Faeiz M. Alserhani

“…Prototypical networks are a type of few-shot learning algorithm that excels at classifying new categories with minimal examples. …”
Get full text

Machine learning-based prediction of FeNi nanoparticle magnetization by Federico Williamson, Nadhir Naciff, Carlos Catania, Gonzalo dos Santos, Nicolás Amigo, Eduardo M. Bringa

“…More than 1600 NPs were created and split into training and testing sets (70%–30% split), with features including the number of Ni/Fe surface and core atoms, potential energy distributions, pair correlation functions, and coordination distributions. Several machine-learning algorithms, including Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost), were applied to predict the average magnetic moment per atom of these NPs. …”
Get full text

Model of e-learning with electronic educational resources of new generation by A. V. Loban, D. A. Lovtsov

“…Conclusion: The developed theoretical provisions and basis components for the model of variation e-learning with electronic educational resource of new generation allow the student to realize functional mastering of knowledge, abilities and skills of trainees on the base of variation approach, and also to choose a personal student-centered way of “finding concept (meaning)” of the given tasks in the process of motivated learning based on formalized information thesaurus-classifier of electronic educational resource.…”
Get full text

Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…We conclude that these machine learning algorithms are helpful tools for classifying galaxy images. …”
Get full text

Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions by Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs, Georgios Feretzakis

“…These techniques were applied to identify clusters based on gene length and distribution according to resistance class, offering insights into the resistance genes’ structural and functional properties. Data preprocessing, such as filtering and normalization, was conducted prior to applying machine learning methods to ensure consistency and accuracy. …”
Get full text

The effectiveness of blended learning technologies in higher education: Assessment methodology by N. V. Bordovskaia, E. A. Koshkina, M. A. Tikhomirova, M. P. Iskhakova

“…Based on the obtained valid data and the results of the approbation, it is concluded that the authors’ methodology has functional certainty, providing controllability in the didactic and resource support for the effective use of different models of blended learning. …”
Get full text

Machine learning-based new classification for immune infiltration of gliomas. by Feng Yuan, Yingshuai Wang, Lei Yuan, Lei Ye, Yangchun Hu, Hongwei Cheng, Yan Li

“…Then establish and verify the classification model through Machine Learning (ML). Then, use DAVID to perform functional enrichment analysis for different immune subtypes. …”
Get full text

Optimizing the Use of History Textbook through Brain-Based Learning by Wildan Insan Fauzi, Ayi Budi Santosa, Tarunasena Tarunasena

“…Such ineffective condition was indicated from functioning book only as a resource material for examination. …”
Get full text

CONCEPT AND STRUCTURE OF AUTOMATED SYSTEM FOR MONITORING STUDENT LEARNING QUALITY by M. Yu. Kataev, A. M. Korikov, V. S. Mkrttchian

“…A block diagram of the learning process is presented; it demonstrates organization and management of the learning process in a higher educational institution. …”
Get full text

Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…This is a common echocardiographic finding with a prevalence of approximately 7-20%, which may indicate early structural and functional remodeling of the left ventricle in certain pathologies. …”
Get full text

Deep LBLS: Accelerated Sky Region Segmentation Using Hybrid Deep CNNs and Lattice Boltzmann Level-Set Model by Fatema A. Albalooshi, M. R. Qader, Yasser Ismail, Wael Elmedany, Hesham Al-Ammal, Muttukrishnan Rajarajan, Vijayan K. Asari

Subjects: “…deep learning…”
Get full text

INCLUSION OF THE TOPIC «THE SIMPLEST FUNCTIONAL EQUATIONS» IN THE MODEL PROGRAMS FOR STUDYING THE SUBJECT «ALGEBRA AND THE BEGINNINGS OF ANALYSIS» by Тетяна Бохонова, Юрій Бохонов, Ірина Матвєєва, Олексій Томащук, Вікторія Тихонова, Олег Лещинський, Валентина Гроза

“…Analysis of the issue of including the topic "The simplest functional equations" in the model curricula for studying the subject "Algebra and the beginnings of analysis" for specialized classes with in-depth study of mathematics. …”
Get full text