Intelligent Infrastructure for Traffic Monitoring Based on Deep Learning and Edge Computing by Jaime Villa, Franz García, Rubén Jover, Ventura Martínez, José M. Armingol

“…This exponential growth is due to the rapid development of deep learning in recent years, as well as the improvements in computer vision models. …”
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Evolutionary search algorithm for learning activation function of an artificial neural network by Yurshin Viacheslav

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Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning by Minhyeok Lee

“…Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency. …”
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Stock Price Prediction in the Financial Market Using Machine Learning Models by Diogo M. Teixeira, Ramiro S. Barbosa

“…This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. …”
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Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking by Baojie Fan, Yingkui Du, Yang Cong

“…A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented. …”
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Student modelling in a web-based platform for learning games composing by V. Atanasov, I. Ivanova

“…The student model is evolved as a composition of sets, formally representing all the elements of the process of acquiring knowledge using a learning game Results: The general concept and a schematic notation of the proposed student model is presented and a detailed description is also given. …”
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Deep brain stimulation on cognition in epilepsy: A concentration on learning and memory by Beibei Shen, Yi Shi, Yanlu Fu, Yina Cao, Yi Wang, Jiajia Fang

“…More than 60 % of epilepsy patients may experience impairment in learning, memory, attention, and executive control. …”
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Efficient Onboard Multitask AI Architecture Based on Self-Supervised Learning by Gabriele Inzerillo, Diego Valsesia, Enrico Magli

“…There is growing interest toward the use of artificial intelligence (AI) directly onboard satellites for quick analysis and rapid response to critical events such as natural disasters. This article presents a blueprint to the mission designer for the development of a modular and efficient deep learning payload to address multiple onboard inference tasks. …”
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A Learning Method for Neural Networks Based on a Pseudoinverse Technique by Chinmoy Pal, Ichiro Hagiwara, Naoki Kayaba, Shin Morishita

“…A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. …”
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Recognition of Suspension Liquid Based on Speckle Patterns Using Deep Learning by Jinhua Yan, Ming Jin, Zhousu Xu, Lei Chen, Ziheng Zhu, Hang Zhang

“…We presented a machine learning-based method to recognize suspension by distinguishing dispersoid-dependent speckle patterns using a convolutional neural network. …”
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FAIRness Along the Machine Learning Lifecycle Using Dataverse in Combination with MLflow by Lincoln Sherpa, Valentin Khaydarov, Ralph Müller-Pfefferkorn

“…This poses novel challenges related to keeping the trained model Findable, Accessible, Interoperable and Reusable (FAIR), especially for the automation of the entire machine learning lifecycle within the concept of Machine Learning Operations (MLOps). …”
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Using Instructional Scaffolding in hybrid learning environment: A critical Review by Dina Ayman, Mahmoud Mohaseb, Jehan El-Bassuony

“…One of these learning environments is hybrid learning environment. …”
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Customization of health insurance premiums using machine learning and explainable AI by Manohar Kapse, Vinod Sharma, Rutuj Vidhale, Varun Vellanki

“…This study presents an analysis of health insurance premiums across various customer segments. …”
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Towards Automated Frequency Response Analysis of Power Transformers with Deep Learning by Micah Phillip, Arvind Singh, Craig J. Ramlal

“…Interpreting FRA test results, though, is still largely dependent on expert identification of graphical features. Machine learning, however, presents an opportunity to automate and improve this feature identification process. …”
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What is a printed textbook?: electronic books at the university teaching and learning by Helena Pečko Mlekuš

“…At the end, some possible tasks and challenges for academic libraries are presented.…”
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Integration of AI Deep Learning in Studying the Morphology of Philosophy of Science Perspectives by adhia nugraha, Rohanda Rohanda, Abdul Kodir, R. Edi Komarudin

“…The study discusses the integration of deep learning-based AI in morphological studies from a philosophical perspective. …”
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A Distributed Conjugate Gradient Online Learning Method over Networks by Cuixia Xu, Junlong Zhu, Youlin Shang, Qingtao Wu

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A Review Toward Deep Learning for High Dynamic Range Reconstruction by Gabriel de Lima Martins, Josue Lopez-Cabrejos, Julio Martins, Quefren Leher, Gustavo de Souza Ferreti, Lucas Hildelbrano Costa Carvalho, Felipe Bezerra Lima, Thuanne Paixão, Ana Beatriz Alvarez

“…High Dynamic Range (HDR) image reconstruction has gained prominence in a wide range of fields; not only is it implemented in computer vision, but industries such as entertainment and medicine also benefit considerably from this technology due to its ability to capture and reproduce scenes with a greater variety of luminosities, extending conventional levels of perception. This article presents a review of the state of the art of HDR reconstruction methods based on deep learning, ranging from classical approaches that are still expressive and relevant to more recent proposals involving the advent of new architectures. …”
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Learning Improvement Heuristics for Multi-Unmanned Aerial Vehicle Task Allocation by Boyang Fan, Yuming Bo, Xiang Wu

“…Specifically, a Transformer-based model is proposed to learn design NeuroSelect Heuristic for DPSO to improve the evolutionary process. …”
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Learning With Partial-Label and Unlabeled Data: Contrastive With Negative Example Separation by Bangfa Jiang, Chengkun Liu, Jing Chai

“…Semi-Supervised Partial Label Learning (SSPLL) is an important branch of weakly supervised learning, where the data consists of both partial label examples and unlabeled ones. …”
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