Learning Graph Structures With Autoregressive Graph Signal Models by Kyle Donoghue, Ashkan Ashrafi

“…This is achieved by utilizing the autoregressive coefficients of time-series graph signals in GL-AR’s learning algorithm. Existing graph learning techniques typically minimize the smoothness of a graph signal on a recovered graph structure to learn instantaneous relationships. …”
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Social risks of e-learning in a digital society by E. V. Chmykhova

“…The characteristic of risk types has been made in relation to such characteristics of modern society as the complexity of self-organization processes, the open nature and non-linearity of transformation processes.The understanding of various types of risks has been explained, that are associated with the problems of implementing the goals of humanistic educational policy; incomplete integration of methodological tools for e-learning; the break in educational traditions; conflicts between subjects of educational relations; the lack of adaptability of digital technologies to the content of social interaction; the complexity of the nature of distance learning technologies in the modern supercomplex socio-global system; impact on the formalization of learning new types of rationality; objectively inherent in modern society’s global social risks; conflicts of social movements with the interests of the subjects realizing the functions of education; with the indivisibility of the security of complex society in different spheres and levels of social interaction. …”
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Deep Reinforcement Learning: A Chronological Overview and Methods by Juan Terven

“…Introduction: Deep reinforcement learning (deep RL) integrates the principles of reinforcement learning with deep neural networks, enabling agents to excel in diverse tasks ranging from playing board games such as Go and Chess to controlling robotic systems and autonomous vehicles. …”
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Hammering Test for Tile Wall Using Deep Learning by Atsushi Ito, Masafumi Koike, Masako Saito, Katsuhiko Hibino

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Information as Interpretation: Measuring Learning Behavior for Knowledge Insight by Kensuke Takii, Changhao Liang, Hiroaki Ogata

“…The results showed the possibility that the information content calculated by ITLAK is an indicator that can capture the behavioral and knowledge-related aspects of learning. In particular, it was suggested that the information content metric functions in immediate feedback and captures aspects of learning distinct from the number of contacts with knowledge. …”
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Student Performance Prediction Using Machine Learning Algorithms by Esmael Ahmed

“…However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. …”
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Improving Deep Learning for Forecasting Accuracy in Financial Data by Shih-Lin Lin, Hua-Wei Huang

“…The proposed method combines deep learning with empirical mode decomposition (EMD) to understand and predict financial trends from financial data. …”
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Attitude control of catamaran based on deep reinforcement learning by Leihong QIN, Songtao ZHANG, Xiaofeng NAN, Qiming ZHONG

“…MethodsBy designing reward functions and neural network structures and adjusting relevant hyper-parameters, in combination with the catamaran model, through experiments, the control effect of the deep reinforcement learning DDPG algorithm and the GA-LQR algorithm under three different control modes and the robustness under different operating conditions and initial states were compared. …”
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Continuous Speech for Improved Learning Pathological Voice Disorders by Syu-Siang Wang, Chi-Te Wang, Chih-Chung Lai, Yu Tsao, Shih-Hau Fang

“…This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…Accurate assessment of phytoplankton abundance and community structure is critical for understanding the health and functioning of lake ecosystems. To monitor phytoplankton dynamics efficiently, fluorescence sensors offer rapid and high-throughput estimates of chlorophyll-a concentrations, a widely used proxy for phytoplankton biomass. …”
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Video Temporal Grounding with Multi-Model Collaborative Learning by Yun Tian, Xiaobo Guo, Jinsong Wang, Bin Li, Shoujun Zhou

“…Given an untrimmed video and a natural language query, the video temporal grounding task aims to accurately locate the target segment within the video. Functioning as a critical conduit between computer vision and natural language processing, this task holds profound importance in advancing video comprehension. …”
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Learning-induced sensory plasticity of mouse olfactory epithelium by A. V. Romashchenko, Р. Е. Kireeva, M. В. Sharapova, Т. A. Zapara, A. S. Ratushnyak

“…Traditionally, studies of the neurobiology of learning and memory focus on the circuitry that interfaces between sensory inputs and behavioral outputs, such as the amygdala and cerebellum. …”
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Machine learning prediction of pKa of organic acids by Juda Baikété, Alhadji Malloum, Jeanet Conradie

“…Here, we present four tree-based machine learning models for pKa prediction of organic molecules. …”
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Electronic nose and machine learning for modern meat inspection by Ivan Shtepliuk, Guillem Domènech-Gil, Viktor Almqvist, Arja Helena Kautto, Ivar Vågsholm, Sofia Boqvist, Jens Eriksson, Donatella Puglisi

“…In this study, we propose a novel method to modernize meat inspection using an electronic nose combined with machine learning (ML), with focus on pig meat as a case study. …”
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Deep learning approach for automated hMPV classification by Sivarama Prasad Tera, Ravikumar Chinthaginjala, Irum Shahzadi, Priya Natha, Safia Obaidur Rab

“…To mitigate this, the framework incorporates advanced techniques such as data augmentation, weighted loss functions, and dropout regularization, which help to balance the dataset, improve model robustness, and enhance classification accuracy. …”
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Introduction to deep learning methods for multi‐species predictions by Yuqing Hu, Sara Si‐Moussi, Wilfried Thuiller

“…We also show how adapted loss functions can address imbalance issues. We applied these deep learning models to a plant community dataset comprising 130,582 vegetation surveys encompassing 2522 species located in the French Alps. …”
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Deciphering the evolution of metaverse - A techno-functional perspective in digital marketing by Mohammad Wasiq, Abu Bashar, Brighton Nyagadza, Amar Johri

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Behavioural user segmentation of app users based on functionality interaction patterns by Li-Yoong Ooi, Choo-Yee Ting, Helmi Zakariah, Eashvaren Chandar

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About managing the number of simultaneously functioning software robots of different types by A. S. Zuev, D. A. Leonov

“…The study sets out to justify the relevance and investigate approaches for solving the problem of managing the number of simultaneously functioning software robots of various types under conditions of limited computational resources and changes in sets of executable tasks.Methods. …”
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Chess Position Evaluation Using Radial Basis Function Neural Networks by Dimitrios Kagkas, Despina Karamichailidou, Alex Alexandridis

“…The game of chess is the most widely examined game in the field of artificial intelligence and machine learning. In this work, we propose a new method for obtaining the evaluation of a chess position without using tree search and examining each candidate move separately, like a chess engine does. …”
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