Overview on game reinforcement learning methods for edge computing of low-orbit constellation by GU Xueqiang, ZHANG Wanpeng, TAN Siyu, LUO Junren, ZHOU Yanzhong

“…As a new paradigm in the field of artificial intelligence, game reinforcement learning is an advanced mainstream method to solve the edge computing problem of low-orbit constellation. …”
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Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification by Md Takbir Alam, Md Ashibul Islam Khan, Nahian Nakiba Dola, Tahia Tazin, Mohammad Monirujjaman Khan, Amani Abdulrahman Albraikan, Faris A. Almalki

“…This research covers the findings and analyses of multiple machine learning models for fetal health classification. The method was developed using the open-access cardiotocography dataset. …”
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Machine learning models and methods for solving optimization and forecasting problems of the work of seaports by M. N. Lukashevich, M. Y. Kovalyov

“…We review the literature on models and methods of machine learning and their application to optimization of port operations. …”
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An Overview of Integrating Deep Learning Methods With Close-Range Hyperspectral Imaging for Agriculture by Shah Faisal, Melanie Po-Leen Ooi, Ye Chow Kuang, Sanush K. Abeysekera, Dale Fletcher

“…Extracting spatial-spectral information of objects-of-interest from hyperspectral images requires sophisticated computational methods. The last decade saw the rapid advancement of deep learning methods due to their superior automatic feature extraction capability from images, and hence it is no surprise that these methods have been adapted and used for hyperspectral image analysis. …”
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Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments by Yirga Yayeh Munaye, Metadel Addis, Yenework Belayneh, Atinkut Molla, Wasyihun Admass

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Strategies to alleviate flickering: Bayesian and smoothing methods for deep learning classification in video by Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce Cox, Trevor Bihl

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Scenario modeling of the drug prescription process for children: application of machine learning methods by А. А. Kondrashov, М. М. Kurashov, Е. Е. Loskutova

“…Objective: determining the most appropriate machine learning method to solve the problem of drug prescribtion for children, evaluating its performance and potential for implementation into scenario modeling systems of the pharmaceutical care structure.Material and methods. …”
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Application of Deep Learning Methods for Employee Satisfaction Analysis Based on Text Data by A. A. Kazinets

“…The application of deep learning methods to analyze employee satisfaction based on text data is investigated. …”
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Research on emulsion concentration detection technology based on interpretable machine learning methods by Jiaxu Kang, Jianwei Li, Meng Wang, Xinwei Guo, Dinghao Liu, Chao Qu, Chengbin Guo

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Hard lessons learned: A model that facilitates the selection of methods of IT project management by Krzysztof Redlarski

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Comprehensive Review of Meta-Learning Methods for Cold-Start Issue in Recommendation Systems by Jamallah M. Zawia, Maizatul Akmar Binti Ismail, Mohammad Imran, Buce Trias Hanggara, Diva Kurnianingtyas, Silvi Asna, Quang Tran Minh

“…The results demonstrate the application of model-independent meta-learning (MAML) and other techniques such as optimization-based methods, few-shot learning frameworks, and gradient-based meta-learning methods to solve the cold start problem. …”
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Research on Predicting Mine Earthquakes Based on Deep Learning Time-Series Methods by Xiufeng Zhang, Wei Li, Yang Chen, Junpeng Zou, Hangrui Zhang, Hao Wang, Chaohong Shi, Shaopeng Yan, Quan Zhang

“…To more accurately monitor and predict mine earthquakes and thereby reduce the potential risk they pose, this paper presents a study on the inversion and localization of seismic sources of mine earthquakes and a study on the prediction of mine earthquakes based on the deep learning method. The latter is set in the context of the Dongtan coal mine. …”
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The Implementation of Eclectic Methods in Arabic Learning Based on All in One System Approach by Zulfa Amalia Wahidah, Umi Baroroh, Abdur Rasheed-Mahmoud Mukadam

Subjects: “…Eclectic Method…”
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Evaluating learning methods to improve pharmacy students’ retention of nonsterile compounding skills by Karina Mendez Cordero, Cindy Ngoc Chau, Damianne Brand, Ayesha Rahman Ahmed

“…This study provides students with the tools and knowledge necessary for compounding and to track the retention of compounding skills along with their performance based on their prior pharmacy experience. Methods: Continued exposure to compounding skills and strategies was provided as part of a compounding lab curriculum to enhance learners’ retention and compounding accuracy. …”
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Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models by John Fischer, Marko Orescanin, Justin Loomis, Patrick Mcclure

“…Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communication costs associated with sharing local datasets. …”
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A review on NLP zero-shot and few-shot learning: methods and applications by G. Ramesh, Mahammad Sahil, Shashank A. Palan, Darshan Bhandary, Teli Abhishek Ashok, J. Shreyas, N. Sowjanya

“…Abstract Zero-shot and few-shot learning techniques in natural language processing (NLP), this comprehensive review traces their evolution from traditional methods to cutting-edge approaches like transfer learning and pre-trained language models, semantic embedding, attribute-based approaches, generative models for data augmentation in zero-shot learning, and meta-learning, model-agnostic meta-learning, relationship networks, model-agnostic meta-learning (MAML), prototypical networks in few-shot learning. …”
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A Comparative Study of Loan Approval Prediction Using Machine Learning Methods by Vahid Sinap

“…It was found that the feature selection methods have a significant impact on the model performances and the Recursive Feature Elimination method was the most successful method. …”
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Data mining methods application in reflexive adaptation realization in e-learning systems by A. S. Bozhday, Y. I. Evseeva, A. A. Gudkov

“…The purpose of this work is to develop the basics of the technology of self-optimization of software systems in the structure of e-learning. The proposed technology is based on the formulated and formalized principle of reflexive adaptation of software, applicable to a wide class of software systems and based on the discovery of new knowledge in the behavioral products of the system.To solve this problem, methods of data mining were applied. …”
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Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure by Juan Cruz-Martinez, Aron Jansen, Gijs van Oord, Tanjona R Rabemananjara, Carlos M R Rocha, Juan Rojo, Roy Stegeman

“…While focusing on proton structure, our method is fully general and is applicable to any deep learning problem relying on hyperparameter optimisation for an ensemble of models.…”
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Review of Spot Electricity Price Prediction Studies Based on Machine Learning Methods by JIA Heping, GUO Yuchen, MA Qianxin, YANG Zhenglin, ZHENG Yaxian, ZENG Dan, LIU Dunnan

Subjects: “…national unified electricity market|spot market|electricity price forecasting|machine learning methods…”
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