Comparison of Random Forest, XGBoost, and LightGBM Methods for the Human Development Index Classification by Yunna Mentari Indah, Rafika Aristawidya, Anwar Fitrianto, Erfiani Erfiani, L.M. Risman Dwi Jumansyah

“…The study applied these algorithms to analyze the most influential variables affecting HDI. …”
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Predictive Analytics in Finance Using the Arima Model. Application for Bucharest Stock Exchange Financial Companies Closing Prices by Spulbar Cristi, Ene Cezar Cătălin

“…Moreover, the selected model is part of the time series analysis under prediction algorithms, the purpose of the research being to predict the prices of the selected shares. …”
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MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting by Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang

“…Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. …”
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COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach by Zuhaira M. Zain, Nazik M. Alturki

“…Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. …”
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Simulating urban growth through case-based reasoning by X. Ye, W. W. Yu, W. H. Yu, L. N. Lv

“…Case-based reasoning (CBR) simplifies knowledge acquisition and is suitable for researching complex geographical problems. However, CBR analyses of land-use changes are difficult to apply in the study of urban growth due to shortcomings in the case structure and model algorithms. …”
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Regenerative Braking Systems in Electric Vehicles: A Comprehensive Review of Design, Control Strategies, and Efficiency Challenges by Emilia M. Szumska

“…Based on a systematic analysis of 89 peer-reviewed articles from Scopus, it highlights a shift from basic PID controllers to advanced predictive algorithms like Model Predictive Control (MPC) and machine learning approaches. …”
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Stock price prediction based on dual important indicators using ARIMAX: A case study in Vietnam by Wang Pai-Chou, Vo Tram Thi Hoai

“…To demonstrate the effectiveness of this method, we compared it with four other methods – long-short term memory, genetics algorithms with long-short term memory, XGBoost, and Meta Prophet – in predicting the next day’s closing price of the Vietnam stock index from January 2013 to April 2023. …”
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An Exploration of the Use of Educational Robotics in Preschool Education by Veronica Mihaela Rus, Beatrice Almășan

“…Future research is needed to study the more in-depth effects of STEAM activities on children's cognitive acquisitions and social-emotional skills.…”
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FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

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Edge vs. Cloud: Empirical Insights into Data-Driven Condition Monitoring by Chikumbutso Christopher Walani, Wesley Doorsamy

“…The tested induction machine fault diagnosis models are developed using popular algorithms, namely support vector machines, k-nearest neighbours, and decision trees. …”
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THE IMPLICATIONS OF THE EU AI ACT ON CONVERSATIONAL TECHNOLOGIES LIKE CHATGPT by Emilian MATEICIUC

“…The significance of this research lies in its exploration of the EU AI Act's effort to balance technological progression with the safeguarding of fundamental rights and user privacy. …”
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Three-dimensional reconstruction cloud studio based on semi-supervised generative adversarial networks by Chong YU

“…Because of the intrinsic complexity in computation,three-dimensional (3D) reconstruction is an essential and challenging topic in computer vision research and applications.The existing methods for 3D reconstruction often produce holes,distortions and obscure parts in the reconstructed 3D models.While the 3D reconstruction algorithms based on machine learning can only reconstruct voxelized 3D models for simple isolated objects,they are not adequate for real usage.From 2014,the generative adversarial network (GAN) is widely used in generating unreal dataset and semi-supervised learning.So the focus of this paper is to achieve high quality 3D reconstruction performance by adopting GAN principle.A novel semi-supervised 3D reconstruction framework,namely SS-GAN-3D was proposed,which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge.This new model only takes 2D observation images as the weak supervision,and doesn’t rely on prior knowledge of shape models or any referenced observations.Finally,through qualitative and quantitative experiments and analysis,this new method shows compelling advantages over the current state-of-the-art methods on Tanks ＆amp; Temples and ETH3D reconstruction benchmark datasets.Based on SS-GAN-3D,the 3D reconstruction studio solution was proposed.…”
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Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks by Cempaka Amalin Mahadzir, Ahmad Fateh Mohamad Nor, Siti Amely Jumaat, Noor Syahirah Ahmad Safawi

“…Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. The aim of this research is to develop ANN algorithms that anticipate solar power output and enhance the structure of them by incorporating the derating factor due to dirt (kdirt) into account. …”
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Equalization methods for Filter Bank Multicarrier OQAM by Oras Faiz Kadhem, Thamer M Jamel, Hasan F Khazaal

“… Filter bank multi-carrier offset-quadrature amplitude modulation (FBMC OQAM) is a hot topic in 5G multi-carrier research. In order to achieve high data rates and reliable wireless communication, receiver channel equalization is required. …”
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Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production by Hyeongjun Lim, Sojung Kim

“…Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. …”
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ENERGY SAVING IN EXTRACTIVE DISTILLATION by E. A. Anokhina

“…The classic ED flowsheets and ED flowsheets with partly coupled thermally and material flows (PTCDS). Algorithms are described for synthesis of flowsheets manifold based on the graph theory. …”
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A Survey of Dental Caries Segmentation and Detection Techniques by Vincent Majanga, Serestina Viriri

“…It involves discussing the methods and algorithms used in segmenting and detecting dental caries. …”
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Experimental performance analysis of lightweight block ciphers and message authentication codes for wireless sensor networks by Sooyeon Shin, Minwoo Kim, Taekyoung Kwon

“…For resource-constrained nature of these special networks, the design of lightweight cryptography has become active research topic over the last several years. In this article, we provide experimental performance analysis of several lightweight block ciphers and message authentication codes to help for choosing better algorithms for wireless sensor networks in terms of efficiency. …”
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Deriving and applying core competencies for intellectual property-based startup entrepreneurs: a data-driven approach to technology evaluation by Wongyu Lee, Jaeho Shin, Donghee Yoo

“…Despite IP-based startups’ role in fostering innovation and job creation, current research on these startups primarily emphasizes business feasibility, marketability, and technical viability while overlooking managerial competencies. …”
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Student Dropout Prediction Using Random Forest and XGBoost Method by Lalu Ganda Rady Putra, Didik Dwi Prasetya, Mayadi Mayadi

“…Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. …”
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