An Improved Deep Belief Network IDS on IoT-Based Network for Traffic Systems by Rayeesa Malik, Yashwant Singh, Zakir Ahmad Sheikh, Pooja Anand, Pradeep Kumar Singh, Tewabe Chekole Workneh

“…Hence, there is a need to use an intelligent mechanism based on machine learning (ML) and deep learning (DL), to detect attacks. …”
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Which Upstream Solar Wind Conditions Matter Most in Predicting Bz Within Coronal Mass Ejections by Pete Riley, M. A. Reiss, C. Möstl

“…Recently, a robust machine learning technique was developed for predicting the minimum value of Bz within ICMEs based on a set of 42 “features,” that is, variables calculated from measured quantities upstream of the ICME and within its sheath region. …”
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Predictors of evacuation behavior: dataset on respondents’ route choice and web interaction by Dajana Snopková, Martin Tancoš, Lukáš Herman, Vojtěch Juřík

“…It supports the development of models to predict human behavior in emergencies using machine learning and predictive modeling and is accessible for both academic and commercial use.…”
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Toward cultural interpretability: A linguistic anthropological framework for describing and evaluating large language models by Graham M Jones, Shai Satran, Arvind Satyanarayan

“…This article proposes a new integration of linguistic anthropology and machine learning (ML) around convergent interests in both the underpinnings of language and making language technologies more socially responsible. …”
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A Low-Power General Matrix Multiplication Accelerator with Sparse Weight-and-Output Stationary Dataflow by Peng Liu, Yu Wang

“…General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. …”
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Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model by WANG Hai, SHEN Yanqing, QI Shansheng, PAN Hongzhong, HUO Jianzhen, WANG Zhance

“…According to the nonlinear and non-stationary characteristics of monthly runoff sequences, the quadratic decomposition method was combined with machine learning to construct a model for predicting monthly runoff. …”
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A New Type of Eye Movement Model Based on Recurrent Neural Networks for Simulating the Gaze Behavior of Human Reading by Xiaoming Wang, Xinbo Zhao, Jinchang Ren

“…Experimental results are then presented to demonstrate that the proposed model can achieve similar prediction accuracy while requiring fewer features than current machine learning models.…”
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A hybrid Genetic Algorithm and Fuzzy Logic approach to ergonomic design of workstations in metal casting operations by Intan Berlianty, Irwan Soejanto, Indun Titisariwati, Favian Ersanta Andhika Putra, Wahyu Tri Utami, Mohamad Kamil Insani

“…Future research could further enhance the system by incorporating machine learning for improved predictive capabilities and expanded optimization of ergonomic parameters.…”
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Increasing the Accuracy of Brain Stroke Classification using Random Forest Algorithm with Mutual Information Feature Selection by Fachruddin Fachruddin, Errissya Rasywir, Yovi Pratama

“…Brain stroke stands out as a leading cause of death, distinguishing it from common illnesses and highlighting the critical need to utilize machine learning techniques to identify symptoms. Among these techniques, the Random Forest (RF) algorithm emerged as the main candidate because of its optimal accuracy values. …”
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A multi-dimensional decision framework based on the XGBoost algorithm and the constrained parametric approach by Xinxin Wang, BingBing Zhang, Zeshui Xu, Ming Li, Marinko Skare

“…Under the framework, XGBoost algorithm, one of the machine learning methods, is conducted to capture the importance of attributes by using the historical data, and further calculate the attribute weights. …”
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A Survey on Sensor Selection and Placement for Connected and Automated Mobility by Mehmet Kiraz, Fikret Sivrikaya, Sahin Albayrak

“…The findings of this article also highlight numerous challenges in creating a universal framework, the lack of application of novel machine learning methods, and the complexity of modeling multi-sensor settings.…”
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An improved spatially downscaled solar-induced chlorophyll fluorescence dataset from the TROPOMI product by Siyuan Chen, Liangyun Liu, Lichun Sui, Xinjie Liu, Yan Ma

“…Compared with a machine learning (ML) SIF product and OSIF, TroDSIF can better reproduce the OSIF signals with higher R2, lower root mean square error (RMSE), and nearly zero residuals at different latitudes. …”
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Examination of Research Conducted on the Use of Artificial Intelligence in Science Education by Faruk Arıcı

“…The advancement of artificial intelligence (AI) has been significantly driven by developments in machine learning and neural networks. As AI becomes increasingly pervasive, its applications are diversifying, with notable penetration in sectors such as health, education, social media, robotics, and entertainment. …”
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Comparative analysis of regression algorithms for drug response prediction using GDSC dataset by Soojung Ha, Juho Park, Kyuri Jo

“…Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient. …”
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Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…In the contemporary context of a burgeoning energy crisis, the accurate and dependable prediction of Solar Radiation (SR) has emerged as an indispensable component within thermal systems to facilitate renewable energy generation. Machine Learning (ML) models have gained widespread recognition for their precision and computational efficiency in addressing SR prediction challenges. …”
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Towards automated recipe genre classification using semi-supervised learning. by Nazmus Sakib, G M Shahariar, Md Mohsinul Kabir, Md Kamrul Hasan, Hasan Mahmud

“…Furthermore, we have demonstrated traditional machine learning, deep learning and pre-trained language models to classify the recipes into their corresponding genre and achieved an overall accuracy of 98.6%. …”
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A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms by Saad Alghamdi, Ben Soh, Alice Li

“…This review also discusses the use of advanced models based on machine learning, deep learning, and meta-heuristics approaches to improve course completion rates, optimise learning outcomes, and provide personalised educational experiences.…”
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A review on inverse analysis models in steel material design by Yoshitaka Adachi, Ta‐Te Chen, Fei Sun, Daichi Maruyama, Kengo Sawai, Yoshihito Fukatsu, Zhi‐Lei Wang

“…Abstract This paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link these elements for enhanced materials engineering. …”
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A New Air Quality Prediction Framework for Airports Developed with a Hybrid Supervised Learning Method by Yong Tian, Weifang Huang, Bojia Ye, Minhao Yang

“…This article proposes a new framework with a combination of the standard assessment procedure and machine learning methods for fast and accurate prediction of air quality in airports. …”
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A Trusted Federated Learning Method Based on Consortium Blockchain by Xiaojun Yin, Xijun Wu, Xinming Zhang

“…Federated learning (FL) has gained significant attention in distributed machine learning due to its ability to protect data privacy while enabling model training across decentralized data sources. …”
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