Deep soil contributions to global nitrogen budgets by Maya Almaraz, Chao Wang, Michelle Y. Wong

“…Using a random forest machine learning approach we estimate a total deep soil nitrate pool of 15.2 ( ± 1.1 SD) Pg of N. …”
Get full text

The Application of New Educational Concepts in Digital Educational Media by Chun Yang

“…The process is as follows: based on the collected education data, mining the specific factors that will affect the application ability of teachers’ digital education resources and building a multiple machine learning regression model using these objective and significant features to predict the score of teachers’ digital education resources application ability. …”
Get full text

Training Neural Networks with a Procedure Guided by BNF Grammars by Ioannis G. Tsoulos , Vasileios Charilogis

“…Artificial neural networks are parametric machine learning models that have been applied successfully to an extended series of classification and regression problems found in the recent literature. …”
Get full text

Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics. by Dinh Viet Cuong, Branislava Lalić, Mina Petrić, Nguyen Thanh Binh, Mark Roantree

“…Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems.…”
Get full text

Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models by Wang Yuxin

“…This study examines the effectiveness of four machine learning models—Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (Bi-LSTM)—in forecasting traffic patterns using both web-based and real-world datasets. …”
Get full text

A Novel Prescribed-Time Convergence Acceleration Algorithm with Time Rescaling by Xuehui Mei, Pengrui Zhang, Haijun Jiang, Zhiyong Yu

“…In machine learning, the processing of datasets is an unavoidable topic. …”
Get full text

An Analysis of the Economic Impact of US Presidential Elections Based on Principal Component and Logical Regression by Jing-Jing Wang, Yan Liang, Jin-Tao Su, Jia-Ming Zhu

“…In order to investigate the impact of the US presidential election on the economy, this paper first constructs an analysis model of the economic impact on the United States based on stepwise regression and principal component analysis to analyze the focus of different candidates’ attention on the economic issues and its possible impact on the US economy in the election year and after the election; secondly, a Chinese economic impact analysis model based on factor analysis and machine learning logistic regression was constructed to analyze the impact of the US presidential election on the Chinese economy. …”
Get full text

6G native intelligence network architecture enabled by intent abstraction and knowledge by Jingya YANG, Xiaogang TANG, Yiqing ZHOU, Ling LIU, Wang Jiangzhou

“…6G will evolve to an intelligent network, featured by native intelligence and openness.The study of standardization of intelligent network emphasizes the importance of intent-driven network for network intelligence.However, current intent-based networking takes the intent as “What to do” rather than “What you want”.Moreover, current knowledge defined network (KDN) can partially fulfill “How to configure the network” according to “What to do”.Therefore, a 6G native intelligent network architecture based on the intent abstraction and knowledge was proposed, aiming to achieve“How to configure the network” according to “What you want”.Firstly, an intent abstraction module was designed to obtain “What to do” from “What you want”, composed of intent acquisition, intent translation, intent mapping, and intent modeling.Secondly, the cognitive module was proposed to achieve “How to configure the network” according to “What to do”, which got network knowledge through joint optimization of machine learning and logical reasoning.Finally, enabling key technologies such as intent mapping, network information measurement, network policy generation, and network policy verification were introduced to support the implementation of 6G native intelligence.…”
Get full text

Estimating the Physical Properties of Nanofluids Using a Connectionist Intelligent Model Known as Gaussian Process Regression Approach by Tzu-Chia Chen, Ali Thaeer Hammid, Avzal N. Akbarov, Kaveh Shariati, Mina Dinari, Mohammed Sardar Ali

“…This work aims to develop a robust machine learning model for the prediction of the relative viscosity of nanoparticles (NPs) including Al2O3, TiO2, SiO2, CuO, SiC, and Ag based on the most important input parameters affecting them covering the size, concentration, thickness of the interfacial layer, and intensive properties of NPs. …”
Get full text

IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data by SeyedEhsan Roshan, Jafar Tanha, Farzad Hallaji, Mohammad-reza Ghanbari

“…Imbalanced datasets pose significant challenges in the field of machine learning, as they consist of samples where one class (majority) dominates over the other class (minority). …”
Get full text

An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extractio... by Nhat-Duc Hoang

“…A data set consisting of 200 image samples has been collected to train and validate the predictive performance of two machine learning algorithms including the least squares support vector machine (LS-SVM) and the artificial neural network (ANN). …”
Get full text

La prochaine génération de téléphonie mobile (5G) et ses implications (Infrastructure, Réglementation) by Edward M. Roche, Benjamin H. Dickens-Jr., Walker Townes

“…Software Defined Networks (SDN) will be set up and torn down, grown and lessened according to demand.Complex network management will be done by Machine Learning (ML) and Artificial Intelligence (AI). …”
Get full text

Direct perception of affective valence from vision by Saeedeh Sadeghi, Zijin Gu, Eve De Rosa, Amy Kuceyeski, Adam K. Anderson

“…We train a visual valence (VV) machine learning model of low-level image statistics on nearly 8000 emotionally charged photographs. …”
Get full text

Class Weighting Approach For Handling Imbalanced Data On Forest Fire Classification Using EfficientNet-B1 by Arvinanto Bahtiar, Muhammad Ihsan Prawira Hutomo, Agung Widiyanto, Siti Khomsah

“…This study contributes to the development of more effective methods for forest fire monitoring and provides insights for future research in machine learning applications in environmental contexts. …”
Get full text

Analysis Technology of Tennis Sports Match Based on Data Mining and Image Feature Retrieval by Hong Huang, Risheng Deng

“…In order to improve the performance of tennis game technical analysis, based on machine learning algorithms, this paper combines image analysis to identify athletes’ movement characteristics and image feature recognition processing with image recognition technology, realizes real-time tracking of athletes’ dynamic characteristics, and records technical characteristics. …”
Get full text

Traffic Sign Recognition in Rainy Conditions Based on Federated Learning by Chen Yilin

“…The proposed method is demonstrated by experimental results to enhance performance in challenging weather conditions while also maintaining data privacy in machine learning applications. Overall, this paper underscores the potential of integrating federated learning with CNNs to improve traffic sign recognition capabilities.…”
Get full text

Optimal Embedding of Graphs with Nonconcurrent Longest Paths in Archimedean Tessellations by Muhammad Faisal Nadeem, Ayesha Shabbir, Muhammad Imran

“…These techniques have wide applications in fields such as machine learning, data mining, and network analysis. Do we have small (if possible minimal) k-connected graphs with the property that for any j vertices there is a longest path avoiding all of them? …”
Get full text

Active oversight and quality control in standard Bayesian optimization for autonomous experiments by Sumner B. Harris, Rama Vasudevan, Yongtao Liu

“…Abstract The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. …”
Get full text

Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model by Akshat Gaurav, Brij B. Gupta, Razaz Waheeb Attar, Ahmed Alhomoud, Varsha Arya, Kwok Tai Chui

“…The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines. …”
Get full text

Classification of Gastrointestinal Diseases Using Hybrid Recurrent Vision Transformers With Wavelet Transform by Biniyam Mulugeta Abuhayi, Yohannes Agegnehu Bezabh, Aleka Melese Ayalew, Miraf Alemayehu Lakew

“…This study highlights the potential of combining RVTs with standard machine learning techniques and wavelet transform to enhance the automated diagnosis of GI diseases. …”
Get full text