Hierarchical partition of urban land-use units by unsupervised graph learning from high-resolution satellite images by Mengmeng Li, Xinyi Gai, Kangkai Lou, Alfred Stein

“…This paper presents a novel method for deriving these units based on unsupervised graph learning techniques using high-resolution satellite images and open street boundaries. …”
Get full text

Accuracy Prediction of Compressive Strength of Concrete Incorporating Recycled Aggregate Using Ensemble Learning Algorithms: Multinational Dataset by Menghay Phoeuk, Minho Kwon

“…One such material, recycled aggregates, has been extensively studied over the past two decades for its potential to replace natural aggregates in cement-based composites. However, the unique properties of recycled aggregates make traditional concrete mix design methods ineffective in determining their target compressive strength. …”
Get full text

Enhancing Water Bodies Detection in the Highland and Coastal Zones Through Multisensor Spectral Data Fusion and Deep Learning by Xiaofei Han, Nazih Y. Rebouh, Yasmeen Ahmed, Muhammad Nasar Ahmad, Zainab Tahir, Yahia Said, Ishfaq Gujree

“…This study presents a deep-learning-based semantic segmentation framework that leverages multiband Sentinel-2 imagery for delineating glaciers and coastal lakes. …”
Get full text

Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous by Weiqi Chen, Zhiyue Xu, Kang Wang, Lei Gao, Aisheng Song, Tianbao Ma

“…A systematic theoretical simulation method accurately describing atomic interactions for hydrogen-carbon systems is crucial for the design of carbon-based materials and their industrial applications. …”
Get full text

Accelerated design of eutectic high-entropy alloys using high-throughput phase diagram calculations and machine learning by Mingxu Wu, Bang Guan, Junfeng Wang, Shubin Wang, Chao Yang, Chuan Zhang, Da Shu, Chengbo Xiao, Baode Sun

“…Designing eutectic high-entropy alloys (HEAs) with a stable dual-phase structure is prospective for high-temperature structural materials, but remains a challenge due to the complexity of multicomponent compositional space. This study introduces a machine learning (ML) based classification model to assist the high-throughput thermodynamic calculations (HTCs), improving the efficiency by identifying eutectic, near-eutectic and non-eutectic compositions in the VNbTiTaSi system using the parameters that determine the eutectic forming ability. …”
Get full text

Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys by Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy

“…This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. …”
Get full text

Development and validation of a comprehensive tool to study the various elements influencing the utilization of E-learning among undergraduate health professions students by Archana Prabu Kumar, Prabu Kumar Chokkalingam Mani, Komal Atta, Abirami Omprakash, K. Maheshkumar, Hany Atwa, K. N. Maruthy, Padmavathi R.

“…The developed CKAPQ provides a new, standardized method to evaluate knowledge, attitudes, and practices related to E-learning among medical and dental students.…”
Get full text

Integrating traditional omics and machine learning approaches to identify microbial biomarkers and therapeutic targets in pediatric inflammatory bowel disease by Lanlan Li, XuZai Deng, Shuge Wang, Tao Huang

“…Although gut microbiome research has advanced, identifying reliable biomarkers remains difficult due to microbial complexity.MethodsWe used RNA-seq-based microbial profiling and machine learning (ML) to find robust biomarkers in pediatric IBD. …”
Get full text

A novel hybrid machine learning approach for δ13C spatial prediction in polish hard-water lakes by Himan Shahabi, Ataollah Shirzadi, Alicja Ustrzycka, Natalia Piotrowska, Janusz Filipiak, Marzieh Hajizadeh Tahan

“…In this study, we propose a novel hybrid machine learning (ML) algorithm known as the ARAMT model, which combines two key components: a meta-classifier of additive regression (AR) and a base classifier of alternating model trees (AMT). …”
Get full text

Solving Collaborative Scheduling of Production and Logistics via Deep Reinforcement Learning: Considering Limited Transportation Resources and Charging Constraints by Xianping Huang, Yong Chen, Wenchao Yi, Zhi Pei, Ziwen Cheng

Get full text

Prediction of Crop Yield by Support Vector Machine Coupled with Deep Learning Algorithm Procedures in Lower Kulfo Watershed of Ethiopia by Abebe Temesgen Ayalew, Tarun Kumar Lohani

“…The planned model is improved through conducting deep learning methods incorporated to the existing practice for different crop condition. …”
Get full text

Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations by Claudio Ronchetti, Sara Marchio, Francesco Buonocore, Simone Giusepponi, Sergio Ferlito, Massimo Celino

“…This materials discovery approach is disruptive and significantly faster than traditional physics-based computational methods.…”
Get full text

Lightweight deep learning system for automated bone age assessment in Chinese children: enhancing clinical efficiency and diagnostic accuracy by Pang Hai, Zhang Bin, Liu Kesheng, Li Cong, Xu Fei

“…To address these limitations, this study introduces a novel lightweight two-stage deep learning framework based on the Chinese 05 BAA standard. …”
Get full text

Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

“…The development of refractory high-entropy alloys (RHEAs) through conventional trial-and-error approaches I s highly inefficient given the vast compositional space. To address this difficulty, a data-driven machine learning (ML) model was established to predict the compressive yield strength (σ0.2) of RHEAs, which provides a design strategy with two pivotal descriptors concerning lattice distortion and thermodynamic parameters. …”
Get full text

Applying genetic algorithm to extreme learning machine in prediction of tumbler index with principal component analysis for iron ore sintering by Senhui Wang

“…Finally, the model is examined using actual production data of a year from a sinter plant, and is compared with the algorithms of single ELM, GA-BP and deep learning method. A comparison is conducted to confirm the superiority of the proposed model with two traditional models. …”
Get full text

Inverse design of high-strength medium-Mn steel using a machine learning-aided genetic algorithm approach by Jin-Young Lee, Seung-Hyun Kim, Hyun-Bin Jeong, KeunWon Lee, KiSub Cho, Young-Kook Lee

“…The k-means clustering method then grouped them into five distinct specimens based on similarities. …”
Get full text

Autonomous Detection of Mineral Phases in a Rock Sample Using a Space-prototype LIMS Instrument and Unsupervised Machine Learning by Salome Gruchola, Peter Keresztes Schmidt, Marek Tulej, Andreas Riedo, Klaus Mezger, Peter Wurz

“…Consequently, these methods represent effective strategies for data reduction, highlighting their potential application on board spacecraft to obtain direct and quantitative information on the chemical composition and mineralogy of planetary surfaces and to optimize mission returns through the unsupervised selection of valuable data.…”
Get full text

COMPARISON OF RANDOM FOREST AND NAÏVE BAYES METHODS FOR CLASSIFYING AND FORECASTING SOIL TEXTURE IN THE AREA AROUND DAS KALIKONTO, EAST JAVA by Henny Pramoedyo, Danang Ariyanto, Novi Nur Aini

“…The methods used to classification and predict soil texture with machine learning algorithms are Random Forest (RF) and Naïve Bayes (NB). …”
Get full text

Origin Traceability of Chinese Mitten Crab (<i>Eriocheir sinensis</i>) Using Multi-Stable Isotopes and Explainable Machine Learning by Danhe Wang, Chunxia Yao, Yangyang Lu, Di Huang, Yameng Li, Xugan Wu, Weiguo Song, Qinxiong Rao

“…The Chinese mitten crab (<i>Eriocheir sinensis</i>) industry is currently facing the challenges of origin fraud, as well as a lack of precision and interpretability of existing traceability methods. Here, we propose a high-precision origin traceability method based on a combination of stable isotope analysis and interpretable machine learning. …”
Get full text

An Improved Multi-Objective Adaptive Human Learning Optimization Algorithm and Its Application in Optimizing Formulation Schemes for Rotary Hearth Furnaces by Jun Yao, Songcheng Zhou, Ling Wang, Xianxia Zhang

“…An improved multi-objective adaptive human learning optimization algorithm (IMOAHLO) is proposed, which enhances local optimization through neighborhood search and an adaptive learning mechanism. …”
Get full text