A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data by Viral Galayia, Ruslan Masinjila, Soheil Khatibi, Thiago Eustaquio Alves de Oliveira, Xianta Jiang, Vinicius Prado da Fonseca

“…The dataset can be used to pre-train a reinforcement machine learning model to perform peg-in-hole tasks and to study how pretraining affects a manipulator's ability to infer tactile signals and improve the success rates of the manipulator.…”
Get full text

Analyses of poverty indicators using PPI methodology by T. Merkulova, D. Kosiashvili

“…Developing the model was carried out using machine learning methods in several steps: 1) data processing and statistical analyses; 2) selection of significant indicators by the classification model; 3) clustering by k-mean algorithm; 4) hierarchical clustering; 5) comparing outcomes of modeling and interpretation of results. …”
Get full text

Channel Mixer Layer: Multimodal Fusion Toward Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content by Peng Liu, Tatsuhiro Yokoyama, Takuya Sori, Mamoru Yamamoto

“…Thanks to the development of machine learning for video prediction, spatiotemporal predictive models are applied on the future TEC map prediction based on the graphic features of past frames. …”
Get full text

Development of a Comprehensive Comparison Software for Automated Decision-Making in Impulse Testing of Power Transformers, Including a Review of Practices from Analog to Digital by Welson Bassi

“…The developed methodology and implemented metrics can form the basis for future machine learning or artificial intelligence (AI) applications. …”
Get full text

Using Depth Cameras for Recognition and Segmentation of Hand Gestures by Khalid Twarish Alhamazani, Jalawi Alshudukhi, Talal Saad Alharbi, Saud Aljaloud, Zelalem Meraf

“…That is why, in the academy, this problem has been addressed using machine learning techniques. The experiments carried out have shown very encouraging results indicating that the choice of this type of architecture allows obtaining an excellent efficiency of parameters and prediction times. …”
Get full text

Geochemical exploration of rare earth element resources in highland karstic bauxite deposits in the Sierra de Bahoruco, Pedernales Province, Southwestern Dominican Republic. by Mark Chappell, Harold Rojas, Charles Andros, Autumn Acree, Yoko Masue-Slowey, Christine Young, Paige Fowler, Elizabeth Lotufo, Wesley Rowland, Michelle Wynter, Marcelo Salles, Leopoldo Gonzalez

“…We employed compositional data analysis (CoDA) and machine learning models to estimate total REE concentrations, demonstrating that pXRF and the color sensor, when properly calibrated, are effective tools for remote geochemical exploration. …”
Get full text

Optimizing Multilayer Perceptron with Cost-Sensitive Learning for Addressing Class Imbalance in Credit Card Fraud Detection by Wowon Priatna, Hindriyanto Dwi Purnomo, Ade Iriani, Irwan Sembiring, Theophilus Wellem

“…This imbalance often leads to machine learning algorithms overlooking fraudulent transactions, resulting in suboptimal performance. …”
Get full text

A Bayesian active learning platform for scalable combination drug screens by Christopher Tosh, Mauricio Tec, Jessica B. White, Jeffrey F. Quinn, Glorymar Ibanez Sanchez, Paul Calder, Andrew L. Kung, Filemon S. Dela Cruz, Wesley Tansey

“…Existing approaches use ad hoc fixed experimental designs then train machine learning models to impute unobserved combinations. …”
Get full text

Measuring China’s Policy Stringency on Climate Change for 1954–2022 by Bo Li, Enxian Fu, Shuhao Yang, Jiaying Lin, Wei Zhang, Jian Zhang, Yaling Lu, Jiantong Wang, Hongqiang Jiang

“…This paper employs an integrated framework comprising lexicon, text analysis, machine learning, and large-language model applied to multi-source data to quantify China’s policy stringency on climate change (PSCC) from 1954 to 2022. …”
Get full text

Improving the Forecast Accuracy of PM_2.5 Using SETAR-Tree Method: Case Study in Jakarta, Indonesia by Dinda Ayu Safira, Heri Kuswanto, Muhammad Ahsan

“…PM_2.5 exhibits significant nonlinear fluctuations; thus, this study employed two machine learning approaches: self-exciting threshold autoregressive tree (SETAR-Tree) and long short-term memory (LSTM). …”
Get full text

Tipping point-induced abrupt shifts in East Asian hydroclimate since the Last Glacial Maximum by Fuzhi Lu, Huayu Lu, Yao Gu, Pengyu Lin, Zhengyao Lu, Qiong Zhang, Hongyan Zhang, Fan Yang, Xiaoyi Dong, Shuangwen Yi, Deliang Chen, Francesco S. R. Pausata, Maya Ben-Yami, Jennifer V. Mecking

“…Here we present an ensemble reconstruction of East Asian summer monsoon (EASM) rainfall since the Last Glacial Maximum (LGM) using nine statistical and machine learning methods based on multi-proxy records from a maar lake in southern China. …”
Get full text

Learning Automata Based Incremental Learning Method for Deep Neural Networks by Haonan Guo, Shilin Wang, Jianxun Fan, Shenghong Li

“…Deep learning methods have got fantastic performance on lots of large-scale datasets for machine learning tasks, such as visual recognition and neural language processing. …”
Get full text

Investigation of ANN Model Containing One Hidden Layer for Predicting Compressive Strength of Concrete with Blast-Furnace Slag and Fly Ash by Hai-Van Thi Mai, Thuy-Anh Nguyen, Hai-Bang Ly, Van Quan Tran

“…In this investigation, an approach using the artificial neuron network (ANN), one of the most powerful machine learning algorithms, is applied to predict the compressive strength of concrete containing BFS and FA. …”
Get full text

CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification by Keuntae Kim, Yong Suk Choi

“…Active learning has been a research area conducted across various domains for a long time, from traditional machine learning to the latest deep learning research. …”
Get full text

Spectral enhancement of PlanetScope using Sentinel-2 images to estimate soybean yield and seed composition by Supria Sarkar, Vasit Sagan, Sourav Bhadra, Felix B. Fritschi

“…The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. …”
Get full text

Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. …”
Get full text

Land use/cover change simulation research: A system literature review based on bibliometric analyses by Meili Chen, Narimah Samat, Mohammad Javad Maghsoodi Tilaki, Liyuan Duan

“…Future research should focus on establishing high spatiotemporal resolution big data systems, multi-scenario theoretical frameworks, multi-scale multi-level spatiotemporal dynamic models, and AI and machine learning. Enhancing the coupling of models improves understanding and prediction of complex LUCC processes, supporting global environmental research, resource management, and policymaking. …”
Get full text

Power flow analysis and volt/var control strategy of the active distribution network based on data-driven method by Chen Hui, Zhu Weiping, Liu Liguo, Shi Mingming, Xie Wenqiang, Zhang Chenyu

“…Firstly, the CatBoost machine learning model for the distribution network power flow analysis is proposed, and the nonlinear mapping relationship between the distribution network state and power flow results is described from the data-driven perspective. …”
Get full text

Orthogonal Wavelet Transform-Based Gaussian Mixture Model for Bearing Fault Diagnosis by Weipeng Li, Yan Cao, Lijuan Li, Siyu Hou

“…The Gaussian mixture model (GMM) is an unsupervised clustering machine learning algorithm. This procedure involves the combination of multiple probability distributions to describe different sample spaces. …”
Get full text

Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems by Kosmas Alexopoulos, Panagiotis Mavrothalassitis, Emmanouil Bakopoulos, Nikolaos Nikolakis, Dimitris Mourtzis

“…However, in recent years, the progress in smart systems enabled by artificial intelligence (AI) and machine learning (ML) solutions has revolutionized the scheduling approach. …”
Get full text