Machine Learning Exploration of Experimental Conditions for Optimized Electrochemical CO2 Reduction by Vuri Ayu Setyowati, Shiho Mukaida, Kaito Nagita, Takashi Harada, Shuji Nakanishi, Kazuyuki Iwase

“…This study constitutes the first demonstration of optimal experimental conditions for electrochemical CO2 reduction with desired characteristics using the experimental results as training data.…”
Get full text

Fairness-Oriented Charging Station Location Optimization Driven by Deep Reinforcement Learning by Siyue Yuan, Jiaying Fu, Xiaoyin Ma

Get full text

Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization by Chuanwei Zhang, Dingshuai Liu, Paraskevas Tsangaratos, Ioanna Ilia, Sijin Ma, Wei Chen

“…From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. …”
Get full text

Identification of students' needs for multimedia development in craft and entrepreneurial topic: information technology-assisted learning by Mochamad Kamil Budiarto

“…Data collection techniques using a needs analysis questionnaire and analyzed quantitatively using statistics to find out the means and percentage of responses from the sample. …”
Get full text

Analysis of student support activities at school in terms of extroverted and introverted personalities by Siska Anita, Suci Damayanti, Rahma Indah Sari, Rodinatul Munawaroh, Munawar Jailani

Get full text

Prediction of Magnetic Fields in Single-Phase Transformers Under Excitation Inrush Based on Machine Learning by Qingjun Peng, Hantao Du, Zezhong Zheng, Haowei Zhu, Yuhang Fang

“…The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction.…”
Get full text

Integrating Copula-Based Random Forest and Deep Learning Approaches for Analyzing Heterogeneous Treatment Effects in Survival Analysis by Jong-Min Kim

“…This paper presents deep learning models—specifically, Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network–LSTM (CNN-LSTM) with a Copula-Based Random Forest (CBRF) model to estimate Heterogeneous Treatment Effects (HTEs) in survival analysis. …”
Get full text

Multimodal scene recognition using semantic segmentation and deep learning integration by Aysha Naseer, Mohammed Alnusayri, Haifa F. Alhasson, Mohammed Alatiyyah, Dina Abdulaziz AlHammadi, Ahmad Jalal, Jeongmin Park

“…In order to overcome these obstacles, this study presents a novel multimodal deep learning technique that enhances scene recognition accuracy and robustness by combining depth information with conventional red-green-blue (RGB) image data. …”
Get full text

Quantitative Emotional Salary and Talent Commitment in Universities: An Unsupervised Machine Learning Approach by Ana-Isabel Alonso-Sastre, Juan Pardo, Oscar Cortijo, Antonio Falcó

“…To achieve this, machine learning (ML) techniques are employed, as Principal Component Analysis (PCA) for dimensionality reduction and clustering techniques for individual segmentation have been employed in such tasks. …”
Get full text

A Spatially Informed Machine Learning Method for Predicting Sound Field Uncertainty by Xiangmei Chen, Chao Li, Haibin Wang, Yupeng Tai, Jun Wang, Cyrille Migniot

Get full text

Bioinformatics and Deep Learning Approach to Discover Food-Derived Active Ingredients for Alzheimer’s Disease Therapy by Junyu Zhou, Chen Li, Yong Kwan Kim, Sunmin Park

Get full text

Improving guest satisfaction by identifying hotel service micro-elements failures through Deep Learning of online reviews by Sergey Kazakov, Pedro Cuesta-Valiño, Vera Butkouskaya, Daniel Muravsky

Get full text

A benchmark for computational analysis of animal behavior, using animal-borne tags by Benjamin Hoffman, Maddie Cusimano, Vittorio Baglione, Daniela Canestrari, Damien Chevallier, Dominic L. DeSantis, Lorène Jeantet, Monique A. Ladds, Takuya Maekawa, Vicente Mata-Silva, Víctor Moreno-González, Anthony M. Pagano, Eva Trapote, Outi Vainio, Antti Vehkaoja, Ken Yoda, Katherine Zacarian, Ari Friedlaender

“…This makes it difficult to, for example, identify patterns in what works well for machine learning-based analysis of bio-logger data. It also makes it difficult to evaluate the effectiveness of novel methods developed by the machine learning community. …”
Get full text

Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: a multicenter retrospective study by Zhan Zhang, Yue Zhao, Yi-Jun Ma, Chuan-Qi Chen, Zhen-Yi Li, Yv-Kai Wang, Si-Jie Zhang, Hai-Ming Li, Yongmeng Li, Yu Tian, Hui Tian

“…This study aimed to develop and validate machine learning models to predict the presence of STAS using preoperative clinical, radiological, and pathological data in lung cancer patients. …”
Get full text

Learning Developmental Age From 3D Infant Kinetics Using Adaptive Graph Neural Networks by Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Tuiskula, Leena Haataja, Sampsa Vanhatalo, Teemu Roos

Get full text

Machine Learning Methods of Providing Informational Management Support for Students’ Professional Development by I. G. Zakharova

“…The effectiveness of this interaction resulted from its information support, based on reliable information, promptly provided to all the members of learning process.The aim of this paper was to study the machine learning methods potential for the effective management of learning process by the example of implementing information support component designed to diagnose and predict the professional development of students based on automatic text analysis.Methodology and research methods. …”
Get full text

Research and Application of Data-driven Forecast Method for Water Level Increment of Small and Medium-sized Rivers by DING Wu, LIN Hanxiong, WANG Hangang, ZHANG Wei, YANG Bin

“…Focusing on the forecast of flash flood disasters in small and medium-sized rivers,this paper adopts three data-driven forecast models to forecast the maximum water level increment.With Paitan Town,Zengcheng District,Guangzhou City as the research area,the mathematical modeling and accuracy evaluation of the three models are carried out.The analysis shows that the forecast accuracy of machine learning is the highest,but the forecast results are not interpretable.The forecast accuracy of the similarity analysis model is medium,and the prediction results have good cognition and interpretability.The overall prediction accuracy of the “rainfall-water level increment” relationship model is the lowest,but its operation is simple.The calculation of the similarity analysis model and machine learning model is relatively complex,relying on computer-aided calculation,and the results of the “rainfall-water level increment” relationship model can be plotted as graphs.Considering the forecast accuracy and convenience,multiple models such as the machine learning model and similarity analysis model can be applied for the forecast to improve the forecast accuracy if there is support from auxiliary equipment such as computers and mobile phone applications.Without support from computers and other basic equipment,the “rainfall-water level increment” relationship model can provide a convenient forecast method for flash flood forecast and early warning in small and medium-sized rivers.…”
Get full text

Chemoreactomic analysis of acyzole in comparison with zinc derivatives of nonsteroidal anti-inflammatory drugs by P. A. Galenko-Yaroshevsky, I. Yu. Torshin, A. N. Gromov, O. A. Gromova, R. A. Murashko, A. V. Zelenskaya, L. O. Alukhanyan, O. V. Shelemekh

Get full text

Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt by Shaimaa A. El-Dabaa, Farouk I. Metwalli, Ali Maher, Amir Ismail

“…It is challenging to investigate these subtle features, including channel systems, with conventional-amplitude seismic data. Over the past few years, the use of machine learning (ML) to analyze multiple seismic attributes has enhanced the facies analysis by mapping patterns in seismic data. …”
Get full text

Rethinking Domain‐Specific Pretraining by Supervised or Self‐Supervised Learning for Chest Radiograph Classification: A Comparative Study Against ImageNet Counterparts in Cold‐Star... by Han Yuan, Mingcheng Zhu, Rui Yang, Han Liu, Irene Li, Chuan Hong

“…To mitigate the cost, cold‐start active learning (AL), comprising an initialization followed by subsequent learning, selects a small subset of informative data points for labeling. …”
Get full text