Search Results - "Machine Learning Methods"

Classification Handbook for Beginners by Arslan, Burak, Aydemir, Emrah, Cebeci, Halil İbrahim, Arı, Oğuzhan, Mizanalı, Sinem

“…Artificial intelligence and machine learning have become indispensable pillars of today's rapidly evolving technological landscape. At the heart of these…”
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Comprehensive comparative study of multi-label classification methods by Bogatinovski, Jasmin, Todorovski, Ljupčo, Džeroski, Sašo, Kocev, Dragi

“…Multi-label classification (MLC) has recently attracted increasing interest in the machine learning community. Several studies provide surveys of methods and…”
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Predicting redox potentials by graph‐based machine learning methods by Jia, Linlin, Brémond, Éric, Zaida, Larissa, Gaüzère, Benoit, Tognetti, Vincent, Joubert, Laurent

“…The evaluation of oxidation and reduction potentials is a pivotal task in various chemical fields. However, their accurate prediction by theoretical…”
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The M4 Competition: 100,000 time series and 61 forecasting methods by Makridakis, Spyros, Spiliotis, Evangelos, Assimakopoulos, Vassilios

“…The M4 Competition follows on from the three previous M competitions, the purpose of which was to learn from empirical evidence both how to improve the…”
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ML-assisted optimization method for the prediction of 3-dimensional fractal structures from microscopic images by Singh, Abhishek, Kailas, Saket Kohinkar, Thajudeen, Thaseem

“…Aggregated aerosol particles released from high temperature processes and combustion processes are often described as quasi-fractal aggregates, where the shape…”
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Integrative analysis of gene expression and DNA methylation through one‐class logistic regression machine learning identifies stemness features in medulloblastoma by Lian, Hao, Han, Yi‐Peng, Zhang, Yu‐Chao, Zhao, Yang, Yan, Shan, Li, Qi‐Feng, Wang, Bao‐Cheng, Wang, Jia‐Jia, Meng, Wei, Yang, Jian, Wang, Qin‐Hua, Mao, Wei‐Wei, Ma, Jie

“…Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer…”
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Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations by Jafar, Abbas, Bibi, Nabila, Naqvi, Rizwan Ali, Sadeghi-Niaraki, Abolghasem, Jeong, Daesik

“…Accurate and rapid plant disease detection is critical for enhancing long-term agricultural yield. Disease infection poses the most significant challenge in…”
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The State-of-the-Art Review on Applications of Intrusive Sensing, Image Processing Techniques, and Machine Learning Methods in Pavement Monitoring and Analysis by Hou, Yue, Li, Qiuhan, Zhang, Chen, Lu, Guoyang, Ye, Zhoujing, Chen, Yihan, Wang, Linbing, Cao, Dandan

“…In modern transportation, pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians. Pavement service quality…”
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Predicting individual learning performance using machine‐learning hybridized with the teaching‐learning‐based optimization by Arashpour, Mehrdad, Golafshani, Emad M., Parthiban, Rajendran, Lamborn, Julia, Kashani, Alireza, Li, Heng, Farzanehfar, Parisa

“…Reliable prediction of individual learning performance can facilitate timely support to students and improve the learning experience. In this study, two…”
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Machine learning - based shale wettability prediction: Implications for H2, CH4 and CO2 geo-storage by Pan, Bin, Song, Tianru, Yue, Ming, Chen, Shengnan, Zhang, Lijie, Edlmann, Katriona, Neil, Chelsea W., Zhu, Weiyao, Iglauer, Stefan

“…Shale wettability determines shale gas productivities and gas (H2, CH4 and CO2) geo-storage efficiencies. However, shale wettability is a complex parameter…”
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A comparative analysis of unsupervised machine‐learning methods in PSG‐related phenotyping by Ghorvei, Mohammadreza, Karhu, Tuomas, Hietakoste, Salla, Ferreira‐Santos, Daniela, Hrubos‐Strøm, Harald, Islind, Anna Sigridur, Biedebach, Luka, Nikkonen, Sami, Leppänen, Timo, Rusanen, Matias

“…Summary Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover…”
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Identification of the potential risk areas for soil heavy metal pollution based on the source-sink theory by Jia, Xiaolin, Fu, Tingting, Hu, Bifeng, Shi, Zhou, Zhou, Lianqing, Zhu, Youwei

“…[Display omitted] •The contents and spatial distributions of soil heavy metals were well predicted.•Thirteen environmental factors related to heavy metal…”
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The investigation of the effect of particle size on wear performance of AA7075/Al2O3 composites using statistical analysis and different machine learning methods by Aydin, Fatih

“…[Display omitted] •7075 alloy was reinforced by different size of Al2O3 particles via hot pressing.•The effect of particle size on 7075 alloy was discussed in…”
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Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems by Uncuoglu, Erdal, Citakoglu, Hatice, Latifoglu, Levent, Bayram, Savas, Laman, Mustafa, Ilkentapar, Mucella, Oner, A. Alper

“…In this study, it was investigated that how machine learning (ML) methods show performance in different problems having different characteristics. Six ML…”
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Evaluation of an automated genome interpretation model for rare disease routinely used in a clinical genetic laboratory by Meng, Linyan, Attali, Ruben, Talmy, Tomer, Regev, Yakir, Mizrahi, Niv, Smirin-Yosef, Pola, Vossaert, Liesbeth, Taborda, Christian, Santana, Michael, Machol, Ido, Xiao, Rui, Dai, Hongzheng, Eng, Christine, Xia, Fan, Tzur, Shay

“…The analysis of exome and genome sequencing data for the diagnosis of rare diseases is challenging and time-consuming. In this study, we evaluated an…”
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Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020 by Meng, Xiaoyu, Gao, Xin, Li, Sen, Li, Shengyu, Lei, Jiaqiang

“…[Display omitted] •Mapping of Mongolian desertification, based on GEE platform.•The maximum entropy method can obtain the most accurate desertification…”
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Estimating the output power and wind speed with ML methods: A case study in Texas by Malakouti, Seyed Matin

“…Because there is such a significant problem with global warming and emissions of greenhouse gases, we decided to reduce the adverse effects on the environment…”
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Scope of machine learning in materials research—A review by Mobarak, Md Hosne, Mimona, Mariam Akter, Islam, Md. Aminul, Hossain, Nayem, Zohura, Fatema Tuz, Imtiaz, Ibnul, Rimon, Md Israfil Hossain

“…This comprehensive review investigates the multifaceted applications of machine learning in materials research across six key dimensions, redefining the…”
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Machine learning approaches in microbiome research: challenges and best practices by Papoutsoglou, Georgios, Tarazona, Sonia, Lopes, Marta B., Klammsteiner, Thomas, Ibrahimi, Eliana, Eckenberger, Julia, Novielli, Pierfrancesco, Tonda, Alberto, Simeon, Andrea, Shigdel, Rajesh, Béreux, Stéphane, Vitali, Giacomo, Tangaro, Sabina, Lahti, Leo, Temko, Andriy, Claesson, Marcus J., Berland, Magali

“…Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature…”
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SpecML: web tool for predicting the spectral properties of BODIPYs by Ksenofontov, Alexander A., Eremeeva, Yulia V., Bocharov, Pavel S., Makarov, Dmitriy M.

“…In this paper, we present the results of training machine learning (ML) models for accurate prediction of several key photophysical characteristics (absorption…”
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