21 papers · ranked by Valyu relevance
Sancho Salcedo‐Sanz, Jorge Pérez‐Aracil, Guido Ascenso, Javier Del Ser + 8 more
'Javier Del Ser' 'David Casillas-Pérez' 'Christopher Kadow' 'Dušan Fister' 'David Barriopedro' 'Ricardo García‐Herrera' 'Marcello Restelli' 'Mateo Giuliani' 'Andrea Castelletti'] Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other…
Nelly Elsayed, Zag ElSayed, Murat Özer
—Diabetes is one of the chronic diseases that has been discovered for decades. However, several cases are diagnosed in their late stages. Every one in eleven of the world's adult population has diabetes. Forty-six percent of people with diabetes have not been diagnosed. Diabetes can develop several other severe…
Xianli Liu, Guo Zhou, Yongquan Zhou, Qifang Luo
Introduction Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. However, the ELM also has some shortcomings, such as structure selection, overfitting and low…
Fulai Peng, Cai Chen, Danyang Lv, Ningling Zhang + 3 more
'Xikun Zhang' 'Zhiyong Wang'] In the recent years, gesture recognition based on the surface electromyography (sEMG) signals has been extensively studied. However, the accuracy and stability of gesture recognition through traditional machine learning algorithms are still insufficient to some actual application…
Edward Ratner, Emma Lee Farmer, Christopher Douglas, Amaury Lendasse
Performance Authors: ['Edward Ratner' 'Emma Lee Farmer' 'Christopher Douglas' 'Amaury Lendasse'] Abstract— Utilizing machine learning techniques has always required choosing hyperparameters. This is true whether one uses a classical technique such as a KNN or very modern neural networks such as Deep Learning. Though in…
Ergun Biçici
Prediction Models Authors: ['Ergun Biçici'] Abstract—Extreme Learning Machines (ELM) provide a fast alternative to traditional gradient-based learning in neural networks, offering rapid training and robust generalization capabilities. Its theoretical basis shows its universal approximation capability. We explore the…
Debendra Muduli, Santosh Kumar Sharma, Sujata Dash, Bernardo Lemos + 1 more
Glaucoma is a prominent threat to vision and ranks as the third leading cause of blindness in India. Early detection is crucial to limit its progression. Retinal image analysis, particularly computer-aided diagnosis (CAD), has gained significant attention due to its potential in effectively screening for and managing…
Zongying Liu, Jiangling Hao, Dongrui Yang, Ghalib Ahmed Tahir + 1 more
With the exponential growth of the Internet population, scientists and researchers face the large-scale data for processing. However, the traditional algorithms, due to their complex computation, are not suitable for the large-scale data, although they play a vital role in dealing with large-scale data for…
Shaofei Zang, Xinghai Li, Jianwei Ma, Yongyi Yan + 2 more
'Yuan Wei'] As a single-layer feedforward network (SLFN), extreme learning machine (ELM) has been successfully applied for classification and regression in machine learning due to its faster training speed and better generalization. However, it will perform poorly for domain adaptation in which the distributions…
Rongbo Lu, Liang Luo, Bolin Liao
This study introduces an intelligent learning model for classification tasks, termed the voting-based Double Pseudo-inverse Extreme Learning Machine (V-DPELM) model. Because the traditional method is affected by the weight of input layer and the bias of hidden layer, the number of hidden layer neurons is too large and…
Hernandez-Hernandez, Rolando A., Rubio-Solis, Adrian
Multilayer Extreme Learning Machine (ML-ELM) and its variants have proven to be an effective technique for the classification of different natural signals such as audio, video, acoustic and images. In this paper, a Hybrid Multilayer Extreme Learning Machine (HML-ELM) that is based on ELMbased autoencoder (ELM-AE) and…
Yinggao Yue, Li Cao, Haishao Chen, Yaodan Chen + 4 more
'Heming Jia' 'Laith Abualigah' 'Xuewen Xia'] The features of the kernel extreme learning machine-efficient processing, improved performance, and less human parameter setting-have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend…
Vinod Kumar, Sougatamoy Biswas, Dharmendra Singh Rajput, Harshita Patel + 1 more
'Harshita Patel' 'Basant Tiwari'] Novel coronavirus 2019 has created a pandemic and was first reported in December 2019. It has had very adverse consequences on people's daily life, healthcare, and the world's economy as well. According to the World Health Organization's most recent statistics, COVID-19 has become a…
Vetrano, Marco, Zingales, Tiziano + 4 more
The study of exoplanetary atmospheres traditionally relies on forward models to analytically compute the spectrum of an exoplanet by fine-tuning numerous chemical and physical parameters. However, the high-dimensionality of parameter space often results in a significant computational overhead. In this work, we…
Caglar Uyulan
Human-machine interfaces contribute to the improvement of the life quality of physically disabled users. In this study, a non-invasive brain-machine interface (BMI) design methodology was proposed to control a robot arm through magnetoencephalography (MEG) based on directionally modulated MEG activity that was acquired…
J. Meiyazhagan, S. Sudharsan, A. Venkatesan, M. Senthilvelan
Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Authors not listed
Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Authors not listed
Hybrid machine-learning/molecular-mechanics (ML/MM) methods extend the classical QM/MM paradigm by replacing the quantum desription with neural network interatomic potentials trained to reproduce accurately quantum-mechanical (QM) results. By describing only the chemically active region with ML and the surrounding…
Anubhav Jain
The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials…