Article
Chemistry, Physical
Marc Duquesnoy, Teo Lombardo, Fernando Caro, Florent Haudiquez, Alain C. Ngandjong, Jiahui Xu, Hassan Oularbi, Alejandro A. Franco
Summary: This study proposes a functional data-driven framework to capture the influence of manufacturing parameters on the properties of lithium-ion battery composite electrodes, while ensuring a match with experimental data. The results demonstrate that this approach can significantly improve computational efficiency without sacrificing accuracy.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Environmental Sciences
Yiran Liu, Jian Wang, Cheng Yang, Yu Zheng, Haipeng Fu
Summary: This study proposed a high-accuracy TEC prediction model based on machine learning, utilizing methods such as principal component analysis, solar activity parameters, and spatial interpolation. The model showed high consistency with observed values and outperformed the traditional IRI model.
Article
Engineering, Electrical & Electronic
Syed Aziz Shah, Jawad Ahmad, Fawad Masood, Syed Yaseen Shah, Haris Pervaiz, William Taylor, Muhammad Ali Imran, Qammer H. Abbasi
Summary: The health status of elderly people can be determined by the decline in independence in Activities of Daily Living, and Radar images can be used to detect their body movements. Through experiments, it was found that utilizing CNN algorithm with Principal Component Analysis and Data Augmentation achieved the highest accuracy rate.
IEEE SENSORS JOURNAL
(2021)
Article
Chemistry, Analytical
Gilbert A. Angulo-Saucedo, Jersson X. Leon-Medina, Wilman Alonso Pineda-Munoz, Miguel Angel Torres-Arredondo, Diego A. Tibaduiza
Summary: Improvements in computing capacity have enabled the development of machine learning algorithms for structural health monitoring (SHM). This study focuses on configuring a data acquisition system, developing a damage classification methodology, and using machine learning algorithms to detect and classify damages. The results validate the effectiveness of the SKN and XYF networks in damage classification tasks.
Article
Computer Science, Artificial Intelligence
Lin Li, Turghun Tayir, Yifeng Han, Xiaohui Tao, Juan D. Velasquez
Summary: This article proposes an effective multimodality information fusion method for automated machine translation based on semi-supervised learning. The method combines multimodality information, texts, and images through a multimodal attention network, improving the accuracy of machine translation.
INFORMATION FUSION
(2023)
Article
Polymer Science
Khaled Younes, Mayssara Antar, Hamdi Chaouk, Yahya Kharboutly, Omar Mouhtady, Emil Obeid, Eddie Gazo Hanna, Jalal Halwani, Nimer Murshid
Summary: The study aimed to estimate the adsorption potential of nanocellulose (NC), chitosan (CS), and graphene (G) oxide-based aerogels. Principal component analysis (PCA) revealed hidden patterns and showed differences between the different types of aerogels. The exclusion of outliers increased the total variance, indicating the effectiveness of the approach and the impact of outliers.
Article
Energy & Fuels
Amaris Dalton, Bernard Bekker
Summary: This paper investigates the wind speed predictive skill provided by 16 exogenous meteorological variables in machine learning algorithms. Factors such as choice of regression model, choice of domain size, forecast period, meteorological variable elevation, and testing site location are considered. The best performing wind speed predictors were found to be 950 hPa- vertical velocity, divergence, the u- & v-wind speed components, and geopotential heights.
Article
Plant Sciences
R. P. Zhang, J. H. Zhou, J. Guo, Y. H. Miao, L. L. Zhang
Summary: Grassland biomass monitoring is crucial for assessing health and carbon cycling, but satellite remote sensing in drylands poses challenges. Statistical and machine learning models have been used, but the predictive power for different grassland types remains unclear. Therefore, ground-truthed data points were analyzed to identify key variables using PCA. The accuracy of various models was evaluated for biomass inversion, and the RF model showed the highest accuracy for grassland biomass inversion in Xinjiang.
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Mathematics, Interdisciplinary Applications
Lu Ye, Saadya Fahad Jabbar, Musaddak M. Abdul Zahra, Mou Leong Tan
Summary: A new model based on sea level pressure was developed for predicting daily rainfall, which showed better performance compared to other AI algorithms. BRNN was found to be the most effective model with a normalized root mean square error (NRMSE) of 0.678.
Article
Automation & Control Systems
Decheng Liu, Jun Shang, Maoyin Chen
Summary: A powerful principal component analysis (PCA)-based ensemble detector (PCAED) is developed for detecting incipient faults in TEP, which cannot be detected by an individual PCA detector. Simulations fully verify the effectiveness of PCAED in detecting faults at the incipient stage.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Public, Environmental & Occupational Health
Xianbin Song, Jiangang Zhu, Xiaoli Tan, Wenlong Yu, Qianqian Wang, Dongfeng Shen, Wenyu Chen
Summary: In this study, we analyzed the gene expression profiling data of COVID-19 positive and negative patients using machine learning methods. We identified 24 feature genes that can effectively classify COVID-19 patients and analyzed their possible biological functions and signaling pathways.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Polymer Science
Khaled Younes, Yahya Kharboutly, Mayssara Antar, Hamdi Chaouk, Emil Obeid, Omar Mouhtady, Mahmoud Abu-samha, Jalal Halwani, Nimer Murshid
Summary: Water scarcity is a global problem with significant impacts on the economy, society, and environment. It affects agriculture, industry, and households, leading to a lower quality of life. To address water scarcity, collaboration between governments, communities, and individuals is crucial for water conservation and sustainable water management. In this study, the potential of Green Aerogels in water treatment for ion removal was investigated.
Article
Meteorology & Atmospheric Sciences
Mingyue Su, Chao Liu, Di Di, Tianhao Le, Yujia Sun, Jun Li, Feng Lu, Peng Zhang, Byung-Ju Sohn
Summary: This study introduces a fast and accurate radiative transfer model using principal component analysis (PCA) or machine learning (such as neural networks) to accelerate simulations for high-resolution hyperspectral measurements. The model is three orders of magnitude faster compared to traditional methods, with comparable accuracy. It simplifies the radiative transfer scheme and is highly flexible for hyperspectral instruments with similar spectral ranges.
ADVANCES IN ATMOSPHERIC SCIENCES
(2023)
Article
Biology
Shoukun Chen, Kaili Xu, Xiwen Yao, Siyi Zhu, Bohan Zhang, Haodong Zhou, Xin Guo, Bingfeng Zhao
Summary: This study conducted field tests on miners in high-altitude and cold areas to measure fatigue-related psychophysiological parameters, and utilized support vector machine and random forest techniques for fatigue identification with feature signal fusion. The results show that ECG-FD and EMG are the best indicators of fatigue, and the optimal three-factor combination classification has the best performance and robustness. Support vector machine learning effectively identifies miner fatigue based on fatigue-related factor combinations.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Mohammadali Ganjali, Alireza Mehridehnavi, Sajed Rakhshani, Abed Khorasani
Summary: Stable decoding of movement parameters is crucial for the success of brain-machine interfaces (BMIs). This study proposes an automatic unsupervised algorithm that addresses the issue of neural activity instability by aligning manifolds and reducing dimensions. The method shows higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer, offering a promising solution for achieving stable and accurate movement decoding in BMI applications.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Automation & Control Systems
Angela Fernandez, Neta Rabin, Dalia Fishelov, Jose R. Dorronsoro
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2020)
Article
Multidisciplinary Sciences
Rafael Y. Brzezinski, Lapaz Levin-Kotler, Neta Rabin, Zehava Ovadia-Blechman, Yair Zimmer, Adi Sternfeld, Joanna Molad Finchelman, Razan Unis, Nir Lewis, Olga Tepper-Shaihov, Nili Naftali-Shani, Nora Balint-Lahat, Michal Safran, Ziv Ben-Ari, Ehud Grossman, Jonathan Leor, Oshrit Hoffer
SCIENTIFIC REPORTS
(2020)
Article
Biophysics
Zehava Ovadia-Blechman, Oshrit Hoffer, Moshe Halak, Karin Adrai, Yair Zimmer, Daniel Silverberg, Neta Rabin
Summary: This study introduces a novel technique using thermal imaging to evaluate the palm's blood distribution following arteriovenous access surgery, finding that the greatest change after surgery was in the fingertips region and the changes in palm blood distribution in both hands were synchronized, indicating a bilateral effect. An unsupervised machine learning model revealed two variables determining the recovery pattern following the surgery.
JOURNAL OF BIOMECHANICS
(2021)
Article
Multidisciplinary Sciences
Rafael Y. Brzezinski, Neta Rabin, Nir Lewis, Racheli Peled, Ariel Kerpel, Avishai M. Tsur, Omer Gendelman, Nili Naftali-Shani, Irina Gringauz, Howard Amital, Avshalom Leibowitz, Haim Mayan, Ilan Ben-Zvi, Eyal Heler, Liran Shechtman, Ori Rogovski, Shani Shenhar-Tsarfaty, Eli Konen, Edith M. Marom, Avinoah Ironi, Galia Rahav, Yair Zimmer, Ehud Grossman, Zehava Ovadia-Blechman, Jonathan Leor, Oshrit Hoffer
Summary: The newly developed non-contact thermal imaging tool showed high sensitivity and area under the curve in detecting COVID-19 infection. This technology could be used for non-invasive screening of COVID-19 in low-income regions with limited medical resources.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Artificial Intelligence
Anat Ratnovsky, Sarit Malayev, Shahar Ratnovsky, Sara Naftali, Neta Rabin
Summary: Through the use of machine learning methods and EMG signals from facial muscles, this study achieved high accuracy in speech recognition through feature processing and multi-subject classification. This has potential applications for individuals who have lost their ability to speak.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Alex Knish, Neta Rabin
Summary: Thermal imaging is used for hand identification by capturing the thermal heat distribution pattern instead of relying on geometric features. A novel image processing algorithm is developed to identify and locate hand posture and extract multiple features. Classification and identification in the reduced-dimensional data achieve high accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Vivian Simon, Neta Rabin, Hila Chalutz-Ben Gal
Summary: The growing use of AI-enabled recruitment systems, particularly through social networks like LinkedIn and Facebook, has become an important aspect of modern talent recruitment. However, the data overflow in these systems, utilizing NLP methods, may result in unconscious gender bias. This study aims to analyze and detect textual bias in different groups using various methods.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Mathematics, Applied
Neta Rabin, Angela Fernandez, Dalia Fishelov
Summary: Many phenomena in Physics and Engineering can be modeled by Partial Differential Equations, and numerical schemes are applied for approximating the exact solutions. This work proposes a multiscale iterative approach to enhance the accuracy of coarse grid computations. A modified multimodal Laplacian Pyramids algorithm is also suggested for predicting future values of the solution. The results show comparable convergence rates and significantly reduced computational time compared to fine grid computations.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Geochemistry & Geophysics
Y. Bregman, Y. Radzyner, Y. Ben Horin, M. Kahlon, N. Rabin
Summary: Discrimination between earthquakes and explosions is crucial for nuclear test monitoring, but current methods are not always effective for regional events. This study introduces a diffusion maps-based discrimination method and enhances its automation and efficiency for application in the Sea of Galilee seismic events.
PURE AND APPLIED GEOPHYSICS
(2023)
Article
Geochemistry & Geophysics
Itay Niv, Yuri Bregman, Neta Rabin
Summary: This study explores the impact of mine activity on global seismicity and proposes a method for automatic identification of mine blasts using manifold learning techniques. By processing and analyzing a large amount of seismic data, a stable model is established to accurately identify explosions from specific mines.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Meeting Abstract
Cardiac & Cardiovascular Systems
R. Y. Brzezinski, N. Rabin, N. Lewis, R. Peled, A. Tsur, A. Kerpel, E. M. Marom, S. Shenhar-Tsarfaty, N. Naftali-Shani, G. Rahav, E. M. Grossman, Y. Zimmer, Z. Ovadia-Blechman, J. Leor, O. Hoffer
EUROPEAN HEART JOURNAL
(2021)
Article
Geochemistry & Geophysics
Y. Bregman, O. Lindenbaum, N. Rabin
Summary: This study applies an advanced machine learning technique called diffusion maps for array-based earthquake-explosion discrimination, focusing on International Monitoring System (IMS) stations as part of the Comprehensive Nuclear-Test-Ban Treaty verification regime. The proposed method involves preprocessing and nonlinear dimensionality reduction steps for discrimination in a low-dimensional space, enhancing the discrimination capabilities of seismic arrays.
PURE AND APPLIED GEOPHYSICS
(2021)
Article
Geochemistry & Geophysics
Ofir Lindenbaum, Neta Rabin, Yuri Bregman, Amir Averbuch
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2020)
Article
Education & Educational Research
Maya Golan, Gonen Singer, Neta Rabin, Dvir Kleper
ASSESSMENT & EVALUATION IN HIGHER EDUCATION
(2020)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)