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arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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Sub-structure Characteristic Mode Analysis of Microstrip Antennas Using a Global Multi-trace Formulation

arXiv

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arXiv 2022年

作者： Zhao, Ran Lu, Yuyu Cheng, Guang Shang Zhu, Wei Hu, Jun Bagci, Hakan Chengdu611731 China The Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China The Anhui Province Key Laboratory of Target Recognition and Feature Extraction Lu’an237010 China Thuwal23955 Saudi Arabia

A characteristic mode (CM) method that relies on a global multi-trace formulation (MTF) of surface integral equations is proposed to compute the modes and the resonance frequencies of microstrip patch antennas with finite dielectric substrates and ground planes. Compared to the coupled formulation of electric field and Poggio-Miller-Chang-Harrington-Wu-Tsai integral equations, global MTF allows for more direct implementation of a sub-structure CM method. This is achieved by representing the coupling of the electromagnetic fields on the substrate and ground plane in the form of a numerical Green function matrix, which yields a more compact generalized eigenvalue equation. The resulting sub-structure CM method avoids the cumbersome computation of the multilayered medium Green function (unlike the CM methods that rely on mixed-potential integral equations) and the volumetric discretization of the substrate (unlike the CM methods that rely on volume-surface integral equations), and numerical results show that it is a reliable and accurate approach to predicting the modal behavior of electromagnetic fields on practical microstrip antennas. Copyright © 2022, The Authors. All rights reserved.

关键词： Microstrip antennas

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Export- and import-based economic models for predicting global trade using deep learning

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Expert Systems with Applications 2023年第1期218卷

作者： Yang, Cheng-Hong Lee, Cheng-Feng Chang, Po-Yin Department of Information Management Tainan University of Technology Tainan City710302 Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung80708 Taiwan Ph. D. Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung80708 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung80708 Taiwan Department of Business Administration National Kaohsiung University of Science and Technology Kaohsiung80778 Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen County 892 Taiwan

Forecasting global foreign trade is essential for developing government trade policies and management strategies for multinational corporations. However, achieving an accurate trade forecast is challenging because of the complex structural relationships between exports, imports and other economic variables. Many traditional forecasting models, such as time series, econometric, and machine learning, provide less accurate forecasts for trade data. This paper proposes an ensemble learning approach to improve forecasting performance by hybridizing the structural relationships between trade and deep learning models to predict foreign trade for ten major countries. The proposed method first establishes a cointegration relationship between exports and imports and their structural variables. The cointegrated models are then used to predict the future of trade, which is used as a benchmark model for comparison. A hybrid deep learning algorithm uses the cointegrated variables as input variables to predict trade data, and then are compared with time-series forecasts and economic structural models. The experimental results reveal that the ensemble learning method can achieve excellent forecasting performance for the tested periods of trade data. In most cases, the root means square error and mean absolute percentage error values are smaller than the time series and economic structural models. © 2023 Elsevier Ltd

关键词： Forecasting

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites 16

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

引用

16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Kim, Dasom Hou, Jin Lee, Geon Agrawal, Ayush Kim, Sunghwan Zhang, Hao Bao, Di Baydin, Andrey Wu, Wenjing Tay, Fuyang Huang, Shengxi Hagenmüller, Daivd Chia, Elbert E. M. Kim, Dai-Sik Seo, Minah Mohite, Aditya D. Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Sensor System Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Physics and Astronomy Seoul National University Seoul08826 Korea Republic of Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Ulsan44919 Korea Republic of Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Smalley-Curl Institute Rice University HoustonTX77005 United States Université de Strasbourg Cnrs Strasbourg67000 France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Department of Physics and Astronomy Rice University HoustonTX77005 United States

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (纸本)9798350372076

关键词： Organic-inorganic materials

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SPGP: Structure Prototype Guided Graph Pooling

arXiv

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arXiv 2022年

作者： Lee, Sangseon Lee, Dohoon Piao, Yinhua Kim, Sun Institute of Computer Technology Seoul National University Korea Republic of BK21 FOUR Intelligence Computing Seoul National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of AIGENDRUG Co. Ltd. Korea Republic of

While graph neural networks (GNNs) have been successful for node classification tasks and link prediction tasks in graph, learning graph-level representations still remains a challenge. For the graph-level representation, it is important to learn both representation of neighboring nodes, i.e., aggregation, and graph structural information. A number of graph pooling methods have been developed for this goal. However, most of the existing pooling methods utilize k-hop neighborhood without considering explicit structural information in a graph. In this paper, we propose Structure Prototype Guided Pooling (SPGP) that utilizes prior graph structures to overcome the limitation. SPGP formulates graph structures as learnable prototype vectors and computes the affinity between nodes and prototype vectors. This leads to a novel node scoring scheme that prioritizes informative nodes while encapsulating the useful structures of the graph. Our experimental results show that SPGP outperforms state-of-the-art graph pooling methods on graph classification benchmark datasets in both accuracy and scalability. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks

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Chemosphere 2024年 352卷 141484页

作者： Monteiro, Thalyssa Oliveira Alves, Pedro Augusto Araújo da Silva de Almeida Nava Barradas Filho, Alex Oliveira Villa-Vélez, Harvey Alexander Cruz, Glauber Postgraduate Program in Mechanical Engineering (PPGMEC) Department of Mechanics and Materials Federal Institute of Education Science and Technology of Maranhão (IFMA) Maranhão São Luís Brazil Postgraduate Program in Computer Science and Computational Mathematics (PPG-CCMC) Department of Computer Science University of São Paulo (USP) São Carlos São Paulo Brazil Data Analysis and Artificial Intelligence Laboratory (DARTi) Department of Computational Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil Department of Chemical Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil Processes and Thermal Systems Laboratory (LPSisTer) Department of Mechanical Engineering Federal University of Maranhão (UFMA) Maranhão São Luís Brazil

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. H... 详细信息

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO₂, NO_x, SO_x, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO₂, NO, and SO₂) produced during the direct biomass combustion (N₂/O₂:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001–12.41% MAPE, 0.001–5.82 mg Nm⁻³ MAE, and 0.03–52.30 mg Nm⁻³ RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO₂, NO, and SO₂ emissions from NEB biomasses were approximately 0.01%, 10⁻⁶%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models. © 2024 Elsevier Ltd

关键词： Brazilian northeastern Environmental statistics Evaluation metrics Gaseous emissions Predictive models Thermoconversion

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Eye-Zheimer: A Deep Transfer Learning Approach of Dementia Detection and Classification from NeuroImaging 7

Eye-Zheimer: A Deep Transfer Learning Approach of Dementia D...

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7th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2020

作者： Alon, Helcy D. Ligayo, Michael Angelo D. Misola, Maribel A. Sandoval, Allan A. Fontanilla, Marites V. Technological Institute of the Philippines Graduate Program Manila Philippines Quezon City University Department of Electronics Engineering Quezon City Philippines FEU Institute of Technology Department of Computer Engineering Manila Philippines St. Paul University Department of Information Technology Quezon City Philippines

ISBN: (纸本)9780738105048

Dementia is a common term for memory loss, speech, problem-solving, and other cognitive skills that are serious enough to interfere with everyday life, and Alzheimer's is the leading cause of dementia. Alzheimer's disease is presumed to develop 20 years or more before symptoms occur, with degenerative changes that are unapparent to the person affected. The deep learning approach for early detection and Alzheimer's disease classification has recently gained significant attention. This study proposed disease detection trained by utilizing the YOLO v3 algorithm that aims to detect Alzheimer's disease based solely on Magnetic Resonance Imaging (MRI). Pascal VOC format and LabelImg tool are used for annotating the datasets, categorizing the image as non-demented and mild-demented. Model 4 was used in the system having 98.617% training accuracy, 98.8207% validation accuracy, and mAP of 96.17%. To test the accuracy of the used model, images of MRI scans are presented and it recorded 80% testing accuracy. © 2020 IEEE.

关键词： Object detection

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Minimizing Number of Nodes in Decision Trees with Hypotheses

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Procedia computer Science 2021年 192卷 232-240页

作者： Mohammad Azad Igor Chikalov Shahid Hussain Mikhail Moshkov Jouf University College of Computer and Information Sciences Department of Computer Science Sakaka 72441 Saudi Arabia Intel Corporation 5000 W Chandler Blvd Chandler AZ 85226 USA Habib University Dhanani School of Science and Engineering Computer Science Program Karachi 75290 Pakistan King Abdullah University of Science and Technology (KAUST) Computer Electrical and Mathematical Sciences & Engineering Division Thuwal 23955-6900 Saudi Arabia

In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses about values of all attributes. This approach is similar to one studied in exact learning, where membership and equivalence queries are considered. We propose dynamic programming algorithms for the minimization of the number of nodes in such decision trees and discuss results of computer experiments.

关键词： Decision tree Hypothesis Number of nodes

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Ramsey envelope modulation in NV diamond magnetometry

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Physical Review B 2022年第5期106卷 054110-054110页

作者： Jner Tzern Oon Jiashen Tang Connor A. Hart Kevin S. Olsson Matthew J. Turner Jennifer M. Schloss Ronald L. Walsworth Department of Physics University of Maryland College Park Maryland 20742 USA Quantum Technology Center University of Maryland College Park Maryland 20742 USA Department of Electrical Engineering and Computer Science University of Maryland College Park Maryland 20742 USA Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College Park Maryland 20742 USA Lincoln Laboratory Massachusetts Institute of Technology Lexington Massachusetts 02421 USA

Nitrogen-vacancy (NV) spin ensembles in diamond provide an advanced magnetic sensing platform, with applications in both the physical and life sciences. The development of isotopically engineered NV15 diamond offers advantages over naturally occurring NV14 for magnetometry, due to its simpler hyperfine structure. However, for sensing modalities requiring a bias magnetic field not aligned with the sensing NV axis, the absence of a quadrupole moment in the N15 nuclear spin leads to pronounced envelope modulation effects in time-dependent measurements of NV15 spin evolution. While such behavior in spin echo experiments are well studied, analogous effects in Ramsey measurements and the implications for magnetometry remain underexplored. Here, we derive the modulated NV15 Ramsey response to a misaligned bias field, using a simple vector description of the effective magnetic field on the nuclear spin. The predicted modulation properties are then compared to experimental results, revealing significant magnetic sensitivity loss if unaddressed. We demonstrate that double-quantum coherences of the NV S=1 electronic spin states dramatically suppress these envelope modulations, while additionally proving resilient to other parasitic effects such as strain heterogeneity and temperature shifts.

关键词： NV centers Quantum metrology Quantum sensing Nitrogen vacancy centers in diamond Magnetization measurements Numerical approximation & analysis Perturbation theory

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Weight vector tuning and asymptotic analysis of binary linear classifiers

arXiv

引用

arXiv 2021年

作者： Niyazi, Lama B. Kammoun, Abla Dahrouj, Hayssam Alouini, Mohamed-Slim Al-Naffouri, Tareq Y. Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Center of Excellence for NEOM Research at KAUST King Abdullah University of Science and Technology Thuwal Saudi Arabia

Unlike its intercept, a linear classifier’s weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data. Copyright © 2021, The Authors. All rights reserved.

关键词： Random variables

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