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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiawei Ling Minchao Ye Yuntao Qian Qipeng Qian College of Information Engineering China Jiliang University China College of Computer Science Zhejiang University China Department of Applied Mathematics University of Arizona USA

Small-sample-size problem is a big challenge in hyperspectral image (HSI) classification. Deep learning-based methods, especially Transformer, may need more training samples to train a satisfactory model. Cross-domain classification has been proven to be effective in handling the small-sample-size problem. In two HSI scenes sharing the same land-cover classes, one with sufficient labeled samples is called the source domain, while the other with limited labeled samples is called the target domain. Thus, the information on the source domain could help the target domain improve classification performance. This paper proposes a cross-domain Vision Transformer (CD-ViT) method for heterogeneous HSI classification. CD-ViT maps the source samples to the target domain for supplementing training samples. In addition, cross-attention is used to align the source and target features. Moreover, knowledge distillation is employed to learn more transferable information. Experiments on three different cross-domain HSI datasets demonstrate the effectiveness of the proposed approach.

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Determining the financial performance of the firms in the Borsa Istanbul sustainability index:integrating multi criteria decision making methods with simulation

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Financial Innovation 2024年第1期10卷 3592-3635页

作者： Ahmet Kaya Dragan Pamucar Hasan Emin Gürler Mehmet Ozcalici Department of Banking-Finance Applied Sciences FacultyAkdeniz UniversityAntalyaTurkey Faculty of Organizational Sciences University of BelgradeBelgradeSerbia College of Engineering Yuan Ze UniversityTaoyuanTaiwan Department of Computer Science and Mathematics Lebanese American UniversityByblosLebanon Department of International Trade and Logistics Applied Sciences FacultyAkdeniz UniversityAntalyaTurkey Department of International Trade and Logistics Faculty of Economics and Administrative SciencesKilis 7 Aralık UniversityKilisTurkey

Regardless of the industry in which a company operates,evaluating corporate performance is one of the most critical and vital processes;the most essential and prominent performance evaluation is related to financial *** performance analysis is complex and critical for decision-makers in different financial performance factors;thus,a methodological framework is needed to solve such complex decision ***,this research aims to rank the companies included in the sustainability index(excluding banks)in Turkey by considering their financial *** criteria weights were determined using the full consistency method(FUCOM)by considering the evaluations of four *** firms were ranked using nine multi-criteria decision-making *** consensus among the nine rankings was ensured with the Copeland *** decision matrix includes financial ratios and the stock market performance of the firms;100,000 FUCOM weights were created with random evaluations to validate the *** results indicate that the most crucial criterion is the current ratio by considering expert *** simulation indicates that alternative 16(alternative 21)is superior(inferior)to the other alternatives,even though the weights are determined with random *** with expert evaluations is similar to the mean of the weight simulation *** results demonstrate that the proposed framework can be performed as a basis for financial performance ranking.

关键词： Financial performance BIST sustainability index Simulation MCDM techniques

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Improved Genetic Algorithm Based Beam Selection Scheme for Millimeter Wave Massive MIMO System 12th

Improved Genetic Algorithm Based Beam Selection Scheme for M...

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12th International Symposium on Intelligence Computation and Applications, ISICA 2021

作者： Li, Kangshun Xie, Shumin Wang, Wenxiang School of Computer and Information Dongguan City University Guangdong Dongguan523808 China College of Mathematics and Informatics South China Agricultural University Guangdong Guangzhou510642 China College of Information Engineering Jiangxi College of Applied Technology Jiangxi Ganzhou341000 China

ISBN: (纸本)9789811941085

Hybrid beamforming is considered as a potential technology in millimeter wave massive multiple input multiple output (MIMO) system, because it can achieve an effective balance between spectral efficiency(SE) and energy efficiency(EE). However, due to the constant amplitude of analog precoding matrix, the design of hybrid beamforming is still a challenging. In this paper, the improved genetic algorithm (IGA) is exploited to design a beam selection scheme, which can search an approximate optimal solution to maximize the EE of system. The comparative results indicate that the proposed IGA-based scheme can achieve the satisfying SE, as well as, the EE of proposed scheme is better than that of existing beam selection schemes. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Millimeter waves

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Pneumonia Detection in Chest X-ray using InceptionV3 and Multi-Class Classification 3

Pneumonia Detection in Chest X-ray using InceptionV3 and Mul...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Pandeeswaran, C. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The lung is a imperative internal organs in human physiology. The abnormality in the lung will cause severe respiratory problems. Pneumonia is a severe lung infection, and early screening and treatment are essential to heal the illness. This research aims to implement a pre-trained InceptionV3 scheme to detect pneumonia in chest X-ray pictures. This scheme consists of the following phases;(i) Image collection and resizing, (ii) Deep-features extraction using InceptionV3, (iii) Feature reduction with firefly algorithm, (iv) Multi-class classification, and (v) Validation. A four-class classifier is employed in the proposed scheme to classify the X-ray into normal, mild, moderate, and severe classes using 5-fold cross-validation. The experimental outcome of the K-Nearest Neighbor (KNN) classifier confirms that this scheme offered a classification accuracy of 85.18% on the considered image database. © 2022 IEEE.

关键词： Classification (of information)

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UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray 3

UNet with Two-Fold Training for Effective Segmentation of Lu...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Gnanasoundharam, J. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts;(i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study. © 2022 IEEE.

关键词： Decision making

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对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学对比分析

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Journal of Central South University 2023年第8期30卷 2616-2626页

作者： KHALID Abdulkhaliq M-alharbi HINA Gul MUHAMMAD Ramzan SEIFEDINE Kadry ABDULKAFI Mohammed-saeed Mechanical Engineering Department College of EngineeringUmm Al-Qura UniversityMakkah 24382Sandi Arabia Department of Computer Science Bahria UniversityIslamabad 44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman 346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Mathematics College of ScienceQassim UniversityBuraydah 51452Saudi Arabia

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题... 详细信息

当两种不同密度和扩散速率的纳米流体混合时,会发生三重扩散和对流现象。本文主要目的是比较对流水平板上二次混合的三重扩散纳米流体的流体力学和流体磁学。文中建立的模型其他新特征考虑了可变导热系数和非线性热辐射热通量。该问题由一组方程组成,借助MATLAB bvp4c包进行求解。为了使参数与相关剖面的影响可视化,给出了图形说明,并利用图表来评估关键参数变化是如何影响相关场及其相应的物理量。通过对盐1和盐2的Dufour参数的多次估计,可以推断出流体流速的波动。验证了流体动力流相对于流体磁流的优势。此外,还将特定情景下已发表的结果与当前研究结果进行了比较。

关键词：三重扩散纳米流体二次混合对流可变导热系数非线性热辐射

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Microfluidic droplet detection using synthetic data generated by a Generative Adversarial Network

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Karimzadeh, Marziyeh Meybodi, Mohsen Alambardar Shams, Mohammadreza Goli-Malekabadi, Zahra Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan81746-73441 Iran Department of Computer Engineering Shahreza Campus University of Isfahan Isfahan Iran Department of Medical Technology and Tissue Engineering School of Life science Engineering College of Interdisciplinary Science and Technology University of Tehran Tehran Iran

Cellular heterogeneity, even among genetically identical cells, results in variations in their properties and behaviors, making single-cell analysis crucial for obtaining detailed insights. However, isolating single cells from a cell population poses major challenges, as conventional laboratory techniques often risk cell damage and involve complex procedures. Droplet microfluidics has emerged as a promising approach for encapsulating cells, particularly single cells, into individual droplets without causing harm. Despite this, factors like cell sedimentation and aggregation can reduce encapsulation efficiency and lead to deviations from the expected Poisson distribution. To address these challenges, leveraging artificial intelligence and deep learning to monitor, detect, and regulate encapsulation conditions in real-time is critical for enhancing system performance. However, deep learning models require substantial training data, and issues like microfluidic channel clogging and the scarcity of certain cell types often limit data availability. To overcome this limitation, researchers are turning to synthetic data generation to supplement training datasets and address data scarcity challenges effectively. This study emphasizes the potential of integrating synthetic data with cutting-edge deep learning techniques to enhance the accuracy and efficiency of single-cell analysis within droplet microfluidic systems. A diverse dataset integrating synthetic and real images was used to train the YOLOv8s model for automated detection and classification of microfluidic droplets, enhancing accuracy and system performance. The model trained on a combination of real and synthetic data outperformed the one trained using conventional data augmentation methods, achieving an mAP0.5 of 98% due to the increased diversity of training images. It also demonstrated faster and more stable training. Additionally, the YOLOv8 network, with a detection rate of approximately 2338 droplets per sec

关键词： Deep learning

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Breast Lesion Segmentation in DCE-MRI using Multi-Objective Clustering with NSGA-II

Breast Lesion Segmentation in DCE-MRI using Multi-Objective ...

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Si, Tapas Patra, Dipak Kumar Mondal, Sukumar Mukherjee, Prakash Bankura Unnayani Institute of Engineering Department of Computer Science Engineering West Bengal Bankura722146 India Research Centre of Natural and Applied Sciences Department of Computer Science West Bengal Midnapore721102 India Department of Mathematics West Bengal Midnapore721102 India Hijli College Department of Mathematics West Bengal Kharagpur721306 India

ISBN: (纸本)9781665406703

Breast cancer causes the highest death among all types of cancers in women. Early detection and diagnosis leading to early treatment can save the life. The computer-assisted methodologies for breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) segmentation can help the radiologists/doctors in the diagnosis of the disease as well as further treatment planning. In this article, we propose a breast DCE-MRI segmentation method using a hard-clustering technique with a Non-dominated Sorting Genetic Algorithm (NSGA-II). The well-known cluster validity metrics namely DB-index and Dunn-index are utilized as objective functions in NSGA-II algorithm. The noise and intensity inhomogeneities in MRI are removed from MRI in the preprocessing step as these artifacts affect the segmentation process. After segmentation, the lesions are separated and finally, localized in the MRI. The devised method is applied to segment 10 Sagittal T2-Weighted fat-suppressed DCE-MRI of the breast. A comparative study has been conducted with the K-means algorithm and the devised method outperforms K-means both quantitatively and qualitatively. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Study and prediction of prostate cancer using fuzzy inference system

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Materials Today: Proceedings 2022年 56卷 157-164页

作者： Boadh, Rahul Aarya, Deo Datta Dahiya, Mamta Rathee, Rakesh Rathee, Sarita Kumar, Ajay Jain, Sarika Rajoria, Yogendra Kumar Department of the Mathematics School of Basic and Applied Science K. R. Mangalam University Sohna Road Haryana Gurugram122103 India Department of the Mathematics Acharya Narendra Dev College University of Delhi India Department of Computer Science & Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India University Institute of Engineering & Technology Maharshi Dayanand University Haryana 124001 Rohtak India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India

The essential driver of passing among men on the planet is prostate disease. In the chief stage, for early identification of the prostate disease by applying Fuzzy Inference System (FIS) has been depicted in this study. The age of the patient, Prostate Volume (PV), % Free PSA (FPSA) and Prostate Specific Antigen (PSA) are utilized as input parameters in FIS for computing the risk factor of the prostate cancer. In this study Mamdani method is used for evaluation and analysis of the Prostate Cancer Risk (PCR). The present FIS additionally required the base estimation decreases horribleness lessens dismalness, mortality and more dependable from other system. It can likewise be considered in medicine to decrease manual assignments and human mistake. This review advocate that, this FIS might consequence on work on the productivity to recognized the distinctive kind of the malignant growths just as lessening the expense of care of the patients. © 2022

关键词： Risk assessment

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Archery Algorithm:A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

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computers, Materials & Continua 2022年第7期72卷 399-416页

作者： Fatemeh Ahmadi Zeidabadi Mohammad Dehghani Pavel Trojovsky Štěpán Hubálovsky Victor Leiva Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic School of Industrial Engineering Pontificia Universidad Católica de ValparaísoValparaíso2362807Chile Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding a suitable solution to an optimization problem designed in science is a major ***,these must be addressed utilizing proper *** on a random search space,optimization algorithms can find acceptable solutions to *** Algorithm(AA)is a new stochastic approach for addressing optimization problems that is discussed in this *** fundamental idea of developing the suggested AA is to imitate the archer’s shooting behavior toward the target *** proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the *** AA is mathematically described,and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective ***,the proposed algorithm’s performance is compared *** approaches,including teaching-learning based optimization,marine predators algorithm,genetic algorithm,grey wolf optimization,particle swarm optimization,whale optimization algorithm,gravitational search algorithm,and tunicate swarm *** to the simulation findings,the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios,and it can give adequate quasi-optimal solutions to these *** analysis and comparison of competing algorithms’performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

关键词： Archer meta-heuristic algorithm population-based optimization stochastic programming swarm intelligence population-based algorithm Wilcoxon statistical test

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