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A PCA-based fuzzy tensor evaluation model for multiple-criteria group decision making

applied Soft Computing 2023年 132卷

作者： Singh, Meenu Pant, Millie Kong, Lingping Alijani, Zahra Snášel, Václav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand247667 India Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Czech Republic Institute for Research and Applications of Fuzzy Modeling University of Ostrava 30. dubna 22 701 03 Ostrava Czech Republic

Multiple-criteria group decision-making (MCGDM) problems mainly consist of multiple factors and multiple Decision Makers (DMs) or Users, for which dimension extension is necessary when considering all the entries of DMs together. Tensor, a generalized form of a matrix, displays a multi-way array item, which is the most suitable and practical way to represent high-dimensional data without losing any information. In this paper, we first reduce the dimension through Principal Component Analysis (PCA), which helps consider the most-informative criteria. Then, we reintroduced the tensor as a fuzzy-form tensor for the MCGDM problem because of user information uncertainty. We choose Interval-valued Neutrosophic Fuzzy numbers (IVNFNs) as the basis for the tensor form because of their ability to distinguish between truth, indeterminacy, and falsity in the data. Lastly, a Generalized Interval-valued Neutrosophic Fuzzy Weighted Geometric (GIVNFWG) operator is defined. Moreover, a generalized framework for fuzzy-form tensors for high-dimensional MCGDM problems is proposed. The feasibility and efficiency of this proposed process is illustrated for a real-world MCGDM problem of ranking the most efficient Third Party Reverse Logistics Partners (3PRLPs), i.e., recycled fiber-based paper mills for the packaging industry. The obtained results are according to the experts and are validated using sensitivity analysis. This analysis facilitates in assessing the impact on the overall ranking performance of 3PRLPs by considering various combinations of environment and technological sub-criteria. © 2022 Elsevier B.V.

关键词： Principal component analysis

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Esophageal Cancer Diagnosis with a Bilinear Pooling and Attention-Based Convolutional Neural Network 1st

Esophageal Cancer Diagnosis with a Bilinear Pooling and Atte...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Raina, Vikas Byeon, Haewon Bhende, Manisha Yogi, K. Sri Keshta, Ismail Sardana, Kanishka CSE SET Mody University of Science and Technology NH-52 Lakshmangarh Sikar332311 India Convergence Department Korea University of Technology and Education Cheonan Korea Republic of Dr. D. Y. Patil School of Science and Technology Dr. D. Y. Patil Vidyapeeth Pune Tathawade Pune India Faculty Department of Operations Management Symbiosis Institute of Business Management Hyderabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9783031860683

In the context of esophageal cancer diagnosis through gastrointestinal endoscopy, this study aims to examine the issues that are related with variability and misinterpretation of lesions observed under white-light endoscopy. To overcome these obstacles, the study introduces a convolutional neural network (CNN) that combines bilinear pooling and attention mechanisms. The CNN, built on the ResNet50 architecture, incorporates a novel global channel attention module and utilizes bilinear pooling to enhance feature representation by fusing multiple feature layers. The results, based on a dataset of 2101 cases from various clinical centers and white-light endoscopy images, demonstrate the effectiveness of this approach. It achieves high accuracy in classifying esophageal lesions, with classification rates of 94.2% at the image level and 96.9% at the patient level. In terms of esophageal cancer detection, it shows a sensitivity and specificity of 95.4% and 98.8% at the image level and 98.7% and 95.9% at the patient level. Notably, this approach outperforms recent models and methods in comparative experiments. These results show that the suggested network greatly raises the diagnostic accuracy for esophageal cancer under white-light endoscopy while maintaining its robustness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Identification of lung nodules CT scan using YOLOv5 based on convolution neural network

arXiv

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arXiv 2022年

作者： Ewaidat, Haytham Al Brag, Youness El Jordan University of Science and Technology Faculty of Applied Medical Sciences Department of Allied Medical Sciences-Radiologic Technology Irbid22110 Jordan Abdelmalek Essaâdi University of Science and Technology Faculty of Multi-Disciplinary Larache Department of Computer Sciences ksar el kebir Morocco92150 Morocco

Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neural network (CNN), especially for image recognition and it’s one of the most used algorithm in computer vision. Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to learn the features representations for nodule detection labels, in this paper, we introduce a method for detecting lung cancer localization. Chest X-rays and low-dose computed tomography are also possible screening methods, When it comes to recognizing nodules in radiography, computer-aided diagnostic (CAD) system based on (CNN) have demonstrated their worth. One-stage detector YOLOv5 trained on 280 annotated CT SCAN from a public dataset LIDC-IDRI based on segmented pulmonary nodules. Results: we analyze the predictions performance of the lung nodule locations, and demarcates the relevant CT scan regions. In lung nodule localization the accuracy is measured as mean average precision (mAP). the mAP takes into account how well the bounding boxes are fitting the labels as well as how accurate the predicted classes for those bounding boxes, the accuracy we got 92.27% . Conclusion: this study was to identify the nodule that were developing in the lungs of the participants. It was difficult to find information on lung nodules in medical literature, © 2022, CC BY.

关键词： computerized tomography

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Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates

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computers in Biology and Medicine 2025年 189卷 109942-109942页

作者： Hsu, Jen-Fu Lin, Ying-Chih Lin, Chun-Yuan Chu, Shih-Ming Cheng, Hui-Jun Xu, Fan-Wei Huang, Hsuan-Rong Liao, Chen-Chu Fu, Rei-Huei Tsai, Ming-Horng Division of Neonatology and Pediatric Hematology/Oncology Department of Pediatrics Chang Gung Memorial Hospital Yunlin Taiwan Division of Pediatric Neonatology Department of Pediatrics Chang Gung Memorial Hospital Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Applied Mathematics Feng Chia University Taichung Taiwan Department of Artificial Intelligence Application Minth University of Science and Technology HsinChu County Taiwan

Background: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimize the therapeutic strategy and avoid unnecessary use of empirical antibiotics. We aimed to examine whether deep learning (DL) methods can assist the diagnosis of VAP of intubated neonates in the neonatal intensive care unit (NICU). Methods: A total of 670 neonates with mechanical ventilation were prospectively observed in a tertiary-level NICU in Taiwan between October 2017 and March 2022, during which image data were collected. All neonates with clinically suspected VAP were enrolled, and various DL methods were used to test the prediction ability of VAP diagnosis. The accuracy, precision, sensitivity, specificity, F1-score, and area under curves (AUCs) of several DL methods were compared. Results: A total of 900 chest X-ray images derived from 670 neonates with VAP and/or bronchopulmonary dysplasia (BPD) were enrolled, including 399 images from patients with definite diagnosis of VAP based on the strict criteria and 501 images from neonates without VAP. Compared with conventional DNN models such as ResNet, VGG, DenseNet, the RegNetX80 achieved the best specificity of 0.8378, which facilitates a low false positive rate. For accurate diagnosis of neonatal VAP, a combinatorial model of ResNet50 and RegNetX80, created through ensemble learning, further enhanced the AUC to 0.8023 for neonates with VAP on mechanical ventilation. In addition, the consistent XAI results in the left-lower region of chest X-ray image provided informative feedback and increased confidence to AI-assisted doctors. Conclusions: Deep learning methods are applicable with good predictive accuracy using chest X-ray images to help diagnosis of VAP in the NICU, which can help clinicians make decisions regarding the choices of empiric antibiotics for critically ill neonates. Future prospective trials are warranted to document its clinical usefulness and

关键词： Contrastive Learning

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Natural Language Generation (NLG) with Reinforcement Learning (RL) 5th

Natural Language Generation (NLG) with Reinforcement Learnin...

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5th International Conference on Artificial Intelligence and Speech technology, AIST 2023

作者： Khan, Shakir Serajuddin, Mohammad Hasan, Zafarul Alvi, Syed Ahad Murtaza Ayub, Rashid Sharma, Arun Riyadh Saudi Arabia College of Dentistry King Saud University Riyadh Saudi Arabia College of Nursing King Saud University Riyadh Saudi Arabia College of Applied Computer Sciences King Saud University Riyadh Saudi Arabia Department of Science Technology and Innovation Unit College of Computer and Information Sciences King Saud University P. O. Box-2454 Riyadh11451 Saudi Arabia University Centre for Research and Development Chandigarh University Mohali India Delhi India

ISBN: (纸本)9783031751660

Finally, utilizing an interpretative framework as well as a deductive method this study studied the use of reinforcement learning strategies in the development of natural languages. Although secondary data collection provided a solid foundation, the descriptive method allowed for a complete study. The technical solutions used included the incorporation of RL computer programs, advanced text processing, linguistic analysis, contextual adaptability, and moral concerns. As the consequence of our research, we now have a better understanding of RL-NLG systems, which opens the way to more sophisticated and environmentally conscious text production. This research not only enhances the field of NLG, although it also emphasizes the importance of ethical as well as objective applications for artificial intelligence. The insights have application that encompass intelligent machines to the generation of customized content. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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OCD Diagnosis with Multiple Spatial Similarity-Aware Learning and Diffusion Structure-Aware Graph Convolutional Network

OCD Diagnosis with Multiple Spatial Similarity-Aware Learnin...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yu Liang Chen Zhao Tianfu Wang Ziwen Peng Peng Yang Baiying Lei School of Computer Science and Software Shenzhen University Shenzhen China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China Center for the Study of Applied Psychology South China Normal University Guangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Obsessive-Compulsive Disorder (OCD) is a hereditary mental illness, and unaffected first-degree relative (UFDR) is also at high risk. This study constructed a framework based on traditional machine learning and deep learning methods to identify OCD and UFDR. Specifically, we propose Multiple Spatial Similarity-Aware Learning (MSSL) models to construct Brain Functional Connectivity Networks (BFCNs) and use Diffusion Structure-Aware Graph Convolutional Network (DSAGCN) for feature learning. Two regularization terms are added to the MSSL model, one is to limit the similarity of functional connectivity of adjacent brain regions, and the other is to constrain the similarity of signals in adjacent brain regions. In this strategy, redundant information can be removed while constructing the interrelationships of various brain regions, which can reflect the interrelationships between brain *** graph diffusion is introduced to the DSAGCN model, which can not only solve the edge noise problem caused by the graph structure constructed by the custom adjacency matrix but also enrich the node features by using the position information of the nodes on the reconstructed graph *** framework has been validated on our dataset collected from local hospitals, and the experimental results show that our proposed method outperforms the state-of-the-art.

关键词： Representation learning Deep learning Graph convolutional networks Hospitals Noise Brain modeling Feature extraction Data mining Bioinformatics

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Face Spoof Detection Using Gray Level Co-Occurrence Matrix and Discrete Wavelet Transform Feature Extractor

Face Spoof Detection Using Gray Level Co-Occurrence Matrix a...

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International Conference on Computing sciences (ICCS)

作者： Amal Mathew Kaushik Daiv Polkumpally Rohan Goud Piyush Talreja Sai Sanjana Reddy Vatte B.tech Computer Science Ms Ramaiah University of Applied Sciences Bangalore Karnataka India B.E. Computer Science MIT College of Engineering Pune Maharashtra India B.E Computer Science Chaitanya Bharathi Institute of Technology Hyderabad India B.E Computer Science Maharashtra Institute of Technology Pune Maharashtra India

ISBN: (纸本)9781665494465

Face identification using ML (machine learning) is well-known. Attendance structures may benefit from this method. Using this method, you may achieve the desired area, as well as beneficial attributes and a dataset, by preparing two sets of data again for test and training phases. To distinguish between a testing set and a test sets, a photograph is used as a testing set. An ensemble classification method is used to sort the test images into categories like “identified” and “unidentified.” This model can't provide reliable findings since it simply divides data into two categories. The development of GLCM was motivated by the need to use texture properties to identify faces. The existence of the query picture is noted once face detection has taken place. In simulation findings, the new model outperforms the baseline models in terms of accuracy. Keywords—Ensemble classifier, GLCM, Face Spoof, SVM, DWT

关键词： Training Image segmentation Face recognition Training data Machine learning Transforms Feature extraction

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Energy dependence of proton-proton elastic scattering at large momentum transfer

arXiv

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arXiv 2023年

作者： Ferreira, Erasmo Kohara, Anderson Kendi Instituto de Física Universidade Federal do Rio de Janeiro C.P. 68528 Rio de Janeiro Rio de Janeiro21945-970 Brazil AGH University Of Science and Technology Faculty of Physics and Applied Computer Science Mickiewicza 30 Kraków30-059 Poland

The measurements of proton-proton elastic scattering for large momentum transfer at energies in the range ≈ 20 to 60 GeV show a simple behaviour of form dσ/dt ≈ const |t|−8, apparently with no energy dependence. In the present work detailed analysis of the data shows a decrease of the magnitude of the tail with the energy, still with preservation of the power |t|−8. The analysis allows the definition of a band for the energy dependence of the amplitude with central line A(√s) = 0.389 log (100)/ log √s GeV6 . The rate of decrease describes well the data at the distant energy √s = 13 TeV, with reduction of the cross section by a factor 5.71. This result gives prediction for new experiments at high energies, and opens important question for theoretical investigation. Copyright © 2023, The Authors. All rights reserved.

关键词： Elastic scattering

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Accuracy of the TOPSIS Method with Different Input Data 26

Accuracy of the TOPSIS Method with Different Input Data

引用

26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Król, Robert Wieckowski, Jakub Watróbski, Jaroslaw Doctoral School of University of Szczecin ul. Mickiewicza 16 Szczecin70-383 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland

Using fuzzy logic in multi-criteria problems allows real-world problems to be modeled with greater accuracy. In addition, it is possible to solve problems in which all data are incomplete. The difficulty may be the choice of an appropriate technique leading to a ranking calculation. Methods fully integrated with fuzzy logic can be used. It is also possible to convert fuzzy data to crisp values and then apply traditional MCDA methods. In this paper, the performance of the TOPSIS method in a crisp and fuzzy environment was analyzed. The approaches of operating fully on uncertain data when calculating preference values were compared with converting data to crisp values using selected membership functions. The research has shown that significant differences and discrepancies in how the alternatives are classified are noticeable in the rankings. © 2022 The Authors. Published by Elsevier B.V.

关键词： Fuzzy logic

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Interferometric Purcell Suppression of Spontaneous Emission in a Superconducting Qubit

arXiv

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arXiv 2024年

作者： Yen, Alec Ye, Yufeng Peng, Kaidong Wang, Jennifer Cunningham, Gregory Gingras, Michael Niedzielski, Bethany M. Stickler, Hannah Serniak, Kyle Schwartz, Mollie E. O'Brien, Kevin P. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States MIT Lincoln Laboratory LexingtonMA02421 United States

In superconducting qubits, suppression of spontaneous emission is essential to achieve fast dispersive measurement and reset without sacrificing qubit lifetime. We show that resonator-mediated decay of the qubit mode to the feedline can be suppressed using destructive interference, where the readout resonator is coupled to the feedline at two points. This "interferometric Purcell filter" does not require dedicated filter components or impedance mismatch in the feedline, making it suitable for applications such as all-pass readout. We design and fabricate a device with the proposed scheme and demonstrate suppression of resonator-mediated decay that exceeds 2 orders of magnitude over a bandwidth of 400MHz. Copyright © 2024, The Authors. All rights reserved.

关键词： Spontaneous emission

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