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16th International Workshop on Ontology Matching, OM 2021

作者： Sharma, Abhisek Patel, Archana Jain, Sarika Department Of Computer Applications National Institute Of Technology Kurukshetra India Department Of Software Engineering Eastern International University Viet Nam

This paper presents the Large Scale Ontology Matching System (LSMatch) and its results on OAEI 2021 datasets. LSMatch is an element-level and label-based ontology matching system that uses string similarity and synonyms matcher. The current version of the system is focused on finding similarities between the classes of the two ontologies. This is the first participation of LSMatch in the OAEI campaign on five tracks, namely Anatomy, Conference, Disease and Phenotype, Common Knowledge Graphs, and Knowledge Graph. LSMatch has demonstrated promising results in all five tracks. We also discuss the strengths and weaknesses of the LSMatch system. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology

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Toward a Way to Benchmark Multilevel Inverter Topologies Based on Level to Components Ratio

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Canadian Journal of Electrical and computer engineering 2019年第2期42卷 78-92页

作者： Sheir, Ahmed Orabi, Mohamed Youssef, Mohamed Z. Ahmed, Mahrous Department of Electrical Computer and Software Engineering Power Electronics and Drives Applications Lab School of Engineering and Applied Science University of Ontario Institute of Technology OshawaONL1H 1K7 Canada Department of Electrical Engineering Aswan Power Electronics Applications Research Center Faculty of Engineering Aswan81542 Egypt

This paper introduces a generalized platform for the design selection of multilevel inverters (MLIs) topologies for any industry, especially in low-and medium-power applications. This platform is novel as it combines the component count and stress in any MLI in a normalized unique score. Each MLI topology is designed to match specific criteria to meet a specific application, which introduces diversified sets of merits and demerits in each case. Therefore, choosing a suitable MLI topology for the specific application is rather a difficult, time-consuming, and complex process. Moreover, the comparison can only be conducted between MLI topologies that fall under the same category. This paper introduces a comprehensive study to propose a new benchmark (BM) which can be used to compare MLI topologies regardless of their constructions or oriented applications. Each MLI topology gets a BM score based on comparative factors that evaluate its components quantitatively and qualitatively. Thus, comparing different types of MLI topologies is a simple process of comparing their calculated BM scores leading to better well-informed choice. First, the proposed BM-derived forms are discussed. Then, the proposed methodology is illustrated by applying to various types of MLI topologies including conventional and recently introduced ones. © 2003-2010 IEEE Canada.

关键词： Topology

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Wide Bandgap Devices in Electric Vehicle Converters: A Performance Survey

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Canadian Journal of Electrical and computer engineering 2018年第1期41卷 45-54页

作者： Abdelrahman, Ahmed S. Erdem, Zekiye Attia, Yosra Youssef, Mohamed Z. Power Electronics and Drives Applications Laboratory Department of Electrical Computer and Software Engineering School of Engineering and Applied Science University of Ontario Institute of Technology OshawaONL1H 1K7 Canada

This paper introduces a unique quantified study about using low-losses fast-switching wide bandgap (WBG) devices, i.e., gallium nitride (GaN) and silicon carbide (SiC), over traditional Silicon (Si) devices in the switching of dc/dc converters, focusing on electric vehicles' (EVs) machine drive and battery charger. A detailed model of the power train of a Nissan Leaf was developed in PSIM software, with WBG semiconductors' capability. The model was simulated one time using GaN semiconductors and another time using SiC devices. Simulation results are quantified and a comparison between different semiconductors in terms of total losses and efficiency is presented. The developed PSIM model can also be extended to other EVs like Chevy Volt. A proof of concept prototype for a Nissan Leaf dc/dc converter was built in the laboratory and results were collected. Componentwise experimental results are presented and their correlation with simulation findings is demonstrated. In addition, experimental results of the overall power train test bench are found to be matched with the simulation results on a system level as well. © 2003-2010 IEEE Canada.

关键词： Silicon carbide

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Knowledge Graph Completion Method Based on Using Multi-Hop Reasoning 10

Knowledge Graph Completion Method Based on Using Multi-Hop R...

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Golovin, Aleksei Zhukova, Nataly Kulikov, Igor Saint Petersburg Electrotechnical University 'LETI' Department Of Software Engineering And Computer Applications Saint-Petersburg Russia Saint-Petersburg Russia

ISBN: (纸本)9798350372939

In this paper, the method for knowledge graph completion based on using multi-hop reasoning is proposed. The relevance of the problem is due to the widespread use of large sparse knowledge graphs with incomplete data. Within the research, the method for restoring the structure of a knowledge graph using sequence-to-sequence multi-hop reasoning, based on the transformer architecture, is proposed. To evaluate the effectiveness of the proposed method, the study of its properties was carried out and the results were compared with the results of the existing methods used for predicting relations in the knowledge graphs. The proposed solution almost achieves the same results as methods based on embeddings. The evidential paths generated by the proposed method ensure the interpretability of the results. Our method does not rely on existing relations to create an evidential path and has the flexibility to restore missing relations in the path. © 2024 IEEE.

关键词： Knowledge graph

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

Today's marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women's clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation. © 2023 IEEE.

关键词： Long short-term memory

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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Application of Neural Network Keyword Extraction Methods for Student's CV Compilation from Discipline Work Programs 26

Application of Neural Network Keyword Extraction Methods for...

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26th International Conference on Soft Computing and Measurements, SCM 2023

作者： Kholod, Ivan I. Korytov, Pavel V. Sorochina, Maria V. Saint-Petersburg Electrotechnical University LETI Department of Computer Science and Engineering Saint-Petersburg Russia Saint-Petersburg Electrotechnical University LETI Department of Software Engineering and Computer Applications Saint-Petersburg Russia

ISBN: (纸本)9798350322484

The paper considers application of natural language processing methods (NLP) to solve the task of identifying the core skills acquired by students in the process of education. An ensemble solution, consisting of models WikiNEuRAL, KBIR-Inspec, ruGTP3Large, word2vec, and other NLP methods, is used to extract and process Russian language keywords. The described method was developed to automate the compilation of a student's CV in the information system called 'Partner's Account' of the LETI' university. © 2023 IEEE.

关键词： Students

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A geometry processor for image processing and pattern recognition

A geometry processor for image processing and pattern recogn...

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Architectures and Algorithms for Digital Image Processing III 1985

作者： Suk, Minsoo Pyo, Sam S. Department of Electrical and Computer Engineering Syracuse University SyracuseNY13244-1240 United States Center for Advanced Technology in Computer Applications and Software Engineering Syracuse University United States

A one-dimensional systolic geometry processor (SGP) which is useful in image processing and pattern recognition is described. The geometry processor can be used to enhance processing speed and throughput of the host computer by working as an attached processor. SGP algorithms for solving several primitive geometric problems such as convex hull, inclusion and intersection are shown. © 1986 SPIE. All rights reserved.

关键词： Pattern recognition

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Implementation of multicloud strategies for healthcare organisations to avoid cloud sprawl

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International Journal of Cloud Computing 2022年第5-6期11卷 529-536页

作者： Jayanthiladevi, A. Ayoobkhan, Mohamed Uvaze Ahamed ThamaraiSelvi, R. Jimmy, Laishram Mishra, Piyush Robert, Nismon Rio Industry Relations and International Research Collaborations Srinivas University Karnataka India Software Engineering New Uzbekistan University Tashkent Uzbekistan Department of MCA Bishop Heber College Trichy India Department of Computer Science and Engineering Manipur Technical University Government Polytechnic Campus Manipur Imphal India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science Christ University Bengaluru India

Healthcare organisations are being overwhelmed by data, devices and apps, and disjointed multiple cloud services. Well-heeled multicloud can provide a unified cloud model that provides greater control and scalability at reduced costs. Healthcare multicloud is turning into an appealing path for associations to manage the blast of advanced healthcare information in digital health, internet of things, associated gadgets and healthcare applications. As more human services associations grasp distributed computing, they are progressively going to a blend of open, private, and hybrid cloud administrations and foundation. In fact most of the healthcare service organisations plan to utilise different cloud vendors over the upcoming years. While having multiple cloud vendors can give organisations more flexibility and redundancy, managing multiple clouds can be a challenge. It can lead to cloud sprawl, unauthorised cloud use known as shadow IT, disjointed cloud solutions, inefficiencies, and waste. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Health care

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Accurate Anomaly Detection in Medical Images using Transfer Learning and Data Optimization: MRI and CT as Case Studies 5

Accurate Anomaly Detection in Medical Images using Transfer ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Nizamli, Yasser Filatov, Anton Yu. Fadel, Weaam Shichkina, Yulia A. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science Saint Petersburg Russia

ISBN: (纸本)9798350363739

Today, medical imaging techniques are widely used to detect a variety of human conditions and diseases. To speed up the diagnostic process, systems are often automated using deep learning methods, which have been proven to yield outstanding results. However, these models may be limited to a specific domain, prone to overfitting, and difficult to update. In this paper, we present a transfer learning approach that achieves high accuracy for detecting anomalies in both MRI and CT images. Our system utilizes the ConvNeXt network, inspired by the Swin Transformer architecture, as a high-level feature extractor from processed and augmented medical images. The resulting data is then optimized using the Edited Nearest Neighbors algorithm, which selects the most effective observations and discards the rest. Finally, the data is passed to the K-Nearest Neighbors classifier to detect anomalies in numerically represented images. Our model achieves high performance for two different types of medical images and can easily be updated without the need for retraining. © 2024 IEEE.

关键词： Magnetic resonance imaging

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