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Developing diverse ensemble architectures for automatic brain tumor classification

Multimedia Tools and Applications 2024年 1-44页

作者： Bogacsovics, Gergo Harangi, Balazs Hajdu, Andras Department of Data Science and Visualization Faculty of Informatics University of Debrecen PO Box 400 Debrecen4002 Hungary

Brain tumors pose a serious threat in our modern society, with a clear increase in global cases each year. Therefore, developing robust solutions that could automatically and reliably detect brain tumors in their early stages is of utmost importance. In our paper, we revisit the problem of building performant ensembles for clinical usage by maximizing the diversity of the member models during the training procedure. We present an improved, more robust, extended version of our framework and propose solutions that could be integrated into a Computer-Aided Diagnosis system to accurately classify some of the most common types of brain tumors: meningioma, glioma, and pituitary tumors. We show that the new framework based on the histogram loss can be seen as a natural extension of the former approach, as it also calculates the inner products of the latent vectors produced by each member to measure similarity, but at the same time, it also makes it possible to capture more complex patterns. We also present several variants of our framework to incorporate member models with varying dimensional feature vectors and to cope with imbalanced datasets. We evaluate our solutions on a clinically tested dataset of 3,064 T1-weighted contrast-enhanced magnetic resonance images and show that they greatly outperform other state-of-the-art approaches and the base architectures as well, achieving over 92% accuracy, 92% macro and weighted precision, 91% macro and 92% weighted F1 score, and over 90% macro and 92% weighted sensitivity. © The Author(s) 2024.

关键词： Magnetic resonance

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Assessing Conventional and Deep Learning-Based Approaches for Named Entity Recognition in Unstructured Hungarian Medical Reports 22

Assessing Conventional and Deep Learning-Based Approaches fo...

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2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024

作者： Bogacsovics, Gergo Harangi, Balazs Beregi-Kovacs, Marcell Kupas, David Lakatos, Robert Serban, Norbert Daniel Tiba, Attila Toth, Janos University of Debrecen Faculty of Informatics Department of Data Science and Visualization Debrecen Hungary

ISBN: (纸本)9798350317206

In digital healthcare, much patient data is available in text format. The structuring of this data, according to standards, has yet to be widely used, including in Hungary, where it is available in unstructured form. To make these patient records easy to filter and search, they must be processed and structured. Using modern natural language processing and deep learning techniques has resulted in effective systems for implementing such workflows. However, selecting appropriate algorithms for specific text-processing tasks is still a challenging issue. This is due to the scarcity of benchmarks and the variety of architectures available. This article evaluates models for named entity recognition (NER) in digital medical reports written in Hungarian. We evaluate traditional, recurrent neural network, and transformer-based approaches for NER using a dataset comprising 801 positron emission tomography scans and annotated medical reports. The medical reports were annotated to cover six different entity classes and reviewed by clinical experts to ensure accuracy. We present a comprehensive assessment of various methods and provide insight into addressing NER problems in the case of low-resource languages such as Hungarian. © 2024 IEEE.

关键词： Text processing

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Long-term spatiotemporal changes in nitrate contamination of municipal groundwater resources after sewerage network construction in the Hungarian Great Plain

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Environmental science and Pollution Research 2024年第51期31卷 61114-61137页

作者： Mester, Tamás Szabó, György Kiss, Emőke Balla, Dániel Department of Landscape Protection and Environmental Geography Faculty of Science and Technology University of Debrecen DebrecenH‑4032 Hungary Department of Data Science and Visualization Faculty of Informatics University of Debrecen DebrecenH‑4028 Hungary

Over the last decades, as a consequence of wastewater discharges and other anthropogenic sources, severe nitrate (NO3−) pollution has developed in municipal environment causing global concern. Thus, eliminating the potential sources of pollution is one of the major challenges of the twenty-first century, whereby sanitation services are essential for ensuring public health and environmental protection. In the present study, long-term monitoring (2011–2022) of shallow groundwater NO3− contamination in municipal environment was carried following the construction of the sewerage network (2014) in the light of the pre-sewerage situation. Our primary aim was to assess the long-term effects of sewerage on nitrate NO3− levels in the shallow groundwater and evaluate the efficiency of these sanitation measures over time. Based on the results, significant pollution of the shallow groundwater in the municipality was identified. During the pre-sewer period, NO3− concentrations exceeded the 50 mg/L limit in the majority of monitoring wells significantly, upper quartile values ranged between 341 and 623 mg/L respectively. Using Nitrate Pollution Index (NPI) and interpolated NO3− pollution maps, marked spatial north–south differences were detected. In order to verify the presence of wastewater discharges in the monitoring wells, the isotopic ratio shifts (δ) for 18O and D(2H) were determined, confirming municipal wastewater effluent. Variations in NO3−/Cl− molar ratios suggest also contamination from anthropogenic sources, including septic tank effluent from households and the extensive use of manure. data series of 7 years (2015–2022) after the investment indicate marked positive changes by the appearance of decreasing trends in NO3− values confirmed by Wilcoxon signed rank test and ANOVA. By comparing the pre- and post-sewerage conditions, the mean NO3− value decreased from 289.7 to 175.6 mg/L, with an increasing number of monitoring wells with concentrations below the limit. Our

关键词： Groundwater pollution

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Developing a Learning Material in the 2D and 3D Space to Teach IT Technical Language 2

Developing a Learning Material in the 2D and 3D Space to Tea...

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2nd IEEE International Conference on Cognitive Aspects of Virtual Reality, CVR 2023

作者： Boda, Istvan Karoly Toth, Erzsebet Nagy, Laszlo T. Debrecen Reformed Theological University Department of Mathematics and Informatics Debrecen Hungary University of Debrecen Department of Data Science and Visualization Debrecen Hungary

ISBN: (纸本)9798350338638

In our paper we introduce the IT technical language learning material focusing on the specialized use of the English language in the context of information technology (IT) and computing. This material has been developed in the framework of the 3D virtual library project started in 2013 as part of the Cognitive Infocommunications (CogInfoCom) research. The current version of the virtual library has a hypertext or 2D implementation which is freely available in the internet. The advanced 3D presentation of the material uses the 3D features of the MaxWhere Seminar System, arranging and displaying the content of the language learning material in the Fly Podium 3D space. First, we provide a short overview on the virtual library project as an introduction to the IT technical language learning material. In the next section we describe the background and aim of the development of the material, then we present in detail the organization and structure of the 2D and 3D implementation of the learning material. Some inventions and ideas may deserve attention such as the topic map of selected terms, concepts, and texts functioning as a navigational tool, the interactive tests (where the first one is for practice, and the second one is actually a memory game), and the mapping mechanism of the hypertext-based material into the 3D virtual space of the MaxWhere system. © 2023 IEEE.

关键词： E-learning

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Visual evaluation of graph representation learning based on the presentation of community structures

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Visual Informatics 2024年第3期8卷 29-31页

作者： Yong Zhang Lihong Cai Yuhua Liu Yize Li Songyue Li Yuming Ma Yuwei Meng Zhiguang Zhou Intelligent Big Data Visualization Lab Hangzhou Dianzi UniversityHangzhouChina Science&Informatization Department Zhejiang Provincial Energy Group CompanyHangzhouChina State Key Lab of CAD&CG Zhejiang UniversityHangzhouChina

Various graph representation learning models convert graph nodes into vectors using techniques like matrix factorization,random walk,and deep ***,choosing the right method for different tasks can be *** within networks help reveal underlying structures and *** how different models preserve community properties is crucial for identifying the best graph representation for data *** paper defines indicators to explore the perceptual quality of community properties in representation learning spaces,including the consistency of community structure,node distribution within and between communities,and central node distribution.A visualization system presents these indicators,allowing users to evaluate models based on community *** studies demonstrate the effectiveness of the indicators for the visual evaluation of graph representation learning models.

关键词： Graph representation learning Community structure Visual perception

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Assessing Conventional and Deep Learning-Based Approaches for Named Entity Recognition in Unstructured Hungarian Medical Reports

Assessing Conventional and Deep Learning-Based Approaches fo...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Gergő Bogacsovics Balázs Harangi Marcell Beregi-Kovács Dávid Kupás Róbert Lakatos Norbert Dániel Serbán Attila Tiba János Tóth Department of Data Science and Visualization Faculty of Informatics University of Debrecen Debrecen Hungary

关键词：

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Improving A Bilingual Leaning Material in The Three-Dimensional Space Using Google Translate 13

Improving A Bilingual Leaning Material in The Three-Dimensio...

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13th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2022

作者： Boda, István Károly Tóth, Erzsébet Nagy, László T. Debrecen Reformed Theological University Department of Mathematics and Informatics Debrecen Hungary University of Debrecen Department of Data Science and Visualization Debrecen Hungary

ISBN: (纸本)9781665492386

In our paper we introduce a bilingual language learning material based on the three dimensional virtual library model (3DVLM). This is an inherent part of the virtual library project started in 2013 in the framework of the Cognitive Infocommunications (CogInfoCom) research. The current version of the 3DVLM uses the 3D features and the hypertext-based presentation capabilities of the MaxWhere Seminar System. The latter is especially important because our material is based on web technology and organized so that it is going to be a scale-free network of interconnected nodes. First, we would like to provide a short overview on the virtual library in general, then introduce the organization and structure of the bilingual language learning material we have developed to support English language learning for Hungarian learners at an advanced level. The learning material is based on selected passages from classical literary works, presented both in English and in Hungarian, which we think may improve both English language skills and cultural awareness of language learners through the background knowledge they acquire. In order to check and test the bilingual language learning material we used the Google Translate service which proved to be a very efficient tool for both improving the learning material and deepening the language learning process. © 2022 IEEE.

关键词： E-learning

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Analyzing the Impact of Security Measures on Wi-Fi Quality of Service 2

Analyzing the Impact of Security Measures on Wi-Fi Quality o...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Quach, Anna T. Olaveson, Israel G. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Ai Data & Visualization Idaho National Laboratory Idaho FallsID United States

ISBN: (纸本)9798331529529

This study examines the relationship between Quality of Service (QoS) and security in Wi-Fi networks. Using a detailed dataset consisting of QoS metrics of file transfers under various security configurations, we analyze the impact of these settings on network performance. Our findings provide a quantification to accurately show that different encryption protocols, such as TKIP and CCMP, significantly affect QoS, with disabling security generally improving performance. The study provides a framework for optimizing security settings to enhance both security and QoS in future wireless networks. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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A Lightweight AI Model for Anomaly Detection in Wireless Networks 2

A Lightweight AI Model for Anomaly Detection in Wireless Net...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho National Laboratory Ai Data & Visualization Idaho FallsID United States

ISBN: (纸本)9798331529529

Detecting network anomalies is critical for wireless network security and reliability. Traditional AI methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying AI models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical AI model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models. © 2024 IEEE.

关键词： 5G mobile communication systems

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