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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Implementation Analysis of KEBI Application on Non-Standard Words Detection in Student Essay Tasks on Language Variety 7

Implementation Analysis of KEBI Application on Non-Standard ...

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7th IEEE Information Technology International Seminar, ITIS 2021

作者： Sa'diyah, Ilmatus Fahrudin, Tresna Maulana Latinah Illah, Ibnu Zahy Atha Lima, Cagiva Chaedar Bey Acarya, Burhan Syarif Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Department of Data Science Surabaya Indonesia Universitas Narotama Faculty of Computer Science Department of Informatics Engineering Surabaya Indonesia

ISBN: (纸本)9781665408073

Non-standard word errors in writing sometimes escape the attention of writers or editors due to several factors, including unintentional or limited time in editing. Non-standard word is one of the Indonesian spelling error. Therefore, the application for detecting non-standard words in writing, KEBI, was created to minimize the height of non-standard words in writing. In this study, KEBI was analyzed in terms of application implementation to five various language essay documents as student assignments for Indonesian language courses. There are three things that are analyzed, namely the interface page, processing time, and the level of accuracy of detection and correction using the precision and recall method (confusion matrix). The overall research method includes literature study, data collection, testing, measurement of display, time, and accuracy. Based on the results of the analysis, the interface page display shows good quality. The processing time required by KEBI to detect non-standard words is in the range of 0.02-0.06 seconds. Meanwhile, KEBI's non-standard word detection accuracy rate is 86%, while KEBI's non-standard word correction accuracy rate is 90%. © 2021 IEEE.

关键词： Quality control

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Testing MPT-GRE Multipath Solution in Vehicular Network V2I Communication 2

Testing MPT-GRE Multipath Solution in Vehicular Network V2I ...

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2nd IEEE Conference on Information Technology and data science, CITDS 2022

作者： Szilagyi, Szabolcs Kovacs, Laszlo University of Debrecen Faculty of Informatics Department of It Systems and Networks Debrecen Hungary University of Debrecen Faculty of Informatics Nvidia Deep Learning Institute Certified Instructor Department of Data Science and Visualization Debrecen Hungary

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

A vehicular network is a communication system comprising of vehicles equipped with radio-interfaces, where the endpoints are capable of exchanging data and communication between each other (Vehicle-to-Vehicle, V2V), as well as with another mobile network or fixed infrastructure (Vehicle-to-Infrastructure, V2I). The numerous applications used in vehicles typically require seamless and increasingly fast and reliable network connections, which poses a challenge for the wireless network technologies at our disposal today. MPT-GRE, developed at the University of Debrecen, is a multi-interface access technology, which could offer a novel solution to satisfy the requirements of the services used in vehicular networking applications. Given that MPT-GRE enables the simultaneous usage of multiple network interfaces and IP-routes for vehicles, it promises to be an effective solution for vehicular networks. In this paper, we are examining the efficiency of MPT-GRE using a self-driving car model in a dual-interface Wi-Fi environment. © 2022 IEEE.

关键词： Model automobiles

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Beyond Sensor data Analysis: Unexpected Challenges in a Honeybee Monitoring Project 22

Beyond Sensor Data Analysis: Unexpected Challenges in a Hone...

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22nd Conference Information Technologies - Applications and Theory, ITAT 2022

作者： Várkonyi, Dániel Tamás Alexy, Márta Horváth, Tomáš ELTE - Eötvös Loránd University in Budapest Faculty of Informatics Department of Data Science and Engineering Budapest Hungary

With shortage in human workforce and increasing ecological challenges application of Internet of Things and Artificial Intelligence technologies in the agricultural sector, known also as precision farming, has become very popular in recent years. Agriculture is, however, a domain with specific challenges which AI engineers and data scientists usually do not face in other domains, such that healthcare, industry or services, just to name a few. In this paper we collect and summarize the challenges and problems which arose in our honeybee monitoring project from which the most important are trust, seasonality, data labeling issues and sudden events. We believe that, despite not focusing on Machine Learning models nor providing technical details, this paper provides an interesting reading for data scientists and will serve as an aid for planning and executing data science projects in the agricultural domain. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： data Analytics

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Prediction of COVID-19 Transmission in the United States Using Google Search Trends

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Computers, Materials & Continua 2022年第4期71卷 1751-1768页

作者： Meshrif Alruily Mohamed Ezz Ayman Mohamed Mostafa Nacim Yanes Mostafa Abbas Yasser El-Manzalawy College of Computer and Information Sciences Jouf UniversitySakaka72314Saudi Arabia Faculty of Engineering Al-Azhar UniversityCairo11651Egypt Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt RIADI Laboratory La Manouba UniversityManouba2010Tunisia Department of Translational Data Science and Informatics GeisingerDanvillePA17822USA

Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health *** to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the *** accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access *** objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related ***,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms *** the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,*** the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,*** 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,*** found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 *** also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.

关键词： Forecasting COVID-19 transmission and mortality in the US stacked LSTM SARS-COV-2 and google COVID-19 search trends

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Analysis of Agricultural Product Package Recommendations Using the FP-Growth Algorithm 5

Analysis of Agricultural Product Package Recommendations Usi...

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5th International Conference on Networking, Information Systems and Security, NISS 2022

作者： Masdiyasa, I Gede Susrama Prabowo, Aris Mandyartha, Eka Prakarsa Ariefwan, Rafka Mahendra Sugiarto Idhom, Mohammad Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Data Science Surabaya Indonesia Universitas Pembangunan Nasional 'Veteran' Jawa Timur Faculty of Computer Science Informatics Surabaya Indonesia

ISBN: (纸本)9781665453639

The size of the agricultural sector in Indonesia provides opportunities for small businesses managed by local residents to provide various needs to support agricultural activities. The rise of agricultural shops has made business competition in the agricultural sector quite competitive. Therefore, a marketing strategy is needed that can increase the sales of various marketed agricultural products. The method applied in this research is Association Rule Mining using the FP-Growth algorithm. This method is applied to obtain association rules that are used as a reference in making recommendations for agricultural product packages at agricultural shops to increase sales. The system workflow starts from preprocessing data to form an itemset which is then processed using the FP-Growth algorithm. The next step is to determine the minimum value of support and minimum confidence as a limit in calculating the FP-Growth algorithm. The system will eliminate a number of itemsets that do not meet the specified threshold to produce frequent itemsets which are then mined into rules as a reference to form the most recommended package of agricultural products. From the research conducted it can be seen that there are 3 itemets that almost always appear and the most recommended agricultural product package is Prowl 250 ml (Herbicide) which is associated with Antracol 70wp 1 kg (fungicide) with a support value of 6.38% and a confidence value of 85, 71% and the lift ratio is 8.95. © 2022 IEEE.

关键词： Association rules

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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A language for graph database evolution and its implementation in Neo4j 42

A language for graph database evolution and its implementati...

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42nd International Conference on Conceptual Modeling: ER Forum, 7th Symposium on Conceptual Modeling Education, SCME 2023, Project Exhibitions, Posters and Demos, and Doctoral Consortium, ER-Companion 2023

作者： Hausler, Dominique Klettke, Meike Störl, Uta University of Regensburg Data Engineering Group Faculty of Informatics and Data Science Bajuwarenstrasse 4 Regensburg93053 Germany University of Hagen Chair of Databases and Information Systems Faculty of Mathematics and Computer Science Universitätsstrasse 1 Hagen58084 Germany

In this article, we present an evolution language for graph databases and a method to realize evolution operations on graph databases and their schema. Graph database management systems like Neo4j can be used for different scenarios: they can store graphs where no semantic constraints are checked. It is also possible to use them to store graphs where the structure of nodes and relationships is regular, for example, because the nodes and relationships of a graph have been generated by an application. In the latter case, these structures may change, that means they can undergo evolution. Currently, there is no established evolutionary language for graphs. However, evolution operations (such as add, rename, delete, merge, copy, split and move) have already been developed for other database models. In this article, we will not only use the established evolution operations but additionally extend the operations appropriated. After analyzing evolution operators explicitly in the context of graph databases, we also created a new graph-specific operation: transform. Moreover, we suggest an evolution language to graph data and show how it can be executed on the graph database schema as well as on the instances (i.e. the graphs themselves). In doing so, some operations can be implemented native based on Cypher, others non-native using the APOC library for Neo4j. © 2023 CEUR-WS. All rights reserved.

关键词： evolution graph databases evolution graph evolution language graph schema description

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Spectral Generalized Category Discovery by training on combined labels 22

Spectral Generalized Category Discovery by training on combi...

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

作者： Mao, Ruixuan Al-Shouha, Modafar Szucs, Gabor Eötvös Loránd University Faculty of Informatics Department of Data Science BudapestH-1117 Hungary Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary

ISBN: (纸本)9798350317206

Hyperspectral imaging poses major challenges for supervised classification methods due to the high dimensionality of the data. This classification was solved in previous works with known classes. In this paper we present a different aim where the goal is to discover and distinguish novel categories for hyperspectral images in scenarios where some classes are unknown. This belongs to Generalized Category Discovery (GCD) problem with a further task for automatic clustering of unlabeled data with partial knowledge. Our contribution is an additional supervised learning stage on combined labels coming from two sources. Our approach consists of three stages, (i) dimension reduction, (ii) clusters assignment with the Hungarian method, and (iii) classification, where a special training set is constructed from ground true labels and the filtered test set with (dummy) labels that are the predicted categories from the clustering part. The proposed method (S-GCD) was evaluated on two datasets, and our experiments demonstrate that classification on combined labels greatly improves both classification and clustering scores. © 2024 IEEE.

关键词： Image classification

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Developing a Visual Dashboard to Improve Research Activity Assessment 11

Developing a Visual Dashboard to Improve Research Activity A...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Nur Rahman, Muhammad Zaki Prakasa, Esa Bijaksana, Moch Arif Bachelor of Data Science Faculty of Informatics Telkom University Bandung Indonesia Bandung Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331542313

In research institutions, the ability to monitor and analyze publication output is crucial for assessing productivity and achieving strategic goals. This paper presents a dashboard designed to visualize and analyze publication data from a research center of the Indonesian public institution. The dashboard addresses several key questions: (1) whether the current number of publications is on track to meet the institution’s targets, (2) the comparison between journal and proceeding publications, (3) individual researcher productivity, (4) research group productivity, and (5) the collaborative efforts among researchers and groups. By leveraging this tool, stakeholders can gain insights into publication trends and identify areas for improvement. The findings reveal significant trends in publication types and collaboration patterns, offering a comprehensive overview of the institution’s research output. This study contributes to the understanding of research productivity metrics and their implications for strategic planning in academic research. © 2024 IEEE.

关键词： Collaboration Metrics Dashboard Visualization Publication data Research Productivity

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