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17th International Workshop on Ontology Matching, OM 2022

作者： Alghamdi, Sarah M. Zhapa-Camach, Fernando Hoehndorf, Robert Computational Bioscience Research Center Computer Electrical & Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology 4700 KAUST Thuwal23955 Saudi Arabia King Abdul-Aziz University Faculty of Computing and Information Technology Rabigh25732 Saudi Arabia

Ontologies play an important role in sharing and reusing knowledge. Several ontologies have been developed to describe a particular domain but from different perspectives from communities of developers and users. This has led to the existence of multiple ontologies covering the same or a different domain with varying degrees of variability. Ontology Alignment is typically used to identify correspondences between semantically related elements of two or more ontologies in order to address this problem. We propose A-LIOn a system that learns alignments by combining lexical and semantic approaches as well as machine learning. The system utilizes OWL EL reasoning for negative sampling which is iteratively used to inform the correction of the learning of the alignments. We demonstrate that A-LIOn produces alignments that are coherent with respect to OWL EL. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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An Efficient Lucas Wavelet Galerkin Method for Solving Time-Delay Optimal Control Problems

SSRN

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SSRN 2022年

作者： Sabermahani, Sedigheh Ordokhani, Yadollah Nourian, Farshid Lakestani, Mehrdad Department of Mathematics Faculty of Mathematical Sciences Alzahra University Tehran Iran Faculty of Mathematics Statistics and Computer Science University of Tabriz Tabriz Iran

Here, we present a numerical scheme to solve optimal control problems with time-varying delay system. This method is based on Lucas wavelets and Galerkin method. Operational matrices of integration and delay for Lucas wavelets are proposed. Then, Galerkin method is used to solve the mentioned problems . Numerical results are included to demonstrate the efficiency of the present technique. © 2022, The Authors. All rights reserved.

关键词： Galerkin methods

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Early Detection and Classification of Diabetic Retinopathy through Analysis of Retinal Medical Images Using Deep Learning

Early Detection and Classification of Diabetic Retinopathy t...

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International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Youssef Siyah Khalid Minaoui Fatima Zahra El Biach Samir Saoudi Laboratory for research in computer science and telecommunications (LRIT) Faculty of Sciences in Rabat Mohammed V University in Rabat Morocco dept. Mathematical and Electrical Engineering STICC Laboratory UMR CNRS 6285 IMT-Atlantique in Brest France

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

In this paper, we propose a model based on deep learning (DL) frameworks for the detection and classification of diabetic retinopathy (DR), along with the assessment of its severity. DL algorithms have been applied to fundus photographs, which are images of the back of the eye, to automatically detect and classify signs of DR. These algorithms can analyze retinal images and identify anomalies, including microaneurysms, hemorrhages, exudates, as well as fluid accumulation in the macula, all of which serve as indicators of the presence of diabetic retinopathy. We utilized the APTOS 2019 dataset sourced from Kaggle, comprising high-resolution retinal images processed with Gaussian filters for diabetic retinopathy detection. Experiments were conducted on six different models, including Visual Geometry Group 16 (VGG16), Visual Geometry Group 19 (VGG19), Residual Network 50 (ResNet50), Inception Version 3 (InceptionV3), basic Convolutional Neural Networks (CNN), and Mobile Network Version 2 (MobileNetV2). The results obtained demonstrate high accuracy, suggesting that this model could contribute to expediting and enhancing the diabetic retinopathy diagnostic process. By automating the detection of anomalies in retinal images, the system proposes an intelligent approach that may help alleviate the workload on medical personnel.

关键词： Geometry Deep learning Diabetic retinopathy Visualization Retina Classification algorithms Convolutional neural networks

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Smartphone Application for the Deaf and the Deaf Caring Community

Smartphone Application for the Deaf and the Deaf Caring Comm...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Pinnus Pryandi Muhammad Bayu Dewantara Harco Leslie Hendric Spits Warnars Arief Ramadhan Nurulhuda Noordin Fahriza Hanis Abdul Razak Information Systems department School of Information Systems. Bina Nusantara University Jakarta Indonesia Computer Science department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Telkom University Bandung Indonesia Faculty of Computer and Mathematical Sciences Universiti Teknologi Mara (UiTM) Selangor Malaysia

In life, various challenges and problems faced by deaf people such as communication skills and other problems, including emotional, mental, and societal development However, technology is needed that can help the process. This Smart Application try to build communication between the deaf to share thoughts in group chats, including getting information about the deaf, so that the communication learning process for the deaf becomes easier. The proposed model is using Unified Modeling Languages (UML) diagrams, such as case diagrams for the current proposed idea process and class diagrams for relational tables or database stores. In addition, the User interface (UI) is implemented in this research work, Personal Home Pages (PHP) as a web server programming and MySQL database as an open-source database.

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LIOUVILLE THEOREM FOR A CLASS OF HESSIAN EQUATIONS

arXiv

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arXiv 2023年

作者： Chu, Jianchun Dinew, Slawomir School of Mathematical Sciences Peking University Yiheyuan Road 5 Beijing100871 China Faculty of Mathematics and Computer Science Jagiellonian University Lojasiewicza 6 Krakow30-348 Poland

In this paper, we study a general class of Hessian elliptic equations, including the Monge-Ampère equation, the k-Hessian equation and p-Monge-Ampère equations. We propose new additional condition on the solution and prove Liouville theorem under this assumption. We show that our general condition covers as special cases numerous sets of assumptions known in the literature which were tailored for specific equations. Thus we obtain a significant generalization of multiple isolated Liouville theorems and conditional interior estimates. Copyright © 2023, The Authors. All rights reserved.

关键词： Liouville equation

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Modelling the mitigation of anti-vaccine opinion propagation to suppress epidemic spread: A computational approach

arXiv

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arXiv 2024年

作者： Alahmadi, Sarah Hoyle, Rebecca Head, Michael Brede, Markus School of Electronics and Computer Science University of Southampton Southampton United Kingdom School of Mathematical Sciences University of Southampton Southampton United Kingdom Clinical Informatics Research Unit Faculty of Medicine University of Southampton Southampton United Kingdom

Information regarding vaccines from sources such as health services, media, and social networks can significantly shape vaccination decisions. In particular, the dissemination of negative information can contribute to vaccine hesitancy, thereby exacerbating infectious disease outbreaks. This study investigates strategies to mitigate anti-vaccine social contagion through effective counter-campaigns that disseminate positive vaccine information and encourage vaccine uptake, aiming to reduce the size of epidemics. In a coupled agent-based model that consists of opinion and disease diffusion processes, we explore and compare different heuristics to design positive campaigns based on the network structure and local presence of negative vaccine attitudes. We examine two campaigning regimes: a static regime with a fixed set of targets, and a dynamic regime in which targets can be updated over time. We demonstrate that strategic targeting and engagement with the dynamics of anti-vaccine influence diffusion in the network can effectively mitigate the spread of anti-vaccine sentiment, thereby reducing the epidemic size. However, the effectiveness of the campaigns differs across different targeting strategies and is impacted by a range of factors. We find that the primary advantage of static campaigns lies in their capacity to act as an obstacle, preventing the clustering of emerging anti-vaccine communities, thereby resulting in smaller and unconnected anti-vaccine groups. On the other hand, dynamic campaigns reach a broader segment of the population and adapt to the evolution of anti-vaccine diffusion, not only protecting susceptible agents from negative influence but also fostering positive propagation within negative regions. Copyright © 2024, The Authors. All rights reserved.

关键词： Vaccines

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Clustering digital mental health perceptions using transformer-based models

Franklin Open

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Franklin Open 2025年 11卷

作者： Ibitoye, Ayodeji O.J. Oladimeji, Oladosu O. Afe, Oluwaseyi F. School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Faculty of Engineering and Design Atlantic Technological University Sligo Ireland Department of Computer Science Lead City University Ibadan Nigeria

The rise in online mental health discussions underscores the need to understand diverse perspectives to inform targeted interventions. Addressing the granularity of existing research on mapping such perspectives, this study proposes a model combining contextual and sentiment analysis, keyword scoring, and clustering techniques to identify themes in online comments. Using transformer-based models (BERT, ALBERT, ELECTRA), the study achieved high-quality clustering of seven distinct mental health perspectives: stigma, empowerment, treatment approaches, recovery, social/environmental factors, advocacy, and cultural dimensions. ELECTRA outperformed others in clustering quality (silhouette score: 0.73;Davies-Bouldin Index: 0.34). The findings reveal cohesive, well-separated clusters that enhance understanding of digital mental health discourse. These insights provide a foundation for data-driven advocacy, tailored interventions, and broader awareness, addressing the complex dynamics of mental health narratives in online spaces. This study bridges a critical research gap by offering a systematic approach to analysing and interpreting mental health perspectives in digital environments. © 2025

关键词： Decision support Digital mental health Mental health Mental health perspectives Text clustering Transformer-based architectures

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Introducing a deep learning method for brain tumor classification using MRI data towards better performance

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Informatics in Medicine Unlocked 2024年 44卷

作者： Dikande Simo, Alain Marcel Tchagna Kouanou, Aurelle Monthe, Valery Kameni Nana, Michael Moffo Lonla, Bertrand Africa Institute of Mathematical Science (AIMS)-Cameroon Limbe Cameroon Department of Physics Faculty of Science University of Buea Cameroon Department of Computer Engineering College of Technology University of Buea Cameroon Department of Training Research Development and Innovation InchTech's Solutions Yaoundé Cameroon Department of Computer Science Faculty of Sciences University of Yaoundé 1 Yaoundé Cameroon

Magnetic resonance imaging (MRI) is widely used to detect brain tumors, but its accuracy depends on the physician's expertise and often requires biopsy confirmation. Deep learning, especially in the field of computer vision, has revolutionized the diagnosis and classification of brain tumors using MRI. This study aims to design a sequential brain tumor detection and classification model based on deep learning and using fully convolutional neural networks. The proposed model consists of two steps: distinguishing non-neoplastic brain from neoplastic brain and determining the tumor type of the latter. Two models were trained using the Brain Tumor MRI Dataset. Four optimizers are studied for three classification tasks (Adam, Nesterov momentum, root-mean-square propagation, and adaptive gradient) to achieve the best results. Adam performed best at distinguishing tumor from non-tumor brains, with 100 % training accuracy and 98 % validation and test accuracy. Nesterov Momentum performed best at differentiating the three tumor types, with 100 % training accuracy and 92 % validation and testing accuracy. Nesterov also performed best on the third classification task, with 100 % training accuracy and 95 % validation and test accuracy. Nesterov-based sequence models show significant results compared to literature works. The proposed Nesterov momentum-based sequence model achieves high accuracy in MRI brain tumor detection and classification. © 2023 The Authors

关键词： Brain tumors classification Deep learning Fully convolutional neural network Magnetic resonance image Nesterov momentum optimization

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Cardiovascular Diseases Risk Analysis using Distance-Based Similarity Measure of Neutrosophic Sets

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Neutrosophic Sets and Systems 2021年 47卷 26-37页

作者： Mustapha, Norzieha Alias, Suriana Yasin, Roliza Md Abdullah, Ilyani Broumi, Said Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Kelantan Kelantan Bukit Ilmu Machang18500 Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Laboratory of Information Processing Faculty of Science Ben M’Sik University Hassan II Casablanca Morocco

One of the highest causes of death in many countries in this modern era is cardiovascular diseases. There are a few symptoms that linked significantly to the cardiovascular diseases. The symptoms and diseases relationship can be represented by neutrosophic set values. In general, neutrosophic set gives remarkable contribution in denoising, clustering, segmentation, and classification in handling data of many real applications including in medical field. This study aims to analyse the cardiovascular disease risks by a new distance-based similarity measure motivated from intuitionistic fuzzy set theory. The proof for all the properties is presented clearly. Then, a case study is conducted by using the data on the severity level of the six symptoms found in two different patients. The neutrosophic data are analysed to determine the patients’ possibility of having any one or combination of the three types of cardiovascular diseases. A comparative study involving three common distance measures is conducted. The results show that the similarity indexes for all measures of both patients are less than 0.5. This situation can further conclude as both patients are possibly not suffering from cardiovascular diseases. © 2021, Neutrosophic Sets and Systems. All Rights Reserved.

关键词： Data handling

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Neurocomputational Modelling of EEG Connectivity: Links Between Depression, Inflammation, and Gut-Microbiome 17th

Neurocomputational Modelling of EEG Connectivity: Links Betw...

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17th International Conference on Brain Informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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