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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： José Lima Thadeu Brito Olga Ferreira Maria J. Afonso Vítor H. Pinto José A. Carvalho Paulo Costa Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal Laboratório Para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança Bragança Portugal Centre for Robotic in Industry and Intelligent Systems Institute for Systems and Computer Engineering Technology and Science (INESC-TEC) Porto Portugal CeDRI SusTEC INESC-TEC Faculty of Engineering of University of Porto (FEUP) Porto Portugal SusTEC ESTiG Instituto Politécnico de Bragança Bragança Portugal ARISE & ECE Dept. Research Center for Systems and Technologies (SYSTEC) FEUP CeDRI SusTEC INESC-TEC FEUP

This paper presents the development of an acquisition system and data logger from an existing set of three continuous stirred-tank reactors in series. The reactors are currently used in chemical engineering educational laboratories to perform kinetic and tracer experiments. In this sense, to accomplish the store data process, the volumetric flow rate and the concentration of tracer, reactants and/or products of the reaction must be acquired as a function of time. In the original experimental setup, only the signal conditioning system was operational, while the acquisition, visualization, and control systems were obsolete and damaged. Thus, a new system composed of an interface and real-time acquisition data is proposed alongside preserving the main reactor structure. A graphical user interface and the automation of the various actuators were developed based on worldwide usage and low cost, respectively. This system, based on a common 8-bit microcontroller and an application developed in Lazarus, allows the storage of the acquired data in a time-series database. In this way, students can analyze the results later or in real time. Moreover, remote access allows controlling the reactor and getting data by the Internet of Things (IoT) resources. Additionally, the proposed system using IoT allows data to be shared with the community as a dataset.

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EDOLAB: An Open-Source Platform for Education and Experimentation with Evolutionary Dynamic Optimization Algorithms

arXiv

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arXiv 2023年

作者： Peng, Mai Yazdani, Delaram She, Zeneng Yazdani, Danial Luo, Wenjian Li, Changhe Branke, Juergen Nguyen, Trung Thanh Gandomi, Amir H. Yang, Shengxiang Jin, Yaochu Yao, Xin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Research Institute Faculty of Engineering and Technology Liverpool John Moores University United Kingdom School of Computer Science and Technology Harbin Institute of Technology China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory China School of Artificial Intelligence Anhui University of Sciences & Technology China Information Systems Management and Analytics in Warwick Business School University of Warwick United Kingdom Faculty of Engineering & Information Technology University of Technology Sydney Australia Obuda University Hungary School of Computer Science and Informatics De Montfort University United Kingdom Department of Artificial Intelligence School of Engineering Westlake University China School of Computer Science University of Birmingham United Kingdom

Many real-world optimization problems exhibit dynamic characteristics, posing significant challenges for traditional optimization techniques. Evolutionary Dynamic Optimization Algorithms (EDOAs) are designed to address these challenges effectively. However, in existing literature, the reported results for a given EDOA can vary significantly. This inconsistency often arises because the source codes for many EDOAs, which are typically complex, have not been made publicly available, leading to error-prone re-implementations. To support researchers in conducting experiments and comparing their algorithms with various EDOAs, we have developed an open-source MATLAB platform called the Evolutionary Dynamic Optimization LABoratory (EDOLAB). This platform not only facilitates research but also includes an educational module designed for instructional purposes. The education module allows users to observe: a) a 2-dimensional problem space and its morphological changes following each environmental change, b) the behaviors of individuals over time, and c) how the EDOA responds to environmental changes and tracks the moving optimum. The current version of EDOLAB features 25 EDOAs and four fully parametric benchmark generators. © 2023, CC BY-NC-SA.

关键词： Optimization algorithms

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NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages

arXiv

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arXiv 2024年

作者： Abdullah, Abdulhady Abas Abdulla, Srwa Hasan Toufiq, Dalia Mohammad Maghdid, Halgurd S. Rashid, Tarik A. Farho, Pakshan F. Sabr, Shadan Sh. Taher, Akar H. Hamad, Darya S. Veisi, Hadi Asaad, Aras T. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler KR Erbil Iraq Department of Horticulture College of Agricultural Engineering Sciences University of Sulaimani- Kurdistan Region Iraq Branch of Basic Medical Science College of medicine University of Sulaimani- Kurdistan Region Iraq Department of Engineering Research Center Deanship of R&D Centers Koya University Kurdistan Region Iraq Department of Kurdish Language Faculty of Education Koya University Kurdistan Region Iraq Department of Kurdish Faculty of Education Koya University Kurdistan Region Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq School of Intelligent Systems College of Interdisciplinary Science and Technologies University of Tehran Iran

Nowadays natural Language Processing (NLP) is an important tool for most people’s daily life routines, ranging from understating speech, translation, name entity recognition (ENR), and text categorizing, to the generative text models such as ChatGPT. Due to existing big data and consequently big corpora for the most usable languages including English, Spanish, Turkish, Persia, and many more, these applications have been utilized accurately. However, the Kurdish language still needs more corpora and big datasets to be included in the NLP applications. This is because the Kurdish language has a rich linguistic structure, varied dialects, and a poor dataset, which poses unique challenges for Kurdish NLP (KNLP) application development. While several types of research have been conducted in KNLP for various applications, the Kurdish ENR (KNER) remained a challenge for most of the KNLP applications including text analysis and text classification. In this work, we address this limitation by proposing a methodology for fine-tuning the pre-trained RoBERTa model for KNER1. To this end, first, a Kurdish corpus is created, followed by designing a modified model architecture, and the training procedures are implemented. To evaluate the trained model, a set of experiments is conducted to show the performance of the KNER model using different tokenization methods and trained models. The experimental results showed that fine-tuned RoBERTa with sentence-piece tokenization method substantially improves KNER performance by 12.8% (F1-score improvement) in comparison to traditional models and consequently establishes a new benchmark for KNLP. © 2024, CC BY.

关键词： Natural language processing systems

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Inter-domain alignment for predicting high-resolution brain networks using teacher-student learning

arXiv

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arXiv 2021年

作者： Demir, Başar Bessadok, Alaa Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse 4011 Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-consuming preprocessing pipeline of neuroimaging data. However, existing deep learning frameworks are solely designed to predict high-resolution (HR) image from a low-resolution (LR) one, which limits their generalization ability to brain graphs (i.e., connectomes). A small body of works has focused on superresolving brain graphs where the goal is to predict a HR graph from a single LR graph. Although promising, existing works mainly focus on superresolving graphs belonging to the same domain (e.g., functional), overlooking the domain fracture existing between multimodal brain data distributions (e.g., morphological and structural). To this aim, we propose a novel inter-domain adaptation framework namely, Learn to SuperResolve Brain Graphs with Knowledge Distillation Network (L2S-KDnet), which adopts a teacher-student paradigm to superresolve brain graphs. Our teacher network is a graph encoder-decoder that firstly learns the LR brain graph embeddings, and secondly learns how to align the resulting latent representations to the HR ground truth data distribution using an adversarial regularization. Ultimately, it decodes the HR graphs from the aligned embeddings. Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher. We further leverage the decoder of the teacher to optimize the student network. In such a way, we are not only bringing the learned embeddings from both networks closer to each other but also their predicted HR graphs. L2S-KDnet presents the first TS architecture tailored for brain graph super-resolution synthesis that is based on inter-domain alignment. Our experimental results demonstrate substantial performance gains over benchmark methods. Our code is available a

关键词： Graphic methods

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Shifting dynamics of self-immolation in the capital of Iran? The rising trend among young, unmarried, less-educated men

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Burns Open 2025年 11卷 100406-100406页

作者： Shokri, MohammadAli Khalili, SaeedehSadat Vahabie, Abdol-Hossein Ghanbari Jolfaei, Atefeh Keshavarz-Akhlaghi, Amir-Abbas Dehmardei, Mostafa Mirfazeli, Fatemeh Sadat National Brain Centre Iran University of Medical Sciences Tehran Iraq Department of Neuroscience Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iraq Cognitive Systems Laboratory Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iraq Mental Health Research Center Psychosocial Health Research Institute Department of Psychiatry School of Medicine Iran University of Medical Sciences Tehran Iraq School of Medicine Iran University of Medical Sciences Tehran Iraq Department of Cognitive Sciences Faculty of Psychology and Education University of Tehran Tehran Iraq School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iraq

In our study, we aimed to explore the psychiatric disorders, risk factors, and predictors of self-immolation among individuals admitted to Shahid Motahari Hospital in Tehran from 2019 to 2020. This cross-sectional study examines 64 hospitalized patients who received psychiatric counseling following self-immolation incidents. The rate of self-immolation varies significantly based on specific demographics. It is observed that in our population men had a higher rate of being unmarried (70.96 % vs 15.15 %), lower levels of education (70.96 % vs 63.63 % did not have a university degree), higher level of unemployment (54.83 % vs 30.30 %), younger age average with most men aging 15–24 (29.06 (SD = 9.33)) vs women 35–44 (35.27(SD = 10.27)) and higher prevalence of addiction (67.74 % vs 36.36 %) compared to women. On the other hand, women who attempted self-immolation mainly were married, involved in housekeeping, and tended to exhibit higher rates of depression (63.63 % vs 32.25 %) than men. Furthermore, these self-immolation incidents are often impulsive (64.1 %) and occur shortly (under an hour) after experiencing a stressor (39.1 %). Self-immolation accidents are frequently carried out using gasoline (50 %). Geographically, the majority of self-immolation cases of our study are concentrated in the central region of Iran (76.6 %), followed by the western region (15.6 %) this may be due to the proximity of these regions to our center while patients of other regions were hospitalized in their referral hospitals and were rarely transferred to the capital. To effectively address the issue of self-immolation and reduce its prevalence, it is essential to identify vulnerable populations and explore targeted preventive measures. Based on our findings, future pilot studies could investigate the feasibility of specific interventions, such as crisis hotlines to reduce impulsivity-related acts of self-immolation. Additionally, small-scale feasibility projects could explore the effect

关键词： Contributing factors Psychiatric disorders Self-immolation

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Text Mining in the Domain of Plant Genetic Resources

Text Mining in the Domain of Plant Genetic Resources

引用

International IEEE Conference on intelligent systems, IS

作者： Ivan Popchev Daniela Orozova Intelligent Systems Department Bulgarian Academy of Sciences Sofia Bulgaria Faculty of Computer Science Burgas Free University Institute of Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria

ISBN: (数字)9781728154565

ISBN: (纸本)9781728154572

Upcoming is the construction of informational and social spaces which integrate heterogeneous technologies and various approaches to the collection, storage and processing of information related to the plant genetic resources of Bulgaria. In the present paper a multistage algorithm for automatic analysis of documents from the area of plant genetic resources is proposed. Determination of the significant words in the document and the type of its content is carried out based on the object-oriented ontologies in the field. By applying software instruments for classification, testing and evaluation, the documents are classified into related and not related to the studied subject area - agriculture and plant genetic resources of Bulgaria.

关键词： Ontologies Genetics Agriculture Task analysis Semantics Text analysis Plants (biology)

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Brain multigraph prediction using topology-aware adversarial graph neural network

arXiv

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arXiv 2021年

作者： Bessadok, Alaa Mahjoub, Mohamed Ali Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia School of Science and Engineering Computing University of Dundee United Kingdom

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches. © 2021, CC BY-NC-SA.

关键词： Graphic methods

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Open-SBS: Smart Building Simulator

Open-SBS: Smart Building Simulator

引用

Information Technology (ACIT)

作者： Houssem Eddine Degha Fatima Zohra Laallam Okba Kazar Issam Khelfaoui Belkacem Athamena Zina Houhamdi Faculty of Sciences and Technology University of Ghardaia Ghardaia Algeria Artificial Intelligence and Information Technologies Laboratory Kasdi Merbah Ouargla University City Algeria Intelligent Computer Science Laboratory LINFI Biskra University Biskra Algeria Department of Information Systems and Security College of Information Technology United Arab Emirate University United Arab Emirates School of Insurance and Economics University of International Business and Economics Beijing China College of Business Al Ain University Al Ain UAE College of engineering Al Ain University Al Ain UAE

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

With the rise of machine learning and deep learning techniques in recent years, a representative dataset has become an inspiring source of prediction and knowledge extraction. Furthermore, ambient intelligence environments have emerged as one of the most important research areas. Many complex tasks and challenges face this area to validate and evaluate field research. One of the major challenges in the smart building domain is the lack of a simulation tool capable of simulating complex contexts, executing semantic rules, and collecting representative datasets for experiments. To address these issues, we present in this paper a cross-platform, open-source Smart Building Simulator (Open-SBS). Open-SBS offer an opportunity for researchers in the field of Ambient Intelligence environments to simulate complex contexts and collect representative datasets, save and share their models of experiments. We propose a new simulation process that is divided into four distinct phases: designing, scenario creation, semantic rule addition, and data-set generation. On the System Usability Scale, we conducted a study to assess the ease of use of Open-SBS (SUS).

关键词： Smart buildings Three-dimensional displays Semantics Ontologies Data models Ambient intelligence Usability

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An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping

引用

Geocarto International 2023年第1期38卷

作者： Hai, Tao Theruvil Sayed, Biju Majdi, Ali Zhou, Jincheng Sagban, Rafid Band, Shahab S. Mosavi, Amir School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Selangor Shah Alam Malaysia Department of Computer Science Dhofar University Salalah Oman Department of Building and Construction Technologies Engineering Al-Mustaqbal University College Hilla Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou Taiwan John von Neumann Faculty of Informatics Obuda University Budapest Hungary German Research Center for Artificial Intelligence Oldenburg Germany Institute of the Information Society University of Public Service Budapest Hungary

A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence cat swarm optimization geospatial machine learning natural hazard Wildfire susceptibility

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

引用

Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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