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3rd International Conference on Applied Mathematics and computer Science, ICAMCS 2023

作者： Rescic, Nina Alberts, Janna Altenburg, Teatske M. Chinapaw, Mai J. M. De Nigro, Antonio Fenoglio, Dario Gjoreski, Martin Gradisek, Anton Jurak, Gregor Kiourtis, Athanasios Kyriazis, Dimosthenis Langheinrich, Marc Mancuso, Elena Mavrogiorgou, Argyro Pechenizkiy, Mykola Pratola, Roberto Ribeiro, Jose Soric, Maroje Taj, Fawad Tammelin, Tuija H. Vastenburg, Martijn Vilanova, Anna Vrijkotte, Tanja G. M. Lustrek, Mitja Jožef Stefan Institute Department of Intelligent Systems Ljubljana Slovenia ConnectedCare Research & Development Nijmegen Netherlands Vrije Universiteit Amsterdam Amsterdam Public Health Research Institute Department of Public and Occupational Health Amsterdam Umc Amsterdam Netherlands Informatica S.p.A. Engineering Ingegneria Rome Italy Università della Svizzera Italiana Faculty of Informatics Lugano Switzerland University of Ljubljana Faculty of Sports Ljubljana Slovenia University of Piraeus Department of Digital Systems Piraeus Greece Eindhoven University of Technology Department of Mathematics and Computer Science Eindhoven Netherlands University of Porto Faculty of Sport Porto Portugal Jamk University of Applied Sciences Likes School of Health and Social Studies Jyväskylä Finland

ISBN: (纸本)9798350324266

The SmartCHANGE project, a Horizon-Europe Research & Innovation project that has been ongoing since May 2023 and is scheduled to conclude in April 2027, aims to develop AI-driven decision-support tools to identify and reduce the long-term risk of non-communicable chronic diseases (NCDs) in children and youth. NCDs have become a significant public health burden in developed countries, with common risk factors including obesity, low physical fitness, and unhealthy lifestyle habits. Childhood and adolescence are critical periods for establishing healthy habits, and early intervention can prevent or delay the onset of NCDs later in life. However, current tools for identifying high-risk individuals mainly focus on adults, leading to missed opportunities for early detection. The SmartCHANGE project aims to address this gap by creating trustworthy AI tools that accurately assess risk factors in children and youth and promote optimised strategies for risk reduction. The paper outlines the plan for the project from the project's proposed methodologies, including dataset utilisation, data cleaning, risk prediction models (basic and complex), and the implementation of federated learning. Additionally, it discusses user experience aspects such as participatory design, suggested applications, and the evaluation of project outcomes. © 2023 IEEE.

关键词： Health risks

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Digital Twins for Dynamic Management of Blockchain Systems

Digital Twins for Dynamic Management of Blockchain Systems

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Simulation Winter Conference

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos University of Birmingham Edgbaston Birmingham UNITED KINGDOM Southern University of Science and Technology Shenzhen CHINA Department of Informatics and Telecommunications University of Thessaly Lamia GREECE School of Computer Science University of Birmingham Edgbaston Birmingham UNITED KINGDOM Department of Computer Science and Engineering Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen CHINA

ISBN: (纸本)9781665476621

Blockchain systems are challenged by the so-called Trilemma tradeoff: decentralization, scalability, and security. Infrastructure and node configuration, choice of the Consensus Protocol, and complexity of the application transactions are cited among the factors that affect the tradeoff balance. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This paper introduces the utilization of DigitalTwins for this purpose. The novel contribution of the paper is the design of a framework and conceptual architecture of a Digital Twin that can assist in maintaining the Trilemma tradeoffs of time-critical systems. The proposed Digital Twin is illustrated via an innovative approach to the dynamic selection of Consensus Protocols. Simulation results show that the proposed framework can effectively support the dynamic adaptation and management of the Blockchain.

关键词： Analytical models Simulation computer architecture Quality of service Digital twins Consensus protocol Complexity theory

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

arXiv

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arXiv 2024年

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Pilani Campus Rajasthan333031 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Skoda Auto University Na Karmeli 1457 Mlada Boleslav293 01 Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science and Engineering Jadavpur University Kolkata32 India

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net’s generalization capability. © 2024, CC BY.

关键词： Plant diseases

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Trusted Execution Environments for Cloud/Fog-based Internet of Things Applications 11

Trusted Execution Environments for Cloud/Fog-based Internet ...

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11th International Conference on Cloud Computing and Services Science, CLOSER 2021

作者： Valadares, Dalton Cézane Gomes Will, Newton Carlos Spohn, Marco Aurélio Santos, Danilo Freire de Souza Perkusich, Angelo Gorgonio, Kyller Costa Federal Institute of Pernambuco Mechanical Engineering Department PE Caruaru Brazil Federal University of Campina Grande Informatics and Electrical Engineering Center Computer Science PB Campina Grande Brazil Federal University of Technology - Paraná PR Dois Vizinhos Brazil Federal University of Fronteira Sul SC Chapecó Brazil

ISBN: (纸本)9789897585104

Cloud services and fog-based solutions can improve the communication and processing efficiency of the Internet of Things (IoT). Cloud and fog servers offer more processing power to IoT solutions, enabling more complex tasks within reduced time frames, which could not be possible when relying solely on IoT devices. Cloud and fog computing benefits are even better when considering sensitive data processing once IoT devices can hardly perform complex security tasks. To improve data security in cloud/fog-based IoT solutions, Trusted Execution Environments (TEEs) allow the processing of sensitive data and code inside protected and isolated regions of memory. This paper presents a brief survey regarding TEEs’ adoption to protect data in cloud/fog-based IoT applications. We focus on solutions based on the two leading TEE technologies currently available in the market (Intel SGX and ARM Trustzone), pointing out some research challenges and directions. Copyright © 2021 by SCITEPRESS – Science and technology Publications, Lda. All rights reserved

关键词： Internet of things

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A Novel Group Decision Making Framework with Opinion Dynamics in Social Trust Network Based on Opinion Reliability

SSRN

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SSRN 2022年

作者： Wang, Han Ju, Yanbing Herrera-Viedma, Enrique Liang, Yingying Dong, Peiwu Krejcar, Ondrej School of Management and Economics Beijing Institute of Technology Beijing100081 China Department of Computer Science and AI University of Granada Granada18071 Spain Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove50003 Czech Republic Institute of Technology and Business in Ceske Budejovice Ceske Budejovice Czech Republic

In social network group decision making (SNGDM) problems, opinion dynamics play an important role in the consensus reaching process (CRP) through evolving and aggregating individual opinions based on their trust relationships. Opinion reliability can be regarded as an important characteristic to indicate the reliability of experts’ opinions in social trust network. However, it has rarely been considered into the opinion evolution process. To quantify the impacts of opinion reliability on group consensus reaching, a novel GDM framework with opinion dynamics in social trust network based on opinion reliability is proposed in this paper. Firstly, opinion reliability of experts in social trust network is defined, and the comprehensive trust value based on the social network structure and individual characteristic is proposed. Secondly, trust propagation and aggregation mechanisms are designed to obtain the social trust matrix. Thirdly, considering opinion similarity and opinion reliability, social network evolution rules and opinion evolution rules based on the extended Hegselmann-Krause (HK) model are presented. Finally, a numerical experiment about supplier performance evaluation and simulation analysis are provided to demonstrate the convergence and illustrate the feasibility and effectiveness of the proposed model. © 2022, The Authors. All rights reserved.

关键词： Reliability

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How we can use text classification in the Back-Office environment of a bank as ‘business as usual’ solution

How we can use text classification in the Back-Office enviro...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Zsolt Krutilla Attila Kovari Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty Óbuda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary Department of Natural Science Institute of Engineering University of Dunaújváros Dunaújváros Hungary GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét Hungary

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a ‘business as usual’ (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models.

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Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance

arXiv

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arXiv 2022年

作者： Sugano, Ryusei Shinkuma, Ryoichi Nishio, Takayuki Itahara, Sohei Mandayam, Narayan B. Faculty of Engineering Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo135-8548 Japan School of Engineering Tokyo Institute of Technology Japan Graduate School of Informatics Kyoto University Kyoto606-8501 Japan WINLAB Department of Electrical and Computer Engineering Rutgers University United States

This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance. Our framework is promising for crime deterrence because UAVs are useful for collecting and distributing data and have high mobility. Our framework relies on machine learning (ML) technology for controlling and dispatching UAVs and predicting crimes. This paper compares the conceptual model of our framework against the literature. It also reports a simulation of UAV dispatching using reinforcement learning and distributed ML inference over a lossy UAV network. Copyright © 2022, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Early Classification of Lung Cancer Based on Cell Morphology Features 1st

Early Classification of Lung Cancer Based on Cell Morphology...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Byeon, Haewon Soni, Mukesh Deb, Nabamita Rivera, Richard Sharma, Nilesh Vijay Kaushal, Chetna Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Department of Information Technology Gauhati University Guwahati India Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Artificial Intelligence and Data Science SNJB′s Late Sau. K. B. Jain College of Engineering Nashik Chandwad India Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

In response to the challenges of limited annotated lung cancer pathology images with complex cell morphology, we propose a lung cancer pathology image classification method based on contrastive learning of cell morphology features. This approach integrates highly reliable unlabelled data into the training data through contrastive learning to address the issue of insufficient labelled data. Building upon the foundation of k-nearest neighbour contrastive learning, we introduce farthest and nearest neighbour contrastive learning, simultaneously using the farthest and nearest neighbour images for contrastive learning. This enhances the performance of contrastive learning by increasing the difficulty of positive sample learning and diversifying the dataset. We employ a ResNet50 encoder with deformable convolutions and dynamic convolutions to improve the extraction of cell morphology features. Experimental results demonstrate that, especially when labelled data is scarce, our classification method effectively leverages cell feature information from both labelled and unlabelled cancer pathology images, resulting in superior classification performance compared to existing methods. When applied to 80%, 60%, and 40% labeled data, respectively, CCCM outperforms other approaches with superior performance metrics, reaching F1 Score of 0.9584, 0.9488, and 0.9386. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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