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6th International Conference on Pattern Analysis and Intelligent Systems, PaiS 2024

作者： Hadjkouider, Abdessalam Mohammed Sahraoui, Yesin Kerrache, Chaker Abdelaziz Korichi, Ahmed Kasdi Merbah University of Ouargla Dpt. of Computer Science And It Ouargla Algeria National Higher School of Ai Algiers Algeria Université Amar Telidji De Laghouat Dpt. of Computer Science Laghouat Algeria

ISBN: (纸本)9798350350265

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) has opened up new horizons in various application domains. However, the optimal selection of UAV services remains a complex challenge, necessitating a nuanced approach considering multiple criteria. This article proposes a novel framework that integrates machine learning (ML) techniques with multi-criteria decision-making (MCDM) methods to address this challenge. Our framework comprehensively evaluates UAV services based on key metrics such as delay, packet loss, throughput, and residual energy, thereby providing a holistic perspective for decision-making. We present experimental results from a comparative study involving Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers. Our findings reveal the effectiveness of our proposed approach, which achieved a classification accuracy of 99.9% with RF. These results underscore the capability of our framework to optimize UAV service delivery, thus enhancing overall performance and user satisfaction. By coupling ML and MCDM, our framework offers a robust and efficient solution for UAV service selection, catering to the diverse needs of users and service providers in the evolving landscape of UAV applications. © 2024 IEEE.

关键词： Decision trees

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A Machine Learning-assisted Game Theoretical Service Selection Strategy in UAV Networks

A Machine Learning-assisted Game Theoretical Service Selecti...

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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Hadjkouider, Abdessalam Mohammed Kerrache, Chaker Abdelaziz Sahraoui, Yesin Adnane, Asma Calafate, Carlos T. Kasdi Merbah University of Ouargla Dpt. of Computer Science and IT Ouargla Algeria Université Amar Telidji de Laghouat Dpt. of Computer Science Laghouat Algeria National Higher School of AI Algiers Algeria Loughborough University Computer Science Department Loughborough United Kingdom Valencia Spain

ISBN: (纸本)9798331542603

The Unmanned Aerial Vehicle (UAV) has emerged as a transformative technology application in healthcare, environmental monitoring, and search and rescue operations, with the growing complexity of their services, Dynamic Environments, and limited resources. This paper introduces a novel approach that integrates machine learning (ML) with game theory to revolutionize the decision-making process in UAV service selection. Our method focuses on enhancing user experience by employing ML algorithms to analyze user requests and predict the most suitable UAV services. Concurrently, game theory is integrated to evaluate and ensure efficient selection regarding delay, throughput, packet loss ratio, and residual energy within UAV *** synergy aims to benefit both users and service providers by optimizing service delivery and user satisfaction. Experimental results demonstrate the efficacy of our model, highlighting its superior performance in terms of accuracy, precision, recall, and F-score. © 2024 IEEE.

关键词： Decision making

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Results From a Large-Scale IoT-based Intervention for Energy Saving and Sustainability Awareness via Behavior Change in 25 K-12 Schools in Europe

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IEEE Internet of Things Journal 2025年第12期12卷 19308-19325页

作者： Mylonas, Georgios Amaxilatis, Dimitrios Paganelli, Federica Chatzigiannakis, Ioannis Koulouris, Pavlos Falkouskaya, Yelizaveta Markopoulos, Panos Athena Research Center Industrial Systems Institute Patras26504 Greece Comp. Tech. Institute and Press "Diophantus" 26504 Greece Spark Works Ltd GalwayH91 AHR1 Ireland Univ. of Pisa Dpt. of Computer Science Pisa56127 Italy CNIT Florence50134 Italy Sapienza University of Rome Dpt. of Computer Control and Management Engineering 00185 Italy Ellinogermaniki Agogi Athens Greece Eindhoven University of Technology AI Systems Institute Industrial Design Noord-Brabant Eindhoven5612 AE Netherlands

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer "all or nothing"approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly. © 2014 IEEE.

关键词： Sustainable development Buildings Energy conservation Europe Meteorology Internet of Things Energy consumption Real-time systems Energy efficiency Psychology

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Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Compliant

Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Comp...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Munilla, J. Burmester, M. Caballero-Gil, P. University of Málaga Dpt. of Telecommunication Engineering Málaga29071 Spain Florida State Unviersity Department of Computer Science TallahasseeFL32304 United States University of La Laguna Dept. of Computer Science and Ai Tenerife La Laguna Spain

ISBN: (纸本)9798350364910

Trust and security are critical deployment require-ments for Industrial Internet of Things (IIoT) networks. A recent protocol, called TRUTH, integrates security mechanisms for authentication and privacy alongside a Dempster-Shafer based trust model, to assess the trustworthiness of collected data. We analyze this protocol and show that it does not comply with the implementation requirements of the 5G standard, and that flaws in the identification of involved parties hinder its adoption by 5G devices. We show how to fix this protocol and tailor it for the specific 5G implementation constraints, by enhancing its computational and communication performance. Finally, we show how to prove the security of this fixed protocol (TRUTH +) using the Tamarin automated verification tool. © 2024 IEEE.

关键词： Authentication Protocol

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MCDM: an ML-based Multi-Criteria Decision Making solution for UAVs Service Selection

MCDM: an ML-based Multi-Criteria Decision Making solution fo...

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International Conference on Pattern Analysis and Intelligent Systems (PaiS)

作者： Abdessalam Mohammed Hadjkouider Yesin Sahraoui Chaker Abdelaziz Kerrache Ahmed Korichi Dpt. of Computer Science and IT Kasdi Merbah University of Ouargla Ouargla Algeria National Higher School of AI Algiers Algeria Dpt. of Computer Science Université Amar Telidji de Laghouat Laghouat Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

关键词： Support vector machines Radio frequency Packet loss Autonomous aerial vehicles Throughput MCDM Delays

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Enhancing Forest Fire Detection and Emergency Response Using Crowdsourcing and Smartphone Sensors

Enhancing Forest Fire Detection and Emergency Response Using...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Abdessalam Mohammed Hadjkouider Yesin Sahraoui Chaker Abdelaziz Kerrache Dpt. of Computer Science and IT Kasdi Merbah University of Ouargla Ouargla Algeria National Higher School of AI Algiers Algeria Dpt. of Computer Science Université Amar Telidji de Laghouat Laghouat Algeria

ISBN: (数字)9798350367928

ISBN: (纸本)9798350367935

Forest fires significantly threaten ecosystems, human life, and property, necessitating rapid detection and response mechanisms. Traditional detection methods, such as satellite imagery and ground-based observation, often suffer from delays and limited accuracy. This paper presents a novel approach to enhancing forest fire detection and emergency response using crowdsourcing and smartphone sensors. By leveraging the widespread availability of smartphones equipped with various sensors, coupled with the power of collective human effort, this method aims to improve the timeliness and accuracy of fire detection and optimize emergency response. Moreover, the proposed system enables real-time fire detection and reporting. A dedicated mobile application allows users to submit fire sightings, including multimedia evidence and precise location data. The integration of crowdsourcing and smartphone sensors represents a promising solution for addressing the challenges of forest fire management, enhancing both detection and emergency response capabilities.

关键词： Crowdsourcing Accuracy Ecosystems Forestry Emergency services Real-time systems Sensors Satellite images Mobile applications Information and communication technology

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A Machine Learning-assisted Game Theoretical Service Selection Strategy in UAV Networks

A Machine Learning-assisted Game Theoretical Service Selecti...

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Emerging Technologies (GCET), Global Congress on

作者： Abdessalam Mohammed Hadjkouider Chaker Abdelaziz Kerrache Yesin Sahraoui Asma Adnane Carlos T. Calafate Dpt. of Computer Science and IT Kasdi Merbah University of Ouargla Ouargla Algeria Dpt. of Computer Science Université Amar Telidji de Laghouat Laghouat Algeria National Higher School of AI Algiers Algeria Computer Science Department Loughborough University Loughborough UK Computer Engineering Department (DISCA) Universitat Politècnica de València Valencia Spain

ISBN: (数字)9798331542603

ISBN: (纸本)9798331542610

关键词： Machine learning algorithms Packet loss Machine learning Medical services Autonomous aerial vehicles Throughput Prediction algorithms User experience Game theory Vehicle dynamics

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Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Compliant

Cryptanalysis of TRUTH: Fixing a Protocol to Make it 5G-Comp...

引用

International Symposium on Networks, computers and Communications (ISNCC)

作者： J. Munilla M. Burmester P. Caballero-Gil Dpt. of Telecommunication Engineering University of Málaga Málaga Spain Department of Computer Science Florida State Unviersity Tallahassee Florida USA Dept. of Computer Science and AI University of La Laguna La Laguna Tenerife Spain

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

关键词： Fault diagnosis Performance evaluation Data privacy Protocols 5G mobile communication Data models Synchronization Object recognition Standards Industrial Internet of Things

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Adiabatic training for Variational Quantum Algorithms

arXiv

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arXiv 2024年

作者： Acosta, Ernesto Gutiérrez, Carlos Cano Botella, Guillermo Campos, Roberto Dpt. Computer Science and AI University of Granada Granada18071 Spain Computer Architecture and Automation Department Complutense University of Madrid Madrid28040 Spain Theoretical Physics Department Complutense University of Madrid Madrid28040 Spain

This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation;a Gate-based Quantum computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN);and an adiabatic Quantum computer where the optimization function is executed to find the best parameters for the VQA. As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect [5]. Some gradient-free classical approaches such as Evolutionary Algorithms have also been proposed to overcome this effect [4]. However, to the knowledge of the authors, adiabatic quantum models have not been used to train VQAs. The paper compares the results of gradient-based classical algorithms against adiabatic optimizers showing the feasibility of integration for gate-based and adiabatic quantum computing models, opening the door to modern hybrid QML approaches for High Performance Computing. © 2024, CC BY-NC-SA.

关键词： Quantum computers

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BACH: grand challenge on breast cancer histology images

arXiv

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arXiv 2018年

作者： Aresta, Guilherme Araújo, Teresa Kwok, Scotty Chennamsetty, Sai Saketh Safwan, Mohammed Alex, Varghese Marami, Bahram Prastawa, Marcel Chan, Monica Donovan, Michael Fernandez, Gerardo Zeineh, Jack Kohl, Matthias Walz, Christoph Ludwig, Florian Braunewell, Stefan Baust, Maximilian Vu, Quoc Dang Nhat To, Minh Nguyen Kim, Eal Kwak, Jin Tae Galal, Sameh Sanchez-Freire, Veronica Brancati, Nadia Frucci, Maria Riccio, Daniel Wang, Yaqi Sun, Lingling Ma, Kaiqiang Fang, Jiannan Kone, Ismael Boulmane, Lahsen Campilho, Aurélio Eloy, Catarina Polónia, António Aguiar, Paulo INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Faculty of Engineering of University of Porto Porto4200-465 Portugal Seek AI Limited Hong Kong Bangalore India Gurgaon India Chennai India Center for Computational and Systems Pathology Dpt. of Pathology Icahn School of Medicine at Mount Sinai Mount Sinai Hospital New York United States Konica Minolta Laboratory Europe Munich Germany Institute of Pathology Faculty of Medicine LMU Munich Munich Germany Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of Chicago IL United States Naples Italy University of Naples "Federico II" Naples Italy Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Laboratory of Integrated Circuits Design Hangzhou Dianzi University Hangzhou310018 China p2MIA Research Group LEM2A Lab Faculté des Sciences Université Moulay Ismail Meknes Morocco qLaboratório de Anatomia Patológica Ipatimup Diagnósticos Rua Júilio Amaral de Carvalho 45 Porto4200-135 Portugal Faculdade de Medicina Universidade do Porto Alameda Prof Hernâni Monteiro Porto4200-319 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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