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Deployment of a LoRa-based Network and Web Monitoring Application for a Smart Farm

Deployment of a LoRa-based Network and Web Monitoring Applic...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Mohamed Saban Otman Aghzout Alfredo Rosado-Muñ oz Dpt. Electronic Engineering ETSE School of Engineering Burjassot Spain Dpt. Computer Science ENSA School of Engineering Tetouan Morocco

The integration of Industry 4.0 technologies in agriculture will reduce the increasing challenges of agricultural process around the globe. The real-time farm management with high degree of automation will greatly improve productivity, agri-food supply chain efficiency and food safety. This paper describes a fully customized LoRa-based IoT system that aims for a low-cost, low power and wide range wireless sensor network targeted for smart farms. The presented system integrates already existing Programmable Logic Controllers (PLC) typically used to control multiple processes and devices such as water pumps, certain machinery, etc. along with a newly developed network of wireless LoRA sensors distributed over the farm. A Telegram bot is also included as novelty for automated user communication via this mobile phone messaging application. The network structure was deployed and tested. The developed integrated system also includes a cloud-based monitoring application to provide remote visualization and control for all the farming environment.

关键词： Smart agriculture Wireless communication Temperature sensors Temperature measurement Wireless sensor networks Process control Water pumps

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FraMark: A Blockchain Marketplace for a 5G Network Management using Fractional NFTs

FraMark: A Blockchain Marketplace for a 5G Network Managemen...

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IEEE World Symposium on Communication engineering (WSCE)

作者： George Michoulis Kyros Tsourdinis George Niotis Konstantinos Kyranou Eleutherios Fountoukidis Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Konstantinos E. Psannis Sotirios K. Goudos R&D Dpt. Sidroco Holdings Ltd Thessaloniki Greece R&D Dpt. Sidroco Holdings Ltd Nicosia Cyprus Dpt. of Computer Science Democritus University of Thrace Kavala Greece Dpt. of Electrical & Computer Engineering University of Western Macedonia Kozani Greece Dpt. of Networks and Digital Media Kingston University London Kingston Upon Thames Surrey UK Department of Applied Informatics University of Macedonia Thessaloniki Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798331542825

ISBN: (纸本)9798331542832

This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework’s claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management.

关键词： 5G mobile communication Distributed ledger Throughput Dynamic scheduling Fabrics Resource management Nonfungible tokens Low latency communication

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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

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.

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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Internet of Musical Things for Children

Internet of Musical Things for Children

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Internet of Sounds, International Symposium on the

作者： Luc Nijs Luca Turchet Dept. of Education and Social Work University of Luxembourg Belval Luxembourg Dpt. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350366525

ISBN: (纸本)9798350366532

One of the musical activities that can be positively impacted by the Internet of Musical Things (IoMusT) is music education. However, although the IoMusT's properties hold a promising potential to enrich music learning processes, the extent to which early childhood music educators and scholars have embraced this emerging type of technology and explored their potential is still very limited. To bridge this gap, we first survey the relevant literature at the confluence of the Internet of Things, music technology, and education. Then, we propose a pedagogical framework to support designing IoMusT applications for early childhood music education. The framework is based on five dimensions: embodied sense-making, nonlinearity, participatory sense-making, privacy and security, as well as accessibility and inclusiveness. Furthermore, we corroborate the framework with a set of pedagogical scenarios showing its usage. Our study aims to foster interdisciplinary research at the confluence of pedagogy and music technology in an application domain, that of early childhood music education, hitherto unexplored.

关键词： Surveys Privacy Ethics Education Music Market research Security Stakeholders Sustainable development Standards

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Leveraging protein-protein interactions in phenotype predict...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Riccardo Smeriglio Joana Rosell-Mirmi Petia Radeva Jordi Abante Dpt. of Physics Universitat de Barcelona Spain Control and Computer Engineering Dpt. Politecnico di Torino Turin Italy Headache Research Group Vall d’Hebron Institut de Recerca Barcelona Catalonia Spain Dpt. of Mathematics & Computer Science Institute of Neurosciences Universitat de Barcelona Barcelona Catalonia Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Dpt. of Biomedical Sciences Institute of Neurosciences Universitat de Barcelona Dpt. of Mathematics & Computer Science Universitat de Barcelona August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Catalonia Spain

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

关键词： Proteins Training Phenotypes Limiting Prevention and mitigation Predictive models Graph neural networks Proposals Alzheimer's disease Genotypes

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DPPL Hallway Tracker: Hospital Contact Tracing During the COVID-19 Pandemic 9

DPPL Hallway Tracker: Hospital Contact Tracing During the CO...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Marinoni, Christian Ponzi, Valerio Comminiello, Danilo Dpt. of Information Engineering Electronics and Telecommunications Sapienza University of Rome Via Eudossiana 18 Roma00184 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

During the COVID-19 pandemic, the use of a people tracking system could have been crucial, particularly in sensitive environments, such as hospitals. DPPL Hallway Tracker is a framework that uses security camera footage to determine which rooms in a corridor a person has entered. It generates a database containing all the people identified and allows quick identification of potential cases of infection based on the time spent in a room and its maximum capacity. DPPL Hallway Tracker is structured in two phases: detection and re-identification. In the first phase, it exploits Mask RCNN to identify people and room doors. In the second one, it uses the deep association metric model from DeepSORT to re-identify a person as he leaves a room. © 2023 Copyright for this paper by its authors.

关键词： COVID-19

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Analyzing the Cause of Students Reluctant to Participate in the Classroom: Machine Learning Approach 10

Analyzing the Cause of Students Reluctant to Participate in ...

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10th International Conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Islam, Majharul Islam, Md. Sanzidul Siddik, Md. Abu Bakkar Mousumi, Sadia Sultana Sharmin Daffodil International University Dpt. of Software Engineering Dhaka Bangladesh Daffodil International University Dept. Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665439701

in the twenty-first century, seems many students are reluctant to participate in classroom matters day by day. Some days ago, a course teacher is providing a lecture, on the most important topics that will be implemented in every step in life that class majority students were nonexistent. We got motivation why it's happened and find out a lot of reasons behind uninterested in class after interrogation. Who are belonging age range 20-25, most of the time they face an obstacle and suffer and other teenagers. The fundamental purpose of this research to take action based on behind reasons, so that students can move in classroom learning matters. In this article, solve will be described and how to motivate students. Then it will be much helpful and far informative for teacher, students, guardians and also education specialist [8]. In this research shown causes, and also recommended how to overcome and solve those reluctant and spread knowledge and enlighten the nation. The aim of the research to help and inform provide to students avoid his/her predicted absent in classroom using Machine learning, particularly we used Liner Algorithm. Then, students will participate in classroom lectures as well as social activity and world would be more peaceful. This research achieved more than the accuracy of 95.9%. © 2021 IEEE.

关键词： Students

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Analysis and Detection of Melanoma through Collective Intelligence with AI

Analysis and Detection of Melanoma through Collective Intell...

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Annual IEEE Symposium on computer-Based Medical Systems

作者： Enrique Fernández-Morales Carlos L. Sanchez-Bocanegra Rafael Pastor-Vargas José-Juan Pereyra-Rodriguez Juan M. Haut José Alberto Benítez-Andrades Communications and Control Systems Dpt. Computer Science Faculty UNED Madrid Spain Communications and Control Systems Dpt. Computer Science Faculty UNED Malaga Spain Virgen Del Rocio Hospital Universitario Seville Spain School of Tecnology University of Extremadura Cáceres Spain Dept. of Electric Systems and Automatics Engineering Universidad de León León Spain

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.

关键词： Accuracy Melanoma Collective intelligence Image capture Transformers Solids Artificial intelligence

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Understanding the Conceptual Structure of Large Language Models through Bibliographical Network

Understanding the Conceptual Structure of Large Language Mod...

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IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS)

作者： V. Duarte-Martínez I.J. Perez P. Ducange M.J Cobo Escuela Superior Politécnica del Litoral ESPOL Facultad de Ingeniería en Electricidad y Computación ESPOL Polytechnic University Guayaquil Ecuador Dpt. of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain Dpt. of Information Engineering University of Pisa Italy

ISBN: (数字)9798350366235

ISBN: (纸本)9798350366242

Large Language Models represent a transformative technology at the forefront of artificial intelligence and natural language processing, with applications spanning diverse domains. This study conducts a comprehensive science mapping analysis of the LLMs research field, leveraging bibliometric techniques to uncover its thematic structure, trends, and global actors involved. Utilizing data from the Web of science, a corpus of 1303 research documents from 2010 to 2023 is analyzed, revealing a notable surge in research activity, particularly in recent years. Key thematic areas driving research within the field are identified, including chatbot, code generation, augmented reality, transformers, and machine learning paradigms. Foundational technologies such as transformers are pivotal in shaping the research landscape, while emerging themes like prompt learning hint at future directions. This study offers valuable insights for researchers, practitioners, and policymakers seeking to navigate the dynamic landscape of LLMs research and harness its full potential for societal benefit.

关键词： Technological innovation Codes Navigation Collaboration Market research Transformers Chatbots

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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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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

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.

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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