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Towards an Efficient and Secure Cache Management Using Apriori-Based Interests Prediction in Named Data Networking

Towards an Efficient and Secure Cache Management Using Aprio...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Chaker Abdelaziz Kerrache Geetanjali Rathee Younes Guellouma Hocine Gasmi Benameur Ziani Dpt. of Computer Science Université Amar Telidji de Laghouat Laghouat Algeria Dpt. of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

Cache management is an important component in any network and it has even more importance in the Future Internet Architectures (FIAs) including Named Data Networking (NDN), because the caches play the key role in reducing the overall network latency and scalability. In this paper, we discuss the functionality of cache management in NDN, its types as well as its importance for the NDN architecture. In addition, we propose a machine learning-empowered cache management and interests predication for NDN to only preserve the cache only to the secure and really needed data. Our proposal uses Apriori algorithm which is supervised learning algorithm to find the association rules and then to recommend the next requested data. Implementation and experiments on real data traffic depicted that the network’ performance and its influence on the cache increased by 3.2% for two content store sizes of 20 and 40 MB. In addition, a larger cache size of 80 MB shows an increase of the cache hit ratio reaching 90% and hence, clearly reducing the network latency.

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Towards a model driven approach for integrating NWN models in CINCO 2

Towards a model driven approach for integrating NWN models i...

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2nd IEEE International Conference on New Technologies of Information and Communication, NTIC 2022

作者： Dembri, Amel Redjimi, Mohammed Université 20 Août 1955 Licus Laboratory Dpt. of Computer Science Skikda21000 Algeria

ISBN: (纸本)9781665489744

In this paper, we propose a model driven approach to facilitate the integration of Nets within Nets (NWN) modeling language in CINCO modeling tool. Despite that, NWN has a sophisticated modeling-simulation tool called Renew, moving NWN models from a platform to another has been a challenge for programmers. The development of our tool is heavily benefits from being implemented with CINCO;a full generation of the application code is provided, a powerful tool is developed with a few efforts and facilities are provided to add semantic to the platform. Combine formal method with a sophisticated model driven tool simplifies the prototypical of domain specific systems, promotes the interoperability capabilities between different technologies and assists designer in the validation of the correctness of the modeled system. © 2022 IEEE.

关键词： Interoperability

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Deep Learning Based Framework for Multi-disease Detection Using CNN-BiLSTM 12th

Deep Learning Based Framework for Multi-disease Detection U...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Yadav, Pooja Sharma, S.C. Yadav, Hemant Electronics & Computer Discipline DPT IIT Roorkee Uttarakhand India Department of Computer Science and Information Technology MJP Rohilkhand University Uttar Pradesh Bareilly India Department of Computer Science Future Institute of Engineering and Technology Uttar Pradesh Bareilly India

ISBN: (纸本)9789819732913

There has been a surge in interest in developing accurate and efficient illness prediction models to help in early diagnosis and treatment planning in recent years. Despite innovations in deep learning techniques, there is enormous potential for leveraging these techniques for illness prediction across different domains. This study presents a deep learning-based technique for predicting multiple diseases, like diabetes, liver, and Kidney, to improve diagnosis accuracy and facilitate prompt treatments. In this paper, the authors employ two deep learning algorithms, CNN, LSTM, and proposed CNN-BILSTM, to determine if patients are at risk of illness. The suggested CNN-BiLSTM approaches outperform others for all three diseases, representing a major improvement, especially for a disease that impacts a large population. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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High Availability in Private Cloud: Approaches and Possibilities

High Availability in Private Cloud: Approaches and Possibili...

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computer science Society (SCCC) International Conference Chilean

作者： Alejandro Mellado-Gatica Julio Rojas-Mora Mario Villanueva-Alveal Dpt. of Computer Science Universidad Católica de Temuco Temuco Chile

ISBN: (数字)9798331527891

ISBN: (纸本)9798331527907

Cloud computing, typically provided by large infrastructure providers, allows companies to utilize services efficiently under the premise of cost reduction. Known as the “public cloud,” this model contrasts with the “private cloud,” often perceived as costly and complex to implement. However, when high availability is required, the complexities of both models align, and the costs associated with public cloud services can become prohibitive. This paper explores the technical feasibility and economic viability of deploying private cloud infrastructure using refurbished hardware. It presents a detailed architecture incorporating Canonical MaaS for Metal-as-a-Service (MaaS), OpenNebula for Infrastructure-as-a-Service (IaaS), and Incus for Platform-as-a-Service (PaaS), with Moodle as the application layer case study. The study finds that private clouds, especially those utilizing refurbished hardware, not only offer a costeffective alternative to public clouds but also enhance security with complete control over data and systems, provide customizable self-management to meet specific organizational needs, utilize existing resources more efficiently for greater sustainability, and ensure technological sovereignty by reducing dependency on public cloud providers. These benefits collectively underscore the strategic advantages of adopting private cloud infrastructures in certain contexts.

关键词： Cloud computing Hardware computer science Software as a service Monitoring Costs Computational modeling Sustainable development Silicon Security

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Implementing a Blockchain Based System for Healthcare Applications Using Digital Twin

Implementing a Blockchain Based System for Healthcare Applic...

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Bouchra Zinab Benattia Nour El Houda Maicha Chaker Abdelaziz Kerrache Geetanjali Rathee Carlos T. Calafate Dpt. of Computer Science Université Amar Telidji de Laghouat Laghouat Algeria Dpt. of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India Dpt. of Computer Engineering Universitat Politècnica de València València Spain

With the increasing focus on healthcare, especially real-time analytics and self-diagnosis, the interest in capturing real-time patient data has increased significantly for both physicians and patients. The development of smart devices that can collect data such as blood pressure, heart rate, blood sugar level, etc., has greatly contributed to improving individual health management capabilities. However, these advances also, bring challenges such as concerns over safety, reliability, and accurate diagnosis. Problems often arise, especially when exchanging information and transmitting data. To overcome these challenges, this project uses blockchain technology to improve data transmission and security. This study explores the use of blockchain-based internet solutions by integrating blockchain into smart health-care. Additionally, the digital twin concept is used to improve diagnosis by simulating the effects of drugs on virtual objects. Based on the results of these simulations, physicians can make informed decisions about appropriate medications and accurately assess a patient's condition. A health monitoring system using digital twin technology and blockchain can be implemented to securely organize the collection of user information and enable accurate diagnosis. This integration ensures secure storage and transmission of your data and improves your health outcomes. In this paper, we study the integrating blockchain technology with Digital Twin for patient health monitoring. We showcase an interface connected to the blockchain database, specifically designed for doctors to review patient health records through patient wallet address and determine the normalcy of the patient's heart rate based on their medical data. Additionally, the interface allows doctors to predict the effect of medication on the patient's heart rate and blood pressure.

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A Novel Hybrid Genetic and A-star Algorithm for UAV Path Optimization 1

A Novel Hybrid Genetic and A-star Algorithm for UAV Path Opt...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Mehmood, Danish Ali, Asharab Ali, Seerat Kulsoom, Farzana Chaudhry, Hassan Nazeer Haider, Ammar Zeb Ul University of Engg. & Technology Telecommunication Engineering Dpt Taxila Pakistan Barani Institute of Technology Computer Science Department Rawalpindi Pakistan

ISBN: (纸本)9798350373042

Unmanned aerial vehicles (UAVs) are essential in 5G/6G communication as they provide affordable and effective solutions. However, collecting data in an unfamiliar environment from various sensor nodes is challenging due to the limited energy resources of UAVs. Therefore, careful planning of the UAV's path is vital to conserve and effectively utilize available energy resources. Our paper presents a hybrid genetic and A-star algorithm that optimizes energy consumption, making it powerful and efficient. The genetic algorithm (GA) generates numerous potential paths for a UAV to take, while the A-star algorithm identifies the most optimal route from that pool. The results show that our algorithm outperforms the state-of-the-art system while increasing the number of data nodes. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Characterizing neural activity in cognitively inspired RL agents during an evidence accumulation task

Characterizing neural activity in cognitively inspired RL ag...

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International Joint Conference on Neural Networks (IJCNN)

作者： James Mochizuki-Freeman Sahaj Singh Maini Zoran Tiganj Dpt. of Computer Science Indiana University Bloomington IN USA

Evidence accumulation is thought to be fundamental for decision-making in humans and other mammals. It has been extensively studied in neuroscience and cognitive science with the goal of explaining how sensory information is sequentially sampled until sufficient evidence has accumulated to favor one decision over others. Neuroscience studies suggest that the hippocampus encodes a low-dimensional ordered representation of evidence through sequential neural activity. Cognitive modelers have proposed a mechanism by which such sequential activity could emerge through the modulation of recurrent weights with a change in the amount of evidence. This gives rise to neurons tuned to a specific magnitude of evidence which resemble neurons recorded in the hippocampus. Here we integrated a cognitive science model inside a Reinforcement Learning (RL) agent and trained the agent to perform a simple evidence accumulation task inspired by the behavioral experiments on animals. We compared the agent's performance with the performance of agents equipped with GRUs and RNNs. We found that the agent based on a cognitive model was able to learn faster and generalize better while having significantly fewer parameters. We also compared the emergent neural activity across agents and found that in some cases, GRU-based agents developed similar neural representations to agents based on a cognitive model. This study illustrates how integrating cognitive models and artificial neural networks can lead to brain-like neural representations that can improve learning.

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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

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.

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

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SECURITY SYSTEMS IN GREEK HEALTH CARE INSTITUTIONS: A SCOPING REVIEW TOWARDS AN EFFECTIVE BENCHMARKING APPROACH 22

SECURITY SYSTEMS IN GREEK HEALTH CARE INSTITUTIONS: A SCOPIN...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Mariettou, Savina Koutsojannis, Constantinos Triantafyllou, Vassilis University of Peloponnese Electrical and Computer Engineering Department Patras Greece Health Physics & Computational Intelligence Laboratory Physiotherapy Department School of Health Rehabilitation Sciences University of Patras Patras Greece Network Technologies and Digital Transformation lab Electrical and Computer Engineering Dpt. University of Peloponnese Patras Greece

Healthcare has undoubtedly brought many advancements through information technology. Specifically, healthcare informatics involves the use of various technologies, data management and communication systems to collect, store, analyze and effectively disseminate information. Therefore, the above serves to improve care as well as patient outcomes, improve efficiency, promote innovation and data analysis, as digitization has become an integral part of our daily lives in today's world. However, with the increased use of interconnected systems and electronic health records, hospitals have become prime targets for cyber-attacks. In this article we will delve into the critical topic of cybersecurity in hospitals as well as nursing facilities. The deepening of this issue was carried out with extensive analysis of the security systems of Greek hospitals. We investigated all their web sites and the security systems already in place on those sites. By checking we found a number vulnerabilities and potential risks. The most important weakness is the lack of legal as well as technical information for all interested citizens and patients. More organizational as well as technical work concerning internal architecture of information systems is needed, towards the improvement of security. In conclusion, we present our proposals on strategies to strengthen healthcare facilities against cyber threats. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Cybersecurity Digitization Healthcare Information Patients Systems

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The Effect of Round-Trip Translation on Fairness in Sentiment Analysis

The Effect of Round-Trip Translation on Fairness in Sentimen...

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2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021

作者： Christiansen, Jonathan Gabel Gammelgaard, Mathias Lykke Søgaard, Anders Dpt. of Computer Science University of Copenhagen Denmark

ISBN: (纸本)9781955917094

Sentiment analysis systems have been shown to exhibit sensitivity to protected attributes. Round-trip translation, on the other hand, has been shown to normalize text. We explore the impact of round-trip translation on the demographic parity of sentiment classifiers and show how round-trip translation consistently improves classification fairness at test time (reducing up to 47% of between-group gaps). We also explore the idea of retraining sentiment classifiers on round-trip-translated data. © 2021 Association for Computational Linguistics

关键词： Sentiment analysis

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