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检索条件"任意字段=2024 IEEE/ACS 21st International Conference on Computer Systems and Applications, AICCSA 2024"

共 150 条记录，以下是1-10 订阅

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024 - Proceedings

2024 IEEE/ACS 21st International Conference on Computer Syst...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

ISBN: (纸本)9798331518240

The proceedings contain 105 papers. The topics discussed include: enhancing four-class motor imagery detection through advanced feature extraction techniques;are my answers medically accurate? answers reliability regarding questions over healthcare knowledge graphs;indoor geolocation demonstrator: enhancing efficiency through real-time tracking and AI optimization;a deep learning system for early detection of diabetic retinopathy;holistic design of economical and secure data centers for developing countries: a free integrative approach;new dynamic Bayesian network for time-to-event prediction;application of artificial intelligence techniques to detect water stress;utilizing multi-step loss for single image reflection removal;and streamlining disaster detection: a lightweight fusion architecture for low-resource deployment.

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Holistic Design of Economical and Secure Data Centers for Developing Countries: A Free Integrative Approach 21

Holistic Design of Economical and Secure Data Centers for De...

引用

2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Bouri, Mohamed Ali Kammoun, Habib M. Aouicha, Mohamed Ben Dept. Computer Sciences and Communications Data Engineering and Semantics Sfax Tunisia University of Sfax Dept. Computer Sciences and Communications Research Groups in Intelligent Machines Sfax Tunisia University of Sfax Dept. Computer Sciences and Communications Data Engineering and Semantics Sfax Tunisia

ISBN: (纸本)9798331518240

This proposal aims to suggest improvements for the article titled 'Low-Cost Data Centers in Developing Countries' with the aim of enhancing its content, relevance, and practicality. Our expertise in data infrastructure, we believe these improve-ments will provide valuable insights and guidance to readers interested in establishing efficient data centers in developing countries. © 2024 ieee.

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A Comprehensive Review of Explainable Artificial Intelligence to Enhance Intrusion Detection in VANET Networks 21

A Comprehensive Review of Explainable Artificial Intelligenc...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Rodrigues, Anthony Ben Rabah, Nourhene Gorrab, Abir Le Grand, Benedicte Université Paris 1 Panthéon-Sorbonne Centre de Recherche en Informatique France National School of Computer Science University of Manouba RIADI Laboratory Tunisia

ISBN: (纸本)9798331518240

This paper explores the convergence of Artificial Intelligence (AI) and Vehicular Ad-Hoc Networks (VANETs), with a particular focus on the impact and potential of Explicable Artificial Intelligence (XAI) to improve intrusion detection. With the advent of VANETs, securing these networks is becoming a major concern, posing complex cybersecurity challenges. Thus, this study examines recent research on XAI approaches to improving intrusion detection in VANETs, focusing on the tradeoff between Explainability and Performance, and how this tradeoff influences the effectiveness of intrusion detection in these environments. © 2024 ieee.

关键词： Network intrusion

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New Dynamic Bayesian Network for Time-To-Event Prediction 21

New Dynamic Bayesian Network for Time-To-Event Prediction

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Ben Abdallah, Walid Njah, Hasna Jamoussi, Salma National Engineering School of Gabes Gabes University Tunisia Sfax University Multimedia InfoRmation Systems and Advanced Computing Laboratory Tunisia Higher Institute of Computer Sciences and Multimedia of Gabes Gabes University Tunisia Higher Institute of Computer Sciences and Multimedia of Sfax Sfax University Tunisia

ISBN: (纸本)9798331518240

Time-To-event prediction is an important analytical approach in medical research and personalized medicine that aims to predict the timing of clinically relevant occurrences and find associated risk variables. In the context of chronic diseases such as multiple sclerosis (MS), precisely modelling time to disease progression, adverse events, or therapeutic responses can have a significant impact on patient management strategies and clinical results. This study investigate the use of Bayesian networks(BN), augmented with a temporal factor, for time-To-event prediction in predicting disease progression in MS patients. We suggest using dynamic Bayesian networks (DBNs) as a versatile temporal modeling framework to capture the complex dynamics and inter-dependencies found in time-To-event processes. © 2024 ieee.

关键词： Prediction models

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PhD Forum: Enhancing Medical Diagnosis Support Through AI-Driven Semantic Classification of Breast Cancer Medical Texts 21

PhD Forum: Enhancing Medical Diagnosis Support Through AI-Dr...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Ouchene, Hiba Benblidia, Nadjia Mezzi, Melyara LRDSI Laboratory Department of Computer Sciences Faculty of Sciences University Blida 1 B.P 270 Route de Soumaa Blida Algeria

ISBN: (纸本)9798331518240

This paper outlines my PhD project, which focuses on using artificial intelligence to classify and extract semantic information from breast radiology reports, specifically within the Algerian healthcare context. Currently, there are no systems in Algeria that automate the classification and processing of these reports. Our goal is to develop a system that not only addresses local needs but also compares favorably with systems from other countries and languages. The project explores tasks such as automatic parsing, classification, summarization, recommendation, and extraction, providing healthcare professionals with tools to manage a large number of reports more efficiently in a short time. Preliminary results are promising, and further work is focused on enhancing these results using large datasets and covering all NLP tasks that can assist in diagnosing breast cancer, with plans to expand to other medical documents and diseases. © 2024 ieee.

关键词： Diseases

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Evaluation of Uncertain-Based IoT Composition Reference Model 21

Evaluation of Uncertain-Based IoT Composition Reference Mode...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Boulaares, Soura Sassi, Salma Chbeir, Richard Benslimane, Djamal Faiz, Sami National School for Computer Science Tunisia University Pau and Pays Adour LIUPPA France Claude Bernard Lyon 1 University LIRIS France Higher Institute of Multimedia Arts Tunisia

ISBN: (纸本)9798331518240

The Internet of Things (IoT) has connected the physical and digital worlds, resulting in a pervasive and self-organized network. Yet, due to the configurable model's behavioral constraints, ambiguity emerges during the design stage while selecting the most relevant composition plan. The constraints placed on the composition plan model restrict its behavior, leading to a single desired composition plan while eliminating the undesired ones. In this paper, we offer an entropy-based uncertainty metric to assess the predictability and efficiency of IoT object composition plans. Our method assesses the degree of uncertainty associated with a specific composition plan and provides insights into optimizing the performance of the IoT system. © 2024 ieee.

关键词： Composition plan Configurable model Entropy-based uncertainty measure IoT Uncertainty

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***: A Cyberbullying Detection Dataset in Tunisian Dialect 21

***: A Cyberbullying Detection Dataset in Tunisian Dialect

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Ben Fraj Trabelsi, Feriel Kouki, Sarra National School of Computer Sciences University of Manouba RIADI Laboratory University Campus of Manouba Manouba2010 Tunisia University of Carthage University Campus of Mrezga Faculty of Economics and Management of Nabeul Road to Hammamet Nabeul8000 Tunisia

ISBN: (纸本)9798331518240

The growing global use of social media has given rise to some problems, such as cyberbullying, which is a form of harassment intended to cause victims to constantly denigrate themselves, isolate themselves from others, or even take their own lives. Preventing the spread of this issue via social media is becoming more and more important. Gathering the necessary resources to comprehend and identify the many forms of dangerous communications is a prerequisite for intelligent detection. To detect cyberbullying in Tunisian dialect talks, this work presents a new dataset called ***. The set includes over 11,000 labeled comments from Facebook and YouTube. Using machine learning and deep learning techniques, it was discovered that our dataset was exceptionally semantically rich and relevant to the goal task. © 2024 ieee.

关键词： Adversarial machine learning

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A Deep Learning System for Early Detection of Diabetic Retinopathy 21

A Deep Learning System for Early Detection of Diabetic Retin...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Emmales, Mayssa Jemili, Farah Korbaa, Ouajdi Kamel, Ferid EPI-International Multidisciplinary School University of Sousse Sousse Tunisia University of Sousse ISITCom MARS Laboratory Sousse Tunisia 3DWave University of Sousse Sousse Tunisia

ISBN: (纸本)9798331518240

This paper aims to develop an innovative solution based on deep learning to assist doctors in detecting diabetic retinopathy. Utilizing the IDRiD and APTOS 2019 datasets, this research addresses three major challenges. The first challenge involves the precise segmentation of retinal lesions, including microaneurysms, hemorrhages, and hard and soft exudates, essential for early and accurate diagnosis. The second challenge is classifying retinal images based on the severity of diabetic retinopathy, utilizing the InceptionV3 architecture. Finally, the early detection of macular edema risk, a major symptom of diabetic retinopathy, is addressed using the MobileNetV2 architecture. This research explores various techniques of data augmentation, image enhancement, and model selection to design robust and precise solutions for healthcare professionals. © 2024 ieee.

关键词： Deep learning

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Crowd Counting Camera Array and Correction 21

Crowd Counting Camera Array and Correction

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Fausak, Andrew T. Tunc, Cihan Fausak, Andrew R. University of North Texas Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798331518240

'Crowd Counting' is a term used to describe the process of calculating the number of people in a given context. Originally answered with basic sensors and cameras, crowd counting has multiple challenges especially when images representing a given crowd span multiple images. In this paper, we propose a Crowd Counting Camera Array and Correction (CCCAC) method using a camera array of scaled, rotated, geometrically corrected, combined, processed, and then evaluated images to determine the number of people within the newly created combined crowd field. We present our approaches for each step and present our mean-squared-error (MSE) and mean-Absolute-error (MAE) results. Experimental results using real data from multiple cameras show that CCCAC methods perform superior to raw image processing methods with an accuracy of approximately 95%. © 2024 ieee.

关键词： Mean square error

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Implementing the Concept of Smart Cities in Amman: Reality and Aspirations Review 21

Implementing the Concept of Smart Cities in Amman: Reality a...

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2024 ieee/acs 21st international conference on computer systems and applications, aiccsa 2024

作者： Al Rawajbeh, Mohammad Al-Zoubi, Hassan Al Zaytoonah University of Jordan Department of Computer Science Amman Jordan Al Zaytoonah University of Jordan Department of Mathematics Amman Jordan

ISBN: (纸本)9798331518240

The tremendous development in the use of smart applications, modern communication technologies, and the Internet of Things revolution has made it necessary for the city administration to use these technologies to improve and develop cities and exploit them to solve problems facing residents and improve their lives. The problems of traffic congestion, climate change, energy sources, natural resources, transportation, and health are among the most important dilemmas that can be faced using the concept of smart cities. This research summarizes the current state of Amman City as a case study to analyze its reality and the extent of its progress in using smart city technologies by studying existing and future projects. There are many challenges facing the city of Amman in its use of smart city systems, such as financing problems, lack of technical expertise in this field, and the lack of integrated and comprehensive planning for a smart city. The researchers developed proposals and recommendations related to technologies and services that contribute to overcoming the dilemmas facing Amman in transitioning toward a smart city similar to cities in the developed world. © 2024 ieee.

关键词： Sustainable city

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