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检索条件"任意字段=Proceedings of the 17th ACM International Conference on Distributed and Event-based Systems"

共 1271 条记录，以下是81-90 订阅

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Balancing Crop Priorities: PSO-based Equitable Irrigation Water Distribution for Efficient Resource Management 17

Balancing Crop Priorities: PSO-Based Equitable Irrigation Wa...

引用

17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Ikidid, Abdelouafi Fazziki, Abdelaziz El Baba, Naima Cadi Ayyad University Computer Science Dept Computing Systems Engineering Laboratory Marrakesh Morocco

ISBN: (纸本)9798350338904

Agriculture consumes a significant proportion of water reserves in irrigated areas. Improving irrigation is becoming essential to reduce this high-water consumption by adapting supplies to crop needs and avoiding losses. this global issue has prompted many scientists to think about sustainable solutions using innovative technologies. this study aims to solve the problem of equitable distribution of irrigation water to crops using the PSO (Particle Swarm Optimization) algorithm. the problem involves the allocation of water between N crops, taking into account the priorities of each crop, the minimum and maximum water thresholds required, and the total amount of water available. A precise modeling of the problem is proposed, followed by a description of the PSO algorithm adapted to this situation. the implementation of the algorithm in Python is also presented. the results and performance of the algorithm are discussed, showing its ability to find a fair and efficient solution for irrigation water allocation. © 2023 IEEE.

关键词： Machine learning

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OPERATING SYSTEM FOR CYBER-PHYSICAL MANUFACTURING (OSCM): A FLEXIBLE event-DRIVEN SHOPFLOOR INFORMATION PLATFORM FOR ADVANCED MANUFACTURING 17

OPERATING SYSTEM FOR CYBER-PHYSICAL MANUFACTURING (OSCM): A ...

引用

ASME 17th international Manufacturing Science and Engineering conference (MSEC) held jointly with the 50th North American Research conference (NAMRC)

作者： Santamaria, Ricardo Toro Ferreira, Placid M. Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA

ISBN: (纸本)9780791885819

Factory technologies have evolved to incorporate a great deal of manufacturing flexibility. Programmable automation in the form of CNC and PLCs along with hardware innovations (quick-change tooling, for example) and various operator assist technologies enable a high level of shop-floor flexibility. Possibly, the most inflexible part of a factory is the manufacturing information system. Customized for manufacturers by system integrators, these systems are often large monolithic systems assembled around an ERP/MRP framework or a precariously integrated set of decision-support software tools with a patchwork of communications enabling information flow between them. On the other hand, cloud-based information service platforms such as those encountered in social networks and service brokers have seen rapid and multiple cycles of evolution resulting in a meteoric rise in their ability to handle increasingly large data scales and rates, while still maintaining their elasticity and flexibility. this rapid evolution of cloud-based information services has ignited a new era in the manufacturing industry as evidenced by emerging manufacturing cyber-physical system technologies such as the Industrial Internet of things (IIoT), and Cloud Manufacturing (CM). these technologies are part of the broader context of what is thought to be the unfolding fourth industrial revolution (Industry 4.0 or Digital Manufacturing). this revolution places at its core, connectivity, information, and machine-based intelligence to create a new paradigm for manufacturing that is highly flexible, scalable, responsive, and intelligent. this paper describes how we leveraged the newest advances in CPS, IIoT, CM, and distributed systems to create a flexible manufacturing information system infrastructure that separates information collection and distribution for decision-making functions. the first part of the paper introduces the architecture for a novel full-stack manufacturing infrastructure that is env

关键词： Cyber-physical systems Manufacturing event-driven systems distributed systems IoT IIoT Cloud Manufacturing RabbitMQ

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A Fusion-based Deep Neural Networks Approach for Face Liveness Detection 17

A Fusion-Based Deep Neural Networks Approach for Face Livene...

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17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Aydin, Melek Taskiran, Murat Kahraman, Nihan Dudukcu, Hatice Vildan Ihs Technology Dept. of R&d İstanbul Turkey Yildiz Technical University Dept. of Electronics and Communication Engineering İstanbul Turkey

ISBN: (纸本)9798350338904

Face recognition systems, which are used for access control, security surveillance, and mobile payments, are popular in recent years among biometric systems with their ability to identify individuals quickly and accurately. However, these systems are vulnerable to spoofing attacks using a fake face input for unauthorized access. Liveness detection is a crucial component of face recognition systems that helps to improve their security against spoofing attacks. the paper first presents a comparative study of state-of-the-art liveness detection techniques and their effectiveness in detecting various types of spoofing attacks. then a match score fusion-based liveness detection algorithm is proposed in which three deep neural networks are combined. As a result of experimental studies using NUAA and CelebA-Spoof datasets to test the performance of the proposed method in face liveness detection, 98.36% accuracy was achieved. the results show that ensemble model can significantly improve the accuracy and robustness of face liveness detection. © 2023 IEEE.

关键词： Biometrics

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A Review of Vision-based Abnormal Human Activity Analysis for Elderly Emergency Detection 17

A Review of Vision-based Abnormal Human Activity Analysis fo...

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17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Nouisser, Raoudha Jarraya, Salma Kammoun Hammami, Mohamed University of Sfax Miracl Laboratory Faculty of Economics and Management Tunisia King Abdulaziz University Faculty of Computing and Information Technology Computer Science Department Jeddah Saudi Arabia University of Sfax Miracl Laboratory Faculty of Sciences Tunisia

ISBN: (纸本)9798350338904

As life expectancy increases, people can live a longer life. However, ageing is associated with various health problems. As a result, older people are vulnerable to emergencies. To ensure the security of older people, it is necessary to analyse abnormal activities. Accordingly, vision sensors serve in elderly care and assisted living. Hence, this study allows the researchers to better understand the field of elderly emergency detection through abnormal activity analysis. In this work, we investigate and compare existing visual approaches and their performance. In addition, we explore available and interesting datasets. Moreover, we discuss existing approaches and datasets to improve real-world applications. We also present challenges to be tackled for future studies in monitoring applications. © 2023 IEEE.

关键词： Information analysis

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Deep Reinforcement Learning-based Load Balancer using Kubernetes 17

Deep Reinforcement Learning-based Load Balancer using Kubern...

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17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Soussia, Moenes Ben Bousselmi, Khadija Idoudi, Hanen University of Manouba Riadi Laboratory Manouba Tunisia University Savoie Mont Blanc Listic Laboratory Annecy France

ISBN: (纸本)9798350338904

the performance of irregular scientific applications can be easily affected by an uneven distribution of loads between computing resources. In this context, load balancing is one of the most important solutions to improve resource utilization. Yet many heuristic and metaheuristic algorithms have been proposed in the literature for dynamic load balancing, choosing the best performing load balancing algorithm for a given application is not a trivial task especially in situations where applications have a dynamic *** paper proposes a new approach of Load Balancing using Deep Reinforcement Learning. the proposed agent schedules tasks to the cluster's node using state of the art scheduling algorithms. based on the user's tasks and the available nodes, the agent must choose an efficient scheduling algorithm as an action from the action space and use it to schedule the pending tasks to cluster's nodes. the agent's action space is composed of state of the art scheduling algorithms. this approach optimizes both the average execution time and the resource utilization while keeping a low Imbalance Degree in the cluster. © 2023 IEEE.

关键词： Scheduling algorithms

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Transfer learning-based Mirai botnet detection in IoT networks 17

Transfer learning-based Mirai botnet detection in IoT networ...

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17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Rabhi, Sana Abbes, Tarek Zarai, Faouzi University of Sfax National School of Electronics and Telecommunication Sfax Tunisia SUP'COM University of Sfax Innov'Com Laboratory Sfax Tunisia

ISBN: (纸本)9798350338904

the Internet of things (IoT) has expeditiously evolved from a recent concept to a prevalent and often crucial side of diverse environments, including industry, healthcare, homes and buildings, etc. Its applications for almost all the fields have actually become a principal target for various threats, mainly botnets. To mitigate this recent threat, diverse techniques were developed, particularly those founded on Machine Learning, Deep Learning and Transfer Learning which have achieved significant results in detecting IoT botnets. In our paper we propose a transfer learning intrusion detection system (IDS) to identify IoT botnets, comparing between deep learning and transfer learning models using two different algorithms, Artificial Neural Network (ANN) and Recurrent Neural Network (RNN). Results prove that the suggested approach based TL reaches high accuracy up to 99% that overcomes the accuracy of DL model for the two algorithms. © 2023 IEEE.

关键词： Internet of things

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An Exploration to Blockchain-based Deep LearningFramework 10

An Exploration to Blockchain-based Deep LearningFramework

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17th INDIACom;2023 10th international conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Patel, Hardeep Chaudhari, Nishtha Kavathiya, Meet Kaur, Hargeet Shah, Kaushal Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat Gandhinagar India

ISBN: (纸本)9789380544472

Deep learning has become extremely popular in recent years due to its potential to replicate the human brain and produce defensible conclusions with the aid of multilayered neural networks. Blockchain is a relatively new, ground-breaking technology that is mostly related to cryptocurrency. It functions as a decentralized, immutable, shared, and distributed ledger, among other distinctive qualities. In this study, we investigate the significance of combining deep learning and blockchain technology. By giving background information on Blockchain Technology, Deep Learning, and Blockchain- based Deep Learning, we analyze and methodize the literature. We outline the assets and liabilities of the most advanced blockchain-based deep learning frameworks to give insightful discussions. Additionally, we contrast the current blockchain-based deep learning frameworks based on four criteria, including the type of blockchain, the consensus protocol, the deep learning technique, and the dataset. this study also emphasizes different applications of blockchain-based deep learning. Finally, we outline significant research issues that must be resolved to create highly reliable deep learning systems. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Blockchain

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Survey semantic Arabic language in deep learning based recommendation system 17

Survey semantic Arabic language in deep learning based recom...

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17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Kadhem, Rasha Mallat, Souheyl Zrigu, Mounir University of ALQadisiyah Department of Computer Science Iraq Research Laboratory in Algebra Numbers Theory and Intelligent Systems Monastir Tunisia University of Monastir Faculty of Sciences of Monastir Department of Computer Science Tunisia

ISBN: (纸本)9798350338904

Due to problems, Arabic-speaking internet users have surged, although nothing is done on it. It is challenging to develop a repliable recognition system (RS) for cursive languages such as Arabic. Variations in text size, fonts, word semantics, user Arabic region, etc. complicate these issues. Deep learning models can model big datasets and handle them. Good features can be selected and learned consecutively by both CNNs and RNNs. In numerous studies, both of these neural networks have proven to be superior than their counterparts. this is true for text recognition, voice recognition, and several NLP tasks (NLP), but they are not qualified to deal with the semantics of the text, so we decided to find the best DL technique for semantic Arabic language from a lot of research to be a survey to other searchers. Our paper compared different algorithms and their accuracy. © 2023 IEEE.

关键词： Semantics

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Substream management in distributed streaming dataflows 22

Substream management in distributed streaming dataflows

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16th acm international conference on distributed and event-based systems, DEBS 2022

作者： Trofimov, Artem Sokolov, Nikita Marshalkin, Nikita Kuralenok, Igor Novikov, Boris Lzy.ai Tel Aviv Israel Yandex Cloud Saint Petersburg Russia No Affiliation Limassol Cyprus Huawei Saint Petersburg Russia Hse University Saint Petersburg Russia

ISBN: (纸本)9781450393089

Most state-of-the-art SPEs use punctuations to divide a stream into bounded substreams of messages, such as epochs and windows. the punctuation approach is powerful but has limitations: it does not support cyclic dataflows, is poorly scalable in some cases due to intensive use of broadcasts, and becomes inefficient when the number of chunks or cluster size becomes significant. We introduce a new substream tracking technique called trAcker that overcomes the limits of punctuations. We experimentally evaluate the properties of trAcker in both synthetic and real-world environments. Experiments show that our technique outperforms punctuations for a large number of substreams and efficiently handles real-world cyclic dataflows. © 2022 acm.

关键词： data streams watermarks stream join state management punctuations substreams

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Arabic-Latin Scene Text Detection based on YOLO Models 17

Arabic-Latin Scene Text Detection based on YOLO Models

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17th international conference on INnovations in Intelligent systems and Applications, INISTA 2023

作者： Turki, Houssem Elleuch, Mohamed Kherallah, Monji Damak, Alima Tunisia Tunisia Sfax University Faculty of Sciences Tunisia Tunisia

ISBN: (纸本)9798350338904

Machine learning and artificial intelligence have led to notable progress in the field of deep learning and the identification of text within natural scene images in the past few years. Despite notable advancements, the effectiveness of deep learning and text detection in natural scene images, particularly for Arabic language, is frequently constrained by the scarcity of comprehensive datasets containing various multilingual scripts. YOLO (You Only Look Once) is a widely used deep learning neural network that has gained immense popularity for its versatility in handling diverse machine learning tasks, primarily in the field of computer vision. the YOLO algorithm has progressively garnered recognition for its exceptional performance to solving a complex problems, noisy data, as well as overcoming various challenges encountered in real-world scenarios. Our experiments provide a concise examination of text detection algorithms based on convolutional neural networks (CNNs), especially different versions of the YOLO models using a data augmentation technique applied to "SYPHAX"dataset, our new dataset of multilingual scripts in the wild. the objective of this paper is to offer insights into the future of YOLO in the field, highlighting potential research directions that can enhance text detection systems. © 2023 IEEE.

关键词： Computer vision

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