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10th International Conference on Optimization and Applications, ICOA 2024

作者： Sadraoui, Youssef Er-Ratby, Mohamed Kadiri, Moulay Saddik Kobi, Abdessamad National School of Applied Sciences Sultan Moulay Slimane University LIPIM Laboratory of Process Engineering Computer Science and Mathematics Khouribga Morocco National School of Applied Sciences Sultan Moulay Slimane University LASTI Laboratory of Science and Engineering Techniques Khouribga Morocco Polytech Angers University of Angers LARIS Angevin Laboratory for Systems Engineering Research France

ISBN: (纸本)9798350387353

The studied process is a grinding and hydroclassification unit designed to ensure that the grain size it processes is in line with the specifications for the pipe's transport mode. Obstacles to the unit's operational process include frequent stops, plant's low reliability, product delivery with an inappropriate grain size, and a lack of control over operating parameters. These factors have a significant impact on the unit's relevance and, consequently, its performance. We created a quality culture among operators with '0 grain quality loss' through the implementation of the quality and processes control study, which was essential to improve the situation. Thus, by completing the seven project stages, we were able to enhance and optimize the grinding and hydroclassification unit's overall performance and capability to regulate the grain size quality. We noticed a notable improvement in regulating the several parameters that affect the targeted particle size after we established tools for unit quality control, such as control and monitoring charts, reaction matrices, intervention flow charts and operational standards. The unit has seen a great deal of satisfaction from the project in terms of quality, process control and resources optimization. Moreover, the incorporation of preventive maintenance into this process has shown to be crucial in guaranteeing the reliability and durability of the installation, thereby substantially improving the unit's overall efficiency. © 2024 IEEE.

关键词： Preventive maintenance

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Blockchain Sharding based Federated Learning Empowered with NFT-based Privacy-Preserving Model Sharing 6

Blockchain Sharding based Federated Learning Empowered with ...

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

作者： Khemaissia, Rofaida Derdour, Makhlouf Djeddai, Ala Tebessa12002 Algeria Larbi Ben m'Hidi University Liaoa Laboratory Oum El Bouaghi Algeria Chadli Bendjedid El-Tarf University Laboratory of Computer Science And Applied Mathematics El Tarf36000 Algeria

ISBN: (纸本)9798350350265

Federated learning is a promising approach that omits the central learning of the collected data through allowing the participating nodes to learn locally their model, and eventually the submitted updates are aggregated to obtain a global trained model. The main goal of this process is maintaining the participant data privacy. Blockchain is supported by lots of federated learning applications in order to handle many issues such as the single point of failure, trust, integrity, privacy and security. On top of that, Blockchain scalability issue is one of the main problems that affects the efficiency of the federated leaning. A few works have been proposed to use Blockchain sharding, yet they have some shortcomings such as static sharding. Besides, the BC approaches marginalize the global model sharing or transfer process between the model owner and the task requester. In this work, we propose FLBCShard a new Blockchain sharding approach that uses proxy nodes and a dynamic shard formation in order to improve the system scalability and improving malicious nodes detection. In addition, for a better protection of the model ownership, we use IPFS and NFT based model sharing between and transfer. The proposed work is implemented using permissioned Hyperledger Fabric and evaluated using MNIST dataset. © 2024 IEEE.

关键词： Blockchain

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Security in the Industrial Internet of Drones

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IEEE Internet of Things Magazine 2023年第3期6卷 110-116页

作者： Svaigen, Alisson R. Boukerche, Azzedine Ruiz, Linnyer B. Loureiro, Antonio A. F. EECS PARADISE Research Laboratory University of Ottawa Ottawa Canada at Gulf University for Science and Technology The Center for Applied Mathematics Kuwait State University of Maringá Manna Research Team Maringá Brazil Federal University of Minas Gerais Department of Computer Science Belo Horizonte Brazil

The Industrial Internet of Things (IIoT) has played a key role in enabling an efficient and interconnected industry through real-time communication and processing systems, thereby building on the principles of Industry 4.0. Nowadays, industrial systems are in the process of transitioning towards Industry 5.0, where humans will once again take center stage in decision-making, supported by Artificial Intelligence (AI)-based methods. In this context, drones have emerged as a feasible device for enhancing environmental sensing tasks, reducing operational costs, alleviating communication bottlenecks, and cooperating with humans through the use of Virtual Reality (VR) and Augmented Reality (AR) platforms. Therefore, Internet of Drones (IoD) network paradigm has been adopted in the industry, giving rise to the Industrial Internet of Drones (IIoD). Given these aspects, there have been changes in the privacy and security requirements for this network environment, which demands a thorough analysis of these modifications, including the challenges that arise and possible solutions to overcome them. Consequently, this study analyzes the privacy and security issues related to IIoD. Namely, we highlight the elements of IIoD which require protection, the threats and the countermeasures. We also present how these aspects differ from the general IoD environment. Lastly, we discuss the challenges regarding IIoD security and privacy, leveraging the future directions to address Industry 5.0 aspects. © 2018 IEEE.

关键词： Drones

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Stability of a Delayed Stochastic Epidemic COVID-19 Model with Vaccination and with Differential Susceptibility

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Journal of applied mathematics and Physics 2024年第2期12卷 509-532页

作者： Modeste N’zi Boubacar Sidiki Kouyaté Ilimidi Yattara Modibo Diarra Laboratory of Applied Mathematics and Computer Science University Felix Houphouet-Boigny Abidjan C&#244te d’Ivoire Department of Mathematics and Computer Science University of Science Technic and Technologies Bamako Mali

In this paper, we treat the spread of COVID-19 using a delayed stochastic SVIRS (Susceptible, Infected, Recovered, Susceptible) epidemic model with a general incidence rate and differential susceptibility. We start with a deterministic model, then add random perturbations on the contact rate using white noise to obtain a stochastic model. We first show that the delayed stochastic differential equation that describes the model has a unique global positive solution for any positive initial value. Under the condition R₀ ≤ 1, we prove the almost sure asymptotic stability of the disease-free equilibrium of the model.

关键词： SIRS Delayed Epidemic Model Nonlinear Incidence rate Lyapunov Function Asymptotic Stability in Probability

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An Artificial Immune System Algorithm for the Permutation Flow Shop Scheduling Problem with Sequence Dependent Setup Times 10

An Artificial Immune System Algorithm for the Permutation Fl...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Mesmar, Khadija Lebbar, Maria Aqil, Said Asebriy, Zahra Allali, Karam National School of Mines of Rabat Rosdm Research Team Lmaid Laboratory of Applied Mathematics and Business Intelligence Rabat Morocco Ensam Casablanca Hassan Ii University Laboratory Artificial Intelligence and Complex Systems Engineering Casablanca Morocco Est Essaouira Cadi Ayyad University Laboratory mathematics Computer sciences and modeling of complex systems Essaouira Morocco Fst Mohammadia Hassan Ii University Laboratory of Mathematics Computer Science and Applications Mohammadia Morocco

ISBN: (纸本)9798350387353

This paper is concerned with a set of independent jobs in a permutation flow shop that have setup times on a set of machines. The M-IAIS algorithm, which is based on immunoglobulins and has been modified, is designed to reduce the makespan. A unique procedure known as B cell repertoire updating, which comprises size C of jobs that are taken out of the sequence and reinserted into all possible positions of the remaining sequences of jobs, has an impact on the M-IAIS algorithm's performance. To increase the convergence velocity, three iterations of the algorithm are suggested. Thorough testing was done, and the three versions' performances on a set (benchmark) are compared. Problem instances are described in Sistemas de Optimizacion Aplicada (http://***/problem-instances) In the paper, percentage deviation is considered to evaluate the performance of the proposed algorithms. Furthermore, the ensuing outcomes demonstrate its suitability for addressing for the Permutation Flow Shop Scheduling Problem with Sequence Dependent Setup Times. © 2024 IEEE.

关键词： Scheduling algorithms

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Asymptotic Analysis of a Stochastic Model of Mosquito-Borne Disease with the Use of Insecticides and Bet Nets

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Journal of applied mathematics and Physics 2024年第1期12卷 305-329页

作者： Boubacar Sidiki Kouyaté Modeste N’zi Department of Mathematics and Computer Science University of Science Technical and Technologies of Bamako Bamako Mali Laboratory of Applied Mathematics and Computer Science University Felix Houphouet-Boigny Abidjan C&#244te d’Ivoire

Ross’ epidemic model describing the transmission of malaria uses two classes of infection, one for humans and one for mosquitoes. This paper presents a stochastic extension of a deterministic vector-borne epidemic model based only on the class of human infectious. The consistency of the model is established by proving that the stochastic delay differential equation describing the model has a unique positive global solution. The extinction of the disease is studied through the analysis of the stability of the disease-free equilibrium state and the persistence of the model. Finally, we introduce some numerical simulations to illustrate the obtained results.

关键词： Vector-Borne Disease Epidemic Model Stochastic Delay Differential Equations Stochastic Stability Lyapunov Functional Technique

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Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

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Tsinghua science and Technology 2022年第5期27卷 855-868页

作者： Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province the College of Mathematics and Information ScienceHebei UniversityBaoding 071002China School of Applied Mathematics Beijing Normal University ZhuhaiZhuhai 519087China Department of Computer Teaching Hebei UniversityBaoding 071002China

The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation *** a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among *** these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering ***,the diversity and amount of input information are enhanced to generate highly useful representations for downstream ***,the topological graph is constructed to express the spatial relationship of *** a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively *** external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering *** results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service *** GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech ***,DBI indicates the dispersion of cluster distribution and compactness within the cluster.

关键词： self-supervised clustering graph convolutional network feature correspondence semantic feature guidance confusion matrix evaluation indicator

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Integration of a Chatbot to Facilitate Access to Educational Content in Digital Universities 26

Integration of a Chatbot to Facilitate Access to Educational...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Babou, Birahim Sylla, Khalifa Sow, M.Y. Ouya, Samuel Lita Laboratory Polytechnic High School Cheikh Anta Diop University of Senegal Senegal of Senegal Department of Applied Mathematics and Computer Science Senegal Unchk of Senegal Infrastructure and Information Systems Department Senegal

ISBN: (纸本)9791188428120

Digital universities have been developed in several countries, particularly on the African continent, to meet the need for massification in the higher education sector. However, the lack of physical space is a major drawback, preventing learners from succeeding and increasing the drop-out rate compared with a conventional university. In these digital universities, learners use distance learning platforms to complete their training. For a good training, mastery of the fundamental modules is essential. With the frequent use of messaging applications, the integration of Artificial Intelligence (AI) could promote and facilitate access to educational content and enhance their learning experience. In this article, we propose a model for integrating a chatbot that will enable learners to access training modules to increase their knowledge and master core modules through formative skills assessments. The model we propose is based on the use of Machine Learning (ML) with the Rasa open-source framework and the Moodle Learning Management System (LMS) platform. © 2024 Global IT Research Institute - GIRI.

关键词： Machine learning

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Semantic and spatial‐spectral feature fusion transformer network for the classification of hyperspectral image

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1308-1322页

作者： Erxin Xie Na Chen Jiangtao Peng Weiwei Sun Qian Du Xinge You Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhanChina Department of Geography and Spatial Information Techniques Ningbo UniversityNingboChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA School of Electronic Information and Communications Huazhong University of Science and TechnologyWuhanChina

Recently,transformer‐based networks have been introduced for the classification of hyperspectral image(HSI).Although transformer‐based methods can well capture spectral sequence information,their ability to fuse different types of information contained in HSI is still *** exploit rich spectral,spatial and semantic information in HSI,a novel semantic and spatial‐spectral feature fusion transformer(S3FFT)network is proposed in this *** the proposed S3FFT method,spatial attention and efficient channel attention(ECA)modules are employed for the extraction of shallow spatialspectral ***,a transformer‐based module is designed to extract advanced fused features and to produce the pseudo‐label and class probability of each pixel for semantic feature ***,the semantic,spatial and spectral features are combined by the transformer for *** with traditional deep learning methods and recently transformer‐based methods,the proposed S3FFT shows relatively better results on three HSI datasets.

关键词： image classification machine learning

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The Role of Predictive Maintenance Optimization Techniques in Enhancing Industrial Productivity 10

The Role of Predictive Maintenance Optimization Techniques i...

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10th International Conference on Optimization and Applications, ICOA 2024

作者： Er-Ratby, Mohamed Kobi, Abdessamad Sadraoui, Youssef Kadiri, Moulay Saddik National School of Applied Sciences Sultan Moulay Slimane University LASTI Laboratory of Science and Engineering Techniques Khouribga Morocco Polytech Angers University of Angers LARIS Angevin Laboratory for Systems Engineering Research Angers France National School of Applied Sciences Sultan Moulay Slimane University LIPIM Laboratory of Process Engineering Computer Science and Mathematics Khouribga Morocco

ISBN: (纸本)9798350387353

The convergence between advanced technology and industrial production holds significant potential to enhance operational efficiency, reduce maintenance costs, and minimize unexpected disruptions. Artificial intelligence, in particular, plays a crucial role in predicting machine failures, thereby contributing to the optimization of operational performance and maximizing overall company output. However, current challenges in maintenance and production are numerous. High maintenance costs are often a major concern for companies, as they can significantly eat into profit margins. Unexpected downtime, caused by sudden machine failures, leads to substantial production losses, affecting not only revenue but also customer satisfaction and the company's reputation. Furthermore, the increasing complexity of industrial systems makes the management of maintenance and production even more difficult, necessitating more advanced and integrated solutions. This article explores the various applications of artificial intelligence in the manufacturing industry, highlighting its impact on business performance. It also examines the steps necessary for implementing predictive maintenance, from data collection and processing to the establishment of predictive models. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize overall company output. © 2024 IEEE.

关键词： Predictive maintenance

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