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An Incentive Mechanism for Federated learning in IIoT: A Long-Term Perspective

An Incentive Mechanism for Federated Learning in IIoT: A Lon...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Chu Wang Shuo He Shupan Li Longlong Xing Saisai Zhu Gaohuan Xu School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Federated learning (FL) is a new machine learning paradigm that can protect data privacy while enabling data-driven insights. Its performance heavily relies on the quality of local models that participants contribute. However, devices may be reluctant to contribute their resources without sufficient incentives. A carefully crafted incentive mechanism is crucial to the practical application of FL. However, existing studies overlook the long-term performance, the requirement of data freshness in some Industrial Internet of things (IIoT) applications, and the capability that IIoT devices can engage in multiple tasks simultaneously. To address these issues, we formulate the incentive mechanism as a stochastic optimization problem which minimizes the long-term energy cost of IIoT devices. Lyapunov optimization is employed to design an online winner selection and resource allocation algorithm. Moreover, we consider the data freshness as a criterion to design reward for devices to motivate their participation. Simulation results show that the proposed mechanism can effectively reduce the average energy cost of devices and ensure continuous participation.

关键词： Performance evaluation Costs Federated learning Data integrity Simulation Stochastic processes Signal processing algorithms Resource management optimization Industrial Internet of things

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Derivation of Constituent Problem Characteristics for the Application of Machine learning Systems 4

Derivation of Constituent Problem Characteristics for the Ap...

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4th international conference on Information and Computer Technologies (ICICT)

作者： Schuh, Guenther Scholz, Paul Burger, Timon Fraunhofer Inst Prod Technol IPT Aachen Germany Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9781665413992

the increasing digitalization across all business sectors creates ever larger amounts of data. When analyzing this data to extract information, traditional data analysis methods easily meet their limits of performance. Conversely, methods from the machine learning (ML) spectrum promise to be a versatile tool for solving highly complex data-related tasks. Yet, organizations fail to identify relevant applications for ML due to a lack of systematic understanding of the applicability of the technology. this research paper draws upon the assumption that real-world problems exhibit distinctive characteristics that indicate their suitability for the application of ML. A framework for describing those constituent problem characteristics is proposed. Investigating the functional differences between traditional data analysis methods and ML, constituent characteristics are derived based on the distinctive technological abilities of ML. In order to differentiate simple ML methods from advanced ML methods regarding their technological abilities, a framework is presented. We suggest that advanced ML methods such as Deep learning or Transfer learning provide different potential benefits than simple methods such as Decision Trees, therefore necessitating this additional distinction.

关键词： machine learning artificial intelligence statistics data requirements application

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Technologies for Introducing Lean Methodology into the Work of University Career Centers

Technologies for Introducing Lean Methodology into the Work ...

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Technology Enhanced learning in Higher Education (TELE), international conference on

作者： Natalia Saraeva Anastasia Zimina Andrey Saraev Lipetsk State Technical University Lipetsk Russia MBEI SCHOOL №33 Lipetsk Russia

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

the concept of lean production has its own methods and tools, following which you can radically change the work of career centers and increase their efficiency several times. Today, the competitiveness of a university graduate is determined by his/her professional competencies. the article discusses technologies for introducing lean methodology into the work of career centers of universities to increase the effectiveness and efficiency of the work of these centers.

关键词： Graphics Lean production Engineering profession Education optimization

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Design and optimization of a Passive Micromixer with a Double-layered Structure for Rapid Mixing 4

Design and Optimization of a Passive Micromixer with a Doubl...

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4th international conference on Electrical, Computer and Telecommunication Engineering, ICECTE 2022

作者： Nishu, Israt Zahan Samad, Fateha Rajshahi University of Engineering and Technology Department of Electronics and Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350320541

Micromixers are microchannel devices that provide effective fluid mixing across a small area and a finite flow path. A micromixer, one of the key elements of a microfluidic system, must achieve the highest mixing with the least pressure drop. this work proposes a passive micromixer with five mixing components and a double-layered structure where the fluid streams are split, twisted perpendicularly, and then recombined to take advantage of fluid laminarity, resulting in greater mixing efficiency at shorter flow distances. the study aims to optimize the proposed design using a numerical inquiry and an efficient method to get a greater mixing index and lower pressure drop. In this work, the Taguchi technique and Grey Relational Analysis are used as the optimization tool. To optimize the performance using the standard L9 orthogonal array, the channel width, the layer depth, the constriction channel width, and the pitch have been chosen as the key parameters. the layer depth is found to be the most important factor in the optimization outcome which is derived from the Grey Relational Grade with the highest Signal-to-Noise ratio. Using Comsol Multiphysics Software, the numerical simulation was run in the 0.1 to 100 Reynolds number range. At higher Reynolds numbers, the optimized micromixer is able to provide a mixing index of 0.97 and a pressure drop of 17 kPa at a total length of 8.7 mm. the outcomes demonstrated that the proposed micromixer is suitable for real-world applications in the chemical and biomedical industries where rapid mixing is required. © 2022 IEEE.

关键词： Reynolds number

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Multimodal Machine learning for Sign Language Prediction 6th

Multimodal Machine Learning for Sign Language Prediction

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6th international conference on Nanotechnologies and Biomedical Engineering, ICNBME 2023

作者： Khalafaoui, Yassèr Grozavu, Nistor Matei, Basarab Rogovschi, Nicoleta ETIS ENSEA UMR 8051 CY Cergy Paris University 2 Avenue Adolphe Chauvin Cergy Pontoise Cedex95302 France LIPN UMR CNRS 7030 Sorbonne Paris Nord University 99 Avenue J-B Clément Villetaneuse93430 France LIPADE Paris-Decartes University 45 rue des Saintes Pères Paris75005 France

ISBN: (纸本)9783031427817

Numerous applications, including translation tools, interpreting services, video remote interpreting, human-computer interaction, online hand tracking of human communication in desktop settings, real-time multi-person recognition systems, games, virtual reality settings, robot controls, and natural language communications, benefit from sign language recognition advantages. Multimodal data contains information from different sources such as video, sensors, electrocardiograms (ECGs), while emotions refer to the non-verbal cues that accompany language use, such as facial expressions and body posture. Integrating these additional sources of information helps to better understand the user’s intent, which improves the performance of the sign language recognition model. To build such a model, a set of multimodal data and emotions must be collected. this data set should be differentiated and cover different individual/isolated signs, emotions and body gestures. the model is designed to integrate multimodal data and emotions, which would involve combining different machine and deep learning algorithms adapted to different types of data. In addition, the model will need to be trained to recognize the different emotions that accompany sign language. Once the model is trained, it can be tested on the test dataset to assess its performance and also plan for a test on real data (with signing people). In this paper we propose a study to use the multi-modal machine learning for sign recognition language. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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A Deep learning-Based Remote Sensing Image Recognition Model for Ships

A Deep Learning-Based Remote Sensing Image Recognition Model...

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Power Electronics, Computer applications (ICPECA), IEEE international conference on

作者： Pingchi Li School of Information Engineering Wuhan University of Technology Wuhan China

With the rapid development of remote sensing technology, remote sensing satellite image datasets have become an important resource for research, providing rich data for the acquisition of surface information. However, for vessel detection and identification, it is usually very difficult to distinguish the background of the picture from the vessel in high-resolution optical images, which leads to a large number of false alarm problems. In this study, we aim to solve the problem of vessel image classification in remote sensing images by using a dataset containing 20,000 images. In this paper, bilateral filter is chosen as the filtering method to maximize the clarity of vessel edges. In this paper, rotation and flipping are mainly chosen to enhance the images. In this paper, the Convolutional Neural Network (CNN) model and the Transformer classification model VIT are constructed respectively, and their performance is evaluated. And the performance of the two models is compared by training on different sized datasets. When the number of datasets is $2.0\times 10^th$ , the recognition accuracy of Transformer classification model VIT is higher than 90.2%, which effectively solves the problem of vessel recognition in remote sensing images.

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Customer Churn Prediction Using Machine learning: A Case Study of Libyan Internet Service Provider Company

Customer Churn Prediction Using Machine Learning: A Case Stu...

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Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Shefaa A. Alteer Alhaam Alariyibi Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Artificial intelligence (AI) is a field of science and technology that simulates human intelligence to develop intelligent machines. Machine learning (ML), on the other hand, is a subset of AI that enables computers to learn from data without being explicitly programmed. In the area of business, data science is the practical application of ML, AI, and statistics, which focuses on extracting valuable insights from data. Nowadays, data science relies heavily on ML techniques, which are used for a variety of applications. One of its applications is Customer Churn Prediction (CCP). Customer churn (or attrition) is the tendency of customers to stop using a company's products or services. It is important to predict churn where businesses can proactively target the customers, who are at high risk of leaving and create marketing campaigns to retain them. this study aims to build a predictive model to predict customer churn for Internet Service Providers (ISPs). Several experiments have been conducted on a dataset obtained from a Libyan ISP company to train the models. the dataset distribution was imbalanced, with only 5% of customers churned. To mitigate this, various oversampling techniques were used. In the proposed system, five diverse classifiers were used. Namely, Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM) tree algorithm, and Extreme Gradient Boosting (XGBoost) algorithms due to their relevance in this predictive context. the experimental findings showed that the XGBoost tree model yielded the most favorable outcomes, achieving an AUC value of 94%.

关键词： Training Machine learning algorithms Web and internet services Companies Predictive models Data science Prediction algorithms

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A Systematic Analysis on Active Federated learning Network in Healthcare Industry

A Systematic Analysis on Active Federated Learning Network i...

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Data Intelligence and Cognitive Informatics (ICDICI), international conference on

作者： D. Surendiran Muthukumar P. Deepa Lakshmi Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Srivilliputhur India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

In recent years, many researchers have demonstrated that privacy acumen can be applied to healthcare applications to enhance patients’ life quality using Machine learning (ML) and Deep learning (DL). As far as we are aware, a tiny degree of patient privacy is contained in medical data, which necessitates extreme data protection against disclosure to intruders. In machine learning and deep learning, to build accurate system data pooling done at server, causes data breach, to overcome this issue, in 2016, Google proposed an idea to share learned models rather than sharing the data called Federated learning (FL). In this systematic analysis, we lay out FL's primary objective concerning privacy. Afterwards, we presented the primary challenges in configuring and deploying federated learning networks and introduce algorithms within advanced federated learning architectures by integrating Convolutional Neural Networks (CNNs), a blockchain-based mechanism, and a non-fungible token (NFT) for the detection of various harmful diseases. moreover, we analysed the configuration settings, performances, Limitations of different algorithms against different datasets. Ultimately, we conclude this analysis by discussing optimization and communication challenges in future FL smart healthcare systems. We believe that reading this through can be beneficial for academicians, industry experts, and neoteric alike, providing them with direction and suggestion for their next endeavours.

关键词： Industries Deep learning Privacy Systematics Federated learning Medical services Internet Servers Nonfungible tokens optimization

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Simplified Plant Health Monitoring using Deep learning

Simplified Plant Health Monitoring using Deep Learning

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international conference on Reliability, Infocom Technologies and optimization (ICRITO) (Trends and Future Directions)

作者： Tej Mandaliya Shanti Verma Department of Computer Applications L J University Ahmedabad

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

this paper examines how superintending is pivotal for plant health in the modern world. One serious cause of low collect yield is an infection brought about by microorganisms, infection, and organisms. Plant disease investigation is one of the major and essential tasks in the part of cultivating. Plant diseases stratification enhance feasible based on Convolutional neural network(CNN) architecture, Xception Model,*** Clarification of plant in early stages and analysing environmental sensing not only helps farmers to get healthy plants and Knowing the various diseases can help farmers in numerous way, and also maximize the production. thus, classifying pre-examining diseases helps farmers to grow healthy fabrication of the environment. the authors of this paper applied a variety of deep learning classification algorithms, such as CNN, Xception, and DenseNet121, to a variety of image datasets that are accessible at https://***/T-EJ/***. the results of the study shows that accuracy of CNN algorithm to detect disease in plant leaves is 89% which is highest as compared with Xception, and DenseNet121.

关键词： Deep learning Plant diseases Stacking User interfaces Streaming media Classification algorithms Sensors

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Parallel K-means on GPU using Warp-Centric Strategies

Parallel K-means on GPU using Warp-Centric Strategies

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Michel B. Cordeiro Wagner M. Nunan Zola Federal University of Parana Curitiba Brazil

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

K-means clustering is a popular unsupervised machine learning method widely used in various applications, such as data mining, image processing, and social sciences. However, clustering can be computationally expensive, especially when applied to large datasets. In this article, we explore the performance optimization of k-means clustering through GPU parallelization strategies using the CUDA framework. Due to a warp-centric approach, the proposed implementation is named warp-KMeans. We have compared warp-KMeans to other best-in-class GPU implementations of the k-means algorithm, including the top-tier NVIDIA RAPIDS, a widely used set of libraries for high-performance data analysis on GPUs. throughout our experiments, we observed that warp-KMeans excels in all scenarios, achieving an acceleration of up to 8.4 compared to the NVIDIA RAPIDS algorithm, when the number of centroids is low, while also exhibiting good scalability and up to 2.94 speedup, concerning various other dataset sizes, higher dimensional points, and number of centroids.

关键词： Machine learning algorithms Scalability Social sciences Memory management Graphics processing units Clustering algorithms Machine learning Parallel processing Labeling optimization

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