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3rd International conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Chen, Yu Wang, Li Wang, Liujun Wang, Yong Cai, Lianxing State Grid Zhejiang Electric Power Co. Ltd. Material Management Department Bidding Management Center Hangzhou310000 China State Grid Zhejiang Producement Company Supply Department Hangzhou310000 China State Grid Jiaxing Electric Power Supply Company Materials Management Department Hangzhou314000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The power system is a vital component of modern society's core infrastructure, as it plays a crucial role in ensuring a steady supply of energy and facilitating the smooth operation of society. The optimization of power supply chain management can effectively reduce power grid operating costs, improve supply chain flexibility, and reduce energy waste. The model based on neural network can learn complex characteristics of power system from large-scale historical data, and predict power demand and supply more accurately, so as to realize rational distribution and efficient utilization of power resources. Traditional methods for optimizing the power supply chain are often hindered by intricate mathematical models and computational efficiency issues. Consequently, they struggle to address the growing operational challenges faced by the increasingly complex power system. The model based on neural network can automatically capture the nonlinear relationship between various links in the power grid through end-to-end learning, so as to better deal with the dynamic changes and uncertainties of the power system. This helps to improve the robustness of the grid and reduce operational risks due to changes in external factors. Based on this, under the guidance of supply chain collaborative innovation theory and supply chain risk management theory, this paper uses process analysis method and other methods to initially identify the risk factors in the whole process of supply chain collaborative innovation, combines BP neural network, builds an evaluation model, and introduces MATLAB to learn and verify the model. It is proved that the evaluation model has application value to the risk prediction and evaluation of supply chain collaborative innovation. The experimental verification ultimately confirms the reliability of the model. © 2025 SPIE.

关键词： Neural networks Neurons Power grids Education and training data modeling Power supplies Industry Mathematical modeling Risk assessment Artificial neural networks

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Simplification process in Supply Chain Management Through Artificial Intelligence 4

Simplification Process in Supply Chain Management Through Ar...

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4th International conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Sharma, Ayushi Kumar, Rakesh Kumar, Deepak Pandey, Purnendu Shekhar Awasthi, Shashank Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Management Studies Dehradun India Army Institute of Technology Pune India G.L. Bajaj Institute of Technology and Management India

ISBN: (纸本)9798331523923

The aim of this study is to examine Artificial Intelligence(AI) applications in the supply Chain process. According to recent research on Supply Chain Management (SCM) in AI technology and various other technologies (machine learning, deep learning, neural network, expert vision, artificial neural network). Supply Chain Management (SCM) is transforming to artificial intelligence (AI), which improves sustainability, accuracy, and efficiency. This study explores AI in inventory management, warehouse management, customer satisfaction etc. AI Transportation, inventory control, and demand forecasting are all made easier by technologies like computer vision, robotics, and machine learning. Global sustainability goals are in line with AI, but adoption is hampered by issues like labour skill gaps, algorithmic biases, cybersecurity threats, and data quality. Training for the workforce, effective data management, and sustainable procedures are among the recommendations. AI is a key component of SCM's future since it can promote innovation, operational excellence, and sustainable development. The study contributes the objectives to analysis about AI reduced overall cost in supply chain management and also to study and analysis about AI enhanced quality and quantity of product in supply chain management. The study also discussed the SCM through the demand and supply concept economically. There are many challenges like biasness in the algorithm, skill person, and compromise with accuracy which need to be addressed. Finally, the study recommends suggestions and discusses the limitations of AI in supply chain management including sustainable future scope. © 2025 IEEE.

关键词： Customer satisfaction

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CSGNet: A network with attention mechanism for automatic modulation recognition 5

CSGNet: A network with attention mechanism for automatic mod...

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5th International conference on Signal processing and Computer Science, SPCS 2024

作者： Li, Xianyi School of Control Science and Engineering Shandong University Shandong Jinan250002 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this paper, a new network called CSGNet is introduced for automatic modulation recognition, designed to improve recognition accuracy in complex electromagnetic environments. This innovative network leverages the strengths of both Convolutional Neural Networks (CNN) and Gate Recurrent Units (GRU) while mitigating their inherent weaknesses. Additionally, it incorporates a Self-Attention mechanism to further enhance its perceptual abilities. The primary advantage of this method is its superior recognition accuracy under low SNR conditions compared to traditional methods. The process starts by extracting basic I/Q data from the RadioML 2018.01a dataset as input. Subsequently, the CSGNet model performs an end-to-end modulation recognition task. The results demonstrate that this method excels at multiple SNR levels, highlighting the significant potential of deep learning in solving automatic modulation recognition challenges. This study underscores the effectiveness of integrating CNN, GRU, and Self-Attention mechanisms in enhancing the robustness and accuracy of modulation recognition systems. Furthermore, it demonstrates how deep learning techniques can be effectively applied to complex signal processing tasks. The findings suggest that CSGNet could be a valuable tool for improving communication systems, especially in challenging environments with low SNR. © 2025 SPIE.

关键词： Modulation Signal to noise ratio Feature extraction Deep learning Matrices data modeling Machine learning data processing Neural networks Quadrature amplitude modulation

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Privacy Preserving Attribute based Access control with data Deduplication using RSA-BLAKE3-OCO for Secure Cloud Storage 1

Privacy Preserving Attribute based Access Control with Data ...

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1st International conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Gayathri, G.N. Channakrishnaraju Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Tumkur India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

Nowadays, cloud storage is one of the popular application models in various fields, where the security and access permission of storage data are widely considered. De-duplication algorithms are designed to mitigate redundant storage of data in the cloud. However, the main problem was data duplication which occupied the storage in many cloud storage systems that require an appropriate data security model. To overcome this problem, Rivest-Shamir-Adleman (RSA) - BLAKE3 based Optimal Cache Oblivious (OCO) demonstrates ensured access and privacy while removing the duplicates in cloud computing. The combination of RSA and BLAKE-3 is used to maintain efficient attribute exchange and prevent data leakage and privacy violations. In addition, the OCO technique is used to hide the access pattern in the cloud storage and copy the data by matching the random pattern. This process ensures data privacy and secure data sharing in cloud storage by avoiding data corruption. The result analysis clearly shows that the proposed RSA-BLAKE3-OCO attains a communication overhead of 11MB, a data integrity rate of 93% and a data confidentiality rate of 95% from 50 files which is better when compared with the existing methods. © 2025 IEEE.

关键词： data integrity

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Enabling Autonomy Through Voice control: AI-Assisted Mobile Platform 20th

Enabling Autonomy Through Voice Control: AI-Assisted Mobile ...

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20th EAI International conference on Computer Science and Education in Computer Science, CSECS 2024

作者： Balcheva, Ivelina Gorbounov, Yassen New Bulgarian University Sofia Bulgaria

ISBN: (纸本)9783031843112

The rapid development of the technological sector and the need to automate a wide range of human-aiding activities defines the development and research of autonomous mobile platforms as an increasingly urgent problem. At the same time, artificial intelligence plays the role of an innovative catalyst that increases the functionality, efficiency and convenience of mobile platforms, making them more intelligent and adaptive. The present paper focuses on the study of an autonomous mobile platform that uses voice control and artificial intelligence techniques for effective object recognition in the environment. This platform aims to improve the functionality of robotic systems, providing a more intuitive way to communicate and control. The research combines theoretical analysis, simulation modeling, software algorithm development, and experimental testing. The development process includes studying existing voice recognition and artificial intelligence methods, as well as their applications in the field of autonomous systems. Improving human-machine communication and decision-making processes facilitates the implementation of voice-assisted control. The proposed approach can find application in the improvement of robotic home assistants, in the control of mobile robots in dangerous or hard-to-reach environments, in the construction of remote laboratories, as well as in a number of other fields of technology. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Software testing

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Deciphering crucial salt-responsive genes in Brassica napus via statistical modeling and network analysis on dynamic transcriptomic data

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PLANT PHYSIOLOGY AND BIOCHEMISTRY 2025年 220卷 109568页

作者： Wang, Pei Liu, Fei Wang, Yongfeng Chen, Hao Liu, Tong Li, Mengyao Chen, Shunjie Wang, Daojie Henan Univ Coll Agr Sch Life Sci Sch Math & StatState Key Lab Crop Stress Adapt & Kaifeng 475004 Henan Peoples R China Henan Univ Henan Engn Res Ctr Ind Internet Things Zhengzhou 450046 Henan Peoples R China

Soil salinization severely impacts crop yields, threatening global food security. Understanding the salt stress response of Brassica napus (B. napus), a vital oilseed crop, is crucial for developing salt-tolerant varieties. This study aims to comprehensively characterize the dynamic transcriptomic response of B. napus seedlings to salt stress, identifying key genes and pathways involved in this process. RNA-sequencing on 43 B. napus seedling samples are performed, including 24 controls and 19 salt-stressed plants, at time points of 0, 1, 3, 6, and 12 h. Differential expression analysis using 33 control experiments (CEs) identified 39,330 differentially expressed genes (DEGs). Principal component analysis (PCA) and a novel penalized logistic regression with k-Shape clustering (PLRKSC) method identify 346 crucial DEGs. GO enrichment, differential co-expression network analysis, and functional validation through B. napus transformation verify the functional roles of the identified DEGs. The analysis reveals highly dynamic and tissue-specific expression patterns of DEGs under salt stress. The identified 346 crucial DEGs include those involved in leaf and root development, stress-responsive transcription factors, and genes associated with the salt overly sensitive (SOS) pathway. Specifically, Overexpression of RD26 (BnaC07g40860D) in B. napus significantly enhances salt tolerance, confirming its role in salt stress response. This study provides a comprehensive understanding of the B. napus salt stress response at the transcriptomic level and identifies key candidate genes, such as RD26, for developing salt-tolerant varieties. The methodologies established can be applied to other omics studies of plant stress responses.

关键词： Brassica napus Transcriptome Penalized logistic regression with k-Shape clustering Salt-responsive gene SOS signaling pathway RD26

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Online Model-Adaptation for an Uncertain Liquid-Liquid Mixer System 11

Online Model-Adaptation for an Uncertain Liquid-Liquid Mixer...

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11th Vienna International conference on Mathematical Modelling, MATHMOD 2025

作者： Topalovic, Daniel Bliatsiou, Chrysoula Villwock, Jörn Kraume, Matthias Knorn, Steffi Technische Universität Berlin Chair of Control Hardenberstraße 36a Berlin10623 Germany Technische Universität Berlin Chair of Chemical and Process Engineering Marchstr. 23 / Ackerstr. 76 Berlin10587 Germany

In this paper, a practical modeling approach for a batch-operated, stirred reactor is investigated that shows significant deviations in system behavior to the same actuation applied over multiple batches. Starting from an easily identifiable, sub-optimal, linear dynamical model to describe the basic dynamic behavior of the system, an augmented model reformulation is defined. This is used to model observed, nonlinear drift effects and static differences that can be seen in the system response over multiple batches, which can finally be estimated using an extended Kalman filter (EKF). The quality of this adaptive model approach that can be used in an online fashion is further assessed using a prediction error for all performed experiments. Copyright © 2025 The Authors.

关键词： Extended Kalman filters

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Stability-Driven CNN Training with Lyapunov-Based Dynamic Learning Rate 35th

Stability-Driven CNN Training with Lyapunov-Based Dynamic Le...

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35th Australasian database conference

作者： Tang, Dahao Yang, Nan Deng, Yongkun Zhang, Yuning Sani, Abubakar Sadiq Yuan, Dong Univ Sydney Fac Engn Sydney NSW Australia Univ Greenwich Sch Comp & Math Sci London SE10 9LS England

ISBN: (纸本)9789819612413;9789819612420

In recent years, Convolutional Neural Networks (CNNs) have become a cornerstone in computer vision tasks, but ensuring stable training remains a challenge, especially when high learning rates or large datasets are involved, as standard optimization techniques like Stochastic Gradient Descent (SGD) can suffer from oscillations and slow convergence. In this paper, we leverage control theory to propose a novel stability-driven training method by modeling the CNN training process as a dynamic control system where we introduce Lyapunov Stability analysis, implemented with Quadratic Lyapunov Function, to guide real-time learning rate adjustments, ensuring stability and faster convergence. We provide both theoretical insights and practical guidelines for the implementation of the learning rate adaptation. We examine the effectiveness of this approach in mitigating oscillations and improving training performance by comparing the proposed Lyanpunov-stability-enhanced SGD, termed SGD-DLR (SGD with Lyapunov-based Dynamic Learning Rate), to traditional SGD with a fixed learning rate. We also conduct experiments on the datasets CIFAR-10 and CIFAR-100 to demonstrate that SGD-DLR enhances both stability and performance, outperforming standard SGD. The code used for the experiment has been released on GitHub: https://***/DahaoTang/ADC-2024-SGD DLR.

关键词： Convolutional Neural Networks control Theory Lyapunov Stability analysis Learning Rate

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Applying Machine Learning in Marketing: An analysis Using the NMF and K-Means Algorithms 2nd

Applying Machine Learning in Marketing: An Analysis Using t...

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2nd Decision Science Alliance International Summer conference, DSA ISC 2024

作者： Gallego, Victor Freixes, Alfons Lingan, Jessica Euncet Business School Barcelona Spain Data Science Research Group Ramon Llull University La Salle International School of Commerce and Digital Economy Barcelona Spain

ISBN: (纸本)9783031782374

The integration of machine learning (ML) techniques into marketing strategies has become increasingly crucial in modern business. Utilizing scientific manuscripts indexed in the Scopus database, this article explores how this integration is being carried out. Initially, a focused search is undertaken for academic articles containing both the terms "machine learning" and "marketing" in their titles, which yields a pool of 58 papers. These papers have been processed using the Supabase platform. The process has included steps like text refinement and feature extraction. In addition, our study uses two key ML methodologies: topic modeling through NMF and a comparative analysis utilizing the k-means clustering algorithm. Through this analysis, three distinct clusters emerged, thus clarifying how ML techniques are influencing marketing strategies, from enhancing customer segmentation practices to optimizing the effectiveness of advertising campaigns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Construction of High-Precision Digital Twin Model and Real-Time data Fusion Technology

Construction of High-Precision Digital Twin Model and Real-T...

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Digital analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Yetong Wang Bing Zheng Kongduo Xing Hainan Vocational University of Science and Technology Haikou Hainan China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

High-precision digital twin model construction and real-time data fusion technology are the core of digital twin technology, which jointly support the whole life cycle simulation, verification, prediction and control of physical entities. In this paper, the construction process of high-precision digital twin model is deeply discussed, including four stages: data acquisition and processing, preliminary model establishment, model refinement and optimization, simulation verification and iterative update. In terms of key technologies and algorithms, this paper analyzes 3D modeling and geometric processing technology, physical properties and behavior simulation technology, and data fusion and real-time update technology. In particular, this paper discusses the role of data fusion algorithms such as Kalman filter in improving the accuracy and reliability of data. In the part of real-time data fusion technology, this paper discusses the data source and diversity, the fusion method and process, and the strategy of using clustering algorithm and machine learning model to improve the efficiency of data fusion. In the face of challenges, this paper points out some problems, such as data security and privacy protection, insufficient generalization ability of models, difficulties in data acquisition and integration, bottlenecks in real-time performance and lack of industry standards, and puts forward corresponding improvement measures. Finally, this paper looks forward to the future development of digital twin technology, and emphasizes the importance of data-driven intelligent modeling methods, cross-industry standardization and innovative applications in intelligent manufacturing, smart cities, smart medical care and other fields.

关键词： Solid modeling Analytical models Accuracy Computational modeling data acquisition data integration data models Real-time systems Digital twins Optimization

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