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检索条件"任意字段=IEEE Conference on Automation Science and Engineering"

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Crop Yield Forecasting Using Bidirectional Gated Recurrent Unit (Bi-GRU) Networks 3

Crop Yield Forecasting Using Bidirectional Gated Recurrent U...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Hussein, Layth Johri, Prashant Priya, A.Anusha Ramya, R. Karpagam, J. Devendran, A. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computing Science and Engineering Galgotias University Greater Noida India Muthayammal College of Arts and Science Department of Computer Science TamilNadu Rasipuram India Psna college of Engineering and Technology Department of Information Technology India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India School of Business Woxsen University Hyderabad India

ISBN: (纸本)9798331542375

Crop yield forecasting is an important aspect of the sustainable agriculture to help farmers and policy makers anticipate both production quantities and qualities, thus providing a technical tool for decision making. In this paper, a new technique of using Bidirectional Gated Recurrent Unit (Bi-GRU) to forecast the growth rate of one or several crops with improved precision and credibility is introduced. The model used here utilizes sequence data of meteorological parameters, soil, satellite index, and yield data also to incorporate temporal dependency and non-linear trends. Imputation of missing values;normalization;feature scaling all helps to remove inconsistencies in data and improve model performance. Folically, the employment of the Bi-GRU model provided a significan improvement over traditional machine learning models and unidirectional models with RMSE of 460.3 kg/ha and R2 of 0.89. Moreover, hyperparameters tuning enhanced the model revealing the importance of bidirectional architecture in managing and capturing the past and future temporal contexts. Similar trends of increased TVB-accuracy arias were observed in sub-Regional analysis considering different crop types and environmental conditions and the accuracy prediction of the yield increased up to 14.1%. This work suggests that Bi-GRU networks can be beneficial to predict crop yields with predictive power, reliability, and fine-grained architectures to handle problems such as climate fluctuations and dataset heterogeneity. The study provides a solution of how to incorporate deep learning algorithms into smart agriculture systems for development, posterity. © 2025 ieee.

关键词： Deep learning

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Implementing EtherCAT Protocol for the Virtualization of Industrial Networks 22

Implementing EtherCAT Protocol for the Virtualization of Ind...

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22nd ieee Consumer Communications and Networking conference, CCNC 2025

作者： Otaki, Shuhei Yamada, Takashi Koyasako, Yushi Hatano, Tomoya Shimada, Tatsuya Yoshida, Tomoaki Graduate School of Science and Engineering Chitose Institute of Science and Technology Chitose Japan Chitose Institute of Science and Technology Silicon Research Center Chitose Japan Ntt Corporation Ntt Access Network Service Systems Laboratories Kanagawa Japan

ISBN: (纸本)9798331508050

In factory networks, various network protocols are used to meet strict requirements for accuracy and latency. The controllers that use these protocols are built with dedicated equipment, making it difficult to mix them with other protocols. Moreover, these controllers often rely on specialized software, complicating the integration of innovative technologies like AI. In this paper, we propose a versatile control system by implementing one of the industrial protocols, EtherCAT, on a general-purpose server. This system can control multiple protocols, allowing new protocols to be added without changing existing equipment. Experiments demonstrated that the system reliably operates multiple devices in a daisy-chain configuration, a characteristic feature of EtherCAT. © 2025 ieee.

关键词： Protocols Software Defined automation vPLC

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BCViMer 1.0: A Deep Network Pipeline for the Identification of Breast Cancer Using MRI Data 3

BCViMer 1.0: A Deep Network Pipeline for the Identification ...

引用

3rd ieee International conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Das, Tejaswini Ratha, Ashis Kumar Dash, Sushree Sasmita Pandey, Rakesh Mohanty, Sabyasachi Nayak, Debasish Swapnesh Kumar Utkal University Department of Statistics Bhubaneswar India School of Computer science and Engineering IILM University Uttar Pradesh Greater Noida India IIMT College of Engineering Department of AI & DS Greater Noida India Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798331533205

Breast cancer ranks among the foremost causes of cancer-related deaths in women globally, with early diagnosis via advanced imaging techniques significantly enhancing patient outcomes. Convolutional neural networks (CNNs) demonstrate considerable potential in the automation of breast cancer detection. Nonetheless, their constrained capacity to capture global image features may hinder their performance. This study presents BCViMer 1.0, a deep learning pipeline utilizing the Vision Transformer (ViT) to overcome existing limitations and enhance breast cancer classification accuracy from MRI images. This research aimed to assess the efficacy of BCViMer 1.0 relative to established CNN architectures, specifically ResNet-50, ResNet-101, VGG-16, and VGG-19. Experimental results indicate that BCViMer 1.0 excels these CNN models, attaining an average classification accuracy that is 4.15% higher. The enhanced performance of BCViMer 1.0 is due to its capacity to capture long-range dependencies and global context in MRI images, facilitated by the self-attention mechanism of the ViT. In conclusion, BCViMer 1.0 exhibits superior diagnostic accuracy compared to conventional CNN-based models, representing a significant advancement in breast cancer detection. This study emphasizes the capabilities of Vision Transformers in medical imaging and establishes a basis for future investigation in clinical applications. © 2025 ieee.

关键词： Convolutional neural networks

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Enhancing Industrial automation Through IIoT-Enabled Cloud Computing and LSTM for Scalable and Real-Time Data Analytics 3

Enhancing Industrial Automation Through IIoT-Enabled Cloud C...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Shanmugapriya, S. Prasad, Rajendra Al-Farouni, Mohhammed H. Velmurugan, R. Singh, K. Ranjith Selvin Pradeep Kumar, S. Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Chengalpattu India School of Agriculture Uttaranchal University Dehradun India College of technical engineering The Islamic University Department of Computer Techniques Engineering Najaf Iraq Karpagam Academy of Higher Education Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (纸本)9798331542375

In this thesis, we investigate the combined application of the Industrial Internet of Things (IIoT), cloud computing and Long Short-Term Memory (LSTM) networks to facilitate advanced industrial automation through the industrial analytics enabled by scalable and real time data processing. IIoT is an industrial approach to generating very large amounts of data from connected industrial devices that can be parsed through cloud computing to yield immediate insights into production processes, equipment health, and operational efficiency. Since this is a time series data, we employ LSTM (A type of recurrent neural network) in this architecture which is able to handle time series data well and is a good fit for predictive maintenance and anomaly detection problems in industrial setting. The proposed system involves three main components: Second, the IoT collection of data from IIoT devices, storage of data in the cloud, and processing of data are stored in the cloud;and finally, real time analysis of data using LSTM models. The LSTM models train on historical datasets containing sensor readings and machine logs, to make accurate equipment failure and process optimization predictions. For large scale data processing we use cloud computing platforms like AWS or Azure to manage where these big databases are located. Evaluation of the system performance is presented in the study with regard to prediction accuracy, latency and a comparison of LSTM with other ML techniques. We can see that that there's strong potential for these technologies to have a powerful impact on industrial automation, downtime, and operational efficiency. Yet, there are still data security and system scalability issues to explore. This research shows that IIoT implants and cloud computing, in conjunction with LSTM, can be designed to transform existing production processes. © 2025 ieee.

关键词： Long short-term memory

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Cloud-Based AI Solutions for Personal Finance Management

Cloud-Based AI Solutions for Personal Finance Management

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2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chhabra, Gurpreet Kaur Mishra, Ashutosh Kumar Tyagi, Varsha Mani, Chandra Tyagi, Vani Sagar, Pramod Kumar Management Education and Research Institute Department of Management Delhi India Srm Business School Department of Management UP Lucknow India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Mangalmay Institute of Engineering & Technology Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

Cloud-based AI answers for private finance control are out-of-the-field tools that leverage synthetic intelligence and cloud computing technologies to manage people's money higher. In this article, I have described various solutions with features ranging from budgeting to personalized financial planning and investment suggestions. Utilizing superior system learning algorithms and cloud-based AI solutions can help understand an individual's fiscal information, including expenses, profits, or savings transactions, for generating personalized insights and advice. This lets people learn about their spending habits and make more informed financial choices. Through the power of the cloud, these solutions can securely store and access financial data from multiple devices, which helps individuals, manage their finances on the go easily. It also enables the client to get real-time updates and notifications, which offers him more comprehensive knowledge regarding his existing financial scenario. These offerings also come with more automation features like reminders to pay payments and computerized enough transfers for a price range, keeping customers from spending further effort and time on their funds. They also offer financial advisors, tax planning tools, and wealth management. © 2025 ieee.

关键词： Decentralized finance

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AI-Powered IT Project Management: Analyzing the Effectiveness of Advanced Project Management Tools to Ensure Project Efficiency

AI-Powered IT Project Management: Analyzing the Effectivenes...

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2025 ieee SoutheastCon, SoutheastCon 2025

作者： Das, Jiban Krisna Elegbe, Ifeoluwa Coffie, Lord Khadka, Rojeena Chen, Lei Ji, Yiming Georgia Southern University Dept. of Information Technology StatesboroGA United States College of Computing and Software Engineering Kennesaw State University United States

ISBN: (纸本)9798331504847

The integration of Artificial Intelligence (AI) into project management has significantly transformed traditional methodologies, particularly within the Information Technology (IT) sector. This study investigates the effectiveness of AI-powered tools, specifically Asana, Jira, and *** to improve project management efficiency through automation, resource optimization, and decision-making processes. Using a systematic literature review and quantitative analysis, the research highlights the capabilities and limitations of these tools in the management of complex IT projects. The study findings reveal that AI-powered platforms facilitate real-time tracking, predictive analytics and dynamic resource allocation, which collectively contribute to reduced project timelines and improved outcomes. However, challenges such as data quality, integration complexities, and the need for skilled personnel remain prevalent. The study was summarized with strategic recommendations for organizations to incorporate automation, leverage predictive analytics, and improve data integrity, thus maximizing the benefits of AI in project management. This research underscores the critical role of AI in modernizing project management practices, which ultimately leads to increased productivity and project success rates in the IT domain. © 2025 ieee.

关键词： Project management

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Highly Reliable Information Interaction and Sharing Based on Distributed Collaboration Communication 26

Highly Reliable Information Interaction and Sharing Based on...

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26th International conference on Industrial Technology, ICIT 2025

作者： Xu, Mingxuan Song, Haifeng Wu, Shourong Zhou, Min Dong, Hairong School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China School of Electronic and Information Engineering Beihang University Beijing100191 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing10081 China Design Institute of Signal & Communication Beijing National Railway Research Beijing100070 China School of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798331521950

To improve the operational efficiency and reliability of high-speed trains (HSTs), the Automatic Train Control System has received considerable attention. Train-to-Train (T2T) communication is one of the critical technologies for autonomous train operation. However, the development of T2T communication technology still faces many challenges, particularly regarding communication distance limitations and reliability issues. To address these challenges, this paper introduces an innovative T2T collaborative communication model for HSTs, focusing on highly reliable information interaction and sharing in distributed collaboration environments. The model employs dynamic and static nodes to enable direct T2T information exchange. Using formal verification and state space analysis to ensure the system's reliability and performance. A Build-in-Test (BIT) mechanism is implemented for fault detection and isolation, enhancing the reliability of the system. By analyzing the performance indicators of the system in different scenarios, the reliability of the system was verified. © 2025 ieee.

关键词： Signaling

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Low Multiplicative Depth Polynomial Evaluation Architectures for Homomorphic Encrypted Data 25

Low Multiplicative Depth Polynomial Evaluation Architectures...

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30th Asia and South Pacific Design automation conference, ASP-DAC 2025

作者： Wang, Jianfei Hou, Jia Zhang, Fahong Meng, Yishuo Su, Yang Yang, Chen School of Microelectronics Xi'an Jiaotong University China School of Cryptography Engineering Engineering University of People's Armed Police China

ISBN: (纸本)9798400706356

In order to reduce the multiplicative depth required by high-order polynomial evaluation for homomorphic encrypted data, we propose two novel and low multiplicative depth polynomial evaluation algorithms: an x4-step nesting algorithm based on parity extraction (x4-NAPE) and a parallel algorithm with high data utilization (PAHU). Compared with the conventional Schoolbook method and Horner's rule, x4-NAPE can reduce about 50% of the ciphertext-ciphertext multiplication (CCM) and 75% of the multiplicative depth in the ideal limiting case. While PAHU does not reduce CCM, it can achieve a logarithmic growth of multiplicative depth, with the increase of polynomial length, the growth rate is much slower than x4-NAPE, Schoolbook method, and Horner's rule. Moreover, the hardware architectures of the above polynomial evaluation algorithms for homomorphic encrypted data are investigated and proposed. The proposed hardware architectures are assessed on the FPGA-based reconfigurable hardware platform for FHE named ReMCA. The assessment results demonstrate that under a fixed upper limit of multiplicative depth, our proposed architectures of x4-NAPE and PAHU support 2.67× and 14.3× the range of polynomial lengths of Schoolbook and Horner's rule, respectively. For polynomial evaluation of the same length, compared with architectures of Schoolbook and Horner's rule, our proposed architectures of x4-NAPE and PAHU can achieve up to 1.13× improvement in execution time, up to 50% reduction in multiplicative depth, and up to 2.39× improvement in the depth-time product. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： polynomial evaluation

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Risk Quantification Based Intraday Flexibility Resource Adjustment Strategy

Risk Quantification Based Intraday Flexibility Resource Adju...

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2025 International conference on Electrical automation and Artificial Intelligence, ICEAAI 2025

作者： Zhang, Yuning Yang, Jiawen Li, Yanxin Liu, Chunming School of Electrical and Electronic Engineering North China Electric Power University Beijing China North China Electric Power University College of Electrical and Electronic Engineering Beijing China

ISBN: (纸本)9798331506797

This paper proposes a risk quantification based intraday scheduling theory for active distribution networks (ADNs) to solve the increasing volatility of renewable energy and the difficulties of distribution network safety. The article employs the Latin Hypercube Sampling (LHS) algorithm to sample the data, and the historical data is sampled to construct a full cycle probability trend sequence, in order to screen out weak nodes/branches of ADN. Thus, in the intraday stage, the safety risks experienced in each period were quantified based on predicted data, and the periods with smooth source load fluctuations and low risk probabilities were identified. The model solution step size was dynamically adjusted to shorten the calculation time. Further accelerate the online solution process of the model and adapt to the fast solution of large-scale complex risk scheduling. Finally, simulation was conducted on the ieee33 node distribution system to obtain a flexible resource adjustment scheme. © 2025 ieee.

关键词： active distribution network flexible resources risk quantification rolling scheduling

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Advancing Visual Marketing Strategies Through Deep Learning-Based Image Recognition for Improved Customer Engagement and Brand Visibility 3

Advancing Visual Marketing Strategies Through Deep Learning-...

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3rd ieee International conference on automation and Computation, AUTOCOM 2025

作者： Pandiri, Ramu Nnadhini, T.J. Velmurugan, R. Menaga, A. Karpagam, J. Vinothkumar, K. Infomatics Corp DallasTX United States Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Coimbatore India St Joseph's College of Engineering Department of Mba Chennai India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Psna College of Engineering and Technology Department of Ece Dindigul India

ISBN: (纸本)9798331542375

This paper aims at analyzing the effectiveness of utilizing deep learning-based image recognition in enhancing the visual marketing thereby determining the extent to which clients' attention and brand awareness are likely to be affected. It uses convolutional neural networking (CNN) to analyze and classify the visual content and the models used include VGG16, ResNet50, InceptionV3. An effective implementation of the data gathering, feature engineering, and algorithm development can be seen in the enhancing of the marketing effectiveness. This entails better accuracy of images in classification. This involves gaining high engagement rates from the customers. probabilities of high visibility of the brands across the various platforms. The study points at the possibility of deep learning technologies in changing visual marketing practices and therefore serves as a useful reference guide to business entity that wish to align themselves with adequate marketing practices in the growing digital landscape. © 2025 ieee.

关键词： Convolutional neural networks

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