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检索条件"任意字段=International Conference on Advanced Research on Computer Science and Information Engineering"

共 160469 条记录，以下是191-200 订阅

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Construction and Application of a Big Data Virtual Teaching Experiment System Based on Container Technology 24

Construction and Application of a Big Data Virtual Teaching ...

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8th international conference on Electronic information Technology and computer engineering, EITCE 2024

作者： Yang, Qunwei Guo, Liqing School of Information and Intelligent Manufacturing Xiamen Huaxia University Fujian Xiamen China General Education Center Xiamen Huaxia University Fujian Xiamen China

ISBN: (纸本)9798400710094

To enhance the efficiency and scalability of virtual teaching experiment systems, a big data virtual teaching experiment system was built using container technology. The system architecture was designed using a container cloud platform that combines Kubernetes and Docker, and big data processing frameworks were utilized to achieve automated scheduling of teaching tasks and resource optimization. Analysis shows that container technology not only improves resource utilization, system response speed, and concurrency processing capabilities, but also enhances the stability and scalability of teaching experiments. This system provides an efficient and flexible experimental platform for modern education, meeting the needs of large-scale data processing and personalized teaching. © 2024 Copyright held by the owner/author(s).

关键词： Spatio-temporal data

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AI for Smart Farming: Machine Learning Models for Precision Crop Yield Prediction

AI for Smart Farming: Machine Learning Models for Precision ...

引用

2025 IEEE international conference on Computational, Communication and information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Bhavana, Munugapati Nijhawan, Ginni Raju, Hemanth Bansal, Saloni LTIMindtree Limited Digital Engineering and Assurance Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Dept. of Comp. Sc. & Engg. Phagwara India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India GLA University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331512965

AI and other advanced technologies are gradually beginning to invade the agriculture sector to serve the increasing demand of the consumers for better methods of food production. A key component of precision agriculture, this work focuses on how to use machine learning models to predict crop yields with high accuracy. Specifically, the nature and volatility of the environmental context - such as the nature and quality of soils, as well as the climatic and water conditions - present a major challenge to the application of the conventional modeling approaches aimed at forecasting productive capacity in agriculture. Still, when comparing, the AI-driven models will allow sorting through much information gathered from different sources, including sensors, satellite imagery, and yield history. This work examines the differences of several machine learning models. Such models are the Random Forests, SVMs, and CNNs and other Deep Learning methodologies respectively. A quality model is evaluated based on scalability, computational performance, and prediction accuracy. Also in scope of the study, there is an opportunity to employ IoT together with data collected with remote sensing to make and provide realtime forecasts and evaluations. By the utilization of the results attained, it is established that the yields expected by the machine learning models are much more precise, which aids in enhancing agricultural decision-making concerning resource utilizing, crop handling, and risk minimization. The purpose of this study is focused on the promotion of the agricultural sustainability and production through the development of the innovative data driven farming practices. © 2025 IEEE.

关键词： Smart agriculture

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Transforming Hospital Visitor Screening with Cloud-Powered Deep Learning for Safety and Security

Transforming Hospital Visitor Screening with Cloud-Powered D...

引用

2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Joseph Rajan, D. Antony Sivanesan, T.M. Mohankumar, N. Srinivasan, M. Sivaraman, Audithan Velmurugan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Symbiosis Institute of Technology Pune India P. S. v College of Engineering and Technology Department of Information Technology Tamilnadu Krishnagiri India Department of Information Technology Jafferpur New Delhi110073 India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331529833

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers. © 2025 IEEE.

关键词： Convolutional neural networks

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A Type Fusion and Span Relation Enhanced Event Extraction Framework for Confused Event 29th

A Type Fusion and Span Relation Enhanced Event Extraction Fr...

引用

29th international conference on Database Systems for advanced Applications (DASFAA)

作者： Meng, Fanshen Lin, Rongheng Beijing Univ Posts & Telecommun Sch Comp Sci State Key Lab Networking & Switching Technol Natl Pilot Software Engn Sch Beijing 100876 Peoples R China

ISBN: (纸本)9789819757787;9789819757794

Event extraction (EE) is a crucial aspect of information extraction facing challenges with overlapped, nested, and confused events. Most prior efforts have focused on overlapped or nested events, overlooking confused events. To address these issues, we introduce a novel event extraction model which employs type fusion to guide argument extraction tasks and utilizes span relation for event splitting to address the issue of confused event. This model simultaneously addresses the challenges of Overlapped, Nested and Confused events for Event Extraction named as ONCEE. Evaluation results on public event extraction datasets FewFC and FNDEE demonstrate a noteworthy improvement in our approach to event extraction. The average F1 score on the datasets increased by 1.8% compared to CasEE and 1.2% compared to OneEE. Besides, our model demonstrates superior performance in extracting confused events.

关键词： information Extraction Event Extraction Confused Event

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Application of generative AI technologies to engineering design 1

Application of generative AI technologies to engineering des...

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1st international conference on Digital Sovereignty, ICDS 2024

作者： Byrne, Daniel Hargaden, Vincent Papakostas, Nikolaos Laboratory for Advanced Manufacturing Simulation and Robotics School of Mechanical and Materials Engineering University College Dublin Dublin Ireland I-Form Advanced Manufacturing Research Centre Ireland

This paper explores the application of artificial intelligence (AI) technologies in the engineering design domain. Two experiments were designed for examining the abilities of diverse generative AI platforms in engineering design applications. The first experiment tested the AI models' abilities to interpret design briefs, perform arithmetic calculations, and finally to provide instructions to design a part in two separate computer-aided design (CAD) platforms, thus gaining an insight into their perception of the functionality of the CAD platforms. The second experiment required the AI tools to design a headphone holder stand from scratch, further a be bing their capabilities in CAD, but also their abilities in product design. The experiments found that these technologies show great potential in the area of engineering design. While they are not yet near the level of a human engineer, they currently exhibit capabilities that may be useful in product concept generation and design proce bes. © 2025 The Author(s).

关键词： Product design

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DFCRN: Deep-learning-based audio denoising for bird monitoring 5

DFCRN: Deep-learning-based audio denoising for bird monitori...

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5th international conference on Signal Processing and computer science, SPCS 2024

作者： Xu, Jinlong Zhang, Huaibin Han, Lin National Supercomputing Center in Zhengzhou Zhengzhou450000 China School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450000 China Information Engineering University Zhengzhou450000 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

As people's awareness of ecological protection increases, bird sound monitoring has received more and more attention. Among them, using bird sound monitoring as part of audio recognition has become a hot research topic. Since bird sounds are usually collected in natural environments, they contain a lot of noise, which will affect the monitoring results. To solve this problem, this paper designs a Convolutional Recurrent Network (CRN) that enhances feature representation along the frequency axis. This method is based on the Short-time Fourier transform (STFT) features of sound signals, focuses on the complex operation features in the time-frequency domain, and designs an Decode-Encode architecture combined with a time-frequency domain enhancement network to reduce the impact of interference information, We called this network DFCRN. Experimental results on the public datasets Birdsdata and xeno-canto-ca-nv show that compared with other denoising models, the noisy signal after DFCRN enhancement achieves the best results in SegSNR and SI-SNR, and the classification accuracy on xeno-canto-ca-nv is improved by 5%, verifying the effectiveness and robustness of this method. © 2025 SPIE.

关键词： Birds

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Detection of Mobile Phone Usage Among Motorcycle Riders: A computer Vision Approach 5th

Detection of Mobile Phone Usage Among Motorcycle Riders: A C...

引用

5th international conference on Electrical and Electronics engineering, ICEEE 2024

作者： Charef, Ayoub Jarir, Zahi Quafafou, Mohamed LAMIGEP EMSI Moroccan School of Engineering Marrakesh Marrakesh Morocco Department of Computer Science Faculty of Science Cadi Ayyad University Marrakech Marrakesh Morocco Department of Computer Science University of Aix-Marseille Marseille Marseille France

ISBN: (纸本)9789819791118

This study examines the prevalence of mobile phone use among motorcycle riders at signalized intersections in Marrakech, utilizing advanced computer vision techniques. Employing the YOLOv8 object detection model and the DeepSORT tracking algorithm, our approach effectively detects and tracks mobile phone usage in real-time traffic scenarios. By analyzing data from strategically placed cameras at high-traffic intersections, the research identifies patterns in mobile phone usage among motorcyclists and evaluates the impact on traffic safety. The findings highlight significant times of day for phone usage and offer insights into behavioral trends, providing a foundation for developing targeted interventions to enhance road safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motorcycles

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Robust Rice Leaf Disease Detection using advanced Preprocessing and Deep CNNs for Class Imbalance Resolution 1

Robust Rice Leaf Disease Detection using Advanced Preprocess...

引用

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

作者： Poorani, S. Singh, Komal Priya Stella Mary, I. Brinda, P. Srimathi, S. Jegajothi, B. Kongu Engineering College Department of Computer Technology Perundurai Erode India Babasaheb Bhimrao Ambedkar University Lucknow India Christ University Department of Computer Science Yeshwanthpur Campus Bangalore India Veltech Hightech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Avadi Chennai India Saveetha School of Engineering Simats Department of Bio Technology Chennai India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331529246

This study addresses the growing challenges posed by plant diseases, particularly in the rice industry, which is vital for many communities. The research propose a robust framework that integrates Deep Convolutional Neural Networks (Deep CNN) with advanced preprocessing techniques to identify rice leaf diseases, including Brown Spot, Leaf Blast, Hispa, and healthy leaves. Our approach employs normalization to enhance convergence during training and data augmentation to improve model generalizability. Additionally, implement the Synthetic Minority Over-sampling Technique (SMOTE) to create synthetic samples for under-represented classes, addressing class imbalance within the dataset. Experimental results demonstrate the model's impressive accuracy, achieving 98.2% for Brown Spot, 97.5% for Leaf Blast, 94.3% for Hispa, and 96.8% for healthy leaves. Furthermore, our method outperforms established CNN architectures such as AlexNet, VGG16, and ResNet50, showcasing the effectiveness of sophisticated preprocessing in enhancing plant disease detection systems and supporting food security initiatives. © 2025 IEEE.

关键词： Convolutional neural networks

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Design and simulation analysis of the architecture of the comprehensive test bench for automotive steering gear using MATLAB software 4

Design and simulation analysis of the architecture of the co...

引用

4th international conference on advanced Manufacturing Technology and Electronic information, AMTEI 2024

作者： Zhang, Chi Wei, Zhansheng Yan, Xiangkun Guo, Yanxiu Lin, Yuxin Zhou, Yu Hu, Yu School of Mechanical and Civil Engineering Jilin Agricultural Science and Technology University Jilin China

ISBN: (数字)9781510688247

ISBN: (纸本)9781510688230

In order to realize a comprehensive and accurate evaluation of automotive steering gear performance, this paper focuses on the architecture design and simulation analysis of automotive steering gear comprehensive test bench. Comparative analysis of existing automobile steering gear, including gear rack steering gear. Through the investigation of the research status of the test bench at home and abroad, it is found that the domestic test bench has made a breakthrough in independent research and development, while the foreign test bench has the advantages of strong versatility. This paper also proposes the architecture design of automobile steering gear comprehensive test bench, including the overall structure design, input shaft steering unit design and output shaft steering unit design. Through MATLAB simulation analysis, the performance of different speed and simulated operation is studied. The results show that the speed has a significant influence on the performance of the steering gear, and the wear and fatigue under specific operating conditions also need attention. This paper provides a new idea and method for the development and testing of automotive steering gear. © 2025 SPIE.

关键词： MATLAB

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research on the Construction Method of Production Equipment Operation Management and Control information Model Based on Knowledge Graph 5th

Research on the Construction Method of Production Equipment ...

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5th international conference on Innovative Intelligent Industrial Production and Logistics

作者： Li, Jun Dou, Keqin Liu, Jinsong Xu, Shunyi Li, Qing Zhou, Yong China Ind Control Syst Cyber Emergency Response T Beijing 100040 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9783031807749;9783031807756

To address the issues of multi-source heterogeneous information, numerous relationships, and complex interaction logic in production equipment operation management and control (PEOMC) activities. This study proposes a combined approach of "forward engineering + reverse engineering" to construct a general information reference model for PEOMC based on knowledge graph. The forward engineering involves analyzing the information resources related to PEOMC functions to design an information meta-model. The reverse engineering involves mining, analyzing, and refining multi-source and heterogeneous PEOMC engineering practice data. Guided by the information meta-model, a knowledge graph is constructed. By mapping and transforming entities, attributes, and relationships from the knowledge graph to the information model, a general information reference model for PEOMC is established. Finally, taking the information model construction of the predictive maintenance scenario in an aero-engine transmission unit manufacturing workshop as an example, the effectiveness and scientific validity of the method proposed in this study are verified.

关键词： Production equipment operation management and control information meta-model Knowledge graph information model

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