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检索条件"任意字段=Proceedings of the 2023 5th International Conference on Big Data Engineering and Technology"

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Integrating Long Short-Time Period Reminiscence Networks for Medical Photograph Classification 5th

Integrating Long Short-Time Period Reminiscence Networks for...

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5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Chhaya Kamalraj, R. Singh, Vaishali Dadhich, Atul Department of Biotechnology Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

L lengthy short-time period memory (LSTM) networks are present-day recurrent neural networks that have been proven effective in numerous regions such as natural language processing, speech popularity, and computer vision. Recently, there was a growing hobby within the software cutting-edge LSTM networks to scientific image type. the gain of ultra-modern usage of LSTM networks in the scientific image category is that they must remember the temporal styles modern day information and effectively integrate the local and worldwide representations of today's photograph. Moreover, they have been proven to be a powerful device to capture temporal patterns in longitudinal, multi-modal information. In this paper, we overview the current methods for clinical picture type and LSTM network usage and the demanding situations and possibilities related to this approach. We additionally provide a top-level view of cutting-edge ability packages for modern LSTM networks in clinical imaging and provide some critical guidelines for future studies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Pest-YOLOv8: A Novel Lightweight Algorithm for Pest Detection 5

Pest-YOLOv8: A Novel Lightweight Algorithm for Pest Detectio...

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5th international Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Li, Hui Wu, Xiyin Guizhou University College of Big Data and Information Engineering Guiyang China

ISBN: (纸本)9798350385557

Intelligent pest detection is a crucial application of object detection in agriculture, significantly improving the efficiency of pest monitoring and ensuring crop quality and yield. However, current pest detection algorithms suffer from low accuracy and large parameter scales. To address these issues, this paper proposes a lightweight pest detection algorithm named Pest-YOLOv8. Firstly, a novel lightweight feature extraction module is introduced based on FasterNet. Secondly, a new lightweight detection head is proposed on the foundation of the YOLOv8 detection head, significantly reducing model parameters and computational complexity while maintaining detection accuracy. Subsequently, the neck portion of the original YOLOv8 is reconstructed using the SimAM attention mechanism, noticeably enhancing the model's feature extraction capabilities in complex natural environments. Finally, a pruning strategy based on layer-adaptive magnitude is designed according to the YOLOv8 network structure, drastically reducing the model size while ensuring performance, making it more suitable for deployment on edge devices. Experimental results on the IP102 dataset subset demonstrate that the Pest-YOLOv8 model achieves a mAP0.5 of 96.6% and a recall rate of 94.5%, outperforming classic models such as YOLOv8n, SSD, and Faster-RCNN. Compared to YOLOv8n, Pest-YOLOv8 shows a 1.4% improvement in mAP0.5 and a 2.4% increase in recall, with a reduction of 57.1% in parameters and 65.5% in computational complexity. this indicates that the improvements in this paper effectively reduce instances of missed and false detections, providing a more efficient solution for deploying models on edge devices like cameras, thereby enhancing pest monitoring, early warning, and prevention services. © 2024 IEEE.

关键词： Object detection

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Student Behavior Analysis System Based on Kmeans Algorithm Under the Background of Smart Campus 5

Student Behavior Analysis System Based on Kmeans Algorithm U...

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5th international conference on Computer Network, Electronic and Automation, ICCNEA 2022

作者： Lv, Yanfeng Xi'an Medical University Information Technology Department Xi'an China

ISBN: (数字)9781665491099

ISBN: (纸本)9781665491099

the development of information technology has made the management of students more systematic in colleges and universities. Student behavior information, such as grade information, trajectory information, competition information, and diversified student behavior information makes college management cumbersome. the application of big data technology can mine student behavior information and realize the construction smart campus. In this regard, this paper designs a student behavior analysis system, and uses the Kmeans algorithm to perform cluster analysis on the data of student behavior characteristics, which provide quantitative data support for the evaluation of scholarships and bursaries and the identification of poor students in the process of student management. the system is used to analyze student behavior, saving a lot of labor costs and providing scientific decision-making suggestions for student management. © 2022 IEEE.

关键词： Cluster analysis

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Chinese image captioning via double decoding based on visual prompts 5

Chinese image captioning via double decoding based on visual...

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5th international conference on Computer Vision and data Mining, ICCVDM 2024

作者： Tan, Weiguo Xiao, Yannan Wen, Yayuan Deng, Zhenrong Huang, Wenming School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9781510682863

the significant progress of vision-language pre-trained models (VLMs) and large language models (LLMs) have provided a feasible new mode for image captioning, which relies on VLMs to process images and then utilizes LLMs to generate captions, simplifying the caption generation process and making it lighter. Based on this mode, to address the issues of deviation between the generated captions and the content of image expressions, incomplete descriptive information, and resource hungry in previous Chinese image captioning models, we propose a Chinese image captioning model via double decoding based on Visual Prompts, DDVP. this model employs the CLIP model as the encoder, the GPT2 model as the decoder, and introduces Visual Prompts, which are keywords related to image content. the model adopts a double decoding approach, first decoding to generate Visual Prompts and then decoding to generate the final captions based on the Visual Prompts. through evaluation, we have demonstrated that our model achieves competitive results on the AIC-ICC dataset, and while maintaining fluency, the generated captions of DDVP can also cover the information in the image more comprehensively and accurately. © 2024 SPIE.

关键词： Visual languages

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Short-term traffic flow prediction based on BAS-VMD-CEEMDAN and IWOA-RF 11

Short-term traffic flow prediction based on BAS-VMD-CEEMDAN ...

引用

11th international conference on Information Systems and Computing technology, ISCTech 2023

作者： Tian, Jia Wang, Deyong Fn, Yanyun Zhao, Xueyi Fang, Jian Shi, Wenxi Xinjiang University School of Information Science and Engineering Urumqi830017 China Xinjiang Lianhaichuangzhi Information Technology Co. Ltd Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control Urumqi830011 China Beijing Academy of Science and Technology Beijing100035 China

ISBN: (纸本)9798350342406

Short-term traffic flow forecasting is crucial for intelligent traffic management. In view of the non-linear and random nature of short-term traffic flow, a prediction model based on BAS-VMD-CEEMDAN and IWOA-RF is proposed. Firstly, the parameters of the VMD, namely the modal number (K) and the value of the quadratic penalty term (α), are optimized using the BAS algorithm, and then the traffic flow is decomposed to obtain several VMF components and one RES component, which extracts the smooth information of the traffic flow. Subsequently, the RES component is decomposed using the decomposition completeness of CEEMDAN to obtain several IMF components, refining the complex information of the RES component. then, the RF model is constructed according to the characteristics of each component, and then the parameters of RF models are optimized using IWOA. Finally, overlay the predictions of each component superimposed to obtain the final predictions. Experimental results show that the short-term traffic flow forecasting model basedon BAS-VMD-CEEMDAN-IWOA-RF has a high accuracy compared to other decomposition models. © 2023 IEEE.

关键词： Forecasting

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Water Assessment Using Geospatial and data Science Tools 5

Water Assessment Using Geospatial and Data Science Tools

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming Chopra, Shakti Raj National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan Lovely Professional University Department of Electronics and Communication Engineering Punjab Phagwara India

ISBN: (纸本)9781665475242

the main objective of this study was to determine the surface water and soil moisture available on Earth, and to test water quality using geospatial and machine learning (ML) tools. Java and Python scripts were developed to design the model. this study presents a smart approach for collecting and assessing water bodies present on Earth. In this study, we identified the surface water and soil moisture sites on Earth and subsequently identified the surface water and soil moisture sites in Taiwan. To test the quality of the water, we designed an ML model. Up on experiment, the random forest model obtained training and test accuracy scores of 100% and 68%, respectively. To improve the test accuracy score further, we used the auto-ML technique and obtained a test accuracy score of 69%. therefore, based on the accuracy scores, we concluded that the auto-ML model was the best. © 2023 IEEE.

关键词： Surface waters

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SINN: A structural information-based neural network for traffic flow forecasting in large-scale road networks 4

SINN: A structural information-based neural network for traf...

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4th international conference on Internet of things and Smart City, IoTSC 2024

作者： Zhu, Weiguo Liu, Caiyuan Zhang, Xingyu Sun, Yongqi Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510681767

Current traffic flow forecasting is a crucial component within intelligent transportation systems (ITSs). However, most existing studies still cannot be employed on large-scale data flow forecasting in real-world scenarios. In this paper, we design a spatiotemporal neural network called SINN for large-scale traffic flow forecasting tasks in real-world scenarios. First, we propose a transformer-based neural network to integrate the historical traffic flow into further traffic flow. Second, we propose a structural information-based hierarchical graph neural network module suitable for large-scale network structures, which can capture relevant node features within large-scale road networks for forecasting future traffic flow. Finally, we collect a large-scale traffic flow dataset and execute evaluation experiments. the experimental results indicate that SINN is capable of processing datasets with thousands of nodes. Compared with the methods that can execute the same scale datasets, SINN can make significant improvements by 44.29%, 26.54%, and 25.23% in the metrics of MAE, RMSE, and MAPE. © 2024 SPIE.

关键词： Graph neural networks

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Optimized Fall Detection Algorithm with Adaptive Sum Vector Magnitude and Axis-Weighted Features from Wearable Accelerometer data 1

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5th international conference on Computer and Communication Technologies, IC3T 2023

作者： Jansi, R. Logeshwari, M. Pavikars, M.M. Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mathematics Sathyabama Institute of Science and Technology Semmancheri Tamil Nadu Chennai600119 India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

this study presents a fall detection approach utilizing body-worn accelerometer data. the proposed method initially involves segmenting accelerometer data into non-overlapping windows. Emphasis is placed on the most informative axis for fall recognition, determined by the axis exhibiting the most prominent features. this is achieved by calculating axis weights based on three crucial features: standard deviation, kurtosis, and entropy. the weight for each axis is computed using a weighted average formula. A novel metric named Adaptive Sum Vector Magnitude (ASVM) is introduced, derived from accelerometer data. this metric takes into account accelerometer readings along the x, y, and z axes. To assess the performance of the ASVM metric, a comparative analysis is conducted with two other metrics: Sum Vector Magnitude (SVM) and Differential Sum Vector Magnitude (DSVM). this work demonstrates the potential of the proposed ASVM metric for fall detection and provides insights into the prioritization of axis data through feature weighting. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fall detection

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Trends in the Sustainability of Related Blockchain Technologies and Bitcoin 5

Trends in The Sustainability of Related Blockchain Technolog...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Pavitra, Mutyala Bardhiya, Himanshu S Gourshettiwar, Palash Computer Science and Design Sawangi India Artificial Intelligence & Data Science Sawangi India Computer Science and Medical Engineering Sawangi India

ISBN: (纸本)9798350383522

Based on an open-source software-powered peer-to-peer payment mechanism, Bitcoin is a digital currency that operates independently of a central bank. Its advantages over fiat money include cheaper transaction costs, increased security, and scalability. Scientists are becoming interested in it despite concerns about illicit usage and societal repercussions. this paper aims to describe and assess existing patterns inside the literature about the sustainability of bitcoin, considering social, economic, and environmental implications. Based on the study, it is evident that the entire system of money transitioning would need an exorbitant quantity of energy to the new cryptocurrency to operate the virtual currency system and mine new bitcoins, meaning that bitcoin is likely to remain a niche currency. A fragmented and democratic maintained public record of transactions is built on blockchain technology, which may open up new and difficult prospects. Block-layer and new state-driven cryptocurrency, the sharing of medical data, the production and distribution of energy in microgrids at the citizen level, and blockchain technology may all gain from the broad adoption of blockchain-based transactions. Bitcoin and associated blockchain technologies have the potential to transcend the concerns raised by many critics when seen as a catalyst for social change. © 2024 IEEE.

关键词： bitcoin digital money energy usage monetary transactions sustainability

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Field programmable gate array-based data processing and early warning system design 5

Field programmable gate array-based data processing and earl...

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5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Huang, Xinyuhe Liu, Yongchong Wuhan Research Institute of Post and Telecommunications Wuhan430074 China Fiberhome Communication Technology Co. Ltd. Wuhan430074 China

ISBN: (纸本)9781510691667

In order to achieve the purpose of physical monitoring for athletes, the elderly and other groups, data transmission uses Bluetooth to achieve wireless, short-distance and fast transmission, and the received waveform is displayed on the VGA(Video Graphic Array)screen for easy viewing. If the set threshold is exceeded, an alarm will be issued to remind the user. this design can be used for heart rate and blood oxygen monitoring of athletes. the main work of this article is reflected in the following aspects: First, the Bluetooth communication is realized, and the data of the host computer is received through the Bluetooth module. the Bluetooth module uses HC06 and follows the UART(universal asynchronous receiver/transmitter) protocol. Secondly, the threshold is set. If the peak-to-peak value of the value or waveform exceeds the threshold, an early warning will be sent to the host computer via Bluetooth, and the LED(Light Emitting Diode) light will change from normal (breathing light flowing water) to three LEDs always on. Finally, the display of waveforms and data. the article studies the waveform display and character display of VGA. the data received through the UART protocol is stored in RAM(Random Access Memory) and displayed on the VGA screen after reading. Finally, through the test of the system, if the results show that the system can receive data, it will be displayed correctly;if the data is not within the threshold range, an early warning will be sent. this research is of great application value. © 2025 SPIE.

关键词： data curation

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