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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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Exploring the Use of Transfer Learning for Automated Brain Tumor Segmentation 5th

Exploring the Use of Transfer Learning for Automated Brain T...

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

作者： Savita Jaison, Feon Nautiyal, Kshitij Sunitha, B.J. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Switch getting to know has become increasingly famous in many aspects of pc vision, including picture segmentation. this paper explores switch-gaining knowledge for automated brain tumor segmentation using a deep getting-to-know structure known as U-net. U-internet is a convolution neural community version that has been used efficiently to ramify scientific image segmentation obligations in the beyond. this work utilizes a U-internet model skilled on a huge-scale brain tumor segmentation dataset. the version plays nicely at the utility of brain tumor segmentation, achieving an average cube coefficient of zero. Ninety-three on check scans. Moreover, qualitative consequences display that the U-net version produces segmentation masks with few false-nice mistakes. these consequences reveal the capability of switch getting to know for automatic mind tumor segmentation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Spatial-Temporal Attention Graph WaveNet for Traffic Forecasting 5

Spatial-Temporal Attention Graph WaveNet for Traffic Forecas...

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5th international conference on data-Driven Optimization of Complex Systems, DOCS 2023

作者： Liu, Shixuan Zhu, Jijun Lei, Wei Zhang, Ping School of Artificial Intelligence Hebei University of Technology Tianjin China Hebei Provincial Communications Planning Design and Research Institute Co. Ltd. Research and Development Center of Transport Industry of Self-driving Technology Chelu Cloud Network Hebei Provincial Engineering Research Center Hebei Shijiazhuang China

ISBN: (纸本)9798350393521

Accurately predicting road traffic conditions is critical for travel planning and efficient allocation of resources, making it a major research focus within smart cities. However, traffic forecasting poses significant challenges due to the complex spatial correlations and nonlinear temporal dependencies inherent to real-time traffic networks. Existing approaches for modeling spatial dependencies have primarily relied on a fixed graph structure, assuming that the relationships between roads are predetermined and static. In contrast, real-world transportation networks exhibit more dynamic interactions that evolve over time. To address this issue, we propose a Spatial-Temporal Attention Graph WaveNet (STAGWN) model for complex traffic network modeling. STAGWN utilizes self-attention network to capture dynamic spatial dependencies. Additionally, long time series are processed via temporal convolution module consisting of stacked dilated casual convolutions in STAGWN. Furthermore, STAGWN incorporates spatial and temporal embeddings to capture spatial-temporal heterogeneity. the proposed model is evaluated using two real-world datasets and compared with 10 baseline methods. Results indicate our approach attains state-of-the-art for 30 and 60 minutes ahead forecasts, demonstrating efficacy on longer prediction horizons. © 2023 IEEE.

关键词： Convolution

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Design of Hybrid Energy Aware Cluster Based Multipath Routing Protocol in IoT Assisted Wireless Sensor Networks 7

Design of Hybrid Energy Aware Cluster Based Multipath Routin...

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7th international conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Khairnar, Prerana Nilesh Sangeethaa, S.N. Rao, Arempula Sreenivasa Srimathi, S. Sivakumar, S. Bharani, G. Sir Visvesvaraya Institute of Technology ETE Nashik India Bannari Amman Institute of Technology Sathyamangalam638401 India Annamacharya Institute of Technology and Sciences Hyderabad India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India VelTech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of EEE Chennai Tamilnadu Avadi600062 India M.Kumarasamy College of Engineering Department of EEE Karur India

ISBN: (纸本)9798350340600

In the past decade, the Internet of things (IoT) has gained a significant research attention due to its ability to revolutionize the cyber and physical world. IoT makes physical items as intelligent objects, which will be capable of collecting, processing and managing the digital data. the communication of big data will be enabled by sensor nodes (SNs) in the IoT-based networks. the lifespan of the wireless sensor nodes is reducing due to the extreme consumption of transmission power. this research study designs and develops a novel Hybrid Energy Aware Cluster based Multipath Routing (HEACMPR) protocol in the IoT assisted WSN. the proposed HEACMPR technique primarily clusters the wireless nodes in the IoT assisted WSN and then selects the optimal route in the network. To obtain this, the proposed HEACMPR technique utilizes the sparrow search algorithm based clustering (SSO-C) technique to select the set of Cluster Heads (CHs) and produce clusters. For route selection, Aquila optimization algorithm based routing (AOA-R) approach is applied to effectually discover the routes to the destination. the simulation results of the HEACMPR technique is examined under different set of performance measures. the extensive outcomes showed the efficiency of the HEACMPR technique over other measures. © 2023 IEEE.

关键词： Internet of things

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Comparing Time Series Assist Vector Machines and Recurrent Neural Networks for Hyper Spectral Image Popularity 5th

Comparing Time Series Assist Vector Machines and Recurrent N...

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

作者： Goswami, Shubhashish Pasha, Afroz Mishra, Awakash Krishnamoorthy, Ramkumar Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

this paper examines the overall performance of two system mastering models, Time series help Vector Machines (SVMs) and Recurrent Neural Networks (RNNs), on classification of Hyper Spectral photographs (HSIs). the fashions are in comparison based totally on their recognition accuracy which includes the time requirements to build fashions. the paper additionally evaluates two varieties of preprocessing techniques: minimum Redundancy maximum Relevancy (MRMR) and precept aspect evaluation (PCA). the effects of the experiments highlight that SVMs and RNNs perform considerably distinct for HSI class and that counting on one version simplest may lead to exclusive type performance. Moreover, the experiments show that with increasing complexity, the quantity of time required to assemble the RNNs will increase significantly. the comparisons in the paper allow further exploration in the discipline and help the choice of the high-quality model with the aid of capacity practitioners and developers. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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Implementing Encryption Algorithms for the Protection of Wireless Networks 5th

Implementing Encryption Algorithms for the Protection of Wir...

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

作者： Ahmad, Naseem Madhura, K. Agarwal, Trapty Zaidi, Taskeen Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

the use of encryption algorithms is more and more turning into a crucial issue of defensive wireless networks. Encryption algorithms may be used to cozy facts that is transferred over a Wi-Fi network while also presenting authentication and integrity protocols for the systems connected to the network. this paper discusses the significance of enforcing encryption algorithms for the safety of a wireless community. It describes the distinct encryption algorithms and protocols which are available, and the criteria for selecting the proper algorithm for exclusive varieties of Wi-Fi networks. It additionally provides unique implementation eventualities based totally on numerous sorts of Wi-Fi community structure in addition to the vast security threats in Wi-Fi networks that may be averted by imposing encryption algorithms. Finally, this paper offers guidelines on selecting and imposing the perfect encryption set of rules for safety of wireless networks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Utilizing Remote Sensing to Monitor Health and Wellbeing in Sustainable Medical Environments 5th

Utilizing Remote Sensing to Monitor Health and Wellbeing in ...

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

作者： Bhatnagar, Gunjan Bengalur, Megha D. Agarwal, Trapty Prabhu, A. Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

the challenge is focused on developing and validating techniques for making use of far flung sensors along with drones and unmanned aerial motors (UAVs) to acquire health-related information in relation to the environment of sustainable clinical facilities along with hospitals, clinics, and long-term care facilities. Moreover, the mission targets to evaluate the health and health of sufferers within those facilities with the aid of tracking the facts accumulated for related tendencies and styles. the challenge collaborates with a variety of partners to ensure the information is accurate and timely, and also involves the development of new algorithms to make use of the information to better apprehend how healthcare experts can fine serve their patients. Ultimately, the mission seeks to reduce value and effort related to healthcare delivery while offering a more green, safe, and sustainable medical environment. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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Impact of user comments on the adoption of electric vehicle charging services: evidence from contextual data from the US 5

Impact of user comments on the adoption of electric vehicle ...

引用

5th international conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Liu, Weiqi Zhang, Yanzi Xu, Na Guo, Yutao School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China School of Mechanical Engineering Beijing Institute of Technology Beijing China School of Economics Beijing Technology and Business University Beijing China School of Business HongKong University Hong Kong

ISBN: (纸本)9781510683006

this study examined user comments from individuals who utilized the charging service, and how these remarks influenced the usage of electric vehicle (EV) charging stations. the transition towards electric vehicles necessitates understanding how to optimize charging infrastructure to promote their widespread adoption. We utilized data collected from the renowned electric vehicle charging station platform PlugShare to explore various factors including user feedback, service provision, site location, and pricing. the research findings suggest that there may not be a direct correlation between user feedback and charging station utilization rates, and the impact of pricing on utilization rates is minimal. therefore, operators should not overly focus on user ratings and pricing. Regarding site service information, operators should prioritize improving the detail and transparency of service operation systems over offering diverse but generalized service descriptions, emphasizing the provision of detailed service descriptions and usage guidelines. In terms of site selection, considering the level of activity in the surrounding environment is more critical than the type of site location. these findings provide important insights for charging station operators and the development of electric vehicles and offer new perspectives for future research. © 2024 SPIE.

关键词： Charging stations

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Quality Prediction of Brewing Koji based on Multitask Learning 5

Quality Prediction of Brewing Koji based on Multitask Learni...

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5th international conference on Internet of things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Zhang, Qi Zeng, Zhuo Si, Ke Luo, Qin Chen, Daoke Chen, Duanbing Wuliangye Yibin Co. Ltd Yibin644000 China University of Electronic Science and Technology of China Chengdu611721 China Chengdu Union Big Data Tech. Inc. Chengdu610041 China

ISBN: (纸本)9798400716485

Koji making is an important craft with critical significance for assessing the quality of liquor. However, under the same physicochemical environment, due to the difference in koji reproductive habits, different levels of koji have different content scores distribution, so, there are great challenges in predicting multiple scores of koji accurately. To address this issue, we proposed a multi-task learning approach by leveraging a shared bottom-level representation layer to extract shared features from the data and joint training on multiple koji rating tasks to learn the relationships between different levels of koji. We conduct experimental evaluations on a real-world koji making dataset. the results demonstrate that the proposed method exceeds other benchmark learning methods in predicting koji scores and capturing the relationships between different levels of koji, thereby enhancing the accuracy and robustness of predictions. © 2023 ACM.

关键词： Forecasting

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Detection of Lung Cancer using Deep Learning based Convolution Neural Network Models 5

Detection of Lung Cancer using Deep Learning based Convoluti...

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5th IEEE international conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Muqeet, Mohd. Abdul Gill, Harmanjyot Kaur Hasnain, Mohammad Abul Nabeel Fatima, Aira Mohammed, Afreen Saifullah, Mohammad Khalid Muffakham Jah College of Engineering and Technology Electrical Engineering Department Hyderabad India

ISBN: (纸本)9798350305449

One of the leading cause for cancer based deaths worldwide is because of Lung cancer. Successful treatment and survival can be significantly improved by early detection of lung cancer. the proposed model has the possibility to aid radiologists by detecting lung cancer in early stages, which can lead to more timely and effective treatment. this thesis focuses in developing a CNN-based model to detect the possibility of lung cancer in medical images. the proposed model uses CNN models, which are trained in advance, such as ResNet50, Inception and Xception, as a feature extractor, and then trains a fully connected neural network on top of the extracted features which classifies if the image is either cancerous or non-cancerous. the model is trained and evaluated based upon a dataset of computed tomography (CT) imaging of the lungs. Experimental results concludes that the model proposed accomplish a high level of accuracy in classifying lung cancer, with an average accuracy of 93.75% for ResNet50, 94.56% for inceptionNetv3 and 97.85% for Xception. the study also compares the accomplishment of the model proposed with other highly developed models, and the results illustrates that the proposed model outperforms other models in terms of accuracy, precision, and recall. the proposed model has the capability to assist radiologists in detecting lung cancer in early stages, which can lead to more timely and effective treatment. the evaluation also gives insight in the usage of deep learning approaches for analysing medical image, particularly in the context of lung cancer detection. © 2023 IEEE.

关键词： Medical imaging

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Invoice Processing Automation Using UI Path Studio 5

Invoice Processing Automation Using UI Path Studio

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

作者： Ramyadevi, R. Anbuselvan, A. Julian, Anitha Selvi, S. Murali Dhar, M.S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Invoices are the fundamental document that play a vital role in encapsulating the details of the products and the business transactions between the seller and the buyer. We cannot check our invoices at any time for emergencies. Substituting the case, by storing the basic details of multiple invoices in an excel sheet might be used for future purposes. this paper provides the advancement of data extraction through automation technologies. We begin this research by the technique Optical Character Recognition (OCR) in automation of data extraction from invoices. this reduces the manual errors during extraction as the entire process works with the automation and provides an accuracy of 95%. the paper discusses the design of the automation that has to be processed for the data extraction including the invoice details, line items and relevant metadata and stored in a structured and accessible format. this storage allows the customer to get through all their product details in a document. Furthermore, the study investigates the integration of extracted data from the invoice to the customer. this integration acts as the strategic role in transforming invoice data into a dynamic asset, enabling customers to glean insights. the paper incorporates real-world examples and case studies to illustrate implementations of scrapping techniques using automation. By addressing challenges such as technological limitations, integration complexities, and data security concerns, the paper provides practical understanding for customers in processing the automation. © 2024 IEEE.

关键词： Accuracy Automation Technologies data Extractions data Storage Invoice Processing Optical Character Recognition (OCR)

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