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检索条件"任意字段=8th International Conference on Computing and Data Engineering, ICCDE 2022"

共 3652 条记录，以下是31-40 订阅

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Leveraging NLP and Quantum computing for Advanced Agricultural Solutions 8

Leveraging NLP and Quantum Computing for Advanced Agricultur...

引用

8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Bhargavi, G. Das, Manisha Banumathi, S. Malarvizhi, E. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India Andhrapradesh Rajam India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Computer Science and Engineering Avadi Chennai India St.Joseph's College of Engineering Department of Ece Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350367904

Quantum computing and natural language processing (NLP) could transform smart farming. Decision-making and resource management improve with convergence, enabling new agricultural solutions. this technique solves traditional computing's problems with large datasets and complex language processing, making it crucial for real-time decision-making. Problems include complex quantum algorithms, NLP models in quantum frameworks, and quantum hardware. this study presents Smart Agriculture computing based on Quantum Natural Language Processing (SAC-QNLP) to overcome the aforesaid challenges. Agricultural data systems use quantum computing, data flow, and quantum-enhanced NLP algorithms. Quantum computing assesses large, complicated datasets faster than standard methods. thus, smart agricultural strategy apps learn more and forecast better. Increased production projections from genetic and environmental factors SAC-QNLP could change farming. A simulation evaluated SAC-QNLP's performance and scalability. Older methods use more resources, produce less precise data, and are less efficient. the results show SAC-QNLP could boost farming production and sustainability. © 2024 IEEE.

关键词： Large datasets

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VQA-Classification-Algorithm-Oriented Performance Benchmarks for Quantum computing 8

VQA-Classification-Algorithm-Oriented Performance Benchmarks...

引用

8th international conference on Cloud computing and Big data Analytics (ICCCBDA)

作者： Zhang, Meng Wang, Jing Yao, Fei Lai, Junsen China Acad Informat Commun Technol CAICT MIIT Res Inst Telecommun Transmiss RITT Beijing Peoples R China

ISBN: (纸本)9781665455336

Quantum computing, taking advantage of its parallel computing, is expected to provide exponential acceleration on some difficult problems. It is one of the important directions for the leapfrog development of computing power in the future. However, due to the limitation of the number of quantum bits, coherence time, fidelity and other factors, the computing power of quantum computers has not been fully utilized at the current stage. In recent years, researchers have proposed a variety of quantum computing performance benchmarks to evaluate and research the performance of quantum computers from the bit, circuit, system, application and other levels. However, the existing performance benchmarks cannot directly evaluate the ability of quantum computers to solve specific problems. Considering the above problems, this paper proposes a VQA-classification-algorithm-oriented performance benchmarks for quantum computing, which takes the classification effect under different data scales as one of the evaluation indicators. this benchmark can comprehensively evaluate the ability of quantum computers to solve data classification problems from the three dimensions of scale, speed, and quality. the benchmark was used to evaluate the superconducting quantum computer and simulator of the IBM and Huawei quantum platform, and its effect was verified experimentally.

关键词： quantum computing variational quantum algorithms performance benchmarks data classification

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Overview of Blockchain Privacy Protection 8

Overview of Blockchain Privacy Protection

引用

8th IEEE international conference on Big data Security on Cloud, 8th IEEE international conference on High Performance and Smart computing, and 8th IEEE international conference on Intelligent data and Security, BigdataSecurity/HPSC/IDS 2022

作者： Yang, Ce Chen, Juan Zeng, Bin Liao, Lei School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China College of Computer Science and Electronic Engineering Hunan University Changsha China Hunan YouDao Information Technology Co. Ltd. Changsha China Hunan Vocational College of Art Changsha China

ISBN: (纸本)9781665480697

In recent years, blockchain technology has attracted extensive attention in academia and industry by virtue of its unique advantages such as decentralization, tamper proof, openness and traceability. the combination of blockchain and data protection has always been the focus of scholars. this paper first analyzes the system structure and characteristics of the blockchain itself, and introduces the development process of the blockchain. Secondly, it analyzes the privacy threats faced by data protection in the blockchain, and introduces the relevant blockchain privacy protection technologies. Finally, it discusses how to combine blockchain and other technologies to enhance privacy protection. © 2022 IEEE.

关键词： Blockchain

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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 ...

引用

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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A Survey on Anomaly Detection in IoT and Cloud computing Security 8

A Survey on Anomaly Detection in IoT and Cloud Computing Sec...

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8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Gagan, P. Varghese, Josy Elsa Aarella, Seema G. Bangalore India University of North Texas Dept. of Computer Science and Engineering DentonTX United States

ISBN: (纸本)9798350376425

Cybersecurity in the Internet of things (IoT) is crucial for ensuring the safety of various applications. the advent of Artificial Intelligence (AI) has enabled innovative security solutions across multiple layers of the IoT ecosystem. As IoT rapidly integrates with edge computing to reduce latency compared to cloud computing, the cloud continues to offer high-speed computation, extensive data analytics, and enhanced security. this survey examines anomaly detection in Cloud-IoT environments, focusing on maintaining safety and functionality. We analyze current security threats, such as data breaches and unauthorized access, while also discussing the benefits and challenges of Cloud IoT, including scalability and privacy concerns. this study aims to explore the effectiveness of cloud-based IoT deployments by reviewing cutting-edge methodologies. © 2024 IEEE.

关键词： Cloud security

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Advances in Multimodal Fusion of EHR and Medical Imaging data Using deep learning techniques for advanced treatment of brain cancer 24

Advances in Multimodal Fusion of EHR and Medical Imaging Dat...

引用

8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Siva Raja, P.M. Vidhya, S. Sumithra, R.P. Anjana, S. Rejini, K. Ramanan, K. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Nagercoil Tamil Nadu Nagercoil India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798400718304

In this research, the performance of different deep learning models, including CNN + RNN, VGG16 + RNN, VGG19 + RNN, ResNet50 + RNN, and InceptionV3 + RNN in predicting brain tumour grades by employing Electronic Health Record (EHR) and imaging data are evaluated. Confusion matrices show the models' abilities to predict, showing the results of the prediction capability of each model over tumour grades. From the result and analysis, InceptionV3+RNN is performing better than all other ML models. With the multimodal fusion techniques, an informed decision can be made about the grades of the cancer cells, and the clinicians can give the proper treatment to the patients. the proposed approach can be implemented in real-time to predict the cancer grades with the highest level of accuracy for better treatment. © 2024 Copyright held by the owner/author(s).

关键词： Electronic health record

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New Method of Song Sentiment Classification Based on Neural Network 8

New Method of Song Sentiment Classification Based on Neural ...

引用

作者： Yu, Ying QILU Institute of Technology School of Computing Science and Information Engineering Jinan China

ISBN: (纸本)9781665480697

In order to classify song text information more effectively, this paper proposes a new neural network classification model based on emotion vector. On the basis of Word2vec, a double word-embedding word vector containing emotion vector is constructed, and the vector is used as the input of Long Short Term Memory (LSTM). LSTM neural network is trained to extract the features of song text and realize emotional classification of song text. the experimental results show that the method can better embody the emotional features of text, and has higher accuracy than the traditional LSTM text classification. © 2022 IEEE.

关键词： Embeddings

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Intelligent Aquaculture Water Quality Detection Scheme Based on TurMass Technology 8

Intelligent Aquaculture Water Quality Detection Scheme Based...

引用

8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Zhang, Yafang Huang, Yongxin Chen, Hongming Zhejiang Ocean University School of Information Engineering Zhoushan316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan316022 China Nankai University College of Electronics Information and Optical Engineering Tianjin300350 China

ISBN: (纸本)9798350302455

To address the shortcomings of wireless data transmission in aquaculture water quality monitoring, we have developed an intelligent water quality detection device based on the STM32 microcontroller system as the processing unit and TurMass transmission technology. this device can detect water quality in real-time and transmit the data to the user end for real-time monitoring so that users can monitor water quality in real-time. through rigorous and systematic experimental measurements, the accuracy of the detected data by the device was found to be extremely high, indicating that it can be used in practical applications. © 2023 IEEE.

关键词： Water quality

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data Mining and Identification of Influencing Factors for Effective Load Forecasting of A Substation Using Machine Learning Regressors 8

Data Mining and Identification of Influencing Factors for Ef...

引用

8th IEEE international conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Kamath, Anantha Krishna Rajalakshmi Samaga, B.L. Canara Engineering College Visvesvaraya Technological University Department of Computer Science and Design Mangalore India India

ISBN: (纸本)9798350350593

Electric load forecasting (ELF), which is a fundamental step in the planning of the energy industry and is important for managing power systems and electric capacity scheduling, has drawn growing academic interest. therefore, for the planning of energy generating capacity and for power system management, the accuracy of electric load forecasting is crucial. Recently, Load forecasting using Machine learning algorithms are the area of interest. Model designed based on featured data provides better result compared to that of based on raw data. thus, data mining and feature extraction of raw data is the trending area for researcher. this paper deals with data mining of historical data of Talapady substation through graphical approach and identifies the influencing factors such as season, month etc. Moreover, this work provides a brief idea to research scholars on how to analyze historical data and to identify the influencing factors through analysis. Proposed work validated using random forest regressor model. © 2024 IEEE.

关键词： Adversarial machine learning

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Bayesian Optimization of SVM for Efficient Detection of Cloud computing Failures 24

Bayesian Optimization of SVM for Efficient Detection of Clou...

引用

8th international conference on Electronic Information Technology and Computer engineering, EITCE 2024

作者： Yuan, Yarui Li, Zhanhui Chengyi College Jimei University Fujian Xiamen China Tencent CSIG Zhejiang Hangzhou China

ISBN: (纸本)9798400710094

As cloud computing has become a fundamental technology underpinning a wide array of online services, ensuring its stability and reliability has emerged as a critical concern. Fault detection plays a pivotal role in maintaining service continuity and performance. However, the dynamic nature and inherent complexity of cloud computing environments pose significant challenges to effective fault detection. Despite the contributions of existing fault detection methods, they are often hampered by limitations in efficiency and accuracy. this paper proposes an online fault detection model based on Bayesian Optimization (BO) that adjusts the parameters of the Support Vector Machine (SVM) model to improve the efficiency and accuracy of fault detection. Pearson correlation and Principal Component Analysis (PCA) methods are employed to select appropriate attributes from cloud system operational data, thereby optimizing the model and improving detection performance. the experimental results prove that the BO-SVM has achieved an accuracy rate of 93% on the training set. the method we proposed demonstrates higher Accuracy, Precision, Recall, and F-score compared to several representative existing fault detection methods. this paper not only demonstrates the practicality of Bayesian Optimization (BO) in adjusting SVM model hyperparameters but also provides an effective solution for cloud computing fault detection. © 2024 Copyright held by the owner/author(s).

关键词： Support vector machines

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