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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

共 11084 条记录，以下是671-680 订阅

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the Porting and Optimization of RISC-V UEFI Boot 7

The Porting and Optimization of RISC-V UEFI Boot

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Zhang, Luming Zhejiang University College of Information Science and Electronic Engineering Hangzhou China

ISBN: (纸本)9781665478571

the galloping development of RISC-V processors has put forward higher requirements for bootup firmware. this paper introduces the basic architecture and boot process of UEFI under the QEMU emulation platform, and describes the defects of the RISC-V UEFI boot process based on the EDK2 Sifive implementation. then the principles and methods of porting and optimization in different stages of RISC-V UEFI boot is analyzed and summarized. the adaptations are made to realize RISC-V UEFI boot in the QEMU's virtio environment for the first time. Meanwhile operations are simplified, and features are expanded. Finally, QEMU is used to start the RISC-V UEFI to verify the improvements. © 2022 IEEE.

关键词： Firmware

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Advancing Biomedical Imaging through High-Resolution Image Segmentation and Computational Modelling Using Convolutional Nueral Networks 7

Advancing Biomedical Imaging Through High-Resolution Image S...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Sridhara, S. V. Kiranmayi Raju, B. Elisha Charitha, G.S. Sharon, B. Rosey Mowlika, P.S.S.N. Chandra, K. Ramesh Aditya College of Engineering and Technology Surampalem India Vishnu Institute of Technology Bhimavaram India Godavari Institute of Engineering and Technology Rajahmundry India Dnr College of Engineering and Technology Electronics and Communication Engineering Bhimavaram India Srkr Engineering College Bhimavaram India

ISBN: (纸本)9798350350067

High resolution image segmentation and computer modelling have greatly aided advancements in biomedical imaging, especially with the use of Convolutional Neural Networks (CNNs). In order to improve the precision and accuracy of biological image processing, this work investigates the integration of CNNs. CNN-enabled high-resolution image segmentation provides better delineation of intricate anatomical structures a necessary step toward precise diagnosis and treatment planning. this study provides a thorough examination of CNN's numerical analytical performance in comparison to cutting-edge techniques. According to the results, CNNs achieve 94% segmentation accuracy, which is far higher than the average of 85% achieved by classical approaches. CNNs also provide a 30% reduction in processing times. Furthermore, CNNs beat traditional methods with an accuracy of 80%, demonstrating a prediction accuracy of 92% in computational modelling. the results highlight CNNs' potential to transform biomedical imaging by improving picture quality and opening up complex modelling methods, which will lead to more effective and efficient healthcare solutions. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy Efficient Routing Protocol for Multisink Wireless Sensor Networks 7th

Energy Efficient Routing Protocol for Multisink Wireless Sen...

引用

7th international conference on computing in engineering and technology, iccet 2022

作者： Rajanna, M. Shiva Murthy, G. Varaprasad, G. VTU PG Center Muddenahalli Chickballapur India BMS College of Engineering Bengaluru India

ISBN: (纸本)9789811927188

Wireless Sensor Network (WSN) is one of the major research areas. WSN contains thousands of sensor devices. these nodes can communicate with other nodes directly or indirectly through BaseStation (BS). Routing is one of the critical areas in the WSN. Many investigators proposed routing algorithms for single sink WSN. However, routing data in the multiple sink node is still more challenging. In this work, Energy Efficient Routing Protocol for Multisink Wireless Sensor Networks has routed the information through an optimal path between source to sink node. the optimal path is found considering parameters like residual energy, energy consumption rate, and hop count. Proposed EERP performs 48% improvement compared to EORA in average residual energy spent in routing. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy efficiency

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A Study on Integrating Machine Learning Techniques for Waste Management 7

A Study on Integrating Machine Learning Techniques for Waste...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Vaidya, Ruchika Dattangire, Rahul Biradar, Divya Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Data Engineering Publicis Sapient HoustonTX77002 United States University of Texas Department of Computer Science ArlingtonTX76019 United States

ISBN: (纸本)9798350372816

the current study focuses on how various Machine Learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. the system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. the accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. the discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Cloud Data Centers for Environment Sustainability through Performance-Aware Virtual Machine Allocation 7

Optimizing Cloud Data Centers for Environment Sustainability...

引用

7th international conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2023

作者： Kumar, Vaneet Jindal, Balkrishan Chandigarh University Department of Computer Science and Engineering Gharuan140413 India Punjabi University Guru Kashi Campus Computer Engineering Section YCoE Talwandi Sabo151302 India

ISBN: (纸本)9798350305517

Cloud computing faces challenging issues concerning high-energy consumption and service-level agreement (SLA) violations, particularly as the demand for computational power continues to grow, necessitating large-scale cloud data centers. Existing energy-aware approaches tend to focus on performance optimization during VM migration phases but it can be effectively maintained at VM allocation level as preprocessing. thereby, this paper proposed Energy Efficient SLA-aware virtual machine Allocation approach using a metaheuristic Firefly algorithm (FFO-EESA). the experimental findings showcase promising outcomes in the context of dynamic workloads and varying numbers of VMs (ranging from 1 to 290) allocated on individual hosts. the proposed method's experimental results were obtained through computer-based simulations conducted in MATLAB version 2017(a). the outcomes showed that, there was a reduction of 12.34% in migrations and a saving of 4.36% in idle hosts with the FFO-EESA approach. proposed algorithm resulted in an average energy savings of 4.39% compared to the FF and MBFD techniques. © 2023 IEEE.

关键词： Optimization

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A Natural Language Processing System for Truth Detection and Text Summarization 7

A Natural Language Processing System for Truth Detection and...

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Rohith, H.P. Sooda, Kavitha Karunakara Rai, B. Srinivas, D.B. Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India Department of Electronics & Communication Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (纸本)9781665464086

In the futuristic world, differentiating between accurate information and false information would be challenging. To identify the fake news a truth detection system will be extremely useful. Different artificial intelligence models were compared, and the highest performing algorithm was chosen. this model was subsequently updated using a larger data-set in order to achieve better results in more real-world applications. the most essential assumptions are that the user will only utilize the subject field in which the model will work. Earlier works have implemented the process of fake news and truth detection separately from the process of summarizing the said news/information. No method was available for streamlining the process to detect whether given information was true or not and to then summarizes the given information automatically. this study has compared and contrasted the differences in accuracy and overall performance of each algorithm for fake news detection and summarization of text. Using smaller dataset, LSTM cells outperformed all other models with an accuracy of 99%. For the larger dataset, the modified LSTM has worked really well with 98.6 percent accuracy. © 2023 IEEE.

关键词： Natural language processing systems

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Assessment on the anti-epidemic measures: Mathematical model establishment for COVID-19 epidemic and application of recurrent neural network 7

Assessment on the anti-epidemic measures: Mathematical model...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Yao, Senlan South China University of Technology Shien-Ming Wu School of Intelligent Engineering Guangzhou China

ISBN: (纸本)9781665478571

How to evaluate the effectiveness of epidemic prevention measures scientifically and reasonably has become the most urgent task for governments around the world. the rapid spread of the coronavirus has brought great challenges to the global community. In this paper, the effectiveness of the following three epidemic prevention measures, including makeshift hospitals, closed cities, and wearing masks, is evaluated by establishing relevant mathematical models and applying the circulating neural network. For the scenes of closed cities and makeshift hospitals, the improved model of SEIR eight cabins is established in this paper, and the parameters are updated in real-time by Long Short-Term Memory. the following results are obtained: the closed city measures greatly reduce the probability of the transfer of susceptible persons to latent ones, and the epidemic is effectively controlled. Regarding the issue of wearing masks, this paper established the MUEIR model and solved with a particle swarm optimization algorithm. It is concluded that the number of infected people decreased by 42% compared with the natural situation, indicating the effectiveness of wearing masks. In conclusion, the effectiveness of the above three epidemic prevention measures is scientifically evaluated, and artificial intelligence technology is combined to achieve intelligent dynamic prediction of the epidemic development trend. © 2022 IEEE.

关键词： Long short-term memory

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SI-LERT (Assistive Device for Hearing Impaired Drivers) 7

SI-LERT (Assistive Device for Hearing Impaired Drivers)

引用

7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Abhiram, S.P. Umamaheswari, S. Muhammed Shybin, M.S. Saju Campbell, Aromal Sreena, V.G. Raji, N.R. Marian Engineering College Dept of Electronics and Communication Engineering Kerala India Dept of Computer Science Kerala India

ISBN: (纸本)9798350372816

the"SI-LERT"(Assistive Device for Hearing Impaired Drivers), represents an innovative solution aimed at enhancing the safety and accessibility of individuals with hearing impairments while driving, particularly in detecting emergency vehicles. Combining a Raspberry Pi 4 and USB microphone for siren recognition, this device transmits signals to an ESP32 microcontroller integrated into driving glove via WiFi. Upon detection, the microcontroller activates micro-vibrators embedded in the glove, providing tactile feedback, and simultaneously displays an image of the approaching emergency vehicle on an OLED display. this innovative approach not only empowers drivers with hearing impairments to respond effectively to emergencies but also marks a significant advancement in assistive technology, offering a comprehensive solution that combines tactile and visual feedback to enhance road safety and driver independence. While the device shows potential, it needs more work and testing to fix problems like limited number of datasets. © 2024 IEEE.

关键词： Microcontrollers

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Parallel Distributed Cooperative Control of Multiple Vehicles with Low Communication Traffic 7

Parallel Distributed Cooperative Control of Multiple Vehicle...

引用

7th CAA international conference on Vehicular Control and Intelligence, CVCI 2023

作者： Jie, Ma Sichao, Tang Feng, Gao Yuan, Liping State Key Laboratory of Automotive Safety and Energy College of Mechanical and Vechile Engineering Chongqing University Chongqing China College of Mechanical and Vechile Engineering Chongqing University Chongqing China Procurement Center Changan Automobile Co. Ltd. Chongqing China

ISBN: (纸本)9798350340488

this paper presents a parallel distributed framework for cooperative control of of connected and automated vehicles (CAV) with low communication traffic. With this framework, the original centralized cooperative control problem is decomposed to a series of separable sub-optimization ones by introducing a set of consensus variables and linear transformation to fully use the computation resources of CAV. Additionally, to reduce the communication traffic between different computing nodes, a communication filtering strategy is designed by limit the transmission of the consensus variables by considering the fact that the interaction process tends to convergence when the variation among adjacent steps is small. To ensure the converge of numerical optimization process, a non-negative decreasing function is used to evaluate the variation of consensus variables. the effectiveness of the proposed cooperative control system is validated and analyzed by several comparative simulations with the original one by considering several indices including cooperation, optimality and communication efficiency. © 2023 IEEE.

关键词： Alternating direction method of multipliers Cooperative control Distributed computation framework Intelligent vehicle

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Multi-Network Task Offloading with Improved Recording Neural Network 7

Multi-Network Task Offloading with Improved Recording Neural...

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2022 7th international conference on Information Science, Computer technology and Transportation, ISCTT 2022

作者： Wang, Longhai Zhang, Luyong School of Information and Communication Engineering Beijing University of Posts and Telecommunication Beijing100876 China

ISBN: (纸本)9783800760565

For the current development of edge computing, it has become a new trend for multiple wireless devices to form a network to connect to the entire system [1]. At the same time, task offloading in this scenario is partially different from traditional edge computing. For multiple systems, how to efficiently allocate tasks has become the primary problem that needs to be solved in this scenario. this paper proposes an improved task offloading algorithm based on neural network. By using neural network to learn the process of task offloading, and at the same time calculate the cost after each decision, the decision that makes the cost decreasing will be recorded, the decision with the original task size is used as the input of the neural network together. Experiments show that the trained neural network can quickly and efficiently obtain the offloading result after each task input while ensuring the lowest delay and energy consumption. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Edge computing

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