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Practice and Exploration of Intelligent Perception technology in Improving Urban Water System Landscape 3

Practice and Exploration of Intelligent Perception Technolog...

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3rd international conference on Intelligent computing, Communication and convergence, ICI3C 2023

作者： Hu, Jian Yan, Yujuan Lishui Vocational and Technical College Zhejiang Lishui323000 China Central South University of Forestry and Technology Hunan Changsha410004 China

ISBN: (纸本)9798350343922

There are many factors worth considering in the design of water system landscapes, and water system landscapes are often an important component of urban landscapes. Therefore, this article aimed to improve the current urban water system landscape design through intelligent perception technology, making it more adaptable to the environment and more popular among tourists. Therefore, in the end of this article, a comparative test was conducted between intelligent perception technology and digital devices, and the former was set as the experimental group and the latter as the control group to observe which technology can make a similar landscape more popular. The result was that in the same month in different years, the average number of tourists in the experimental group was 2103, while the average number of tourists in the control group was only 1715. This study found that intelligent perception technology can effectively improve the design of urban water system landscapes. © 2023 IEEE.

关键词： digital devices intelligent perception technology landscape design urban landscape water system landscape

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Delay-Optimized Task Scheduling Based on Deep Reinforcement Learning in computing Power Networks 3rd

Delay-Optimized Task Scheduling Based on Deep Reinforcement ...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Tang, Qinqin Xu, Deyun Xie, Renchao Feng, Li Chen, Tianjiao Huang, Tao Beijing University of Posts and Telecommunications Beijing China China Mobile Research Institute Beijing China

ISBN: (纸本)9789819624676

With the rapid advancement of information and communication technology, there arises an increasing demand for heightened computing capabilities to facilitate a myriad of intelligent services. The computing power network (CPN) amalgamates an array of resources including cloud and edge devices to enable collaborative sensing, routing, and scheduling. This integration significantly amplifies the utilization of computing network resources while concurrently augmenting service quality. In the realm of multi-node distributed joint computing within CPNs conventional scheduling methodologies frequently neglect the interplay between computing and the network, resulting in suboptimal task scheduling. To address this issue, this study proposes a delay-optimized task scheduling mechanism based on deep reinforcement learning. Simulation results demonstrate that this framework achieves faster convergence speed, particularly under dual-depth networks, outperforming other algorithms in reducing system delays, energy and enhancing scheduling efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computing power

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Multi-class Waste Segregation using EfficientNetb3 Model through Waste Segregation Dataset 7

Multi-class Waste Segregation using EfficientNetb3 Model thr...

引用

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

作者： Prasher, Shikha Nelson, Leema Chitkara University Insititue of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9798350372816

Efficient waste segregation is critical for successful recycling and sustainable waste management methods. In this paper, we investigate the use of EfficientNetB3, a cutting-edge deep learning model, to obtain high accuracy in trash segregation tasks. Using the Adam optimizer and training the model for 30 epochs, achieving 97% accuracy rate in categorising various waste types. The Adam optimizer's adaptive learning rate approach improves model efficiency and convergence, allowing for rapid training and durable performance. Deep learning algorithms, particularly EfficientNetB3, have the potential to automate trash segregation procedures, streamline recycling efforts, and enhance environmental sustainability. This study helps to advance intelligent waste management systems by demonstrating the efficacy of AI-driven approaches in addressing difficult environmental concerns. Future research will concentrate on incorporating AI technology into practical problems waste management infrastructures to improve efficiency and support circular economy concepts. © 2024 IEEE.

关键词： Circular economy

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Enhancement of Network Security in MANET using Adaptive Coronavirus Herd Immunity Optimizer (ACHIO) Algorithm for IoT Applications 3

Enhancement of Network Security in MANET using Adaptive Coro...

引用

3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Niharika, K. Mahalakshmi, T. Praveen, Palem Arun, M. Talari, Venkata Syamala Raju Krishnan, V. Gokula V. R. Siddhartha Engineering College Department of Eie Vijayawada India P v P Siddhartha Institute of Technology Department of Ece AP Vijayawada India Vignana Bharathi Institute of Technology Department of Cse Telangana Hyderabad India Panimalar Engineering College Department of Ece Tamil Nadu Chennai India S.R.K.R Engineering College Department of Ece Bhimavaram India Saveetha School of Engineering Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350375190

Mobile Ad Hoc Networks (MANETs) represent decentralized, highly mobile wireless networks that operate independently without pre-existing infrastructure, offering self-configurable, self-repairing, and self-maintaining capabilities. As MANETs evolve, integrating them with the Internet of Things (IoT) gives rise to a novel MANET-IoT system, aiming to reduce network implementation costs and enhance user mobility. This study proposes a security protocol for MANETs in IoT environments, leveraging enhanced chaotic maps for encryption and decryption strategies. An improved algorithm, the Adaptive Coronavirus Herd Immunity Optimizer (ACHIO), is implemented for optimized key management under chaotic maps. Evaluation of the convergence rate against text input size reveals ACHIO's gradual decrease in convergence rate with increasing text size, showcasing a convergence rate ranging from 0.42 to 0.16. The proposed ACHIO algorithm demonstrates the highest accuracy across text sizes, averaging 86.02%, outperforming the CHHSO and SASFO algorithms with average accuracies of 79.53% and 79.46%, respectively. Additionally, the ACHIO algorithm exhibits a moderate vulnerability to attacks, with a gradual increase in the cost function ranging from 0.08 to 0.0976 with growing text input size. These findings highlight the efficacy of the proposed ACHIO algorithm for secure key management in MANET-IoT systems. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Edge computing Resource Management Based on Genetic Algorithm 13

Edge Computing Resource Management Based on Genetic Algorith...

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13th IEEE international conference on Electronics Information and Emergency Communication, ICEIEC 2023

作者： Cai, Lei Yao, Kangning Xian, Wenbin Gong, Chao North China Institute of Computing Technology The 4th System Department Beijing China University of Science and Technology Beijing Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350331714

Resource management in edge computing has always been a hot research topic. In the scenario of multiple edge servers and multiple tasks, this paper proposes a task offloading model that optimizes the total cost by weighting energy consumption and latency to reduce energy consumption and latency. Based on the proposed task offloading model, an improved genetic algorithm is proposed to solve the optimal task offloading strategy. Simulation experiments have shown that applying improved genetic algorithms to task offloading models can accelerate convergence speed, find the optimal computing offloading strategy, and improve system performance. © 2023 IEEE.

关键词： Genetic algorithms

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Adaptive Dynamic Aquila Optimization Incorporating with Improved Sine Cosine 12

Adaptive Dynamic Aquila Optimization Incorporating with Impr...

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12th IEEE international conference on Intelligent Data Acquisition and Advanced computing Systems: technology and Applications, IDAACS 2023

作者： Huang, Lang Xu, Hui Qin, Zhengbin Jin, Yang Hubei University of Technology Wuhan430068 China

ISBN: (纸本)9798350358056

To address the issues of the Aquila Optimization (AO) easily falling into local optima, slow convergence speed, and low convergence accuracy, this paper proposes a fusion of adaptive dynamic Aquila Optimization incorporating with improved Sine Cosine (AO-SC). The AO-SC algorithm combines the adaptive weight mechanism and the nonlinearly decreasing search factor of the sine cosine algorithm to perform secondary optimization, effectively improving convergence accuracy and speed, and escaping from local optima. Additionally, the Cauchy-Gaussian mutation operator is also utilized to enhance the proposed AO-SC algorithm's global search capability. Finally, the introduction of nonlinear adaptive weight factors and the combination of archimedean spiral pattern with the variable step size feature of the original AO algorithm generate new local solutions to improve the local search capability and search accuracy of the AO algorithm. Through testing and comparison with seven other algorithms on ten benchmark functions, the final experimental results demonstrate that, the proposed AO-SC algorithm has significant advantages in terms of convergence speed, accuracy, and stability. © 2023 IEEE.

关键词： Aquila Optimization archimedean spiral Cauchy-Gaussian mutation nonlinear time-varying adaptive weights Sine Cosine Algorithm

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Optimized Long Short-Term Memory Using Hybrid Salp Swarm Optimization and Tabu Search for Medical Text Classification 10

Optimized Long Short-Term Memory Using Hybrid Salp Swarm Opt...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Yogameera, T. Shanthi, D. Theni Kammavar Sangam College of Technology Department of Computer Science and Engineering Theni India Psna College of engineering and technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350384369

Recently, many patients have sought treatment ideas through social media. The medical texts include a wealth of information, including a huge number of medical musculatures and symptoms. Developing an intelligence model to classify patient-provided text content and recommend the appropriate department is critical. Hence, an efficient medical text classification method is proposed using long short-term memory (LSTM) optimized by hybrid salp swarm algorithm (SSA) and tabu search (TS) algorithms called SSA-TS. The hybrid SSA-TS approach is used to optimize the hyperparameters of LSTM for enhancing the accuracy and convergence rate. The developed optimized LSTM approach is used to classify the patient-provided text. The experimental results conducted over health news in Twitter datasets and the performance of developed optimized LSTM are compared with some variants of LSTM methods. The experimental results reveal good generalization performance when compared to the baseline models on average, demonstrating that our model performs better and is more robust © 2024 IEEE.

关键词： Tabu search

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MODGF: Multi-Objective Optimization Approach for Directed Greybox Fuzzing 25

MODGF: Multi-Objective Optimization Approach for Directed Gr...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zuo, Xueying Song, Yubo Mao, Run Qin, Zhongyuan Sun, Xin School of Cyberspace Security Southeast University Nanjing China Internet Technology Center State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou China

ISBN: (纸本)9798350330014

Directed Greybox Fuzzing employs code analysis to predict critical paths or potential vulnerabilities. Multi-objective optimization is a challenge to Directed Greybox Fuzzing. Current approaches employ evolutionary algorithms which may trap in local optima during mutation operation procedure. In this paper, we propose MODGF, a Directed Greybox Fuzzing method based on the elitist multi-objective evolutionary algorithm. The key insight of our approach is to get a more diverse distribution along the Pareto front during evolutionary iterations, which speed the convergence of the test cases. In MODGF, we use function distance, basic block distance, and seed distance as objective functions, and calculate distance metrics of the nearest neighbor solutions to infer the distribution density of solutions. We further introduce convergence measure and diversity measure as fitness values to ensure that the generated test cases are more uniformly distributed with more vulnerability types. In comparison to AFL and AFL-GO, the experiment reveals that MODGF exhibits significant performance enhancements in terms of convergence time, target function coverage, and vulnerability triggering efficiency. © 2023 IEEE.

关键词： Multiobjective optimization

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A device driver for the AB21 SoC in Supreme-K system 14

A device driver for the AB21 SoC in Supreme-K system

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14th international conference on Information and Communication technology convergence, ICTC 2023

作者： Kim, Youngho Lim, Eunji Ahn, Shinyoung Park, Yoomi Electronics and Telecommunications Research Institute Supercomputing Technology Research Center Daejeon Korea Republic of

ISBN: (纸本)9798350313277

This paper presents an XPU device driver (XDD) that efficiently supports the OpenCL programming model and development tools for the AB21 SoC, a heterogeneous computational accelerator used in the homegrown prototype of the Supreme-K supercomputer. We propose a dynamic polling-based termination detection mechanism for parallel computing kernel tasks to improve the scheduling performance of OpenCL work groups in a split driver model architecture for heterogeneous compute accelerators connected to the host system via PCIe. Furthermore, we enhance the architecture of XDD to support debugging through the hardware DM of the RISC-V ISA core. This enhancement facilitates the efficient development and optimization of various parallel computing applications. © 2023 IEEE.

关键词： AB21 SoC Device driver Heterogeneous Accelerator OpenCL XPU

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AI-Smart Factory: Design and verification of Korean SME AI smart factory using level diagnosis system 18

AI-Smart Factory: Design and verification of Korean SME AI s...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th international conference on Sustainable Energy Information technology (SEIT)

作者： Park, Byungjun Kim, Taeyong Jong-pil, Jeong Sungkyunkwan Univ Coll Sport Sci 2066 Seobu Ro Suwon 16419 South Korea Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 South Korea

This paper presents a new AI-based smart factory construction model for SMEs based on the Korean manufacturing industry and proposes an effective construction process that implements the informatization, automation, and intelligentization characteristics of smart factories. The Korean AI-based smart factory construction process starts with the accumulation of digital information by attaching sensors to the manufacturing processs, and then creates a customized AI model for the company through cloud accumulation of sensor data and big data analysis through CNN techniques. The Korean SME Smart Factory is an AI-based smart factory through AHP analysis and smart factory level diagnosis system, which is a construction process, which means a consumer-oriented intelligent factory that combines next-generation digital technology and manufacturing technology beyond the existing level of factory automation (FA). Various products can be produced on one production line, and modularization is expected to change from mass orders to individual flexible production systems. The transition to smart factories is expected to dramatically improve the productivity of manufacturing, as well as realize energy savings and a more human-centered work environment. By monitoring and controlling manufacturing sites in a virtual space, it is expected to facilitate factory management and lead to improved quality and cost competitiveness. (c) 2023 The Authors. Published by Elsevier B.V.

关键词： Wafer stage Semiconductor Ellipsometer Machine Vision Auto-Focusing

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