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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是101-110 订阅

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An Optimal Real-Time Routing and Clustering Protocol for UAV Networks Based on Security 4

An Optimal Real-Time Routing and Clustering Protocol for UAV...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sophia, S. Leelipushpam Paulraj, Getzi Jeba Yuvarajan, D. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance;however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. this research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. the proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. the proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios. © 2025 IEEE.

关键词： Emergency services

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Dynamic Spectrum Access in Cognitive Radio Networks: A Reinforcement learning Approach 4

Dynamic Spectrum Access in Cognitive Radio Networks: A Reinf...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Kumar, Gaurav Kumar, Sandeep Shrivastava, Anurag Srivastava, Arun Pratap Badhoutiya, Arti Pant, Rajesh Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Research Impact and Outreach Punjab Rajpura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Lloyd Institute of Engineering & Technology Greater Noida India GLA University Department of Electrical Engineering Mathura India Uttaranchal University Uttaranchal Institute of Management Uttarakhand India

ISBN: (纸本)9798350307757

this study also evaluates the role of RL algorithms like Q-learning, DQN and Policy Gradient Methods including DDPG in relation to implementation within DRSA for CRNs. By using metrics such as average reward, convergence time and spectrum efficiency a realistic evaluation has been done in the simulated environment. the findings show that DDPG outperformed other algorithms in terms of average rewards, fast convergence and high spectrum utilization efficiency proving its adaptability under dynamic settings. It is in line with the wider trend of using sophisticated approaches that emphasize high-level learning, collaborative optimization models and efficient distributed methods for the studies related to spectrum. this study gives helpful information on RL algorithms' pros and cons in the field of DSM, highlighting their ability to redefine wireless communication frameworks. this provides a basis for creating intelligent and self-optimization communication networks, that are of immense importance in addressing spectrum scarcity, creation innovative wireless technologies. © 2024 IEEE.

关键词： Reinforcement learning

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Comparison Between Single and Multi-objective Clustering algorithms: MathE Case Study 1

引用

4th international conference on optimization learning algorithms and applications

作者： Azevedo, Beatriz Flamia Rocha, Ana Maria A. C. Fernandes, Florbela P. Pacheco, Maria F. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Aveiro Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M P-5300253 Braganca Portugal

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

this paper compares the results obtained for four single clustering algorithms with a multi-objective clustering approach. For this, a dataset describing the student's behavior within the Linear Algebra topic on the MathE e-learning platform is used. this dataset aids in understanding student performance and engagement in MathE to support the development of an intelligent system to tailor the platform's resources to users's needs. the four algorithms suggested two clusters as the optimal solution for the dataset. However, this binary categorization did not provide meaningful insights into the proposal of the MathE platform;that is, it did not provide a customized system according to individual needs. thus, this study uses the multi-objective clustering algorithm, which results in a set of non-dominated solutions, providing decision-makers with a broader range of options to choose the solution that best meets their needs. the results demonstrate the main benefits of the proposed human-in-the-loop multi-objective approach since it provides several optimal solutions and allows the decision-maker to apply fundamental knowledge to define the most appropriate solution to the problem based on previous knowledge.

关键词： multi-objective clustering automatic clustering optimization bio-inspired algorithm

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optimization of Placement and Routing Techniques: Congestion Estimation and Control in VLSI Design 5th

Optimization of Placement and Routing Techniques: Congestion...

引用

5th international conference on Data Science, Machine learning and applications

作者： Nagendra, Vanganuru Manavalan, Saravanan Vel Tech Rangarajan Dr Sagunthala R Inst Sci & Te Dept ECE Chennai Tamil Nadu India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the rapid advancement of integrated circuit (IC) technology has increased the demand for efficient and effective VLSI optimization methods. this paper presents a systematic literature review as well as a conceptual framework for optimizing placement and routing congestion estimation and control in VLSI design. this paper review existing algorithms, optimization techniques for placement and routing, congestion estimation, and control mechanisms. the goal is to comprehend the advantages, disadvantages, and applicability of these algorithms in addressing VLSI design problems. Furthermore, the research proposal seeks to create a comprehensive approach that incorporates various optimization techniques to improve the overall performance of integrated circuits. the estimation and control of routing congestion receive special attention because it has a significant impact on signal flow and overall circuit quality. the preferred programming platform for implementing the proposed methodology is the MATLAB technical computing language, along with the required toolboxes. this research aims to contribute to the advancement of efficient and reliable IC designs by addressing the key challenges in VLSI design using an optimized placement and routing framework.

关键词： Placement Integrated Circuit optimization Congestion VLSI

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Performance Study in Heterogeneous Vehicular Networks Using Dual Connectivity and Deep Reinforcement learning 20

Performance Study in Heterogeneous Vehicular Networks Using ...

引用

20th IEEE international Wireless Communications and Mobile Computing (IEEE IWCMC) - Green and Intelligent Communications

作者： Arwa, Amaira Hend, Koubaa Faouzi, Zarai Univ Sfax ENETCOM NTSCOM Res Unit Sfax Tunisia Univ Sousse ISITCOM Hammem Sousse Sousse Tunisia

ISBN: (纸本)9798350361261;9798350361278

Vehicular networks are a vast and significant research subject that attracts the transportation and telecommunications industries. the transportation industry's primary goal is to ensure road safety. For this purpose, as a solution, it was necessary to combine cellular networks and vehicular technologies, which led to Cellular Vehicle-to-Everything (C-V2X). thus, vehicles that support 4th generation (4G) or 5th generation (5G) capacities must be capable of transparently adjusting to the existing dual infrastructure by enforcing the new 5G radio and current 4G Long-Term Evolution (LTE) technology. In this context, Dual connectivity (DC) was invented to enable vehicles to be connected simultaneously using the two technologies and to resolve the problem of frequent Handovers (HO). this study aims to enhance the network's performance by reducing HO's number. To do this, we opted to use and test our network with two Deep Reinforcement learning (DRL) algorithms: Proximal Policy optimization (PPO) and Advantage Actor-Critic (A2C). Simulation results and the comparison between these two algorithms reveal that PPO is the algorithm that performs the best in almost all scenarios.

关键词： 5G V2X Heterogeneous Network Handover Dual Connectivity Deep Reinforcement learning Performance

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WTSA: A Whale-enhanced Tree-Seed Algorithm with Timed Iterative Update Mechanism 4

WTSA: A Whale-enhanced Tree-Seed Algorithm with Timed Iterat...

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Li, Hao Li, Chenxi Yu, Zhenhao Wang, Ruyi Wang, Nan Ji, Yvzhu Jilin University of Finance and Economics School of Management Science and Information Engineering Changchun China

ISBN: (纸本)9798331531225

this paper presents an improved Tree Seed optimization Algorithm, namely the Whale optimization Algorithm with Adaptive Search Strategy (WTSA), designed to address the limitations of traditional Tree Seed optimization algorithms. WTSA incorporates an advanced adaptive search mechanism and a dynamic adjustment framework to enhance the balance between exploration and exploitation, overcoming issues such as premature convergence and entrapment in local optima. the algorithm adopts an Iterative Chaos Initialization strategy, combining deterministic and stochastic methods to achieve a more uniform and diversified initial population distribution. Additionally, WTSA introduces a dynamic search step-size adjustment, significantly improving convergence speed and solution quality. the integration of multi-strategy search methods enables WTSA to effectively navigate complex search spaces and identify global optima. through extensive comparative experiments with advanced algorithms, including the original Whale optimization Algorithm (WOA), Particle Swarm optimization (PSO), and TSA variants, the results demonstrate WTSA's superior performance in solution accuracy and computational efficiency. the findings highlight WTSA's robustness and versatility in solving various optimization problems, making it a competitive choice for complex real-world applications. © 2024 IEEE.

关键词： optimization algorithms

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Hybrid Cooperative Scatter Search Algorithm with Elite learning Mechanism 4

Hybrid Cooperative Scatter Search Algorithm with Elite Learn...

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4th international conference on Computer, Control and Robotics (ICCCR)

作者： Zhou, Gang Xie, Songbo Zhang, Xianxia Li, Ke Shanghai Univ Sch Mech & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China Shandong Laigang Yongfeng Steel Corp Dezhou Shandong Peoples R China

ISBN: (纸本)9798350373141;9798350373158

A novel hybrid cooperative scatter search (SS) algorithm with an elite learning mechanism (HSSA) is proposed for addressing complex continuous global optimization problems. the HSSA integrates the evolutionary mechanism of the backtracking search algorithm (BSA) into the reference set update process of SS. In addition, an elite individual-guided learning mechanism is proposed to guide the evolutionary direction of the population through the information provided by the current optimal individual. An opposition-based learning mechanism is adopted to avoid the population from falling into stagnation. Experimental results conducted on the CEC2017 benchmark test set demonstrate the effectiveness of the HSSA algorithm.

关键词： scatter search algorithm elite learning mechanism opposition-based learning global optimization

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Proceedings - 2024 4th international conference on Soft Computing for Security applications, ICSCSA 2024

Proceedings - 2024 4th International Conference on Soft Comp...

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4th international conference on Soft Computing for Security applications, ICSCSA 2024

ISBN: (纸本)9798331515720

the proceedings contain 117 papers. the topics discussed include: analysis of artificial intelligence hybrid security cloud system intelligent technology and its applications;use of docker containerization and load balancer to scale a flask application;agriculture wastage management system using android application;cloud resource allocation using deep learning techniques – a study;dynamic resource optimization for cloud encryption: integrating ACO and key-policy attribute-based encryption;women’s safety application using flutter and dart;artificial intelligence and big data applications in recruitment process management;temporal analysis and prediction of lung tumor growth using LSTM networks: a deep learning approach;an introspection of existing plant identification mobile app consumers and potentials;cutting-edge neural networks elevating breast cancer diagnosis accuracy through image analysis;and wireless stethoscope with digital feedback.

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Predicting Child Development Status: Can Machine learning Helpƒ 4

Predicting Child Development Status: Can Machine Learning He...

引用

4th international conference on Emerging Smart Technologies and applications, eSmarTA 2024

作者： Coban, Onder Sehitoglu, Ensar Yaganoglu, Mete Ataturk University Department of Computer Engineering Erzurum Turkey

ISBN: (纸本)9798350354133

Machine learning (ML) has recently been used to solve different problems including detection of child development status, meeting children's individual learning needs, detecting and intervening developmental problems early, improving rehabilitation processes, and so on. Hence, in this study, we build and use ML models to predict the development status of children based on a recently shared dataset that includes various attributes. To improve the performance of our models, we applied simple and efficient imputing, feature extension, and enlarging techniques on the dataset. the results show that four of ten ML algorithms reach the perfect performance (i.e., an f1-score of 1.0) and this indicates that ML is capable of solving this problem in an automatic and highly accurate way. We therefore believe that the findings of this study can uncover the applicability of ML to solve problems within the related field. © 2024 IEEE.

关键词： Adversarial machine learning

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Data Enhancement Strategy Using Reinforcement learning and Xgboost with BlockChain Security 4

Data Enhancement Strategy Using Reinforcement Learning and X...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Selvapriya, S. Manvizhi, N. Ajay, S. Kavya, V.K. Gilmary, Rosario Ifet College of Engineering Dept. of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798350349900

the enormous volumes of data generated by Next Generation Sequencing (NGS) technology have transformed genomics and made efficient data analysis techniques necessary. To create data analytics applications, the extraction, transformation, and loading (ETL) process is necessary. In understanding data features, representation, storage, I/O operations, intermediate findings, in-memory analysis, and total processing time, the data format is crucial, as this study emphasizes. Data conformance is required for effective analysis since different machine learning and data mining algorithms require different types and forms of data. In this paper, we explore data format-centric optimization of the ETL process for NGS data analysis. We investigate how computing efficiency and analytical correctness are affected by the choice of data type. By using distributed computing and parallel processing, we evaluate the effectiveness of several data formats in NGS data analysis processes, such as FASTQ, BAM, and VCF. Our study offers suggestions for developing data analytics applications in the genomics field by clarifying the relationship between data format, ETL, and algorithmic needs. We also look at the trade-offs between resource usage and data format choice. In the end, our study contributes to more accurate and efficient genomics research by addressing the urgent need for well-informed data format options in NGS data analysis. © 2024 IEEE.

关键词： Reinforcement learning

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