Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470612
Pawan Bhambu, G. D, Vaishali Singh
This paper presents an extensive performance analysis of evolutionary algorithms (EA) used for automated design of autonomous vehicles (AVs). This research explores the algorithms' abilities to generate AV designs that exhibit safe driving behavior and also meet requirements concerning comfort, efficiency and reliability. The assessment of the EA performance is based on the analysis of two design scenarios-corridor driving and intersection crossing. On each of these scenarios, the performance of two distinct evolutionary algorithms was compared against several baselines, including a hand-crafted controller and several other methods from the literature. The results showed that the EA-generated designs outperform the other methods in terms of safety, efficiency and general performance on both of the test scenarios. Furthermore, the assessment revealed some interesting distinctions between both the tested evolutionary algorithms, which could be useful for practitioners and developers of autonomous vehicles. Overall, the results support the conclusion that evolutionary algorithms can be a reliable and effective tool for automated generation of safe and efficient AV designs
本文对用于自动驾驶汽车（AV）自动设计的进化算法（EA）进行了广泛的性能分析。这项研究探讨了算法生成自动驾驶汽车设计的能力，这些设计既能表现出安全驾驶行为，又能满足舒适性、效率和可靠性方面的要求。对 EA 性能的评估基于两个设计场景的分析--走廊驾驶和交叉路口穿越。在每个场景中，两种不同进化算法的性能都与几种基线方法进行了比较，包括手工制作的控制器和文献中的其他几种方法。结果表明，在两个测试场景中，进化算法生成的设计在安全性、效率和总体性能方面都优于其他方法。此外，评估还揭示了两种经测试的进化算法之间的一些有趣区别，这对自动驾驶汽车的从业人员和开发人员很有帮助。总之，评估结果支持这样的结论，即进化算法是自动生成安全高效的自动驾驶汽车设计的可靠而有效的工具。
{"title":"An Enhanced Performance Analysis of Evolutionary Algorithms for Automated Design of Autonomous Vehicles","authors":"Pawan Bhambu, G. D, Vaishali Singh","doi":"10.1109/ICOCWC60930.2024.10470612","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470612","url":null,"abstract":"This paper presents an extensive performance analysis of evolutionary algorithms (EA) used for automated design of autonomous vehicles (AVs). This research explores the algorithms' abilities to generate AV designs that exhibit safe driving behavior and also meet requirements concerning comfort, efficiency and reliability. The assessment of the EA performance is based on the analysis of two design scenarios-corridor driving and intersection crossing. On each of these scenarios, the performance of two distinct evolutionary algorithms was compared against several baselines, including a hand-crafted controller and several other methods from the literature. The results showed that the EA-generated designs outperform the other methods in terms of safety, efficiency and general performance on both of the test scenarios. Furthermore, the assessment revealed some interesting distinctions between both the tested evolutionary algorithms, which could be useful for practitioners and developers of autonomous vehicles. Overall, the results support the conclusion that evolutionary algorithms can be a reliable and effective tool for automated generation of safe and efficient AV designs","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"22 8","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470648
Syed Harron, Gaurav Shukla, Harshita Kaushik
A sensor community is a set of interconnected sensors that can degree, monitor, and report phenomena within the environment. Sensor networks have many packages consisting of actual-time environmental tracking. A good way to effectively use these networks for monitoring, an optimized network layout needs to be employed to ensure that the network can meet the necessities of its operational demands. This paper proposes a fashionable-motive sensor network design framework for real-time environmental tracking. The proposed framework gives a step-by-step technique to optimize a sensor network design for the project at hand. It begins with a version of the surroundings that the sensor community will screen, which is used to generate sensor node configurations and topologies. These configurations and topologies are then used to lay out the sensor community such that its deployment is optimized for the surroundings. The design framework makes use of a community optimization set of rules to attain a configuration that maximizes the network's performance and is capable of meeting its operational requirements. The set of rules operates with the aid of looking through the distance of all feasible sensor community topologies, looking for those that have the first-rate value-benefit ratio. The algorithm can be further progressed by way of the usage of the device, gaining knowledge of strategies, which makes it capable of managing extra complex eventualities.
{"title":"Optimized Sensor Network Design for Real-Time Environmental Monitoring","authors":"Syed Harron, Gaurav Shukla, Harshita Kaushik","doi":"10.1109/ICOCWC60930.2024.10470648","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470648","url":null,"abstract":"A sensor community is a set of interconnected sensors that can degree, monitor, and report phenomena within the environment. Sensor networks have many packages consisting of actual-time environmental tracking. A good way to effectively use these networks for monitoring, an optimized network layout needs to be employed to ensure that the network can meet the necessities of its operational demands. This paper proposes a fashionable-motive sensor network design framework for real-time environmental tracking. The proposed framework gives a step-by-step technique to optimize a sensor network design for the project at hand. It begins with a version of the surroundings that the sensor community will screen, which is used to generate sensor node configurations and topologies. These configurations and topologies are then used to lay out the sensor community such that its deployment is optimized for the surroundings. The design framework makes use of a community optimization set of rules to attain a configuration that maximizes the network's performance and is capable of meeting its operational requirements. The set of rules operates with the aid of looking through the distance of all feasible sensor community topologies, looking for those that have the first-rate value-benefit ratio. The algorithm can be further progressed by way of the usage of the device, gaining knowledge of strategies, which makes it capable of managing extra complex eventualities.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470862
Lijuan Li
Family education has been attached great importance in today's society, and family education is an indispensable part of minors, and also plays a great role in strengthening the construction of spiritual civilization. The community is also inseparable from the support of family education. The traditional model cannot solve the problem of family education development in the community. Therefore, this paper proposes a Louvain algorithm for family education analysis. Firstly, machine learning is used to analyze family education, and indicators are divided according to family education requirements to reduce interference factors in family education. Then, machine learning analyzes community family education to form a family education program and comprehensively analyzes the family education results. MATLAB simulation shows that under certain evaluation criteria, the effect and rationality of family education of Louvain $primemathrm{s}$ algorithm on community are better than those of traditional models.
{"title":"A Study of the Home Education Community Based on Louvain's Algorithm","authors":"Lijuan Li","doi":"10.1109/ICOCWC60930.2024.10470862","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470862","url":null,"abstract":"Family education has been attached great importance in today's society, and family education is an indispensable part of minors, and also plays a great role in strengthening the construction of spiritual civilization. The community is also inseparable from the support of family education. The traditional model cannot solve the problem of family education development in the community. Therefore, this paper proposes a Louvain algorithm for family education analysis. Firstly, machine learning is used to analyze family education, and indicators are divided according to family education requirements to reduce interference factors in family education. Then, machine learning analyzes community family education to form a family education program and comprehensively analyzes the family education results. MATLAB simulation shows that under certain evaluation criteria, the effect and rationality of family education of Louvain $primemathrm{s}$ algorithm on community are better than those of traditional models.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"17 17","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The role of technical evaluation in the fine water injection flow field control technology of reservoirs is very important, but there is a problem of inaccurate evaluation of results. The traditional control technology cannot solve the technical evaluation problem in the fine water injection flow field control technology of reservoirs, and the evaluation is unreasonable. Therefore, this paper proposes a BP neural network algorithm for innovative technical evaluation and analysis. Firstly, the control theory is used to evaluate the technical personnel, and the indicators are divided according to the technical evaluation requirements to reduce the interference factors in the technical evaluation. Then, the control theory evaluates the technology of fine water injection flow field regulation of the reservoir, forms a technical evaluation scheme, and comprehensively analyzes the technical evaluation results. MATLAB simulation shows that under certain evaluation criteria, the technical evaluation accuracy and oil recovery of the fine water injection flow field control method of the reservoir by the BP neural network algorithm are better than those of the traditional control technology.
技术评价在水库精细注水流场控制技术中的作用非常重要，但也存在结果评价不准确的问题。传统的控制技术无法解决油藏精细注水流场控制技术中的技术评价问题，评价结果不合理。因此，本文提出了一种 BP 神经网络算法，用于创新技术评价分析。首先，利用控制论对技术人员进行评价，根据技术评价要求划分指标，减少技术评价中的干扰因素。然后，控制理论对油藏精细注水流场调节技术进行评价，形成技术评价方案，并对技术评价结果进行综合分析。MATLAB仿真表明，在一定评价标准下，BP神经网络算法油藏精细注水流场调控方法的技术评价精度和采油率均优于传统调控技术。
{"title":"Research on Fine Water Injection Flow Field Regulation Technology of Reservoir Based on Computer Neural Network Simulation","authors":"Ruijie Geng, Minglin Li, Yaqiong Wei, Mingzhu Li, Guoyong Li, Chenglin Yu","doi":"10.1109/ICOCWC60930.2024.10470813","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470813","url":null,"abstract":"The role of technical evaluation in the fine water injection flow field control technology of reservoirs is very important, but there is a problem of inaccurate evaluation of results. The traditional control technology cannot solve the technical evaluation problem in the fine water injection flow field control technology of reservoirs, and the evaluation is unreasonable. Therefore, this paper proposes a BP neural network algorithm for innovative technical evaluation and analysis. Firstly, the control theory is used to evaluate the technical personnel, and the indicators are divided according to the technical evaluation requirements to reduce the interference factors in the technical evaluation. Then, the control theory evaluates the technology of fine water injection flow field regulation of the reservoir, forms a technical evaluation scheme, and comprehensively analyzes the technical evaluation results. MATLAB simulation shows that under certain evaluation criteria, the technical evaluation accuracy and oil recovery of the fine water injection flow field control method of the reservoir by the BP neural network algorithm are better than those of the traditional control technology.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"64 21","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
neural networks have emerged as practical tools for discovering styles in agricultural crop monitoring. The motive of this take a look at was to research whether or not neural network strategies will be implemented to seize and perceive patterns in various parameters related to crop monitoring. Data was accrued from four corn fields inside the Midwest US from October 2007 to October 2009. Linear and non-linear neural networks were used to analyze the information, to identify enormous styles associated with crop manufacturing. Effects showed that the neural networks have been able to as they should be perceived styles inside the records, with the non-linear network generating satisfactory results. The results confirmed that the most important parameters related to crop production were the width of the kernel, duration of the cob, and precise leaf location. These findings advocate that neural networks may be a promising technique for understanding complicated crop monitoring relationships.
{"title":"Discovering Patterns with Neural Networks in Agricultural Crop Monitoring","authors":"Akhilendra Pratap Singh, Neeraj Kaushik, Rahul Pawar","doi":"10.1109/ICOCWC60930.2024.10470833","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470833","url":null,"abstract":"neural networks have emerged as practical tools for discovering styles in agricultural crop monitoring. The motive of this take a look at was to research whether or not neural network strategies will be implemented to seize and perceive patterns in various parameters related to crop monitoring. Data was accrued from four corn fields inside the Midwest US from October 2007 to October 2009. Linear and non-linear neural networks were used to analyze the information, to identify enormous styles associated with crop manufacturing. Effects showed that the neural networks have been able to as they should be perceived styles inside the records, with the non-linear network generating satisfactory results. The results confirmed that the most important parameters related to crop production were the width of the kernel, duration of the cob, and precise leaf location. These findings advocate that neural networks may be a promising technique for understanding complicated crop monitoring relationships.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"63 12","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470568
K. R, Anubhav Sony, Pradeep Kumar Shah
this paper presents an in-intensity evaluation of cutting-edge tunable linear circuit design methodologies applied to the design of various ultra-modern filters. First, a brief creation of linear circuit design and the contemporary filters utilized in its software is given, accompanied by an assessment of modern cutting-edge technology for tuning linear circuits. Numerous design exchange-contemporary associated with tunable linear circuit design are mentioned. Secondly, a complete review of modern-day existing techniques hired for the choice of present-day most beneficial passive additives in filter design is supplied, in conjunction with the benefits and drawbacks of today's approach. The focus then shifts to a comprehensive examination of today's most promising tunable linear circuit design strategies, featuring various methodologies inclusive of disbursed parameter models, parametric optimization algorithms, and software program-based methods. The paper concludes with feedback on the potential destiny instructions for cutting-edge tunable linear circuit design.
{"title":"Analysis of Tunable Linear Circuit Design Methodologies for Filters","authors":"K. R, Anubhav Sony, Pradeep Kumar Shah","doi":"10.1109/ICOCWC60930.2024.10470568","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470568","url":null,"abstract":"this paper presents an in-intensity evaluation of cutting-edge tunable linear circuit design methodologies applied to the design of various ultra-modern filters. First, a brief creation of linear circuit design and the contemporary filters utilized in its software is given, accompanied by an assessment of modern cutting-edge technology for tuning linear circuits. Numerous design exchange-contemporary associated with tunable linear circuit design are mentioned. Secondly, a complete review of modern-day existing techniques hired for the choice of present-day most beneficial passive additives in filter design is supplied, in conjunction with the benefits and drawbacks of today's approach. The focus then shifts to a comprehensive examination of today's most promising tunable linear circuit design strategies, featuring various methodologies inclusive of disbursed parameter models, parametric optimization algorithms, and software program-based methods. The paper concludes with feedback on the potential destiny instructions for cutting-edge tunable linear circuit design.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"32 9","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470868
Nikita Jain, R. Kamalraj, Ajay Agrawal
This paper offers a technique to categorize an expansion of clinically critical cancers using a deep perception community (DBN) technique. The DBN version became educated with transcriptome datasets from most human cancer mobile strains to generate a set of rules for different sorts and ranges of cancers. Inside the DBN model, every schooling sample was encoded with a set of features, along with gene and isoform expression information. The entered statistics are then handed thru the layers of DBN that generate a probabilistic inference of the samples based totally on the relationships among features and output values. The mistake and misclassification rates were evaluated using leave-one-out cross-validation, with an average accuracy of ninety two.2% This method provides a speedy and computationally inexpensive manner to classify differing types and ranges of cancer, which is of specific importance for early detection and diagnosis in medical care. Deep notion Networks (DBNs) are machine-mastering algorithms that use more than one layer of neural networks to analyze complex styles from statistics. DBNs are specifically beneficial for classifying clinically-essential cancers, as they allow for the correct and effective detection of several cancerous cells. DBNs obtain this through skilled layers of statistics to extract precise features from datasets, along with pictures of the cancerous cells or biomarkers of metabolic pathways. Using those extracted capabilities, DBNs can correctly distinguish between every day and cancerous cells and which sort of cancers the cells constitute. With a greater understanding of cancerous cells, medical practitioners can higher diagnose and treat a ramification of cancers, mainly to improve affected person care. .
{"title":"Classifying Clinically Important Cancers Using Deep Belief Networks","authors":"Nikita Jain, R. Kamalraj, Ajay Agrawal","doi":"10.1109/ICOCWC60930.2024.10470868","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470868","url":null,"abstract":"This paper offers a technique to categorize an expansion of clinically critical cancers using a deep perception community (DBN) technique. The DBN version became educated with transcriptome datasets from most human cancer mobile strains to generate a set of rules for different sorts and ranges of cancers. Inside the DBN model, every schooling sample was encoded with a set of features, along with gene and isoform expression information. The entered statistics are then handed thru the layers of DBN that generate a probabilistic inference of the samples based totally on the relationships among features and output values. The mistake and misclassification rates were evaluated using leave-one-out cross-validation, with an average accuracy of ninety two.2% This method provides a speedy and computationally inexpensive manner to classify differing types and ranges of cancer, which is of specific importance for early detection and diagnosis in medical care. Deep notion Networks (DBNs) are machine-mastering algorithms that use more than one layer of neural networks to analyze complex styles from statistics. DBNs are specifically beneficial for classifying clinically-essential cancers, as they allow for the correct and effective detection of several cancerous cells. DBNs obtain this through skilled layers of statistics to extract precise features from datasets, along with pictures of the cancerous cells or biomarkers of metabolic pathways. Using those extracted capabilities, DBNs can correctly distinguish between every day and cancerous cells and which sort of cancers the cells constitute. With a greater understanding of cancerous cells, medical practitioners can higher diagnose and treat a ramification of cancers, mainly to improve affected person care. .","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"31 8","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Move-layer architectures, called protocol layering, represent a thrilling way of managing networking problems on more than one OSI model layer. Mainly, go-layer architectures provide a way to optimize community performance for software-specific eventualities. In these architectures, community overall performance is advanced by optimizing or tuning sure network parameters harmoniously throughout more than one layer, with each layer gambling its function to maximize performance. This approach is critical in present-day networks and conversation technologies, in which community and alertness overall performance will become increasingly important. This paper outlines current go-layer architectures, describes their essential blessings and functions, and discusses their utility in shaping community and alertness performance. Specifically, we discuss the blessings of go-layer architectures in enhancing application-unique network performance in today's mobile networks and virtualized environments. In addition, we provide examples of how move-layer architectures can be used to optimize network performance in unique utility situations. Lastly, we provide a brief dialogue of capacity troubles that may arise when using these architectures.
移动层架构被称为协议分层，是在一个以上的 OSI 模型层上管理网络问题的一种令人激动的方法。主要而言，移动层架构提供了一种针对软件特定情况优化社区性能的方法。在这些架构中，通过在多个层中协调优化或调整确定的网络参数来提高社区的整体性能，每个层都在发挥自己的功能，以最大限度地提高性能。这种方法在当今的网络和对话技术中至关重要，因为在这些技术中，社区和警戒性的整体性能将变得越来越重要。本文概述了当前的去层架构，描述了它们的基本优势和功能，并讨论了它们在塑造社区和警觉性能方面的作用。具体而言，我们讨论了去层架构在当今移动网络和虚拟化环境中提高应用独特网络性能的优势。此外，我们还举例说明了如何利用移动层架构来优化独特应用情况下的网络性能。最后，我们就使用这些架构时可能出现的容量问题进行了简要对话。
{"title":"Cross-Layer Architectures for Enhancing Application-Specific Network Performance","authors":"Ajay Kumar Upadhyay, Sanjeev Kumar Mandal, Vishal Sharma","doi":"10.1109/ICOCWC60930.2024.10470747","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470747","url":null,"abstract":"Move-layer architectures, called protocol layering, represent a thrilling way of managing networking problems on more than one OSI model layer. Mainly, go-layer architectures provide a way to optimize community performance for software-specific eventualities. In these architectures, community overall performance is advanced by optimizing or tuning sure network parameters harmoniously throughout more than one layer, with each layer gambling its function to maximize performance. This approach is critical in present-day networks and conversation technologies, in which community and alertness overall performance will become increasingly important. This paper outlines current go-layer architectures, describes their essential blessings and functions, and discusses their utility in shaping community and alertness performance. Specifically, we discuss the blessings of go-layer architectures in enhancing application-unique network performance in today's mobile networks and virtualized environments. In addition, we provide examples of how move-layer architectures can be used to optimize network performance in unique utility situations. Lastly, we provide a brief dialogue of capacity troubles that may arise when using these architectures.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"55 35","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470491
Ramakant Upadhyay, Arun Kumar Pipersenia, M.S. Nidhya
Reinforcement learning (RL) is a famous and influential technique for fixing complicated problems in synthetic intelligence. But it typically calls for considerable records and computational assets to be effective. A key to a hit RL is a suitable illustration of the environment country records. A famous approach to that is using multilayer notion (MLP) architectures. In this paper, we recognize MLP architectures as an essential constructing block for plenty of RL algorithms. We examine the effectiveness of MLP architectures for RL and present processes to improve their overall performance. First, we recommend a Multi-Layer Reinforcement gaining knowledge of (the MLRL) approach, wherein the MLP structure is included within the RL policy shape. 2d, we inspect an Ensemble of MLPs method, which combines a couple of MLPs into an unmarried RL policy. We practice each of these strategies to select RL duties and problem domains and display that they could result in stepped-forward learning performance. Our outcomes advice that MLP architectures provide a powerful illustration for reinforcement getting to know and that the MLRL and Ensemble processes can similarly improve the performance of those architectures.
{"title":"Analyzing Multilayer Perception Architectures for Reinforcement Learning","authors":"Ramakant Upadhyay, Arun Kumar Pipersenia, M.S. Nidhya","doi":"10.1109/ICOCWC60930.2024.10470491","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470491","url":null,"abstract":"Reinforcement learning (RL) is a famous and influential technique for fixing complicated problems in synthetic intelligence. But it typically calls for considerable records and computational assets to be effective. A key to a hit RL is a suitable illustration of the environment country records. A famous approach to that is using multilayer notion (MLP) architectures. In this paper, we recognize MLP architectures as an essential constructing block for plenty of RL algorithms. We examine the effectiveness of MLP architectures for RL and present processes to improve their overall performance. First, we recommend a Multi-Layer Reinforcement gaining knowledge of (the MLRL) approach, wherein the MLP structure is included within the RL policy shape. 2d, we inspect an Ensemble of MLPs method, which combines a couple of MLPs into an unmarried RL policy. We practice each of these strategies to select RL duties and problem domains and display that they could result in stepped-forward learning performance. Our outcomes advice that MLP architectures provide a powerful illustration for reinforcement getting to know and that the MLRL and Ensemble processes can similarly improve the performance of those architectures.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"26 5","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140530181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1109/ICOCWC60930.2024.10470615
R. Naaz, K. R, Surendra Yadav
This technical abstract explores the impact of extended quick-term reminiscence (LSTM) parameters on net overall performance for automated textual content summarization within the Korean language. It observes and considers the parameters of phrase embedding size, sentence length, and encoding intensity. Embedding length substantially affects the community's overall performance, and a pair of dimensional representations of word embedding can improve summarization accuracy. Increasing sentence duration additionally showed enhancements, with the very best accuracy executed at triple sentence embedding lengths. Eventually, encoding intensity had a low effect on network performance, with only barely better results visible with double and triple encodings. Overall, this observation concluded that gold standard network performance for textual content summarization in the Korean language is pleasant and finished via a mixture of two-dimensional embedding with an elevated sentence length and unmarried encoding intensity.
{"title":"Exploring the Impact of LSTM Parameters on Network Performance for Automatic Text Summarization","authors":"R. Naaz, K. R, Surendra Yadav","doi":"10.1109/ICOCWC60930.2024.10470615","DOIUrl":"https://doi.org/10.1109/ICOCWC60930.2024.10470615","url":null,"abstract":"This technical abstract explores the impact of extended quick-term reminiscence (LSTM) parameters on net overall performance for automated textual content summarization within the Korean language. It observes and considers the parameters of phrase embedding size, sentence length, and encoding intensity. Embedding length substantially affects the community's overall performance, and a pair of dimensional representations of word embedding can improve summarization accuracy. Increasing sentence duration additionally showed enhancements, with the very best accuracy executed at triple sentence embedding lengths. Eventually, encoding intensity had a low effect on network performance, with only barely better results visible with double and triple encodings. Overall, this observation concluded that gold standard network performance for textual content summarization in the Korean language is pleasant and finished via a mixture of two-dimensional embedding with an elevated sentence length and unmarried encoding intensity.","PeriodicalId":518901,"journal":{"name":"2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC)","volume":"113 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140529734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：