AI, Machine Learning and Applications最新文献

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Research on Task Scheduling Strategy based on the Trustworthiness of MapReduce 基于MapReduce可信度的任务调度策略研究
AI, Machine Learning and Applications Pub Date : 2021-08-28 DOI: 10.5121/csit.2021.111304
Qin Jun, Song Yanyan, Zong Ping
{"title":"Research on Task Scheduling Strategy based on the Trustworthiness of MapReduce","authors":"Qin Jun, Song Yanyan, Zong Ping","doi":"10.5121/csit.2021.111304","DOIUrl":"https://doi.org/10.5121/csit.2021.111304","url":null,"abstract":"With the rapid development and popularization of information technology, cloud computing technology provides a good environment for solving massive data processing. Hadoop is an open-source implementation of MapReduce and has the ability to process large amounts of data. Aiming at the shortcomings of the fault-tolerant technology in the MapReduce programming model, this paper proposes a reliability task scheduling strategy that introduces a failure recovery mechanism, evaluates the trustworthiness of resource nodes in the cloud environment, establishes a trustworthiness model, and avoids task allocation to low reliability node, causing the task to be re-executed, wasting time and resources. Finally, the simulation platform CloudSim verifies the validity and stability of the task scheduling algorithm and scheduling model proposed in this paper.","PeriodicalId":104179,"journal":{"name":"AI, Machine Learning and Applications","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130735572","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}
引用次数: 0
Divide-and-Conquer Federated Learning Under Data Heterogeneity 数据异构下的分而治之联邦学习
AI, Machine Learning and Applications Pub Date : 2021-08-28 DOI: 10.5121/csit.2021.111302
Pravin Chandran, Raghavendra Bhat, A. Chakravarthy, Srikanth Chandar
{"title":"Divide-and-Conquer Federated Learning Under Data Heterogeneity","authors":"Pravin Chandran, Raghavendra Bhat, A. Chakravarthy, Srikanth Chandar","doi":"10.5121/csit.2021.111302","DOIUrl":"https://doi.org/10.5121/csit.2021.111302","url":null,"abstract":"Federated Learning allows training of data stored in distributed devices without the need for centralizing training-data, thereby maintaining data-privacy. Addressing the ability to handle data heterogeneity (non-identical and independent distribution or non-IID) is a key enabler for the wider deployment of Federated Learning. In this paper, we propose a novel Divide-andConquer training methodology that enables the use of the popular FedAvg aggregation algorithm by over-coming the acknowledged FedAvg limitations in non-IID environments. We propose a novel use of Cosine-distance based Weight Divergence metric to determine the exact point where a Deep Learning network can be divided into class-agnostic initial layers and class-specific deep layers for performing a Divide and Conquer training. We show that the methodology achieves trained-model accuracy at-par with (and in certain cases exceeding) the numbers achieved by state-of-the-art algorithms like FedProx, FedMA, etc. Also, we show that this methodology leads to compute and/or bandwidth optimizations under certain documented conditions.","PeriodicalId":104179,"journal":{"name":"AI, Machine Learning and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114816202","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}
引用次数: 0
How Many Features is an Image Worth? Multi-Channel CNN for Steering Angle Prediction in Autonomous Vehicles 一张图像值多少个特征?自动驾驶汽车转向角度预测的多通道CNN
AI, Machine Learning and Applications Pub Date : 2021-08-28 DOI: 10.5121/csit.2021.111301
Jason Munger, Carlos Morato
{"title":"How Many Features is an Image Worth? Multi-Channel CNN for Steering Angle Prediction in Autonomous Vehicles","authors":"Jason Munger, Carlos Morato","doi":"10.5121/csit.2021.111301","DOIUrl":"https://doi.org/10.5121/csit.2021.111301","url":null,"abstract":"This project explores how raw image data obtained from AV cameras can provide a model with more spatial information than can be learned from simple RGB images alone. This paper leverages the advances of deep neural networks to demonstrate steering angle predictions of autonomous vehicles through an end-to-end multi-channel CNN model using only the image data provided from an onboard camera. Image data is processed through existing neural networks to provide pixel segmentation and depth estimates and input to a new neural network along with the raw input image to provide enhanced feature signals from the environment. Various input combinations of Multi-Channel CNNs are evaluated, and their effectiveness is compared to single CNN networks using the individual data inputs. The model with the most accurate steering predictions is identified and performance compared to previous neural networks.","PeriodicalId":104179,"journal":{"name":"AI, Machine Learning and Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133594543","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}
引用次数: 0
Impact of Blockchain Technology in Healthcare Sector during Covid-19 Pandemic Covid-19大流行期间区块链技术对医疗保健行业的影响
AI, Machine Learning and Applications Pub Date : 2021-08-28 DOI: 10.5121/csit.2021.111306
Zeba Mahmood
{"title":"Impact of Blockchain Technology in Healthcare Sector during Covid-19 Pandemic","authors":"Zeba Mahmood","doi":"10.5121/csit.2021.111306","DOIUrl":"https://doi.org/10.5121/csit.2021.111306","url":null,"abstract":"Globally, the pandemic has affected management of risks. Progressively Blockchain is being applicable over the management of healthcare, as an imperative method for improving organizationalprotocols and for providing the convenient support for a productive and efficient decision-making process hinge on facts. In healthcare, different approaches to emergency preparedness can be recognized; indeed, each emergency is distinguished by different stages. In healthcare, we intend to role: explicitly, it will be responsible to enhance COVID19-safe clinical proceeding. The primary approaches obtainable from various blockchain-based models, and distinctly those associated by clinical individuals in the future throughout the current COVID-19 pandemic either on the would be capable to perform an outstanding assumption of furthermore infectious conditions. We believe that in real infectious disease outbreaks, blockchain technology undertaking, have been documented here and part in the future.","PeriodicalId":104179,"journal":{"name":"AI, Machine Learning and Applications","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508578","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}
引用次数: 1
A Fragmentation Region-based Skyline Computation Framework for a Group of Users 基于碎片化区域的用户群Skyline计算框架
AI, Machine Learning and Applications Pub Date : 2021-08-28 DOI: 10.5121/csit.2021.111303
Ghoncheh Babanejad Dehaki, H. Ibrahim, N. Udzir, F. Sidi, A. Alwan
{"title":"A Fragmentation Region-based Skyline Computation Framework for a Group of Users","authors":"Ghoncheh Babanejad Dehaki, H. Ibrahim, N. Udzir, F. Sidi, A. Alwan","doi":"10.5121/csit.2021.111303","DOIUrl":"https://doi.org/10.5121/csit.2021.111303","url":null,"abstract":"Skyline processing, an established preference evaluation technique, aims at discovering the best, most preferred objects, i.e. those that are not dominated by other objects, in satisfying the user’s preferences. In today’s society, due to the advancement of technology, ad-hoc meetings or impromptu gathering are becoming more and more common. Deciding on a suitable meeting point (object)for a group of people (users) to meet is not a straightforward task especially when these users are located at different places with distinct preferences. A place which is close by to the users might not provide the facilities/services that meet all the users’ preferences; while a place having the facilities/services that meet most of the users’ preferences might be too distant from these users. Although the skyline operator can be utilised to filter the dominated objects among the objects that fall in the region of interest of these users, computing the skylines for various groups of users in similar region would mean rescanning the objects of the region and repeating the process of pair wise comparisons among the objects which are undoubtedly unwise. On this account, this study presents a region-based skyline computation framework which attempts to resolve the above issues by fragmenting the search region of a group of users and utilising the past computed skyline results of the fragments. The skylines, which are the objects recommended to be visited by a group of users, are derived by analysing both the locations of the users, i.e. spatial attributes, as well as the spatial and non-spatial attributes of the objects. Several experiments have been conducted and the results show that our proposed framework outperforms the previous works with respect to CPU time.","PeriodicalId":104179,"journal":{"name":"AI, Machine Learning and Applications","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125470941","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}
引用次数: 0
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