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共 1120 条记录，以下是401-410 订阅

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Generative data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning 4

Generative Data Augmentation for Non-IID Problem in Decentra...

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4th international conference on data Intelligence and Security, ICDIS 2022

作者： Wang, Zirui Duan, Shaoming Wu, Chengyue Lin, Wenhao Zha, Xinyu Han, Peiyi Liu, Chuanyi School of Computer Science and Technology Shenzhen China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

Swarm learning (SL) is an emerging promising decentralized machine learning paradigm and has achieved high performance in clinical applications. SL solves the problem of a central structure in federated learning by combining edge computing and blockchain-based peer-to-peer network. While there are promising results in the assumption of the independent and identically distributed (IID) data across participants, SL suffers from performance degradation as the degree of the non-IID data increases. To address this problem, we propose a generative augmentation framework in swarm learning called SL-GAN, which augments the non-IID data by generating the synthetic data from participants. SL-GAN trains generators and discriminators locally, and periodically aggregation via a randomly elected coordinator in SL network. Under the standard assumptions, we theoretically prove the convergence of SL-GAN using stochastic approximations. Experimental results demonstrate that SL-GAN outperforms state-of-art methods on three real world clinical datasets including Tuberculosis, Leukemia, COVID-19. © 2022 IEEE.

关键词： Peer to peer networks

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An Interactive Short Answer Grading System Based on Active Learning Models 4

An Interactive Short Answer Grading System Based on Active L...

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4th international conference on Computer science and Technologies in Education, CSTE 2022

作者： Lui, Andrew Kwok-Fai Ng, Sin-Chun Cheung, Stella Wing-Nga Hong Kong Metropolitan University Dept of Electronic Eng and Computer Science Hong Kong School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom

ISBN: (数字)9781665481885

ISBN: (纸本)9781665481885

Grading automation can improve learning experience with quick around-the-clock feedback and superior grading consistency. Obtaining annotated data for training short answer grading models is costly. Active learning has been proven an effective approach to build accurate models with few annotated data. this paper presents an active learning approach of short answer grading that comprises of a few novelties. the first is a specialized active learning formulation adapted to short answer grading principles. the second is a proposal to exploit human expertise in fine-tuning several active learning model parameters for adaptation to the specifics of each grading task. the third is an interactive short answer grading system that is designed for building better quality grading model by informing users with data visualizations. the prototype presented in the paper should provide a useful conceptual demonstration for real-life deployment of active learning for short answer grading and further research in an enhanced interactive form of active learning. © 2022 IEEE.

关键词： Graphical user interfaces

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Storage I/O Acceleration Scheme Based on SPDK in OpenStack Cloud Platform 4

Storage I/O Acceleration Scheme Based on SPDK in OpenStack C...

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4th international Academic Exchange conference on science and Technology Innovation, IAECST 2022

作者： He, Junli Gong, Weifeng Qi, Kaiyuan Su, Guangfeng Song, Wenping Zhengzhou University School of Electrical and Information Engineering Zhengzhou China Inspur Electronic Information Industry Co. Ltd Beijing China Jinan Inspur Data Technology Co. Ltd Jinan China Zhengzhou Inspur Data Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9798350320008

Aiming at the problems of long storage I/O path, low IOPS (Input/Output Per Second) and high latency of virtual machines in OpenStack cloud platform, a storage performance acceleration scheme based on SPDK (Storage Performance Development Kit) is proposed: offload the back-end driver of virtualized I/O to user state at virtualization layer by mounting SPDK type cloud hard disk, replace interrupt mechanism with polling mechanism, and use lock-free queue to achieve the purpose of shortening I/O path, reducing context switching frequency, and reducing memory copy. the experimental results show that compared with virtual machines mounted with regular cloud hard disks, virtual machines using this acceleration solution saw a 32.75% and 549.02% increase in read IOPS and write IOPS, a 40.73% and 35.18% increase in read and write bandwidth, and a 32% and 18.81% decrease in read latency and write latency, respectively. © 2022 IEEE.

关键词： Virtual machine

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Large-Scale data Intensive Heterogeneous Task Scheduling Method Based on Parallel GATS-TS Algorithm 4

Large-Scale Data Intensive Heterogeneous Task Scheduling Met...

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4th international conference on Communications, Information System and Computer Engineering, CISCE 2022

作者： Xu, Xuelai Tian, Qingyang Xing, Yun Yin, Baolin Hu, Andong Zhiyang Innovation Technology Co. Ltd. Zibo China State Grid Liaoning Electric Power Company Limited Shenyang China State Grid Liaoning Electric Power Co. Ltd. Liaoyang Power Supply Company Liaoyang China

ISBN: (数字)9781665498487

ISBN: (纸本)9781665498487

data-intensive applications need to process ever-increasing massive data, which puts forward high requirements on the performance of computing resources. Traditional scheduling methods can no longer adapt to the randomness of heterogeneous tasks, so a large-scale data-intensive heterogeneous task scheduling method based on parallel GATS-TS algorithm is proposed. According to the large-scale data task scheduling strategy, the tasks of the whole workflow are divided into various stages according to the data dependence, and then the task scheduling is carried out step by step. Large-scale data-intensive heterogeneous tasks are classified and the performance of each node is dynamically evaluated according to the different task types. According to the classification results of heterogeneous tasks, the completion time of tasks to be assigned at each stage was determined, and the scheduling model was established by using the parallel GATS-TS algorithm. the fitness function of the algorithm was the reciprocal of the completion time, and the model output the optimization results of intensive heterogeneous task scheduling. the test results show that the design method can shorten the task completion time and reduce the scheduling length ratio, and achieve good scheduling results under the condition of large number of tasks © 2022 IEEE.

关键词： Scheduling

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One-class Anomaly Detection with Redundancy Reduction and Momentum Mechanism 4

One-class Anomaly Detection with Redundancy Reduction and Mo...

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4th international conference on data-Driven Optimization of Complex Systems, DOCS 2022

作者： Zhang, Xingbao Li, Wei Zhao, Yue Jiangxi University of Science and Technology School of Information Engineering Ganzhou China Jiangxi University of Science and Technology School of Economic and Management Ganzhou China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

the objective of anomaly detection is to identify the sample which differs in some known data. In practice, anomaly class is usually hard to obtain and consumptive to label, while unsupervised learning and one-class classification are most widely used to solve this problem. Only a set of data from the specific class are given in the training phase, and the remaining categories will be considered as abnormal. In this paper, inspired by the success of deep learning and Support Vector data Description (SVDD) of decision boundary-based, a novel idea that combining SVDD with redundant information reduction and momentum update mechanism named RRM-SVDD is proposed to address the anomaly detection problem. With the existence of trivial solutions for SVDD, an objective function is designed to avoid such situation by computing the dimension correlation matrix of the output vector from the feature extraction network, while optimizing it as the identity matrix to make any two dimensions as linearly independent as possible in the pretraining phase, that causes the effective for SVDD to describe the distribution of normal data in the feature space and reduce the probability of model collapse. Meanwhile, the momentum update mechanism is applied to learn the global hyperparameter center C by considering the previous epoch information in the next training period. To evaluate the performance of RRM-SVDD, related experiments on MNIST and CIFAR-10 image benchmark dataset have been conducted, achieved state-of-the-art anomaly detection accuracy and robustness in most categories than comparison methods. © 2022 IEEE.

关键词： Momentum

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Moringa Oleifera As An Alternative to Conventional Coagulants in Big data and data science Analytics 4

Moringa Oleifera As An Alternative to Conventional Coagulant...

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4th international conference on Advances in computing, Communication Control and Networking, ICAC3N 2022

作者： Singh, Tanmay Anandaram, Harishchander Shukla, Sunil Balmiki, Vinod Chandramauli, Awadhesh Joshi, Kapil Uttaranchal University Uttaranchal Institute of Technology Department of Civil Engineering Uttarakhand Dehradun India Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India University of Engineering and Technology Roorkee India Uttaranchal University Uttaranchal Institute of Technology Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9781665474368

Ever since the population explosion began postmodern industrialization since 1950 the population has grown more than threefold. With the global population crossing the Benchmark of 8.0 billion in June 2022, while some nations reap the benefits of progress, there are still nations who face the challenges of a pre-industrialized world, even today there is a quarter of world Population that does not have access to safe and clean drinking water. In this paper, conventionally, treatment of water is carried out with the help of Chemical Coagulants (Primarily Alum), several research have linked the usage of Alum with Neurodegenerative Diseases like Alzheimer's. Hence the need arises to develop a suitable alternative, Seeds of Moringa Oleifera also known as miracle tree due to its Phytochemical Properties contain Mo-CBP3-4 a protein that causes adsorption and neutralization of the colloidal charges thereby clarifying the water, For high initial Turbidity waters the optimal Dosage was the same for Moringa Oleifera (50mg/l) and the sludge produced post treatment is not only innocuous but also three to four times less by volume when compared with Alum and specially we use big data for data analysis. © 2022 IEEE.

关键词： Coagulation

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Power Network Scheduling Strategy Based on Federated Learning Algorithm in Edge computing Environment 4th

Power Network Scheduling Strategy Based on Federated Learnin...

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4th international conference on Big data and Security, ICBDS 2022

作者： Xu, Xiaowei Ding, Han Wang, Jiayu Hua, Liang State Grid Wuxi Power Supply Company Wuxi China

ISBN: (纸本)9789819932993

Distributed new energy consumption scenarios, such as photovoltaic, energy storage, charging stake, etc., are facing the needs of processing massive real-time data, large-scale distributed new energy and access to diverse loads. Based on the business characteristics such as business peak-valley dynamic change, network connection and time-delay differential demand of different business in energy and power business, a reasonable and effective integrated resource scheduling model of computing resources suitable for distributed new energy consumption scenario is studied to support power planning and dynamic dispatch application. In this article, we propose an arithmetic planning strategy based on federal learning. Specifically, we first introduce a computing priority network scheduling framework in edge cloud computing environments. Secondly, we process the absorption data of corresponding energy nodes by random forest algorithm, adjust the connection relationship between a large number of internal nodes, control and dispatch the nodes, and then conduct integrated training through federal learning to dispatch the computing power of the overall network, so as to achieve fast and accurate algorithm dispatch. then, under the same environment conditions, the simulation experiments of deep neural network and random forest algorithm are compared. A large number of simulation results show that the system can effectively assist the smart grid in reasonable algorithm dispatch and improve the resource utilization efficiency. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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Smart Device and Tracer to Overcome COVID-19 Using Digital Technology for Better Protection 4

Smart Device and Tracer to Overcome COVID-19 Using Digital T...

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4th international conference on Advancements in computing, ICAC 2022

作者： Avinash, Kulana Dithmal, Chethika Wijerathne, Pathum Kaushan, Nipuna De Silva, Hansi Kasthurirathna, Dharshana Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

A number of nations have experienced challenging circumstances as a result of the coronavirus disease (COVID-19), which has turned into a global pandemic. As a result of the social changes it has caused, this crisis will also have an impact on future generations. With the help of this technology, health organizations can quickly locate individuals who are infected with COVID-19 and provide them with medical care. the objective of this work is to develop a COVID-19 Tracer that is capable of COVID-19 detection and mitigation. the goal of this research is to reduce the number of COVID-19related fatalities in Sri Lanka while also enabling users who are infected with the disease to access appropriate care and hospitalization. this software uses digital technologies to acquire accurate data and provide precise interpretations based on that data. through the proposed method, patients can be treated using the application to get a precise diagnosis of their disease, maintaining social distance, stabilizing the mental level of the patient through AI, predicting the epidemic, providing COVID-19 vaccinations, as well as ambulance services through this application. Using every preventative measure available, this mobile application has now been developed to safeguard against COVID-19. © 2022 IEEE.

关键词： COVID-19

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Comparison of Bagging and Boosting in Imbalanced Multilabel of Al-Quran dataset 4

Comparison of Bagging and Boosting in Imbalanced Multilabel ...

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4th international conference Advancement in data science, E-Learning and Information Systems, ICADEIS 2022

作者： Choirulfikri, Muhammad Rizqi Adiwijaya Suryani, Arie Ardiyanti School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9781665463874

this paper address multi-label text classification in the case of Al-Quran in English translation. this research built from two models which are Bagging and Boosting in comparison. those models are chosen because previous research gives a good performance, especially in the imbalanced dataset. Imbalanced data is one problem in classification and this research focuses on that problem. this research started by preprocessing data, then TF-IDF is used for feature extraction. To measure the prediction of classification, this research uses the Hamming Loss to see the performance. Based the several experiments, the best performance result is obtained by Random Forest with a Hamming Loss value of 0.1019. then, For the fastest computational time is given by Extreme Gradient Boosting (XGBoost) with 224.76 seconds for running the system. thus, Bagging and Boosting have their advantage in classification. © 2022 IEEE.

关键词： Classification (of information)

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Analysis of Audio data and Prediction of the Genre using Novel Random Forest and Decision Tree 4

Analysis of Audio Data and Prediction of the Genre using Nov...

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4th international conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Pavan, V. Dhanalakshmi, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu 602105 India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

the main objective of the study is to classify the music genre using the features that are extracted from audio files. the classification is done using Novel Random Forest and Decision Tree and corresponding results are compared in terms of accuracy. Materials and Methods: the GTZAN dataset used in this study is obtained from the MARSYAS website, which is used for Music Information Retrieval, consists of 1000 music files in *** format. It is also referred to as the standard dataset to the date. the acoustic features of music called Mel-frequency cepstral coefficients(MFCC) that create patterns and help to predict the genre are extracted from the Music files. the data analysis, model training, and testing process are done entirely on the Jupyter platform. the Sample size was 20 per group. the pretest power obtained was 0.08. Results: From the experimental results it is observed that Novel Random forest gives an accuracy of 71.78% while Decision tree gives an accuracy of 59.89%. the classification process is done with both Novel Random Forest and Decision Tree, where the sample size N is 20 for two groups proposed (N=20) and comparison (N=20). the pretest power obtained is 0.08. Conclusion: In this study, it is found that the Random Forest model outperforms the Decision tree model in terms of accuracy by predicting the music genre efficiently. © 2022 IEEE.

关键词： Decision trees

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