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检索条件"任意字段=19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020"

共 800 条记录，以下是91-100 订阅

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Histological breast cancer classification using CNN and MLP based ensembles 19

Histological breast cancer classification using CNN and MLP ...

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19th ieee/ACS international conference on Computer Systems and applications (AICCSA)

作者： Zerouaoui, Hasnae Idri, Ali El Alaoui, Omar Mohammed VI Polytech Univ Benguerir Modeling Simulat & Data Anal Benguerir Morocco Mohammed V Univ Rabat ENSIAS Software Project Management Res Team Rabat Morocco

ISBN: (纸本)9798350310085

Breast cancer affects thousands of people worldwide each year, artificial intelligence used for digital pathological computer-aided diagnosis for breast cancer classification is a valuable domain. the advancement of machine learning and deep learning methods, as well as pathology slide digitization for primary diagnosis, is a major development toward meeting the demand for more accurate breast tumor diagnosis, classification, and prediction. this paper proposes and evaluates a new approach consisting of deep hybrid homogenous ensemble method based on seven deep learning models for feature extraction (DenseNet 201, Inception V3, Inception ResNet V2, MobileNet V2, ResNet 50, VGG16, and VGG19), a multi-layer perceptron (MLP) for classification and two combination rules (hard and weighted voting) for histological classification using the BreakHis dataset four magnification factors: 40X, 100X, 200X and 400X. the outcomes proved the potential of the proposed new approach since it outperformed its singles achieving an accuracy value of 98,3% for the MFs 40X and 100X and 97,7% for the MFs 200X and 400X.

关键词： Breast Cancer Classification Heterogeneous ensembles voting methods Deep learning Histopathological Images

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machine learning Based thread Pool Tuning via Program Analysis 23

Machine Learning Based Thread Pool Tuning via Program Analys...

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23rd ieee international conference on High Performance Computing and Communications, 7th ieee international conference on Data Science and Systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Akash, Lakindu Fernando, Duneesha Jayasinghe, Malith Keppitiyagama, Chamath thangarajah, Kishanthan University of Colombo School of Computing Colombo Sri Lanka WSO2 Inc. Mountain ViewCA United States

ISBN: (纸本)9781665494571

Intelligent tuning of parameters affecting performance can improve the performance of applications thereby improving the user experience. In this paper, we present a methodology which estimates the optimal thread pool size for an API integration during compile time by parsing its Abstract Syntax Tree (AST). Towards this, we trained 4 machine learning (ML) models using performance data and program features extracted by parsing the AST, and then used the models to estimate the optimal thread pool size. Out of the evaluated ML models;Random Forest Regression (RFR) provides 7.78%, 1.29 and 0.87 as MAPE, MSE and MAE values respectively, which correspond to highly accurate forecasts. Decision Tree and Extreme Gradient Boost (XGBoost) models also provide highly accurate forecasts. the optimal thread pool size predicted by our approach results in a lower average latency for a given API integration. For certain use cases, we achieve a latency improvement of 36% compared to the latency obtained when analytical models are used. © 2021 ieee.

关键词： Analytical models Maximum likelihood estimation Predictive models Syntactics Feature extraction Data models User experience

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On-demand Intelligent Routing Algorithms for the Deterministic Networks 23

On-demand Intelligent Routing Algorithms for the Determinist...

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作者： Wu, Zhongli Huang, Wenrui Dai, Zhewei Cao, Yuanyuan Dai, Bin Mo, Yijun School of Electronic Information and Communications Huazhong University of Science and Technology Hubei Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9781665494571

the deterministic network is oriented to various services in the network, ensuring the deterministic requirements of different services in terms of delay, packet loss rate, cost overhead, throughput, security, and reliability. this paper proposes an on-demand routing learning model with a three-layer logical plane that can generate on-demand routing strategies with deterministic QoS (Quality of Services) requirements for different traffic flows. With the help of deep reinforcement learning algorithms, the OdR-TD3 and OdR-SAC algorithms are proposed to generate the suitable routing strategies to meet the deterministic QoS requirements of applications' traffic. the experimental evaluation results show the OdR-TD3 and OdR-SAC algorithm have significant advantages over the DV and SPF algorithms, considering the achievement rate of deterministic QoS requirements. © 2021 ieee.

关键词： machine learning algorithms Smart cities Packet loss Quality of service Reinforcement learning Routing throughput

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Scaling machine learning at the Edge-Cloud: A Distributed Computing Perspective 19

Scaling Machine Learning at the Edge-Cloud: A Distributed Co...

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19th Annual international conference on Distributed Computing in Smart Systems and the Internet of things, DCOSS-IoT 2023

作者： Marozzo, Fabrizio Orsino, Alessio Talia, Domenico Trunfio, Paolo Dimes University of Calabria Rende Italy

ISBN: (纸本)9798350346497

the widespread diffusion of Internet of things (IoT) devices has led to an exponential growth in the volume of data generated at the edge of the network. With the rapid spread of machine learning (ML)-based applications, performing compute and resource-intensive learning tasks at the edge has become a critical issue, resulting in the need for scalable and efficient solutions that can overcome the resource constraints of edge devices. this paper analyzes the problem of scaling ML applications and algorithms at the edge-cloud continuum from a distributed computing perspective. In particular, we first highlight the limitations of traditional distributed architectures (e.g., clusters, clouds, and HPC systems) when running ML applications that use data generated at the edge. Next, we discuss how to enable traditional ML algorithms combining the benefits of edge computing, such as low-latency processing and privacy preservation of personal user data, with those of cloud computing, such as virtually unlimited computational and storage capabilities. Our analysis provides insights into how properly separated parts of a ML application can be deployed across edge-cloud architectures in order to optimize its execution. More-over, examples of ML applications and algorithms appropriately adapted for the edge-cloud continuum are shown. © 2023 ieee.

关键词： Internet of things

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A Survey of Deep learning Techniques Based On Drone Images For the Search And Rescue Of Victims From Collapsed Structures 19

A Survey of Deep Learning Techniques Based On Drone Images F...

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19th ieee-India-Council international conference (INDICON)

作者： Toby, Tom Gopalakrishnan, Uma Rao, Sethuraman N. Amrita Vishwa Vidyapeetham Ctr Wireless Networks & Applicat WNA Amritapuri India

ISBN: (纸本)9781665473507

the extent and frequency of the disasters occurring in the world have increased drastically and this trend is expected to continue. the major key player in disaster management is the collection of timely and accurate disaster damage status of the disaster affected regions. Even today disaster damage assessment is largely based on manual operations and data gathering. Automating the disaster assessment, mapping and response by combining information obtained from ground-based sources and aerial sources is a promising method. this work is a survey of the state of the art in automation of disaster damage assessment and utilization of data based on aerial sources for structural damage detection in collapsed buildings for extracting meaningful information to assist disaster rescuers. the various approaches for data processing from the data collected are studied for identifying effective methodologies. Deep learning methods are studied and key factors are to be discussed in the view point of drone images based collapsed building detection. the various approaches for feature learning, the challenges, bottlenecks based on various performance metrics are studied. the potential optimizations and solutions are extensively studied presented in this paper. Drone based deep learning processing is also provided prominence in the critical study for practical applications.

关键词： Deep learning CNN collapsed buildings transfer learning machine learning artificial intelligence drones UAVs disaster mapping building damage detection convnets

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Sentiments Affect Stock Returns: Evidence Based on Big Data 23

Sentiments Affect Stock Returns: Evidence Based on Big Data

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作者： Sun, Jing Xu, Yingying Zhao, Yueqiang Zhou, Yuliang Ortiz, Jaime Chen, Weipeng School of Economics and Management University of Science and Technology Beijing Beijing China School of Finance and Business Shanghai Normal University Shanghai China School of Management National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781665494571

this study provides new insights into the causality between sentiments and stock returns. Based on machine learning, we classify the blogs from Weibo into five sentiments of anger, disgust, fear, joy, and sadness. Using wavelet coherence and phase difference, we find close linkages between the number of blogs under certain sentiment and stock returns, which have both frequency and time-varying features. Over the medium horizons, the number of blogs under anger, disgust, fear, joy, and sadness lead to changes in stock returns positively. Nevertheless, over the long time horizons, stock returns lead changes in the number of blogs with a certain sentiment. We find no evidence supporting the difference between positive and negative sentiments considering their effects on stock returns. © 2021 ieee.

关键词： Couplings Time-frequency analysis Correlation Social networking (online) Blogs machine learning Lead

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Exploring Adversarial Graph Autoencoders to Manipulate Federated learning in the Internet of things 19

Exploring Adversarial Graph Autoencoders to Manipulate Feder...

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19th ieee international Wireless Communications and Mobile Computing (ieee IWCMC)

作者： Li, Kai Yuan, Xin Zheng, Jingjing Ni, Wei Guizani, Mohsen CISTER Res Ctr Porto Portugal Carnegie Mellon Univ CMU CyLab Secur & Privacy Inst Pittsburgh PA USA Commonwealth Sci & Ind Res Org CSIRO Black Mt ACT Australia MBZUAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350333398

Mobile edge computing (MEC) enables the Internet of things (IoT) with seamless integration of multiple application services. Federated learning is increasingly considered to improve training accuracy in MEC-IoT while circumventing the disclosure of private data, where the IoT nodes collaboratively train a machine learning model without disclosing their private data. In this paper, we propose a new cyber-epidemic attack that progressively manipulates federated learning and reduces the training accuracy of the benign MEC-IoT. the proposed cyber-epidemic attack explores adversarial graph autoencoders (GACE) to generate malicious local model updates that extract correlated features with the benign local and global models. the proposed GACE attack epidemically infects all the benign IoT nodes along with the training iterations in federated learning, while highly enhancing concealment of the attack.

关键词： Mobile edge computing (MEC) Internet of things (IoT) federated learning adversarial graph autoencoders cyber-epidemic attacks

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Cardiovascular Disease Prediction using Ensemble learning Techniques: A Stacking Approach 19

Cardiovascular Disease Prediction using Ensemble Learning Te...

引用

19th ieee international Colloquium on Signal Processing and Its applications, CSPA 2023

作者： Rustamov, Zahiriddin Rustamov, Jaloliddin Sultana, Most Sarmin Ywei, Jeanne Balakrishnan, Vimala Zaki, Nazar University of Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9781665476928

Cardiovascular diseases (CVDs) are group disorders affecting the heart or involving constricted blood arteries. Early disease detection increases the likelihood of survival. As a result, newer methods such as machine learning emerged, capable of processing and analysing vast quantities of complex medical data and providing a more accurate prediction of diseases, including CVDs. However, due to factors such as overfitting and bias, the single classifier could not ensure optimum prediction. thus, this study proposes a stacking ensemble classifier, which combines several single classifiers to produce an optimal predictive model. the Framingham Heart Study dataset was used to train the machine learning algorithms. the exploratory data analysis indicates that CVD was more common in males and diabetic individuals. Furthermore, individuals above the age of 65 were more susceptible to CVDs. Feature selection, missing value imputation, and data sampling were performed as part of data preprocessing. the results show that the proposed stacked ensemble classifier achieved 88.33% accuracy, 89.95% precision, 86.27% recall and 88.07% F1-score. Furthermore, the significance test results indicate that the proposed model performs significantly better than most models evaluated in this research. Finally, the comparative analysis showed that the proposed ensemble classifier performs better than most studies using the same dataset. the proposed model achieved a high F1-score, indicating it can accurately predict the cases with and without CVD. © 2023 ieee.

关键词： Forecasting

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A convolutional generative adversarial framework for data augmentation based on a robust optimal transport metric 23

A convolutional generative adversarial framework for data au...

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作者： Su, Liyilei Fu, Xianjun Hu, Qingmao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Zhejiang College of Security Technology Wenzhou325016 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665494571

Enhancement of the vanilla generative adversarial network (GAN) to preserve data variability in the presence of real world noise is of paramount significance in deep learning. In this study, we proposed a new distance metric of cosine distance in the framework of optimal transport (OT), and presented and validated a convolutional neural network (CNN) based GAN framework. In comparison with state-of-the-art methods based on Graphics Processing Units (GPU), the proposed framework could maintain the data diversity and quality best in terms of inception score (IS), Fréchet inception distance (FID) and enhancing the classification network of bone age, and is robust to noise degradation. the proposed framework is independent of hardware and thus could also be extended to more advanced hardware such as specialized Tensor Processing Units (TPU), and could be a potential built-in component of a general deep learning networks for such applications as image classification, segmentation, registration, and object detection. © 2021 ieee.

关键词： Measurement Deep learning Tensors Smart cities Neural networks Graphics processing units Object detection

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Federated learning for Human Mobility 19

Federated Learning for Human Mobility

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19th Annual international conference on Distributed Computing in Smart Systems and the Internet of things, DCOSS-IoT 2023

作者： Proteasa, Vlad-Alexandru Ciobanu, Radu-Ioan Dobre, Ciprian Marin, Radu-Corneliu University Politehnica of Bucharest Faculty of Automatic Control and Computers Romania

ISBN: (纸本)9798350346497

Nowadays, many interesting uses can be found for human mobility data in mobile networks, ranging from human flow prediction to statistics regarding crowded areas. However, location data are considered sensitive, and many users are naturally reticent to share this information. Nevertheless, federated learning is a collaborative approach that allows consumers to not share their personal data with anyone, but still contribute to machine learning algorithms. thus, in this paper we look at how various neural networks may be used to predict human mobility (and, in particular, the next location) using conventional machine learning algorithms like RNN, LSTM, or GRU. then, we expand the implementation towards federated learning, which is useful in distributed systems where data are collected from the mobile devices of people. the results of both approaches are reviewed in order to validate the implementation and then to determine how the accuracy of the networks is altered by the move towards federated learning, and whether it is a justifiable trade off for the increased benefits of data security and privacy, as well as for the applicability in real-life mobile networks. © 2023 ieee.

关键词： Wireless networks

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