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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33664 条记录，以下是4991-5000 订阅

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machine learning based Prediction of COVID-19: A Study on Italy's Pandemic Problems 8

Machine Learning based Prediction of COVID-19: A Study on It...

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8th international conference on Smart Structures and Systems, ICSSS 2022

作者： Gopirajan, P.V. Sivaranjani, Govindarajan Manickam, M. Parkavi, K. Vel Murugesh Kumar, N. Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Sustainable Engineering Chennai India Karpaga Vinayga College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9781665497619

Starting from December 2019, COVID-19 has claimed several thousand lives all around the world. Though the novel Corona virus (SARS-CoV-2) has emerged from China, it has affected most of the western countries like European Union and America. Italy is one of the most affected countries accounting for 16523 deaths until 7th April 2020. This study involves machine learning based approach to analyse the effectiveness of lockdown in containing the COVID-19 problem. It is assumed that, the lockdown was implemented 9 previous dates and total affected cases were predicted accordingly. Also, total deaths were predicted for these conditions. From the results, it is evident that the rate of increase of total cases remained 1.128 on an average during 21 days lockdown starting from 9th March 2020. Furthermore, the rate decreased after the lockdown period. The model also predicted that a 9 days prior lockdown would have reduced the total deaths from 16523 to 2239. During a pandemics, like COVID-19 the essential action taken by any government should be a strict lockdown as early as possible. Also, the public should maintain proper social distancing to avoid community spread of a pandemic disease which is highly contagious like COVID-19. © 2022 IEEE.

关键词： COVID-19 machine learning algorithms Pandemics machine learning Predictive models Prediction algorithms Market research

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Towards Differentially Private machine learning Models and Their Robustness to Adversaries 22nd

Towards Differentially Private Machine Learning Models and T...

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22nd international conference on Web engineering (ICWE)

作者： Mancino, Alberto Carlo Maria Di Noia, Tommaso Politecn Bari Bari Italy

ISBN: (纸本)9783031099175;9783031099168

The pervasiveness of modern machine learning algorithms exposes users to new vulnerabilities: violation of sensitive information stored in the training data and wrong model behaviors caused by adversaries. State-of-the-art approaches to prevent such behaviors are usually based on Differential Privacy (DP) and Adversarial Training (AT). DP is a rigorous formulation of privacy in probabilist terms to prevent information leakages that could reveal private information about the users, while AT algorithms empirically increase the system's robustness, injecting adversarial examples during the training process. Both techniques involve achieving their goal by modeling noise introduced into the system. We propose analyzing the relationship between these two techniques, studying how one affects the other. Our objective is to design a mechanism that guarantees DP and robustness against adversarial attacks, injecting modeled noise into the system. We propose Recommender Systems as an application scenario because of the severe risks to user privacy and system sensitivity to adversaries.

关键词： Differential privacy Adversarial training Recommender systems Privacy preservation System robustness

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CCVAE: A Variational Autoencoder for Handling Censored Covariates 21

CCVAE: A Variational Autoencoder for Handling Censored Covar...

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21st IEEE international conference on machine learning and Applications (IEEE ICMLA)

作者： Svahn, Caroline Sysoev, Oleg Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Ericsson Res Gothenburg Sweden

ISBN: (纸本)9781665462839

For time or safety critical scenarios when faulty predictions or decisions can have crucial consequences, such as in certain telecommunications scenarios, reliable prediction models and accurate data are of the essence. When modeling and predicting data in such scenarios, data with censored covariates remain an issue as ignoring them or imputing them with lack of precision may cause inaccurate or uncertain predictions. In this paper, we provide a fast, reliable Variational Autoencoder framework which can handle covariate censoring in high dimensional data. Our numerical experiments demonstrate that our framework compares favorably to alternative methods in terms of prediction accuracy for both the response and the covariates, while enabling estimation of the prediction uncertainties. We moreover demonstrate that the method is at least 8 times faster than the benchmark models used in this paper, and more robust to initial imputations and noise than existing models. The method can also be used directly for predicting unseen data, which is a challenge for some state-of-the-art methods.

关键词： variational autoencoder censored covariates zero inflated truncated normal deep learning dynamic range

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High Precision Method of Federated learning Based on Cosine Similarity and Differential Privacy 15

High Precision Method of Federated Learning Based on Cosine ...

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2022 IEEE Congress on Cybermatics: 15th IEEE international conferences on Internet of Things, iThings 2022, 18th IEEE international conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE international conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE international conference on Smart data, Smartdata 2022

作者： Wang, Jia Li, Yazheng Ye, Ronghang Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China Zhengzhou University of Industrial Technology College of Computer Science and Software Engineering Zhengzhou China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Federated learning has emerged as an efficient way to exploit distributed data in recent years. It allows multiple client nodes to collaboratively train an optimized machine learning model without revealing the participants' data. However, there are some shortcomings in the existing federated learning algorithms: the existence of poor quality nodes leads to undesirable effects on the overall gradient descent direction of the model. And the typical iterative training and noising way also make the privacy loss of the model reaches the privacy budget quickly. Hence, the model training may stop before convergence. In this paper, we propose a federated learning gradient adaptive aggregation method based on cosine similarity and a central node privacy protection method based on random differential privacy to solve the above-mentioned problems respectively. The proposed scheme was implemented on two public general datasets (MNIST and SVHN) and a medical dataset (UCI diabetes). Experimental results show that the federated learning adaptive method based on cosine similarity could effectively eliminate the negative influence of nodes with poor data quality on the overall model and maintain stable performance. The central node privacy protection method based on random differential privacy could also improve the performance of the training model under the same privacy budget and privacy loss threshold. © 2022 IEEE.

关键词： learning algorithms

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WELL PATH DESIGN USING Q-learning ALGORITHMS AND BEZIER CURVES WITH OBSTACLES AVOIDANCE 41

WELL PATH DESIGN USING Q-LEARNING ALGORITHMS AND BEZIER CURV...

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ASME 41st international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Cao, Jie Chowdhury, Samiul Ehsan Wiktorski, Tomasz Sui, Dan Univ Stavanger Fac Sci & Technol Dept Energy & Petr Engn Stavanger Norway Univ Stavanger Fac Sci & Technol Dept Elect Engn & Comp Sci Stavanger Norway

ISBN: (纸本)9780791885956

Designing an optimal path has been considered as the one of the key challenges for drilling engineers. Even for a group of competent engineers, it takes several months to plan a well. A robust optimized path can influence the total cost of wellbore, wellbore quality and stability, transport efficiency, and drilling speed. A properly optimized path can be called if it is with a shortest path, avoids well-collisions, low touristy, and has maximum contact with the reservoir. Optimal design of wellbore trajectory is very demanding due to many complex and interacted drilling variables, models' uncertainties, and design constraints. In drilling engineering, well path optimization is typically based on the objectives to minimize of the total length of wellbore, drilling cost and time in consideration of well-collision avoidance. Optimization algorithms such as genetic algorithm, dynamic programming, particle swarm optimization etc. might provide probable solutions to the optimization problem. Recently, machine learning technology becomes more attractive in drilling engineering to deal with complicated cases, like regression, classification, control and optimization problems. The aim of this paper is to investigate the implementation feasibility and efficiency of Reinforcement learning algorithms to design an optimal path with the obstacle detection and avoidance. Our work has applied Q-learning of Reinforcement learning to plan a path with minimum measure depth, and in the meanwhile to avoid different shapes of obstacles. The agent interacts with the environment, achieving maximum reward upon reaching the target with passing through the given obstacles or receiving penalties when it fails. This work shows the behavior of the algorithms to obey the main criterion of the trajectory design as an alternative solution. In addition, one engineering path plan method based on Bezier curve to modify the generated path from Q-learning is proposed, where the minimum dog leg severity

关键词： Algorithms Design Drilling Trajectories (Physics) Reinforcement learning Optimization Collisions (Physics) Engineers Reservoirs Stability Petroleum engineering engineering design Dynamic programming Genetic algorithms Optimization algorithms Particle swarm optimization Shapes Uncertainty Dynamic light scattering machine learning

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Automated learning Approach for Genetic Diseases 19

Automated Learning Approach for Genetic Diseases

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19th IEEE/ACS international conference on Computer Systems and Applications (AICCSA)

作者： Alajramy, Loay Taweel, Adel Jarrar, Radi Lamine, Elyes Megdiche, Imen Birzeit Univ Dept Comp Sci Birzeit Palestine INU Champollion Castres France Toulouse Univ IMT Toulouse France IRIT Toulouse France

ISBN: (纸本)9798350310085

Finding the exact gene mutations that cause a genetic disease has been a challenging task. Despite the development in information technology, the task of extracting gene-disease associations has been mainly a manual process. This is a time-consuming process, in which experts extract gene-disease associations from relevant research papers from the literature manually. The main aim of this paper is to develop an automated approach for extracting and classifying gene-disease associations from relevant literature research papers using both natural language processing and machine learning techniques. This paper extracted data from free-text literature research papers and built four different dataset formats to discover an optimal representation. machine and Deep learning models (NB, KNN, SVM, NN, CNN, and LSTM) with TF-IDF were applied on the built datasets. As a result, the format of the dataset with (Positive and Negative) instances only, was found to be the best representation for extracting gene-disease associations with optimal accuracy between 74% and 91%. For the four dataset representations, Multilayer Neural Networks was able to predict all classes in most experiments with accuracy between 64% and 91%. From the initial results, this work highlights the need for additional work to improve both the performance of these models and the data extraction method to build more accurate and optimal dataset representation.

关键词： Gene-disease associations machine learning Deep learning NLP text mining

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MLIR: machine learning based IR Drop Prediction on ECO Revised Design for Faster Convergence 35

MLIR: Machine Learning based IR Drop Prediction on ECO Revis...

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35th international conference on VLSI Design (VLSID) / 21st international conference on Embedded Systems (ES)

作者： Kundu, Santanu Prasad, Manoranjan Nishad, Sashank Nachireddy, Sandeep Harikrishnan, K. Intel Technol India Pvt Ltd SRR Campus Bangalore Karnataka India

ISBN: (纸本)9781665485050

The on-chip power delivery network (PDN) is an essential element of physical implementation that strongly determines functionality, quality, and reliability of an IC. During signoff phase, several engineering-change-order (ECO) iterations are needed to ensure that each instance of the design should meet IR drop specification. Even though the design remains very similar after ECO changes, conventional PDN analysis in industry standard CAD tool takes several hours of simulation runtime to determine IR drop. Our goal is to reduce this runtime in each iteration to evaluate the ECO changes and fix the violating cells immediately, prior to run conventional PDN signoff tool. Hence, improving the number of iteration and achieving faster PDN convergence. Our contribution in this paper is to develop a machine learning methodology for fast IR drop prediction where we have used regression techniques to predict static IR drop values and classification techniques to predict dynamic IR drop violating cells. We have evaluated the importance of every feature that contributes to the IR drop. We have also interpreted the predicted output using Explainable AI method. While inferencing on a similar to 1M instance industry design, we have achieved 0.997 R-2-Score, -5.21 mV maximum absolute error, and 117 mu V RMSE in static IR Drop prediction. On dynamic IR drop prediction, we have achieved 0.999 accuracy, 0.909 F1_Score, 0.893 Precision, and 0.926 Recall.

关键词： PDN convergence machine learning IR Drop Prediction Explainable AI Regression Classification

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Semantic Transformation Utilizing Graphs 2

Semantic Transformation Utilizing Graphs

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2nd international conference on Advanced Enterprise Information System (AEIS)

作者： Reimer, Tim Univ Calgary Dept Elect & Software Engn Calgary AB Canada

ISBN: (纸本)9798350324372

Semantic transformation for data in motion has been a continuous problem, and various approaches have been proposed to solve it. This paper proposes a graph approach to handle semantic data transformations. By using a set of traversal patterns, an event log will provide the feature set for both machine-learning algorithms and graph algorithms to make missing node predictions. The paper compares the two approaches and presents significant differences in results.

关键词： semantic transformation graph databases know algorithms traversal patterns data transformation homogenous graphs node predictions

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Management of automation activities in the production process for a motor transport manufacturing using smart machine learning model 1

Management of automation activities in the production proces...

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1st IEEE international Interdisciplinary Humanitarian conference for Sustainability, IIHC 2022

作者： Dhiman, Neha Kumar Doda, Devendra Chandigarh Engineering College Department of Computer Science & Engineering Jhanjeri India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9781665456876

The purposeful influence of the material objects to organize and coordinate the activities of groups and individual workers in the production process. It is necessary to carry out many administrative functions. They reveal the essence and content of management activities at any level. Management functions show what and how the object is doing in order to more accurately represent the object, goals are set, impact management is developed and implemented, and its implementation is monitored. Thus, the control function is understood as a special type of operation, which is a set of actions or decisions carried out in the management of the company. In this paper a smart machine learning model was proposed to manage the different management activities. The management functions are assigned to individual employees and each is responsible for the quality performance of his/her function. Activities are fixed by job descriptions. In addition to instructions, regulations are developed in departments that determine the department's position in the organization and establish the responsibility of each department for developing issues within the department's competence. © 2022 IEEE.

关键词： machine learning

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Distributed Music Comments Mining System Based on BGLL Algorithm 8

Distributed Music Comments Mining System Based on BGLL Algor...

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8th Annual international conference on Network and Information Systems for Computers, ICNISC 2022

作者： Peng, Yawei Qu, Chengzhang Wuhan Bussiness University Department of Electronic Information Engineering Wuhan China

ISBN: (纸本)9781665453516

This paper aims to establish a distributed music comments mining system based on BGLL algorithm. With the massive music resource and users of NetEase Cloud Music, our system employs big data technology and machine learning technology to analyze data from the perspective of comments, discover users' attitudes towards the song or the music. The whole system is built based on the Hadoop ecosystem, and it includes four main modules: data acquisition, data storage, data analysis, and data visualization. A python based web crawler is used for data acquisition on NetEase Cloud Music resource, then the distributed HDFS system is built for data processing and analyzing. A refined mining algorithm based on BGLL is implemented for topic mining procedure. At last, a word cloud map is generated based on javaweb technology. Experiments show that our system can perfome well on the comment topic mining application, due to the distributed architecture, our system has a potential capability on dealing with large scale of data. © 2022 IEEE.

关键词： data acquisition

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