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共 3354 条记录，以下是21-30 订阅

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UCAPF: A Unified Processing Platform for Large-scale Virtual Screening

UCAPF: A Unified Processing Platform for Large-scale Virtual...

引用

2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： He, Jiaqi Li, Yangyang Gao, Yuan Zhou, Xu Wang, Zhuoya Guang, Hong Wang, Weidan Dong, Yujie Liu, Hao Ocean University of China College of Computer Science and Technology Qingdao China Qingdao Marine Science and Technology Center Qingdao China Super Computing Marine Biotechnology Co. Ltd. Qingdao China

ISBN: (纸本)9798350337488

With growing data volume for large-scale virtual screening, the associated data processing and management meet challenges. We have developed UCAPF, A unified platform for large-scale virtual screening. The platform provides a parallel processing framework for large-scale virtual screening data. It also enables scheduling of heterogeneous parallel architectures and hierarchical storage of massive data. The processing framework improves data quality. On the CASF-2016 dataset, the standardized molecules processed by UCAPF showed a 9.5% to 14.62% improvement in scoring performance and a 7.9% to 34.6% improvement in ranking performance compared to the raw molecules. For massive data processing, the framework provides parallel efficiency of 81.20% for molecule standardized processing and 79.51% for docking result processing on a 72-unit Hadoop cluster. In addition, the distributed database for data management improves the ability to retrieve 10,094 molecules from seventy million docking result data by a factor of 2.40 compared to the single-node storage model. Finally, we analyze the variation of input/output (I/O) over time for different phases of virtual screening to reflect the effectiveness of the scheduling strategy and tiered storage for the heterogeneous parallel architecture. © 2023 IEEE.

关键词： heterogeneous system large-scale virtual screening Molecular data processing Parallel processing framework

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Satellite and statistical system for advanced river flow prediction 29

Satellite and statistical system for advanced river flow pre...

引用

29th IEEE international conference on Electronics, Electrical Engineering and computing, INTERCON 2022

作者： Velasquez, Ricardo Manuel Arias Ezcurra, Tamara Tatiana Pando Aldon, Luis Francisco Vivanco Universidad Privada Peruana Alemana Lima Peru

ISBN: (数字)9781665486361

ISBN: (纸本)9781665486361

Prediction of the river flow, irradiance and wind velocity is a challenge for all the countries to improve the natural resources, the uses of the satellite and meteorological data allow to improve the statistical processing, in this research article a new methodology is proposed for the river flow forecasting in Lima, it is associated to the Transandean tunnel with the watershed of the Atlantic and Pacific ocean. with the basins of Tamboraque, Sheque and Tulumayo. Our findings are the implementation of river flow prediction with the data from 2016 to 2022 with an hourly data, the forecasting allows to obtain information from the satellite data of ambient temperature, rainfall precipitation, wind velocity and atmospheric pressure and evaluated river flows in three basins, with the MAE and RMSE metrics evaluation, it is Tamboraque (3.63 m/s and 5.55), Sheque (0.92 m/s and 1.29 m/s) and Tulumayo (5.28m/s and 11.28 m/s), these technology could be used for the hydroelectric and water supply companies. © 2022 IEEE.

关键词： Forecasting

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Conducting Morality and Emotion Analysis on Blog Discourse 29th

Conducting Morality and Emotion Analysis on Blog Discourse

引用

29th international conference on Collaboration Technologies and Social computing (CollabTech)

作者： Mbila-Uma, Stella Umoga, Ifeanyichukwu Alassad, Mustafa Agarwal, Nitin Univ Arkansas Little Rock AR 72204 USA

ISBN: (纸本)9783031421402;9783031421419

Social media has exploded in its usage, since the advancement of technology with individuals sharing their opinions and beliefs on its platform through text. This has led to the access of tons of text data that researchers can use to further understand human behaviour or user perspective. Emotion analysis is a common text analysis technique used to discover embedded emotion in content. For this research, we compare emotion analysis and morality assessment techniques in discovering embedded user opinions. Our results show that emotion analysis possesses limitations in its result. On the other hand, morality assessment provides a more comprehensive and accurate analysis of text data. For this research, we proposed that both methodologies are not mutually exclusive, and can complement each other to better understand the complexities of human communication and behavior.

关键词： Text Analysis Emotion Analysis Morality Assessment Blogs South China Sea text-data

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Removal Rate Prediction of Multiway Material data Using the Deep Learning Approach 22

Removal Rate Prediction of Multiway Material Data Using the ...

引用

8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Li, Han Fan, Wenhui Qiyuan Laboratory Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781450396110

Chemical Mechanical Polishing (CMP) process is one of the most critical process steps in advanced packaging manufacturing. The material removal rate (MRR) value, which affects polishing accuracy, can be predicted by data-driven method. There is no general study of predicting MRR value based on different deep learning neural networks. In this paper, multiway data are directly employed as the input of three types of deep learning neural network to predict MRR value. Afterwards, features extracted by deep learning neural network are applied as the input of ensemble machine learning model. The proposed approaches are verified by the public dataset in 2016 PHM Challenge. Experiments results show the deep learning neural networks outperform traditional machine learning model, and integration model between deep learning model and traditional machine learning model pursues better performance. © 2022 ACM.

关键词： Recurrent neural networks

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SnuHPL: High Performance LINPACK for Heterogeneous GPUs 22

SnuHPL: High Performance LINPACK for Heterogeneous GPUs

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36th ACM international conference on Supercomputing (ICS)

作者： Kim, Jinpyo Kwon, Hyungdal Kang, Jintaek Park, Jihwan Lee, Seungwook Lee, Jaejin Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Samsung Adv Inst Technol Suwon South Korea Seoul Natl Univ Dept Comp Sci & Engn Dept Data Sci Seoul South Korea

ISBN: (纸本)9781450392815

These days, it is typical for a large-scale cluster system to have different kinds of GPUs. However, HPL (High-Performance LINPACK), the de-facto standard LINPACK implementation for evaluating the performance of a cluster system, is originally designed to work only for homogeneous CPU-only systems. In this paper, we develop SnuHPL, an optimized HPL for clusters of modern heterogeneous GPUs. To optimize SnuHPL for the heterogeneous GPUs, we design a performance model, a SnuHPL simulator based on the model, and a greedy heuristic algorithm based on the simulator. The algorithm generates the best data distribution for a given cluster configuration by considering computing power, memory capacity, and network performance altogether. We also present a simple technique to increase the energy efficiency of HPL by adjusting the core clock frequency of the GPUs. The evaluation of the data distribution algorithm on small clusters of different GPU combinations shows that it outperforms well-known other data distribution strategies. We show the effectiveness of SnuHPL on a cluster of 1,760 NVIDIA A100-80GB GPUs and 440 A100-40GB GPUs. We also show the effectiveness of the proposed energy optimization technique on a cluster of 144 A100-80GB GPUs.

关键词： Cluster GPU Heterogeneous computing High performance LINPACK

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A Survey of Available Information Recovery of Binary Programs Based on Machine Learning 5

A Survey of Available Information Recovery of Binary Program...

引用

5th IEEE international conference on Artificial Intelligence and Big data, ICAIBD 2022

作者： Shao, Wenqiang Yang, Qichao Guo, Xixi Cai, Ruijie State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665499132

A binary program is an executable file obtained by compiling the source code. In order to reduce the size of software or prevent reverse engineering, software publishers usually only release executable files that have stripped a lot of source information. The rich source-level information is very helpful for software hardening and malware analysis. In recent years, the rapid development of artificial intelligence has promoted the research of binary program information recovery. This paper summarizes the relevant research on effective information recovery of binary programs since 2016. Firstly, the importance of recovering source-level information from binary programs is introduced. Next, the problems are divided into three categories, compiler traceability, signature information recovery and variable information recovery of source program. Finally, based on the latest technology in this field, the future development direction is analyzed. © 2022 IEEE.

关键词： Machine learning

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An Efficient Detection System For Cardio Vascular Disease Using Machine Learning Algorithms 2

An Efficient Detection System For Cardio Vascular Disease Us...

引用

2nd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Sarmiladevi, N. Basker, K. Tamilarasu, R. Nivetha, L. Kongunadu College of Engineering and Technology Department of Artificial Intelligence and Data Science Trichy India Cihan University-Duhok Kurdistan Region Department of Artificial Intelligence and Data Science Iraq

ISBN: (纸本)9781665437899

mining and acquiring knowledge technologies have played an increasingly important role in the healthcare system in latest generations, with the objective of transforming all data into an intelligible format. Medical professionals make 12 percent to 13 percent inaccuracies throughout the prognosis process, according to several design dictates. As a result, an automated ailment forecasting system is required for improved health diagnosis, and greater reliability is emphasised for assessment purposes. Furthermore, numerous researchers have emphasised the importance of prediction performance in order to improve the design of a computerized method. By minimising computational burden and factors that add to computational effort, the developed automation methodology is especially suited to predict cardiovascular diseases. The retrieved and chosen information are input into the classifier model in terms of enhancing forecast accuracy. For varying degree of success, the publicly available baseline heart disease database is being explored. Pattern learning techniques are used to examine the characteristics in both linear and non-linear ways. To improve the prediction rate, Linearly Iterated Support Vector Regression (LISVR) and stacked monkey optimization (SMO) are used for classification. This calculations were carried out in the MATLAB 2016b framework, and the analysis is carried out using a variety of available methods. The predicted model's exactness is 98.5 percent, specificity is 98.8 percent, recall is 99.5%, and F -measure is 99.28 percent. In comparison to current procedures, the obtained findings indicate that perhaps the expected LISVR- SMO operates more effectively than previous techniques and provides a good trading. © 2022 IEEE.

关键词： Metadata

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A Framework for Developing the Next Generation Interactive Soil Moisture Forecasting System Using the Long-Short Term Memory Model 22

A Framework for Developing the Next Generation Interactive S...

引用

22nd IEEE international conference on Machine Learning and Applications, ICMLA 2023

作者： Guna Shekar, M. Lee, Wonjun Kumar, Sanjiv Duan, Yanan Rangwala, Imtiaz California State University Department of Computer Science California United States Biola University Department of Math and Computer Science La MiradaCA United States College of Forestry Wildlife and Environment Auburn University Auburn United States University of Colorado Nc Casc and Cires Boulder United States

ISBN: (纸本)9798350345346

Soil moisture is crucial for agriculture and hydrology, but its accurate prediction is challenging due to inadequate representation of various complex land surface processes and meteorological influences. In this research, we employ the Long Short-Term Memory (LSTM) framework, a specific architecture of deep learning networks that is effective in processing time series data, for predicting soil moisture. We have developed the Next Generation Interactive Soil Moisture Forecasting System to advance skillful soil moisture predictions at sub-seasonal timescales by leveraging advanced analytics and deep learning, with LSTM at its core. We combined the state-of-the-art climate model's (Community Earth System Model Version 2) forecast that incorporates the effects of the large-scale climatic drivers, including sea-surface temperature and atmosphere circulation features into soil water forecast with the LSTM-based Deep Learning model. Our Deep Learning model understands the local forecast biases using the weekly hindcast data from 1999 to 2016. We used this trained LSTM model to test its performance from 2017 to 2021 and enhanced the forecast proficiency and aid in analyzing future soil moisture anomalies, i.e., departure from climatology using data fusion and spatial downscaling. For performance assessment, optimal metrics include Mean Absolute Error (MAE) values near 0 (0-0.6), Root Mean Square Error (RMSE) around 0.5, and Anomaly Correlation Coefficient (ACC) nearing 1. These breakthroughs in system design and modeling facilitate improved soil moisture prediction, benefiting water management and our understanding of land-atmosphere interactions. © 2023 IEEE.

关键词： Climate change data integration Deep learning LSTM framework Meteorological data Predictive modeling Soil moisture Spatial-temporal dynamics User interface

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Machine Learning-Based Fake News Detection on Social Media

Machine Learning-Based Fake News Detection on Social Media

引用

IEEE WIC ACM international conference on Web Intelligence (WI)

作者： Li Zeng Xiaoci Tao Information Retrieval & Knowledge Management Research Lab YorK University Canada

ISBN: (数字)9798331504946

ISBN: (纸本)9798331504953

In the age of data, artificial intelligence has become an effective tool for detecting fake news, enabling a swift response to correct misinformation during critical events such as elections, health crises, and global disasters. This case study provides a detailed analysis of news shared by nine media organizations on Facebook in the crucial week leading up to the 2016 U.S. election. By employing detailed fact-checking and advanced data mining techniques, we investigate the linguistic characteristics of fake news articles to develop models capable of accurately distinguishing between fake and authentic news. Our approach includes an exploratory data analysis (EDA) to identify patterns and linguistic features, followed by the application of multiple machine learning algorithms for classification. The results yield valuable insights into the defining characteristics of fake news and demonstrate the potential of machine learning to enhance the detection and filtering of misinformation.

关键词： data analysis Accuracy Social networking (online) Filtering Voting Computational modeling Linguistics data models Cleaning Fake news

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PFCA: An Intelligent Method of Multidimensional User Profile Construction and Application 7

PFCA: An Intelligent Method of Multidimensional User Profile...

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7th international conference on Big data Analytics, ICBDA 2022

作者： Xia, Yi Zhou, Gang Luo, Junyong Lan, Mingjing Chen, Xiaohui Wang, Shiyu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665479387

User profile plays an important role in analyzing high influencers in the era of big data. For example, the profile can help the political strategists to scrutinize the performance of a candidate or party and overcome their weakness long before election day. In this work, we propose a novel profile construction and application model (PFCA) that aims to discover the overall character of high influencers, and apply PFCA to Donald Trump, one of the most influential people recently, to identify Trump's comprehensive personalities. We first collect and preprocess profile data from static and dynamic aspects of multi-source data, and then categorize multi-source data topics and emotions. Next, we attempt to construct the profile from four aspects: fundamental characteristic, behavior characteristic, value characteristic and situation characteristic, to explore the correlation among these characteristics. Finally, PFCA is verified by the outcomes of the 2016 and 2020 U.S. presidential elections, and is evaluated by relevant experts, which proves that PFCA is relatively effective and efficient to profile high influencers. Moreover, PFCA can be further applied to related analysis and prediction in the 2024 U.S. presidential election. © 2022 IEEE.

关键词： Sentiment analysis

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