检索结果-内蒙古大学图书馆

Advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Vishal Singh Ajay Shankar Singh K Anandhan Computer Science and Engineering Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

The System that automatically write descriptions or write captions for an image according to the contents of that image is known as Auto Image Captioning. It’s literacy problem of the machine that combines computer vision (for understanding image contents) with Natural processing (for textbook product) Auto picture captioning is a fairly new and fleetly adding academic content. Colourful new styles are being launched on a diurnal base in order to attain satisfying outcomes in this discipline. Still, to gain mortal- suchlike results, a lot of attention is still necessary. The thing of this exploration is to discover, in a methodical manner, what different and recent methodologies and models are employed for picture captioning using deep learning. What approach and new ideas are used to make these models better and more accurate. what’s further, ideas and strategy are more likely to give suitable result or outcome. To get so, we did a methodical literature analysis of recent exploration published between 2017 and 2019 from well- known datasource (Scopus, Web of Lores, IEEEXplore). We plant an aggregate of over 60+ primary studies that were related to this study. We found out that CNN understands the content in the picture and detect objects in an image, whereas LSTM/RNN is used to induce language. MS COCO, flicker 8k and flicker 30k, which are used in almost every studies, are the most extensively used datasets. The BLEU (1 – 4) standing rating matrix is the most extensively utilised in all projects or studies. The analysis also show that CNN with LSTM is more preferable than CNN with RNN. We found out that the Encoder Decoder and the attention medium are one of the most suitable and best ways for enforcing this model, and combining them can ameliorate outcomes to more accurate and good level. Experimenters or other people that want to work or contribute to auto image captioning will surely find this exploration very useful and can use it as a guidance.

关键词： Deep learning Computer vision Image databases Aggregates Neural networks Multimedia Web sites Network architecture

来源：评论

学校读者我要写书评

暂无评论

Predicting Epileptic Seizures: Case Studies Harnessing machine learning

Predicting Epileptic Seizures: Case Studies Harnessing Machi...

引用

IEEE international conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Neto, Augusto da Silva, Liliane Moioli, Renan Brasil, Fabricio Rodrigues, Joel J. P. C. Fed Univ Rio Grande do Norte UFRN Natal RN Brazil Inst Telecomunicacoes Aveiro Portugal Santos Dumont Inst IIN ELS Grad Program Neuroengn Edmond & Lily Safra Int Inst Neurosci Macaiba Brazil Univ Fed Piaui Teresina PI Brazil

ISBN: (纸本)9781728150895

Epileptic seizure prediction is a critical patient-specific challenging task, relies on big data streams, and is essential for patient care. With the aid of recent advances, together with forthcoming technologies and powerful computing capabilities, smart healthcare attracts great attention in harnessing Intelligent computing to yield seizure prediction and detection. However, it is unclear how even simple classification methods can be employed to carry out such challenging and mission-critical tasks. This paper investigates the performance impact of mainstream supervised machine learning techniques with different configurations in predicting epileptic seizures. A lab-premised testbed, along with neurophysiological data in dogs, enables the set of tests. Through analysis in the Area Under the ROC Curve (AUC) Key Performance Information (KPI), it was found that classification committees show improved performance capabilities than single classifiers. Overall, we believe this work represents a step towards making seizure prediction more accurate and widely available in different computational platforms.

关键词： Epileptic Seizure Detection and Prediction Supervised machine learning Classifiers

来源：评论

学校读者我要写书评

暂无评论

Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs 40

Lynceus: Cost-efficient Tuning and Provisioning of Data Anal...

引用

40th IEEE international conference on Distributed computing Systems (ICDCS)

作者： Casimiro, Maria Didona, Diego Romano, Paolo Rodrigues, Luis Zwaenepoel, Willy Garlan, David Univ Lisbon INESC ID Lisbon Portugal Univ Lisbon Inst Super Tecn Lisbon Portugal IBM Res Europe Zurich Switzerland Univ Sydney Sydney NSW Australia Carnegie Mellon Univ Inst Software Res Pittsburgh PA 15213 USA

ISBN: (纸本)9781728170022

Modern data analytic and machine learning jobs find in the cloud a natural deployment platform to satisfy their notoriously large resource requirements. Yet, to achieve cost efficiency, it is crucial to identify a deployment configuration that satisfies user-defined QoS constraints (e.g., on execution time), while avoiding unnecessary over-provisioning. This paper introduces Lynceus, a new approach for the optimization of cloud-based data analytic jobs that improves over state-of-the-art approaches by enabling significant cost savings both in terms of the final recommended configuration and of the optimization process used to recommend configurations. Unlike existing solutions, Lynceus optimizes in a joint fashion both the cloud-related (i.e., which and how many machines to provision) and the application-level (e.g. the hyper-parameters of a machine learning algorithm) parameters. This allows for a reduction of the cost of recommended configurations by up to 3.7x at the 90-th percentile with respect to existing approaches, which treat the optimization of cloud-related and application-level parameters as two independent problems. Further, Lynceus reduces the cost of the optimization process (i.e., the cloud cost incurred for testing configurations) by up to 11x. Such an improvement is achieved thanks to two mechanisms: i) a timeout approach which allows to abort the exploration of configurations that are deemed suboptimal, while still extracting useful information to guide future explorations and to improve its predictive model - differently from recent works, which either incur the full cost for testing suboptimal configurations or are unable to extract any knowledge from aborted runs;ii) a long-sighted and budget-aware technique that determines which configurations to test by predicting the long-term impact of each exploration - unlike state-of-the-art approaches for the optimization of cloud jobs, which adopt greedy optimization methods.

关键词： cloud computing machine learning platforms optimization virtual machines Bayesian optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal Fog Services Placement in SDN IoT Network Using Random Neural Networks and Cognitive Network Map 19th

Optimal Fog Services Placement in SDN IoT Network Using Rand...

引用

19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

作者： Fröhlich, Piotr Gelenbe, Erol Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland

ISBN: (纸本)9783030614003

Due to a massive increase in the number of IoT devices and the number of cloud-based services a crucial task arises of optimally placing (both topologically and resource-wise) services in the network so that no of the clients will be victimized and all of them will receive the best possible time of response. Also - there must be a balance not to instantiate a service on every possible machine - which would take too many resources. The task which must be solved is an optimization of parameters such as QoS between service and client, equality of clients and usage of resources. Using the SDN - which is designed to answer some of the problems posed in this section such as QoS and knowledge about the topology of the whole network and newly connected clients - is a gateway to better-adapted service management. machine learning provides less stiff rules to follow and more intelligent behavior of the manager. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Editorial: Computational Genomics and Molecular Medicine for Emerging COVID-19

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第4期18卷 1227-1229页

作者： Wei, Dong-Qing Kaushik, Aman Chandra Selvaraj, Gurudeeban Pan, Yi Shanghai Jiao Tong Univ State Key Lab Microbial Metab Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Shanghai 200240 Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China Jiangnan Univ Wuxi Sch Med Wuxi 214122 Jiangsu Peoples R China Concordia Univ Ctr Res Mol Modeling CERMM Montreal PQ H4B 1R6 Canada Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

The papers in this special section focus on computational genomics and molecular medicine for emerging COVID-19. In 2020, World Health Organization announced Coronavirus disease (COVID)-19 is a pandemic disease, which is devastated the socio-economic life around the world. The disease caused by the zoonotic single-strand RNA virus known as “SARS-CoV-2”. To overcome the pandemic, the diagnosis and therapeutics products needs to be developed in short term. Developing therapeutics for infectious diseases, especially viral diseases always a challenging task for the scientific community. However, the utility of high-performance computational resources, artificial intelligence, and machine-learning algorithms can make the process in an affordable way through the usage of genomics, proteomics, pharmacogenomics, and chemical data. Thus, the special section received potential research articles related to computational genomics, molecular medicine, and COVID-19 from reputed scientist around the world. Different articles were employed machine learning, molecular dynamics, computer aided drug design techniques, and emphasizing viral genomics, mutation, drug target, drug candidates, and patient data, were included in this special section.

关键词： Special Issues And Sections COVID 19 Pandemics Genomics Drugs Bioinformatics machine learning RNA Proteomics Molecular Biology Coronaviruses

来源：评论

学校读者我要写书评

暂无评论

machine learning in Information Visualization: Using Rule-based learning Algorithms to Personalize Interfaces 27

Machine Learning in Information Visualization: Using Rule-ba...

引用

27th international conference on Systems, Signals and Image Processing (IWSSIP)

作者： Silva, Leonardo Souza Nakamura, Ricardo Nunes, Fatima L. S. Univ Sao Paulo Escola Politecn Interact Technol Lab Sao Paulo Brazil Univ Sao Paulo Escola Artes Ciencias & Humanidades Lab Comp Applicat Hlth Care Sao Paulo Brazil

ISBN: (纸本)9781728175393

machine learning has offered great potential in areas such as data mining and information retrieval, while less attention has been given to its use in the design of Information Visualization tools. Based on user needs, a properly designed interface can enhance augmented perception;however, the project is usually conducted without the inclusion of users. machine learning techniques can be useful to promote the users' involvement in the interface development process, contributing to offer personalized visualizations. Based on a dataset from a User Experience experiment with an interactive 3D environment to visualize medical temporal data, we present an approach using rule-based learning algorithms to promote the user's involvement. Our results indicate that Association Rule learning can be a valuable resource to improve the design of Information Visualization interface tools as well as to customize visualization according to the user's preferences.

关键词： Information Visualization Visual Attention Ruled-based learning method Human-Computer Interaction

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis on Tweets for Trains Using machine learning 10th

Sentiment Analysis on Tweets for Trains Using Machine Learni...

引用

10th international conference on soft computing and Pattern Recognition, SoCPaR 2018

作者： Kumar, Sachin Nezhurina, Marina I. College of IBS NUST MISIS Leninsky Prospect 4 Moscow119049 Russia

ISBN: (纸本)9783030170646

Sentiment analysis is a popular theme in the natural language processing (NLP) domain. People at present share their stay experience in restaurants, shopping malls, hotels and their travel experience in taxis, buses, trains and airplanes. Online social media provide a platform for the people to share their experiences of stay and travel in the form of text, images and videos. Twitter is one of the popular and well known social media platforms across the world. In this study, we are using tweets data in respect to comfort services in Indian long route superfast trains. This tweet data is used to analyze the hidden sentiments using machine learning techniques such as support vector machines (SVM), Random forest (RF) and back propagation neural networks (BPNN). The results show that BPNN provides high accuracy with more training on the data. The results achieved from SVM and RF was also satisfactory but BPANN won the race with more training on the data. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Quantum track reconstruction algorithms for non-HEP applications 40

Quantum track reconstruction algorithms for non-HEP applicat...

引用

40th international conference on High Energy Physics, ICHEP 2020

作者： Novotny, Kristiane Tüysüz, Cenk Rieger, Carla Dobos, Daniel Potamianos, Karolos Demirköz, Bilge Carminati, Federico Vallecorsa, Sofia Vlimant, Jean-Roch Fracas, Fabio gluoNNet METU Ankara Turkey ETH Zürich Switzerland University of Lancaster United Kingdom University of Oxford United Kingdom CERN Openlab Caltech University of Padua Italy

The expected increase in simultaneous collisions creates a challenge for accurate particle track reconstruction in High Luminosity LHC experiments. Similar challenges can be seen in non-HEP trajectory reconstruction use-cases, where tracking and track evaluation algorithms are used. High occupancy, track density, complexity and fast growth therefore exponentially increase the demand of algorithms in terms of time, memory and computing resources. While traditionally Kalman filter (or even simpler algorithms) are used, they are expected to scale worse than quadratic and thus strongly increasing the total processing time. Graph Neural Networks (GNN) are currently explored for HEP, but also non HEP trajectory reconstruction applications. Quantum Computers with their feature of evaluating a very large number of states simultaneously are therefore good candidates for such complex searches in large parameter and graph spaces. In this paper we present our work on implementing a quantum-based graph tracking machine learning algorithm to evaluate Traffic collision avoidance system (TCAS) probabilities of commercial flights. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0).

关键词： learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Design and Analysis of a ChatBot with IPL First Inning Score Prediction

Design and Analysis of a ChatBot with IPL First Inning Score...

引用

Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： Kumar Suresh Bhardwaj Vikas Kanishka Kumar Vikas MRIIRS Faridabad India

ISBN: (纸本)9781665428309

A chat bot aims to make conversations with human and machine both. The use of chat bots has emerged rapidly in many fields, including marketing, support systems, education, health care, cultural heritage, and entertainment. The machine is equipped with the information to identify the sentences and make the decision itself as a response to the question. Response policy compares the input sentence from the user. We have integrated our Chat bot with IPL first inning score prediction. In this paper, details of the last decade IPL seasons from 2008-2017 are used for prediction. Six machine learning techniques are used for prediction and their performance is analyzed. The result shows that the prediction accuracy of Random Forest is better than all other models.

关键词： Bot (Internet) machine learning algorithms Computational modeling Medical services Predictive models Telecommunication computing Online services

来源：评论

学校读者我要写书评

暂无评论

Application of machine learning on Google Earth Engine to Produce Landslide Susceptibility Mapping (Case Study: Pacitan)

Application of Machine Learning on Google Earth Engine to Pr...

引用

5th Geomatics international conference 2020, GeoICON 2020

作者： Ilmy, Hafsah Fatihul Darminto, Mohammad Rohmaneo Widodo, Amien Geomatics Department National Cheng Kung University No.1 University Road Tainan City701 Taiwan Department of Geomatics Engineering Sepuluh Nopember Institute of Technology Kampus ITS Keputih Sukolilo Surabaya60111 Indonesia Department of Geophysics Engineering Sepuluh Nopember Institute of Technology Kampus ITS Keputih Sukolilo Surabaya60111 Indonesia

According to the Indonesian Disaster Management Agency (BNPB), Indonesia's losses due to landslides were estimated around hundreds of billion rupiah in 2017. Making landslide as one of the catastrophes with the greatest risk of loss and leaving a couple regions prone to landslides in Indonesia, Pacitan region is one of them. Landslide delineation therefore represents a particularly beneficial application of evolving research trend in disaster reduction, especially for the vulnerable region. In the present times of open-access satellite data, cloud computing and machine-learning algorithms is frequently used for disaster prevention monitoring. By employing Google Earth Engine, this study focuses on the susceptibility of landslide occurrence using a random forest machine-learning framework applied to digital topographic data such as elevation, slope and aspect as the independent variables and landslide inventory data obtained from Ministry of Energy and Mineral Resources Republic of Indonesia as the dependent variable. This study data sets composed from 1000 random points in Pacitan region with 70:30 ratio for training and testing sample points. The model produced good result, with overall accuracy values of 0.94, kappa values of 0.79 and 0.80 for AUC value. This model also showed that elevation is the most important variable in the landslide susceptible area. The results of this study can be used to evaluate the potential future impacts of landslide and help to optimize the management of disaster reduction in the region of Pacitan. © Published under licence by IOP Publishing Ltd.

关键词： Engines

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：