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Forecasting COVID-19 cases at the Amazon region: a comparison of classical and machine learning models [article]

Dalton Garcia Borges de Souza, Francisco Tarcísio Alves Júnior, Nei Yoshihiro Soma
2020 bioRxiv   pre-print
learning techniques viable options.  ...  MATERIAL AND METHODS - We implement the models to data provided by the health surveillance secretary of Amapá, a Brazilian state fully carved in the Amazon rainforest, which has been experiencing high  ...  Machine learning. 2001;45(1):5-32. 27. Yeşilkanat CM. Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm.  ... 
doi:10.1101/2020.10.09.332908 fatcat:qt3ryqddzrc7vnypbbqfl5j6le

MLbench

Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang
2018 Proceedings of the VLDB Endowment  
We then conduct an empirical study using MLBench to understand example machine learning services from Amazon and Microsoft Azure, and showcase how MLBench enables a comparative study revealing the strength  ...  and weakness of these existing machine learning services quantitatively and systematically.  ...  Amazon Machine Learning.  ... 
doi:10.14778/3231751.3231770 fatcat:hzitipxuvvhbreizch6tqdh4fi

An Incorporation of Artificial Intelligence Capabilities in Cloud Computing

Mandeep Kumar
2016 International Journal Of Engineering And Computer Science  
In this paper, discuss about artificial intelligence capabilities in cloud computing, in the form of cloud machine learning platforms and artificial intelligence cloud services.  ...  They provides cloud machine learning platform and artificial intelligence cloud services like computer vision, powerful speech recognition, powerful text analysis, fast dynamic translation, smart search  ...  Amazon Machine Learning is highly scalable and can generate billions of predictions daily, and serve those predictions in real time and at high throughput with Amazon Machine Learning, there is no upfront  ... 
doi:10.18535/ijecs/v5i11.63 fatcat:o5wbltjolfefpedjzcpgn62wzy

MLBench: How Good Are Machine Learning Clouds for Binary Classification Tasks on Structured Data? [article]

Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, Ce Zhang
2017 arXiv   pre-print
We then compare the performance of the top winning code available from Kaggle with that of running machine learning clouds from both Azure and Amazon on mlbench.  ...  Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task, users only specify what machine  ...  Studio and Amazon Machine Learning.  ... 
arXiv:1707.09562v3 fatcat:lolzimetufetlpjlh635xl5fkm

Machine learning in the real world

Vineet Chaoji, Rajeev Rastogi, Gourav Roy
2016 Proceedings of the VLDB Endowment  
This tutorial takes a hands-on approach to introducing the audience to machine learning.  ...  The first part of the tutorial gives a broad overview and discusses some of the key concepts within machine learning.  ...  Gourav Roy is a Software Engineer in the Machine Learning team at Amazon where he builds scalable machine learning platforms and applications.  ... 
doi:10.14778/3007263.3007318 fatcat:afaxvtczn5hhharfciqc4huu3e

Bringing Gaming; VR; and AR to Life with Deep Learning

Danny Lange
2017 Proceedings of the 2017 ACM on Multimedia Conference - MM '17  
Lange is VP of AI and Machine Learning at Unity Technologies.  ...  Prior to Amazon, Danny was Principal Development Manager at Microsoft where he was leading a product team focused on large-scale Machine Learning for Big Data.  ... 
doi:10.1145/3123266.3130873 dblp:conf/mm/Lange17 fatcat:jeiwusoajvdnjfvbt2p23g3lom

Building Machine Learning Based Senti-word Lexicon for Sentiment Analysis

Alaa Hamouda, Mahmoud Marei, Mohamed Rohaim
2011 Journal of Advances in Information Technology  
We propose a Machine Learning Based Senti-word Lexicon (MLBSL) based on the Amazon data set which contains reviews from different domains.  ...  In this paper we proposed a Machine Learning Based Senti-word Lexicon based on the Amazon data set which contains reviews from different domains.  ...  Figure 1 . 1 Creating Machine Learning Based Senti-word Lexicon Using Amazon Data set TABLE 1 : 1 THE RESULTS OF AMAZON AND MOVIES REVIEWS CLASSIFICATION USING MLBSL BASED ON 'NORMAL REVIEWS' AND 'STRING  ... 
doi:10.4304/jait.2.4.199-203 fatcat:q4pyd4ejgnajnjlikn3owcxxya

Sentiment Analysis of Product Reviews to Identify Deceptive Rating Information in Social Media: A SentiDeceptive Approach

2022 KSII Transactions on Internet and Information Systems  
learning classifiers.  ...  learning algorithms by applying a standard crossvalidation approach (KFold and Shuffle Split).  ...  The details about the machine learning algorithms and experiment are discussed below: Machine Learning Algorithms To identify deceptive end-user rating information in the Amazon and Flipkart shopping  ... 
doi:10.3837/tiis.2022.03.005 fatcat:yqf7rdyxqvgx5gy5aytwrjst3a

Cloud-based Healthcare data management Framework

2020 KSII Transactions on Internet and Information Systems  
Finally, a cloud-based healthcare architecture using Amazon Cloud Services is constructed for reference.  ...  The top three public cloud providers-Amazon, Google, and Microsoft offers advanced cloud services for the solution that the healthcare industry is looking for.  ...  Amazon ElasticSearch Bigquery, Cloud ML, Cloud Dataprep Azure Data Explorer,Azure Databricks,Analysis Services Machine Learning Amazon Sage Maker Cloud Machine Learning Engine,AutoML,  ... 
doi:10.3837/tiis.2020.03.006 fatcat:yeput45l7favbmvt2hhwjfsn5y

Machine Learning for the Communication Optimization in Distributed Systems

Zarina Kazhmaganbetova, Shnar Imangaliyev, Altynbek Sharipbay
2018 International Journal of Engineering & Technology  
Machine Learning tools had been retained from the cloud services provider – Amazon Web Services.  ...  learning.  ...  The machine learning tools are available at the Amazon Web Services (further -AWS), including the following options of supervised learning [5] : 1. binary classificationreference of a vector of values  ... 
doi:10.14419/ijet.v7i4.1.19491 fatcat:yrcqecermzgrtc7ecrvfgbacpa

Machine learning with cloud platforms

Mykola Kozlenko
2021 Zenodo  
This paper presents a general overview of how machine learning solutions may be implemented using modern cloud platforms.  ...  machine learning [8] .  ...  AutoML Vision automates the training of custom machine learning models. Vision API offers powerful pretrained machine learning models through REST and RPC APIs.  ... 
doi:10.5281/zenodo.5555430 fatcat:jys6ar46szbrdkupfkhkuxvjim

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples [article]

Nicolas Papernot and Patrick McDaniel and Ian Goodfellow
2016 arXiv   pre-print
We demonstrate our attacks on two commercial machine learning classification systems from Amazon (96.19% misclassification rate) and Google (88.94%) using only 800 queries of the victim model, thereby  ...  Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output.  ...  Amazon Web Services Oracle Amazon offers a machine learning service, Amazon Machine Learning, 4 as part of their Amazon Web Services platform.  ... 
arXiv:1605.07277v1 fatcat:mlnntpsmbnfe3gahi7a3u77rlu

Comparison of cloud computing providers for development of big data and internet of things application

Muhammad Fajrul Falah, Yohanes Yohanie Fridelin Panduman, Sritrusta Sukaridhoto, Arther Wilem Cornelius Tirie, M. Cahyo Kriswantoro, Bayu Dwiyan Satria, Saifudin Usman
2021 Indonesian Journal of Electrical Engineering and Computer Science  
learning.  ...  analyzed several parameters such as technology specifications, model services, data center location, big data service, internet of things, microservices architecture, cloud computing management, and machine  ...  Learning Image Search, Machine Translation, Machine Learning Platform For AI, Intelligent Speech InteractionBeta GCP Amazon SageMaker, Amazon Augmented AI, Amazon CodeGuru (Preview), Amazon  ... 
doi:10.11591/ijeecs.v22.i3.pp1723-1730 fatcat:jfsptxeti5bhffoi5ywor3vboa

Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC [article]

Sau Lan Wu, Shaojun Sun, Wen Guan, Chen Zhou, Jay Chan, Chi Lung Cheng, Tuan Pham, Yan Qian, Alex Zeng Wang, Rui Zhang, Miron Livny, Jennifer Glick (+11 others)
2021 arXiv   pre-print
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups.  ...  study using up to 20 qubits and up to 50000 events, the QSVM-Kernel method performs as well as its classical counterparts in three different platforms from Google Tensorflow Quantum, IBM Quantum and Amazon  ...  Due to the small ttH production rate at the LHC, its observation was highly challenging. The ATLAS and CMS analyses utilize machine learning techniques to improve the sensitivities to ttH production.  ... 
arXiv:2104.05059v1 fatcat:kfhucj2oe5hj7b7xvsgoylgzqy

Product Sentiment Analysis for Amazon Reviews

Arwa S. M. AlQahtani
2021 Zenodo  
This Research Provides an Analysis of the Amazon Reviews Dataset and Studies Sentiment Classification with Different Machine Learning Approaches.  ...  Then, we Trained Various Machine Learning Algorithms, I.E., Logistic Regression, Random Forest, Naïve Bayes, Bidirectional Long-Short Term Memory, and Bert.  ...  On the other hand, machine learning techniques are divided into: supervised learning, and unsupervised learning.  ... 
doi:10.5281/zenodo.5100054 fatcat:5bxaueg4vffyvn6foo5ukugx5y
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