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检索条件"任意字段=International Conference on Machine Learning and Computing（ICMLC 2009）"

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machine learning Based Prediction of H1N1 and Seasonal Flu Vaccination 10th

Machine Learning Based Prediction of H1N1 and Seasonal Flu V...

引用

10th international Advanced computing conference, IACC 2020

作者： Inampudi, Srividya Johnson, Greshma Jhaveri, Jay Niranjan, S. Chaurasia, Kuldeep Dixit, Mayank Fr. Conceicao Rodrigues Institute of Technology Vashi Navi Mumbai India Saintgits College of Engineering Kottayam India Vivekanand Education Society’s Institute of Technology Mumbai India Sri Ramakrishna Engineering College Coimbatore India School of Engineering and Applied Sciences Bennett University Delhi India

ISBN: (纸本)9789811604003

The H1N1 Flu that came into existence in 2009 had a great impact on the lives of people around the world. It was a life-threatening season to hundreds of people mainly below 65 years old which eventually made the World Health Organization (WHO) to declare it as the greatest pandemic in more than 40 years. To find out the vaccination status National 2009 H1N1 Flu Survey (NHFS) was conducted in U.S. In this paper, the data from the above survey was used to develop a model that predicts how likely people got H1N1 and seasonal flu vaccine. For this purpose, various machine learning (ML) and Artificial Neural Network (ANN) models are used to determine the probability of person receiving H1N1 and Seasonal Flu vaccine. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Surveys

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Designing production-friendly machine learning 47th

Designing production-friendly machine learning

引用

47th international conference on Very Large Data Bases, VLDB 2021

作者： Zaharia, Matei Stanford United States Databricks United States

Building production ML applications is difficult because of their resource cost and complex failure modes. I will discuss these challenges from two perspectives: the Stanford DAWN Lab and experience with large-scale commercial ML users at Databricks. I will then present two emerging ideas to help address these challenges. The first is "ML platforms", an emerging class of software systems that standardize the interfaces used in ML applications to make them easier to build and maintain. I will give a few examples, including the open-source MLflow system from Databricks [3]. The second idea is models that are more "production-friendly" by design. As a concrete example, I will discuss retrieval-based NLP models such as Stanford’s ColBERT [1, 2] that query documents from an updateable corpus to perform tasks such as question-answering, which gives multiple practical advantages, including low computational cost, high interpretability, and very fast updates to the model’s "knowledge". These models are an exciting alternative to large language models such as GPT-3. © 2021, VLDB Endowment. All rights reserved.

关键词： Open source software

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A Distributed PCM Clustering Algorithm Based on Spark 19

A Distributed PCM Clustering Algorithm Based on Spark

引用

11th international conference on machine learning and computing (icmlc)

作者： Zhang, Yong Liu, Haoke Chen, Tianzhen Tang, Di Liaoning Normal Univ Sch Comp & Informat Technol Dalian Peoples R China

ISBN: (纸本)9781450366007

With the large-scale growth of data, traditional single-machine data processing methods are difficult to deal with massive data, especially iterative clustering algorithms that require frequent reading and writing operations. On the basis of Spark framework, this paper proposes a distributed possibilistic c-means algorithm based on memory computing, called Spark-PCM. The proposed method improves the related processing of distributed matrix operation and is implemented on the Spark platform. Experimental results show that the proposed Spark-PCM algorithm runs in a linear relationship with the number of nodes and has a good scalability, which indicates that it has higher scalability and adaptability to large-scale data.

关键词： Spark platform possibilistic c-means clustering distributed computing matrix operation

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A Robust Mature Tomato Detection in Greenhouse Scenes Using machine learning and Color Analysis 19

A Robust Mature Tomato Detection in Greenhouse Scenes Using ...

引用

11th international conference on machine learning and computing (icmlc)

作者： Liu, Guoxu Mao, Shuyi Jin, Hui Kim, Jae Ho Pusan Natl Univ Dept Elect Engn Busan South Korea Weifang Univ Sci & Technol Comp Software Inst Shouguang Peoples R China

ISBN: (纸本)9781450366007

A new algorithm for automatic tomato detection in regular color images is proposed, which can reduce the influence of illumination, color similarity as well as suppress the effect of occlusion. The method uses a Support Vector machine (SVM) with Histograms of Oriented Gradients (HOG) to detect the tomatoes, followed by a color analysis method for false positive removal. And the Non-Maximum Suppression Method (NMS) is employed to merge the detection results. Finally, a total of 144 images were used for the experiment. The results showed that the recall and precision of the classifier were 96.67% and 98.64% on the test set. Compared with other methods developed in recent years, the proposed algorithm shows substantial improvement for tomato detection.

关键词： Tomato detection harvesting robots machine learning color analysis

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EXPLORING AND EVALUATING THE SCALABILITY AND EFICINECY OF APACHE SPARK USING EDUCATIONAL DATASETS 18

EXPLORING AND EVALUATING THE SCALABILITY AND EFICINECY OF AP...

引用

international conference on machine learning and Cybernetics (IMLC)

作者： Zhang, Jian Yang, Zijiang Benslimane, Younes York Univ Sch Informat Technol Toronto ON Canada

ISBN: (纸本)9781728128160

The combination of data mining and machine learning technology with web-based education system is becoming an imperative research area to enhance the quality of education beyond the traditional concept. With the worldwide fast growth of the Information Communication Technology (ICT), data come with significant large volume, high velocity and extensive variety. In this paper, four popular data mining methods are applied on Apache Spark using large volume of datasets from Online Cognitive learning Systems to explore the scalability and efficiency of Spark. Various volumes of datasets are tested on Spark MLlib with different running configurations and parameter tunings. The output of the paper convincingly presents useful strategies of computing resource allocation and tuning to make full advantage of the in-memory system of Apache Spark with the tasks of data mining and machine learning on educational datasets.

关键词： Data mining Big Data machine learning Spark

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Predicting Personality Using Facebook Status Based on Semi-supervised learning 19

Predicting Personality Using Facebook Status Based on Semi-s...

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11th international conference on machine learning and computing (icmlc)

作者： Zheng, Heci Wu, Chunhua Beijing Univ Posts & Telecommun 10 Xitucheng Rd Beijing Peoples R China

ISBN: (纸本)9781450366007

Personality analysis on social media is a research hotspot due to the importance of personality research in psychology as well as the rapid development of social media. Many studies have used social media status to analyze user's personality, but most of them are conducted on inadequate label data and linguistic features. In this paper, to explore the usage of unlabeled data on personality analysis, a personality analysis framework based on semi supervised learning is introduced. Besides, for making full use of the language information in social media status, the well-known n-gram model is adopted to extract linguistic features. The experimental results demonstrate the semi-supervised learning can take advantage of unlabeled data and improve the accuracy of prediction model.

关键词： Personality social media status semi-surpervised learning

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machine learning Techniques for Heart Disease Datasets: A Survey 19

Machine Learning Techniques for Heart Disease Datasets: A Su...

引用

11th international conference on machine learning and computing (icmlc)

作者： Khan, Younas Qamar, Usman Yousaf, Nazish Khan, Aimal Natl Univ Sci & Technol Coll Elect & Mech Engn Dept Comp & Software Engn Islamabad Pakistan Univ Wah Dept Comp Sci Wah Cantt Pakistan

ISBN: (纸本)9781450366007

Heart Failure (HF) has been proven one of the leading causes of death that is why an accurate and timely prediction of HF risks is extremely essential. Clinical methods, for instance, angiography is the best and most effective way of diagnosing HF, however, studies show that it is not only costly but has side effects as well. Lately, machine learning techniques have been used for the stated purpose. This survey paper aims to present a systematic literature review based on 35 journal articles published since 2012, where state of the art machine learning classification techniques have been implemented on heart disease datasets. This study critically analyzes the selected papers and finds gaps in the existing literature and is assistive for researchers who intend to apply machine learning in medical domains, particularly on heart disease datasets. The survey finds out that the most popular classification techniques are Support Vector machine, Neural Networks, and ensemble classifiers.

关键词： Heart failure heart diseases risk prediction neural network deep learning machine learning healthcare

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DISCOVERING EMOTIONAL LOGIC RULES FROM PHYSIOLOGICAL DATA OF INDIVIDUALS 18

DISCOVERING EMOTIONAL LOGIC RULES FROM PHYSIOLOGICAL DATA OF...

引用

international conference on machine learning and Cybernetics (IMLC)

作者： Costadopoulos, Nectarios Islam, Md Zahidul Tien, David Charles Sturt Univ Sch Comp & Math Bathurst NSW Australia

ISBN: (纸本)9781728128160

This paper discusses our work on discovering a set of emotional logic rules, derived from physiological data of individuals from a wearable technology perspective. We concentrated the analysis on physiological data such asplethysmography, respiration, galvanic skin response, and temperature that can be detected by wearable sensors. We sourced our data from the DEAP dataset, which is a popular labelled Affective computing dataset. Our approach implemented a fusion of preprocessing and data mining techniques, to discover logic rules relating to the valence and arousal emotional dimensions. Our findings indicate that while there are similar changes in heart rates or galvanic skin response across individuals during emotional stimuli, every individual has a unique and quantifiable physiological reaction.

关键词： Knowledge discovery Decision trees Physiological data logic rules Wearables Affective computing Plethysmography Respiration Galvanic skin response Temperature

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Crypto currency portfolio allocation Using machine learning 3

Crypto currency portfolio allocation Using Machine Learning

引用

3rd international conference on Advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Rao, Pradhyumna Bhasin, Nishit Goswami, Puneet Aggarwal, Lakshita SRM University Computer Science Engg Haryana Delhi-NCR India

ISBN: (纸本)9781665438117

Objective: Unprecedented social and economic health patterns change or when a new normal arises, people prefer to seek safety and wealth preservation. Crypto is the buzzword nowadays where people worldwide are investing their money in a digital payment transaction and have ticked all the boxes when it comes to cases such as security, seemingly fast transactions, and has quickly emerged as one the world's fastest-growing financial markets. Bitcoin, the first cryptocurrency launched in 2009 showcased the proof of work of a decentralised system and what blockchain is capable of doing in a digital ecosystem. Scope: Our paper aims at suggesting currencies to future investors based on the sentiments on popular social media and predicting the overall growth of any coin to analyze its growth prospects. Method: In this paper, we have attempted a meticulous approach to predict the price movements in cryptocurrencies considering its fluctuating factors. Since no unique model can predict the market changes effectively, we focussed on creating a SETP (Sentiment Evaluation for Trend Prediction) model, which analyzes the sentiment surrounding the crypto market and the apprehensions that engulf an investor while investing. The statistical data analysis of the top 5 coins in the market has been undertaken effectively using IBM Watson services. Further enhancements have been made using the jupyter notebook to predict the market highs of the top trending coins. The sentimental evaluation has been done by extracting Twitter trends and hashtags thereby judging the sentiments of the microblogging site and paving a way to predict the exact emotion of investors all over the globe. Findings: Finally, we believe that this research has immense capability to predict the market speculation using the various technologies that are still in the rudimentary phase and has a lot of potential in invoking the interest of researchers and providing useful data/cognizance for future investors. © 2021 IEEE

关键词： Forecasting

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ALBS: An Active learning Framework Based on Syncretic Sample Selection Strategy 19

ALBS: An Active Learning Framework Based on Syncretic Sample...

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11th international conference on machine learning and computing (icmlc)

作者： Pan, Longfei Wang, Xiaojun Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781450366007

machine learning has achieved outstanding performance in many fields, but its success heavily relies on the large number of annotated training samples. However, for many professional fields, data annotation is not only tedious and time consuming, but also demanding specialty-oriented knowledge and skills, which are not easily accessible. To significantly reduce the cost of annotation, we propose a novel active learning framework called ALBS. ALBS uses the syncretic strategy which incorporates "most discriminative" and "most representative" to seek "worthy" samples from unlabeled dataset and update the model incrementally to enhance the performance continuously. We have evaluated our method on two different audio datasets, demonstrating that the syncretic strategy can makes the promotion of model model's performance more robust and faster than the other strategies, and subsampling the historical labeled dataset can reduce unnecessary computing costs and storage space.

关键词： Active learning incremental learning classification syncretic strategy

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