Big Data Classification and Internet of Things in Healthcare

Every single day, a massive amount of data is generated by different medical data sources. Processing this wealth of data is indeed a daunting task, and it forces us to adopt smart and scalable computational strategies, including machine intelligence, big data analytics, and data classification. The authors can use the Big Data analysis for effective decision making in healthcare domain using the existing machine learning algorithms with some modification to it. The fundamental purpose of this article is to summarize the role of Big Data analysis in healthcare, and to provide a comprehensive analysis of the various techniques involved in mining big data. This article provides an overview of Big Data, applicability of it in healthcare, some of the work in progress and a future works. Therefore, in this article, the use of machine learning techniques is proposed for real-time diabetic patient data analysis from IoT devices and gateways.

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Big Data Analysis for Trend Recognition Using Machine Learning Techniques

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327910666200304141238 ◽

2020 ◽

Vol 10 (4) ◽

pp. 540-550

Author(s):

Cerene Mariam Abraham ◽

Mannathazhathu Sudheep Elayidom ◽

Thankappan Santhanakrishnan

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Data Analysis ◽

Data Analytics ◽

Research Work ◽

Big Data Analytics ◽

Big Data Analysis ◽

Machine Learning Techniques ◽

Derivative Market

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis. Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data. Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

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A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python

Open Source Software for Statistical Analysis of Big Data - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-2768-9.ch007 ◽

2020 ◽

pp. 197-218

Author(s):

Son Nguyen ◽

Anthony Park

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Time Series ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Time Series Forecasting ◽

Machine Learning Algorithms ◽

Time Series Models ◽

Data Sets

This chapter compares the performances of multiple Big Data techniques applied for time series forecasting and traditional time series models on three Big Data sets. The traditional time series models, Autoregressive Integrated Moving Average (ARIMA), and exponential smoothing models are used as the baseline models against Big Data analysis methods in the machine learning. These Big Data techniques include regression trees, Support Vector Machines (SVM), Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), and long short-term memory neural networks (LSTM). Across three time series data sets used (unemployment rate, bike rentals, and transportation), this study finds that LSTM neural networks performed the best. In conclusion, this study points out that Big Data machine learning algorithms applied in time series can outperform traditional time series models. The computations in this work are done by Python, one of the most popular open-sourced platforms for data science and Big Data analysis.

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Acoustic Diversity Classification Using Machine Learning Techniques: Towards Automated Marine Big Data Analysis

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213020600118 ◽

2020 ◽

Vol 29 (03n04) ◽

pp. 2060011

Author(s):

Emna Hachicha Belghith ◽

François Rioult ◽

Medjber Bouzidi

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Big Data Analysis ◽

Machine Learning Techniques ◽

Support Vector ◽

K Nearest Neighbor ◽

Learning Techniques ◽

Acoustic Diversity ◽

Marine Data

During the last years, big data has become the new emerging trend that increasingly attracting the attention of the R&D community in several fields (e.g., image processing, database engineering, data mining, artificial intelligence). Marine data is part of these fields which accommodates this growth, hence the appearance of marine big data paradigm that monitoring advocates the assessment of human impact on marine data. Nonetheless, supporting acoustic sounds classification is missing in such environment, with taking into account the diversity of such data (i.e., sounds of living undersea species, sounds of human activities, and sounds of environmental effects). To overcome this issue, we propose in this paper an approach that efficiently allowing acoustic diversity classification using machine learning techniques. The aim is to reach an automated support of marine big data analysis. We have conducted a set of experiments, using a real marine dataset, in order to validate our approach and show its effectiveness and efficiency. To do so, three machine learning techniques are employed: (i) classic machine learning models (i.e., k-nearest neighbor and support vector machine), (ii) deep learning based on convolutional neural networks, and (iii) transfer learning based on the reuse of pretrained models.

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Big Data Analysis Based on Machine Learning Techniques

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l3630.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 4049-4056

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Web Application ◽

Big Data Analysis ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

User Reviews ◽

Mobile Web ◽

Learning Techniques

Most of the online applications such as Amazon, Snap deal, Flip cart and many others, attract customers by presenting user reviews about the services. These services typically include hotels, flights, cabs, holiday plans and many more. The main objective of this paper is to automatically analyze the feedbacks data given by the customers into positive, negative and neutral categories and gives a summarized review in case of multiple sentences is present in the feedback. In this proposed work various sources of data; namely from Flip cart, Snap deal is considered. The method to analyze the data include collecting the data from the mobile/web application sources, filtering the unwanted data, preprocessing and finally analyzing and summarizing the reviews using supervised machine learning techniques.

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Self-Adaptive Genetic Programming for Manufacturing Big Data Analysis

Symmetry ◽

10.3390/sym13040709 ◽

2021 ◽

Vol 13 (4) ◽

pp. 709

Author(s):

Sanghoun Oh ◽

Woong-Hyun Suh ◽

Chang-Wook Ahn

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analysis ◽

Genetic Programming ◽

Learning Algorithms ◽

Explanatory Power ◽

Big Data Analysis ◽

Machine Learning Algorithms ◽

Superior Performance ◽

Self Adaptive

While black-box-based machine learning algorithms have high analytical consistency in manufacturing big data analysis, those algorithms experience difficulties in interpreting the results based on the manufacturing process principle. To overcome this limitation, we present a Self-Adaptive Genetic Programming (SAGP) for manufacturing big data analysis. In Genetic Programming (GP), the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. These advantages enable intuitive interpretation on manufacturing mechanisms and derive manufacturing principles based on the variables represented by formulas. However, GP occasionally has trouble adjusting the balance between high accuracy and detailed interpretation due to an incommensurable symmetry of the solutions. In order to effectively handle this drawback, we apply the self-adaptive mechanism into GP for managing crossover and mutation probabilities regarding the complexity of tree structure solutions in each generation. Our proposed algorithm showed equal or superior performance compared to other machine learning algorithms. We believe our proposed method can be applied in diverse manufacturing big data analytics in the future.

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