Big data analytics for financial Market volatility forecast based on support vector machine

2020 ◽  
Vol 50 ◽  
pp. 452-462 ◽  
Author(s):  
Rongjun Yang ◽  
Lin Yu ◽  
Yuanjun Zhao ◽  
Hongxin Yu ◽  
Guiping Xu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Big Data ◽  
Financial Market ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Market Volatility ◽  
Support Vector ◽  
Volatility Forecast

scholarly journals Crime Data Forecasting Using Machine Learning and Big Data Analytics

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 591-606
Author(s):  
R. Brindha ◽  
Dr.M. Thillaikarasi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Crime Data

Big data analytics (BDA) is a system based method with an aim to recognize and examine different designs, patterns and trends under the big dataset. In this paper, BDA is used to visualize and trends the prediction where exploratory data analysis examines the crime data. “A successive facts and patterns have been taken in following cities of California, Washington and Florida by using statistical analysis and visualization”. The predictive result gives the performance using Keras Prophet Model, LSTM and neural network models followed by prophet model which are the existing methods used to find the crime data under BDA technique. But the crime actions increases day by day which is greater task for the people to overcome the challenging crime activities. Some ignored the essential rate of influential aspects. To overcome these challenging problems of big data, many studies have been developed with limited one or two features. “This paper introduces a big data introduces to analyze the influential aspects about the crime incidents, and examine it on New York City. The proposed structure relates the dynamic machine learning algorithms and geographical information system (GIS) to consider the contiguous reasons of crime data. Recursive feature elimination (RFE) is used to select the optimum characteristic data. Exploitation of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) are related to develop the optimum data model. Significant impact features were then reviewed by applying GBDT and GIS”. The experimental results illustrates that GBDT along with GIS model combination can identify the crime ranking with high performance and accuracy compared to existing method.”

scholarly journals Boruta-grid-search least square support vector machine for NO2 pollution prediction using big data analytics and IoT emission sensors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Habeeb Balogun ◽  
Hafiz Alaka ◽  
Christian Nnaemeka Egwim
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Predictive Models ◽  
Data Analytics ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Support Vector ◽  
Traffic Data ◽  
Content Type ◽  
Pollution Concentration

PurposeThis paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison.Design/methodology/approachThis research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist.FindingsThe results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO2 pollution.Practical implicationsThis paper's hybrid model provides a basis for giving an informed decision on the NO2 pollutant avoidance system.Originality/valueThis research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration.

Artificial intelligence for hospitality big data analytics: developing a prediction model of restaurant review helpfulness for customer decision-making

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Minwoo Lee ◽  
Wooseok Kwon ◽  
Ki-Joon Back
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Prediction Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Support Vector ◽  
Content Type ◽  
Extreme Gradient Boosting ◽  
Review Helpfulness

Purpose Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data. Design/methodology/approach The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making. Findings By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity. Practical implications The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results. Originality/value To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

scholarly journals PV Forecasting Using Support Vector Machine Learning in a Big Data Analytics Context

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 748 ◽  
Author(s):  
Stefan Preda ◽  
Simona-Vasilica Oprea ◽  
Adela Bâra ◽  
Anda Belciu (Velicanu)
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Power Systems ◽  
Management Strategies ◽  
Big Data Analytics ◽  
Support Vector ◽  
Load Curve ◽  
Big Data Technologies ◽  
Accurate Forecast

Renewable energy systems (RES) are reliable by nature; the sun and wind are theoretically endless resources. From the beginnings of the power systems, the concern was to know “how much” energy will be generated. Initially, there were voltmeters and power meters; nowadays, there are much more advanced solar controllers, with small displays and built-in modules that handle big data. Usually, large photovoltaic (PV)-battery systems have sophisticated energy management strategies in order to operate unattended. By adding the information collected by sensors managed with powerful technologies such as big data and analytics, the system is able to efficiently react to environmental factors and respond to consumers’ requirements in real time. According to the weather parameters, the output of PV could be symmetric, supplying an asymmetric electricity demand. Thus, a smart adaptive switching module that includes a forecasting component is proposed to improve the symmetry between the PV output and daily load curve. A scaling approach for smaller off-grid systems that provides an accurate forecast of the PV output based on data collected from sensors is developed. The proposed methodology is based on sensor implementation in RES operation and big data technologies are considered for data processing and analytics. In this respect, we analyze data captured from loggers and forecast the PV output with Support Vector Machine (SVM) and linear regression, finding that Root Mean Square Error (RMSE) for prediction is considerably improved when using more parameters in the machine learning process.

scholarly journals Digital agriculture based on big data analytics: a focus on predictive irrigation for smart farming in Morocco

2021 ◽  
Vol 24 (1) ◽  
pp. 581
Author(s):  
Loubna Rabhi ◽  
Noureddine Falih ◽  
Lekbir Afraites ◽  
Belaid Bouikhalene
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Support Vector ◽  
Soil Drought ◽  
The Internet ◽  
Data Mining Algorithms ◽  
Smart Farming ◽  
Digital Agriculture ◽  
Mining Algorithms

Due to the spead of objects connected to the internet and objects connected to each other, agriculture nowadays knows a huge volume of data exchanged called big data. Therefore, this paper discusses connected agriculture or agriculture 4.0 instead of a traditional one. As irrigation is one of the foremost challenges in agriculture, it is also moved from manual watering towards smart watering based on big data analytics where the farmer can water crops regularly and without wastage even remotely. The method used in this paper combines big data, remote sensing and data mining algorithms (neural network and support vector machine). In this paper, we are interfacing the databricks platform based on the apache Spark tool for using machine learning to predict the soil drought based on detecting the soil moisture and temperature.

scholarly journals A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Raja Krishnamoorthi ◽  
Shubham Joshi ◽  
Hatim Z. Almarzouki ◽  
Piyush Kumar Shukla ◽  
Ali Rizwan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Prediction Models ◽  
Big Data Analytics ◽  
Support Vector ◽  
Model Assessment ◽  
Learning Models ◽  
Research Issues ◽  
Diabetes Prediction

Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

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