scholarly journals Descriptive and predictive analytics of agricultural data using machine learning algorithms

2020 ◽  
pp. 20-39
Author(s):  
R. Suguna ◽  
R. Uma Rani
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

scholarly journals Predictive Analytics for Maintaining Power System Stability in Smart Energy Communities

2021 ◽  
Author(s):  
Aida Mehdipour Pirbazari
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Energy Supply ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
System Stability ◽  
Energy Forecasting ◽  
The Stability ◽  
Energy Prosumers

Digitalization and decentralization of energy supply have introduced several challenges to emerging power grids known as smart grids. One of the significant challenges, on the demand side, is preserving the stability of the power systems due to locally distributed energy sources such as micro-power generation and storage units among energy prosumers at the household and community levels. In this context, energy prosumers are defined as energy consumers who also generate, store and trade energy. Accurate predictions of energy supply and electric demand of prosuemrs can address the stability issues at local levels. This study aims to develop appropriate forecasting frameworks for such environments to preserve power stability. Building on existing work on energy forecasting at low-aggregated levels, it asks: What factors influence most on consumption and generation patterns of residential customers as energy prosumers. It also investigates how the accuracy of forecasting models at the household and community levels can be improved. Based on a review of the literature on energy forecasting and per- forming empirical study on real datasets, the forecasting frameworks were developed focusing on short-term prediction horizons. These frameworks are built upon predictive analytics including data col- lection, data analysis, data preprocessing, and predictive machine learning algorithms based on statistical learning, artificial neural networks and deep learning. Analysis of experimental results demonstrated that load observa- tions from previous hours (lagged loads) along with air temperature and time variables highly affects the households’ consumption and generation behaviour. The results also indicate that the prediction accuracy of adopted machine learning techniques can be improved by feeding them with highly influential variables and appliance-level data as well as by combining multiple learning algorithms ranging from conventional to deep neural networks. Further research is needed to investigate online approaches that could strengthen the effectiveness of forecasting in time-sensitive energy environments.

Trend and Predictive Analytics of Dengue Prevalence in Administrative Region

2020 ◽  
pp. 236-262
Author(s):  
Kannimuthu Subramanian ◽  
Swathypriyadharsini P. ◽  
Gunavathi C. ◽  
Premalatha K.
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Goodness Of Fit ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Public Health Problem ◽  
Viral Disease ◽  
Machine Learning Algorithms ◽  
Severe Dengue ◽  
Major Public Health Problem

Dengue is fast emerging pandemic-prone viral disease in many parts of the world. Dengue flourishes in urban areas, suburbs, and the countryside, but also affects more affluent neighborhoods in tropical and subtropical countries. Dengue is a mosquito-borne viral infection causing a severe flu-like illness and sometimes causing a potentially deadly complication called severe dengue. It is a major public health problem in India. Accurate and timely forecasts of dengue incidence in India are still lacking. In this chapter, the state-of-the-art machine learning algorithms are used to develop an accurate predictive model of dengue. Several machine learning algorithms are used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed, and it is found that the optimized SVR gives minimal RMSE 0.25. The classifiers are applied, and experiment results show that the extreme boost and random forest gives 93.65% accuracy.

scholarly journals An Intelligent and Data-Driven Mobile Platform for Youth Volunteer Management using Machine Learning and Predictive Analytics

2020 ◽  
Author(s):  
Alyssa Huang ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
High School ◽  
High School Students ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
School Students ◽  
Best Fit

Volunteering is very important to high school students because it not only allows the teens to apply the knowledge and skills they have acquired to real-life scenarios, but it also enables them to make an association between helping others and their own joy of fulfillment. Choosing the right volunteering opportunities to work on can influence how the teens interact with that cause and how well they can serve the community through their volunteering services. However, high school students who look for volunteer opportunities often do not have enough information about the opportunities around them, so they tend to take whatever opportunity that comes across. On the other hand, as organizations who look for volunteers usually lack effective ways to evaluate and select the volunteers that best fit the jobs, they will just take volunteers on a first-come, firstserve basis. Therefore, there is a need to build a platform that serves as a bridge to connect the volunteers and the organizations that offer volunteer opportunities. In this paper, we focus on creating an intelligent platform that can effectively evaluate volunteer performance and predict best-fit volunteer opportunities by using machine learning algorithms to study 1) the correlation between volunteer profiles (e.g. demographics, preferred jobs, talents, previous volunteering events, etc.) and predictive volunteer performance in specific events and 2) the correlation between volunteer profiles and future volunteer opportunities. Two highest-scoring machine learning algorithms are proposed to make predictions on volunteer performance and event recommendations. We demonstrate that the two highest-scoring algorithms are able to make the best prediction for each query. Alongside the practice with the algorithms, a mobile application, which can run on both iPhone and Android platforms is also created to provide a very convenient and effective way for the volunteers and event supervisors to plan and manage their volunteer activities. As a result of this research, volunteers and organizations that look for volunteers can both benefit from this data-driven platform for a more positive overall experience.

A journal bearing performance prediction method utilizing a machine learning technique

2021 ◽  
pp. 135065012110557
Author(s):  
Georgios N Rossopoulos ◽  
Christos I Papadopoulos
Keyword(s):  
Machine Learning ◽  
Journal Bearing ◽  
Predictive Analytics ◽  
Hydrodynamic Lubrication ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Maximum Pressure ◽  
Classification Problems ◽  
Lubrication Regime ◽  
Lift Off

A predictive analytics methodology is presented, utilizing machine learning algorithms to identify the performance state of marine journal bearings in terms of maximum pressure, minimum film thickness, Sommerfeld number, load and shaft speed. A dataset of different bearing operation states has been generated by solving numerically the Reynolds equation in the hydrodynamic lubrication regime, for steady-state loading conditions and assuming isothermal and isoviscous lubricant flow. The shaft has been modelled with four different values of misalignment angle, lying within the acceptable operating range, as defined in the existing regulatory framework. The journal bearing was modelled parametrically using generic geometric parameters of a marine stern tube bearing. The lift-off speed was estimated for each loading scenario to ensure operation in the hydrodynamic lubrication regime and the effect of shaft misalignment on lift-off speed has been evaluated. The generated dataset was utilised for training, testing and validation of several machine learning algorithms, as well as feature selection analysis, in order to solve several classification problems and identify the various bearing operational states.

scholarly journals A Novel Approach for Improving Breast Cancer Risk Prediction using Machine Learning Algorithms : A Survey

2019 ◽  
pp. 113-118 ◽  
Author(s):  
Madhuri Maru ◽  
Saket Swarndeep
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Predictive Analysis ◽  
Support Vector ◽  
K Nearest Neighbors

Breast cancer represents one of the diseases that make a high number of deaths every year. It is the most common type of all cancers and the main cause of women's deaths worldwide. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. Here, a common misconception is that predictive analytics and machine learning are the same thing where in predictive analysis is a statistical learning and machine learning is pattern recognition and explores the notion that algorithms can learn from and make predictions on data. In this paper, we are addressing the problem of predictive analysis by adding machine learning techniques for better prediction of breast cancer. In this, a performance comparison between different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), Naive Bayes (NB) and k Nearest Neighbors (k-NN) on the Wisconsin Breast Cancer (original) datasets is conducted. The main objective is to assess the correctness in classifying data with respect to efficiency and effectiveness of hybrid algorithm in terms of accuracy, precision, sensitivity and specificity.

scholarly journals Robolution: Real Time Predictive Analytics for Industrial Robots

2020 ◽  
Vol 9 (3) ◽  
pp. 923-926
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Industrial Robots ◽  
Financial Loss ◽  
The Given

Robots have been playing a very important role in our day-to-day lives and will be a necessity in the coming future. Whenever we hear automation, the first thing that strikes our mind is a robot performing the given task. But if a robot fails to do the task, it could cost an individual or corporate a huge financial loss. In this study, we have learned the working of various robots and drawbacks that hold them back. For this work, we did make a study of drives used in the robot and after that applied the machine learning algorithms to predict the classification of whether the robot will function properly or not, based on the data of drive(s).

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