An Efficient Microcontroller Based Sun Tracker Control for Solar Cell Systems

The solar energy is fast becoming a different means of electricity resource. Now in world Fossil fuels are seriously depleting thus the need for another energy source is a necessity. To create effective utilization of its solar, energy efficiency must be maximized. An attainable way to deal with amplifying the power output of sun-powered exhibit is by sun tracking. This paper presents the control system for a solar cell orientation device which follows the sun in real time during daytime

AUTO-CDD: automatic cleaning dirty data using machine learning techniques

Cleaning the dirty data has become very critical significance for many years, especially in medical sectors. This is the reason behind widening research in this sector. To initiate the research, a comparison between currently used functions of handling missing values and Auto-CDD is presented. The developed system will guarantee to overcome processing unwanted outcomes in data Analytical process; second, it will improve overall data processing. Our motivation is to create an intelligent tool that will automatically predict the missing data. Starting with feature selection using Random Forest Gini Index values. Then by using three Machine Learning Paradigm trained model was developed and evaluated by two datasets from UCI (i.e. Diabetics and Student Performance). Evaluated outcomes of accuracy proved Random Forest Classifier and Logistic Regression gives constant accuracy at around 90%. Finally, it concludes that this process will help to get clean data for further analytical process

A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance

The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement

Modifying cleaning method in big data analytics process using random forest classifier

Accurate data is a key success factor influencing the performance of data analytics results, especially for the detection and prediction purpose. Nowadays, Big Data analytics (BDA) is used to analyze the sheer volume of data available in an organization. These data quality must be maintained in order to obtain correct alert and valuable insights from the rapidly changing data of high volume, velocity, variety, veracity, and value. This paper aim is to modify existing framework of big data analytics by improving an important step in pre-processing (i.e. Data Cleaning). Initially, feature selection based on Random Forest is used to extract effective features. Then, two classifier algorithms (i.e. Random Forest classifier and Linear SVM classifier) are applied to train using the dataset to classify data quality and to develop an intelligent model. In evaluation, our experimental results show a consistent accuracy of Random Forest and Linear Regression around 90%. Using this approach, we expect to provide a set of cleaned data for further processing. Besides, analysts can benefit from this system in data analytical process in cleaning stage and conclude that the data is cleaned. Finally, a comparison is presented between available functions which are used to handle missing values with the developed system

A Review on Complex Event Processing Systems for Big Data

Over the years, huge volumes of data are continuously generated due to the increasing number of applications, efficient methods are therefore required to determine the event patterns of interest and manage highly dynamic events in real-time. There has been increasing demand for active systems within Internet of Things, which can automatically react to events that come from various sources. Complex Event Processing (CEP) is an impressive technology that can deal with large amount of data from various sources depending on the consistency of data to generate exact result to process dynamic data in real-time. Thus, understanding existing CEP methods and tools is essential to develop a robust and effective CEP system. In this paper, we had briefly described about event processing, CEP with different engines and CEP for uncertainty. This paper reviewed CEP tools available in the market from 2010 to 2017. It has been found that there are many commercialized and open-source CEP tools in current market, where commercialized tools are used for business intelligence purpose and open-source tools are mostly used for academic purposes. Most of the available processing tools are Query-based and very few are working with Machine learning. There is a huge potential for further research in the use of Machine Learning in Complex Event Processing