A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

Evaluation of Four Geomembrane-Mounted PV Systems for Land Reclamation in Southern Arizona

Four different PV （photovoltaic） systems deployed around Tucson Arizona on geomembranes are used to test the feasibility of converting mine tailings and landfills into solar energy generating sites. Differences between these deployed systems include： two types of geomembrane materials, two different module anatomies and two different locations. Module mounting techniques unique to mine tailing sites are described. Several system failures observed during the first two years of operation are explained here in detail. Validated predictions for the operating temperature of these systems and their associated electrical performance are presented. It was determined that PV modules mounted on light-colored thermoplastic with shielded wiring operate at lower temperatures, are structurally stable, and experience fewer wiring failures.

Effect of Boron and Heat Treatment on Mechanical Properties of White Cast Iron for Mining Application

Heat treatment methods were applied to white cast iron for improving the impact and wear resistance. Additionally, chemical composition optimization was made. Furthermore, the effect of boron addition on such applica- tions was investigated. Samples were investigated by using optical and electron microscope methods. Hardness, wear and impact tests were conducted. The results showed that the secondary carbides in the standard alloy were iron-enriched, needle-like carbides M3C when the boron-added alloy contained Fe23 （C, B）6 type, globular secondary carbides. It was concluded that heat treatment B provided higher wear and hardness properties, compared to the stand- ard heat treatment. Optimum mechanical properties were obtained by lower destabilisation temperatures and increasing temperature reduced the wear resistance and hardness.