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Owning property is one of the most important investments that a person can make in their lifetime. Therefore, being able to accurately know the real-time value of any property is crucial for making wise sales and purchases. Since the online real estate database company Zillow first developed a machine learning system to predict property sale prices in real time, it has continually worked to improve...
In this empirical study we develop forecasting models for electricity demand using publicly available data and three models based on machine learning algorithms. It compares accuracy of these models using different evaluation metrics. The data consist of several measurements and observations related to the electricity market in Turkey from 2011 to 2016. It is available in different time granularities...
A large number of metalloproteins contained in Protein Data Bank, taking metal ions as cofactors, have important biological functions. As the second most abundant bound trace metal elements in organism, zinc ion plays an important regulatory role in the biological growth and development, disease control, DNA synthesis. So, research on the area of zinc-binding protein sites has an important significance...
Crime distribution forecasting has a positive impact on social stability and has drew much attention in academia. Existing research methods are not applicable for specific research problems or specific data sets very well. So we build the Vector Motion Model and propose a new algorithm named as TPML-WMA (Transition Probability Matrix Learning and Weighted Moving Average algorithm) to predict a future...
Machine learning is the process which converts the information into intelligent actions. This paper presents a literature review on application of different Machine Learning algorithms on huge amount of data collected by the academic institutes. Predictive analytics using the machine learning algorithms has become a new tool of this modern era, as it assists academic institutions in improving the...
The use of computer-based and online education systems has made new data available that can describe the temporal and process-level progression of learning. To date, machine learning research has not considered the impacts of these properties on the machine learning prediction task in educational settings. Machine learning algorithms may have applications in supporting targeted intervention approaches...
Traditionally, performance has been the most important metrics when evaluating a system. However, in the last decades industry and academia have been paying increasing attention to another metric to evaluate servers: availability. A Web server may serve many users when running, but if it is out of service too much time, it becomes useless and expensive. The industry has adopted several techniques...
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