Evaporation is one of the major components of the hydrological cycle in the nature, thus its accurate estimation is so important in the planning and management of the irrigation practices and to assess water availability and requirements. The aim of this study is to investigate the ability of fuzzy inference system for estimating monthly pan evaporation form meteorological data. The study has been carried out depending on 261 monthly measurements of each of temperature (T), relative humidity (RH), and wind speed (W) which have been available in Emara meteorological station, southern Iraq. Three different fuzzy models comprising various combinations of monthly climatic variables (temperature, wind speed, and relative humidity) were developed

Business organizations have faced many challenges in recent times, most important of which is information technology, because it is widely spread and easy to use. Its use has led to an increase in the amount of data that business organizations deal with an unprecedented manner. The amount of data available through the internet is a problem that many parties seek to find solutions for. Why is it available there in this huge amount randomly? Many expectations have revealed that in 2017, there will be devices connected to the internet estimated at three times the population of the Earth, and in 2015 more than one and a half billion gigabytes of data was transferred every minute globally. Thus, the so-called data mining emerged as a

     Data mining is a data analysis process using software to find certain patterns or rules in a large amount of data, which is expected to provide knowledge to support decisions. However, missing value in data mining often leads to a loss of information. The purpose of this study is to improve the performance of data classification with missing values, ​​precisely and accurately. The test method is carried out using the Car Evaluation dataset from the UCI Machine Learning Repository. RStudio and RapidMiner tools were used for testing the algorithm. This study will result in a data analysis of the tested parameters to measure the performance of the algorithm. Using test variations: performance at C5.0, C4.5, and k-NN at 0% missi

The majority of systems dealing with natural language processing (NLP) and artificial intelligence (AI) can assist in making automated and automatically-supported decisions. However, these systems may face challenges and difficulties or find it confusing to identify the required information (characterization) for eliciting a decision by extracting or summarizing relevant information from large text documents or colossal content.   When obtaining these documents online, for instance from social networking or social media, these sites undergo a remarkable increase in the textual content. The main objective of the present study is to conduct a survey and show the latest developments about the implementation of text-mining techniqu

The drought is a globally phenomenon, its influence will convert large parts of Middle East and North Africa (MENA) region into hot dry deserts under the expectations of the climate change scenarios. Climate limitations, soil erosion affected by weather properties such as unequally and limited rainfall; temperature changing and wind, unsuitable irrigation techniques, excessive grazing, agricultural expansion against to the natural habitats, extensively clearance of natural vegetation, and soil salinity had all contributed to land degradation, reduced water supplies, and limited agricultural production in Iraq. It is estimated that nearly 54.3 % of Iraq's area is threatened by desertification problems.
In this research, for Iraq the Cl

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an

     This review explores the Knowledge Discovery Database (KDD) approach, which supports the bioinformatics domain to progress efficiently, and illustrate their relationship with data mining. Thus, it is important to extract advantages of Data Mining (DM) strategy management such as effectively stressing its role in cost control, which is the principle of competitive intelligence, and the role of it in information management. As well as, its ability to discover hidden knowledge. However, there are many challenges such as inaccurate, hand-written data, and analyzing a large amount of variant information for extracting useful knowledge by using DM strategies. These strategies are successfully applied in several applications as data wa