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The constant change in the components and variables of the economic environment is one of the most important phenomena that characterize this environment at present, and these changes have made the goal of survival in the market a priority due to the prevalence of financial distress in companies. The main purpose of this paper is to test the impact and effectiveness of the model of artificial neural networks and Naive Bayes technology and to see how accurate they are and their ability to classify companies. In this paper, the information of the companies listed on the Iraq stock exchange for 2017, which represents 36 companies with high financial distress, 20 companies with medium financial distress, in addition to 43 non-distressed companies for a group of 99 companies. According to the experimental results, the model of artificial neural networks gives the highest rating accuracy of 96.97% and an error rate compared to Naive Bayes, which gave a rating accuracy of 74.74%. [ABSTRACT FROM AUTHOR]

Financial markets are currently a key source of growth for the local and international economies, as they are the means by which economic units are fed. Iraq is one of the countries attempting to improve and modernize its financial industry in order to stay up with technology advancements and the digital revolution. Forecasting and early detection of financial distress is one of the important methods that contribute to increasing confidence between investors and the market and help to make sound decisions in a timely manner in order to avoid reaching bankruptcy. This paper aims to employ smart models in the detection of financial distress, and to select the best model capable of classifying the financial situation of companies into three categories (non-distress, medium distress and high distress) by selecting (14) financial ratio that directly affects the situation of companies. The researcher used artificial neural networks algorithms such as the reverse error propagation algorithm, Using data mining methods and deep learning algorithms, the C4.5 algorithm, Naive Bayes simple classifier algorithm, Convolutional neural networks algorithm, and a multi-layered algorithm support vector machine were used to classify a company's financial state. The C4.5 method and SVM had the highest rating accuracy by a tiny margin in all levels (98, 96.9, and 91.9) respectively, according to the results of the analysis. The most essential recommendations included the fundamental requirement of using smart technology in recognizing financial challenges of companies in order to support and consolidate the economic stability of enterprises in particular and the market in general in the adoption of the Iraqi stock market. [ABSTRACT FROM AUTHOR]

There was a considerable rise in water consumption by public needs as a result of the increasing demand for water in light of limited water imports, resulting in a shortage in the amount of water resources and storage in the dam. Water import forecasting techniques were utilized to determine the suitable water stock as a result of the scarcity. Data mining techniques will be employed in this research to obtain a smart decision support model that consists of two stages based on natural water imports and for the aim of generating a rational management for the operation of the dam and control of water releases: Assessment and forecasting of the situation, as well as decision-making on the amount of water released In the first phase, time-series models based on Artificial Neural Networks (ANN) theory and the Propagation Algorithm (BP) algorithm were used, and in the second phase, classification techniques represented by the support vector classifier were used to extract predictions of water levels entering the reservoir. MSVM (Multiclass Support Vector Machine) is used to regulate and select the appropriate launch policy. An Artificial Neural Network (ANN) is favored because it has the lowest sum of squared errors (MSE). In the second stage, the researcher applied and used the vector support machine classification method to classify the tank's water discharge output, and discovered that the multiple booster transmission classification method (MSVM) is the most efficient based on the results because it has the highest accuracy in making decisions. The best for waste water and for water consumption needs such as drinking and cooking as needed (few, medium, high) (irrigation, agriculture, industry, electricity). [ABSTRACT FROM AUTHOR]