Mining techniques proved to have a successful impact in different fields for many targets; one of these targets is to gain customers' satisfaction through enhancing the products' quality according to the voice of these customers. This research proposes a framework that is based on mining techniques and adopted Saaty method targeting to gain the customers' satisfaction and consequently a competitive advantage in the real estate market. The proposed framework is applied during the design phase of a real estate residential building project as an improvement tool to design the building according to the customers' requirements representing the voice of customers (VOC). The proposed Saaty method adaptation increased the number of the consistent sample which was incorrectly excluded using the traditional Saaty method. Saaty method adaptation has succeeded in enhancing the house of quality (HOQ) by achieving the consistent technical customers' requirements for residential buildings, while customers' segmentation succeeded in focusing on the homogeneous grouping of customers.

Social networks are currently one of the main News' sources for most of their users. Moreover, News channels also consider social networks as main channels not only for spreading the news but also for measuring the feedback from their followers. Facebook Followers can comment or react to the news, which represents the follower's feedback about this topic. Therefore, it is a fact that measuring the News' credibility is one of the important tasks that could control the propagation of the fake news as well as the number of News' followers. The proposed model in this research highlights the impact of the News' followers on detecting the News' polarity either it is fake or not. The proposed model focuses on applying an intelligent sentiment analysis using Vector Space Model (VSM) which is one of the most successful techniques on the users' comments and reactions through the emoji. Then the degree of credibility is determined according to the correlation coefficient. An experimental study was applied using Facebook News dataset, which included the News and the followers' feedbacks.

The data mining techniques-based systems could have a crucial impact on the employees’ lifestyle to predict heart diseases. There are many scientific papers, which use the techniques of data mining to predict heart diseases. However, limited scientific papers have addressed the four cross-validation techniques of splitting the data set that plays an important role in selecting the best technique for predicting heart disease. It is important to choose the optimal combination between the cross-validation techniques and the data mining, classification techniques that can enhance the performance of the prediction models. This paper aims to apply the four-cross-validation techniques (holdout, k-fold cross-validation, stratified k fold cross-validation, and repeated random) with the eight data mining, classification techniques (Linear Discriminant Analysis, Logistic regression, Support Vector Model, KNN, Decision Tree, Naïve Bayes, Random Forest, and Neural Network) to improve the accuracy of heart disease prediction and select the best prediction models. It analyzes these techniques on a small and large dataset collected from different data sources like Kaggle and the UCI machine-learning repository. The evaluation metrics like accuracy, precision, recall, and F-measure were used to measure the performance of prediction models. Experimentation is performed on two datasets, and the results show that when the dataset is colossal (70000 records), the optimal combination that achieves the highest accuracy is holdout cross-validation with the neural network with an accuracy of 71.82%. At the same time, Repeated Random with Random Forest considers the optimal combination in a small dataset (303 records) with an accuracy of 89.01%. The best models will be recommended to the physicians in business organizations to help them predicting heart disease in employees into one of two categories, cardiac and non-cardiac, at an early stage. The early detection of heart diseases in employees will improve productivity in the business organization.

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Construction companies need to improve the accuracy of their projects' budgeting to achieve the targeted profit. Site overheads are the expenses related to a project but are not allocated to a specific work package. The main objective of this research is to develop a neural network model for commercial projects to predict and estimate project site overhead costs as a percentage of the direct cost. The focal point of the research is focused on the main factors affecting site overhead costs for commercial projects in Egypt. These factors and their weights were identified by experts through the collected structured data. Cost data for 55 projects in the past seven years was collected with various conditions of company rank, direct cost, project duration, project location, contract type, and type of company ownership. The results have shown that the best model developed consists of six input neurons; two hidden layers with six and five neurons respectively, and one output layer representing the percentage of project site overhead. The model was tested on six projects with accuracy of 84%.