Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se

 The financial markets are one of the sectors whose data is characterized by continuous movement in most of the times and it is constantly changing, so it is difficult to predict its trends , and this leads to the need of methods , means and techniques for making decisions, and that pushes investors and analysts in the financial markets to use various and different methods in order to reach at predicting the movement of the direction of the financial markets. In order to reach the goal of making decisions in different investments, where the algorithm of the support vector machine and the CART regression tree algorithm are used to classify the stock data in order to determine

The cost management of cost indicators in housing projects, on the level of planning and design, is the most important quality indicators, for adoption of strategies of planning and design efficient in managing these indicators. So this research points out the need to highlight the most effective and influential cost indicators in housing projects, and to determine strategies in the management of these indicators in order to raise the efficiency of housing projects quality, to seemly the income level target group, taking into consideration the quality of housing standards, to achieve the basic requirements of housing. This paper highlights the importance of the cost  management, the types of housing cost, the method

      The prevalence of diabetes is increasing rapidly and is now recognized as a significant global health problem. Diabetes occurs when a person does not produce enough insulin due to an imbalance in insulin production. This can lead to the failure of organs and tissues such as the kidneys, heart, blood vessels, eyes, nerves, and kidneys. As a result, early diagnosis and classification of type 2 diabetes mellitus (T2DM) are critical to aiding physician assessments. Subsequently, the current study aims to determine irisin levels in patients with T2DM and pre-DM as early predictors for disease cases. The current study included 138 subjects divided into three groups based on fasting blood glucose (FBG) and glycosylated hemoglobi

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Background: Various studies conducted in many parts of the world suggest that there is lack of public awareness and knowledge of various aspects related to diabetes. With proper education, awareness, earlier detection and better care, many complications and co-morbidities can be reduced in diabetic population.Objectives: to evaluate the level of awareness of diabetes mellitus type 2 patients regarding their disease and its' complications.Methods: Cross – sectional survey was conducted during November and December 2011, in the Medical centers of Al Baladiat, Mustansyria and Zuafranya, including 145 type 2 diabetic patients (58.6 % males, 41.4% females) who were subjected to self–structured questionnaires regarding different aspects of