Background: Diabetes and hypertension are two of the commonest diseases in the world. As they unfavorably affect people of different age groups, they have become a cause of concern and must be predicted and diagnosed well in advance. Objective: This research aims to determine the effectiveness of artificial neural networks (ANNs) in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases. Sample: This work used two online datasets which consist of data collected from 768 individuals. We applied neural network algorithms to predict if the individuals have those two diseases based on some factors. Diabetes prediction is based on five factors: age, weight, fat-ratio, glucose, and insulin, while blood pressure prediction is based on six factors: age, weight, fat-ratio, blood pressure, alcohol, and smoking. Method: A model based on the Multi-Layer Perceptron Neural Network (MLP) was implemented. The inputs of the network were the factors for each disease, while the output was the prediction of the disease’s occurrence. The model performance was compared with other classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). We used performance metrics measures to assess the accuracy and performance of MLP. Also, a tool was implemented to help diagnose the diseases and to understand the results. Result: The model predicted the two diseases with correct classification rate (CCR) of 77.6% for diabetes and 68.7% for hypertension. The results indicate that MLP correctly predicts the probability of being diseased or not, and the performance can be significantly increased compared with both SVM and KNN. This shows MLPs effectiveness in early disease prediction.

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The International Software Benchmarking Standards Group (ISBSG) maintains a software development repository with 6,006 software projects. The definition of productivity is a single ratio of output to input and then combined with various cost factors leading to a single value. Of these values we have dataset makes it possible to calculate the productivity of projects, effort, size and quality. By contrast, the concept of performance is more comprehensive than productivity. This study explores a comparison between performance and productivity and how it can affect projects by several other factors that affect its using ISBSG dataset V.12. In this research, tree data analysis techniques were applied: data clustering, neural network. SPSS was used to conduct statistical analysis and data visualization.

Today, it is known that inflammation represents the common mechanism of human systemic diseases, including cancer and autoimmunity. Obviously, the endothelial system is involved in all inflammatory processes. Then, the control of the endothelial functions could constitute a new medical strategy to treat several pathological conditions, including ischemic and thrombotic events. Moreover, in addition to the action of angiogenic and anti-angiogenic factors, the endothelial system has been proven to be physiologically under a double control, represent by the cytokine network and the neuroendocrine system. Most cytokines have appeared to exert angiogenic and inflammatory effects, which are balanced by an anti-angiogenic and an anti-inflammatory action exerted by the pineal hormone melatonin (MLT), cannabinoid agents, and the product of ACE2, the angiotensin 1-7 (Ang 1-7). Then, a neuroendocrine approach with MLT, cannabinoids and Ang 1-7 could constitute a new way in the treatment of endothelial alterations and angiogenesis.