Depression is a widespread and serious phenomenon in public health in all societies. In Saudi society, depression is one of the diseases that the community is may refuse to disclose it. There are no studies have analyzed this disease within the Saudi community. The main research objective is to discover the depression level of Saudi People's. In addition to analyzing the age group and the most gender type affected by the depression in this society. The data collected from social media achieved indirectly without any communication with patients as a sample from this society people. It analyzed using Machine Learning algorithms that give accurate results for this disease. Three classification models have been established to diagnose this disease and the findings of this study presented that the depression levels include five ‎classes and ‎the most affected age group in depression was in the ‎age group from 20-26 years. The results show that young Saudi women are more likely to be depressed. The obtained results are very important to the medical field. Researchers and people working in this field can get benefits out of this research. Especially those who want to understand the depression disease in Saudi society and searching for real solutions to overcome this problem.

Recently, the logistic map is studied to analyse the impact on the chaotic dynamics of various iterated logistic maps using Picard, Mann, and many more. The purpose of this paper is to explore the behavior of a multi-scale population model, i.e. modified logistic map (Mod-LM) and chosen population proportion model, i.e. extended logistic map (Ex-LM) in an I-superior orbit using a bifurcation diagram. The additional parameters of Mod-LM and Ex-LM with the three-step iteration system, increase the degree of freedom which invariably enhances the stability of both the functions. A detailed study of possible scenarios has been conducted to discover the effect of each parameter to the fixed point and its location, periodic cycle, and stability condition by examining the corresponding bifurcation diagram. The experimental result is discussed in terms of convergence point and chaotic range of the given dynamical systems. 

A text summarization system generates short and brief summaries of original document for given user queries. The machine generated summaries uses information retrieval techniques for searching relevant answers from large corpus. This research article proposes a novel framework for generating machine generated summaries using reinforcement learning techniques with Non-deterministic reward function.  Experiments have exemplified with ROUGE evaluation metrics with DUC 2001, 20newsgroup data. Evaluation results of proposed system with hypothesis of automatic summarization from given datasets prove that statistically significant improvement for answering complex questions with f- actual vs. critical values.

Diabetes mellitus is an incurable disease with global prevalence and exponentially increasing incidence. It is one of the greatest health hazards of the twenty-first century which poses a great economic threat on many nations. The premise behind effective disease management in healthcare system is to ensure coordinated intervention targeted towards reducing the incidence of such disease. This paper presents an approach to reducing the incidence of diabetes by predicting the risk of diabetes in patients. Diabetes mellitus risk prediction model was developed using supervised machine learning algorithms of Naïve Bayes, Support Vector Machine and J48 Decision Tree. The decision tree was able to give a prediction accuracy of 95.09% using rules of prediction that give acceptable results, that is, the model was approximately 95% accurate.  The easy-to-understand rules of prediction got from J48 decision tree make it excellent in developing predictive models.

Today the internet has become primary source of communication among people because it holds limitless space and pool of various web applications and resources. The internet allows us to communicate in a fraction of second with another people who is sitting in the other part of the world. At present, the internet has become part of our daily life and its usage is increasing exponentially, therefore it accumulates a number of web applications on daily basis on Web podium. Most of the web applications exist with few weaknesses and that weaknesses give room to several bad buys (hackers) to interrupt that weak part of code in web applications. Recently, SQL Injection, Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF) seriously threaten the most of the web applications.  In this study, we have proposed a black box testing method to detect different web vulnerabilities such as SQL Injection, XSS and CSRF and developed a detection tool i.e. Web Vulnerabilities Finder (WVF) for most of these vulnerabilities.  Our proposed method can automatically analyze websites with the aim of finding web vulnerabilities. By applying the tool to some websites, we have found 45 exploitable XSS, SQL Injection 45, Directory Discloser 38 and Local/remote file inclusion 40 vulnerabilities. The experimental results show that our tool can efficiently detect XSS, SQL Injection, Directory Discloser and LFI/RFI vulnerabilities.