Internet acts as the best medium for proliferation and diffusion of fake news. Information quality on the internet is a very important issue, but web-scale data hinders the expert’s ability to correct much of the inaccurate content or fake content present over these platforms. Thus, a new system of safeguard is needed. Traditional Fake news detection systems are based on content-based features (i.e. analyzing the content of the news) of the news whereas most recent models focus on the social features of news (i.e. how the news is diffused in the network). This paper aims to build a novel machine learning model based on Natural Language Processing (NLP) techniques for the detection of ‘fake news’ by using both content-based features and social features of news. The proposed model has shown remarkable results and has achieved an average accuracy of 90.62% with F1 Score of 90.33% on a standard dataset.

Hypertension is a silent killer, which gives no warning signs to alert a patient and can only be detected through regular blood pressure check¬ups. Uncontrolled and unmonitored hypertension contributed to stroke, chronic kidney disease, eye problem, and heart failure. It is an ongoing challenge to health care systems worldwide. Early detection of hypertension and creating awareness will greatly reduce the effect of hypertension and its related diseases. Also, having a mobile-based system will help patients to know their status, relate with Doctor and enjoy the quick response from the Doctor on hypertension diagnostic effect on their health. The mobile application will help in monitoring patients anytime, anywhere and provide services for each patient based on their personal health condition. The mobile application was designed using unified modeling language and implemented using the Extensible Mark-Up Language and Java programming language for the mobile layout and content, while JavaScript Object Notation was used to implement the data storage and retrieval mechanism of the system. The system was tested using data collected from hospital, which yielded an accuracy of 100%. In conclusion, the system will assist in providing timely, efficient, accurate and comprehensive information about hypertension, which is useful for Doctors and patients in detecting, diagnosing, classifying and managing hypertension and its risk.

In Algeria, as in many other countries, the Emergency Department (ED) of the hospital, is the main entrance to the hospital, which provides Healthcare to patients threatened with death, and which faces several issues, emphasized by resource limitation. Our work presents a description of patient flow inside the ‘ED’ of Chalabi Abdelkader Hospital, Mascara, Algeria. This study aims to prevent the care complication scheme by adopting a workflow approach in order to design the patient flow in the chosen ‘ED’. The objective is to enhance patients’ flows, to improve the quality of the patient supervision, by targeting the minimization of the total and waiting times. A simulation model of the study system will be built based on the acquired data, and it will be validated by domain experts for a maximal rapprochement to the reality. Then, many simulations instances will be realized using Rockwell ARENA simulator to evaluate the impact of the proposed solutions. As a result of this study, we provided to ‘ED’ supervisors many improvement solutions and recommendations to the issues identified in the modeling phase.

Collaborative filtering techniques are successfully employed in recommender systems to assist users counter the information overload by making accurate personalized recommendations. However, such systems are shown to be at risk of attacks. Malicious users can deliberately insert biased profiles in favor/disfavor of chosen item(s). The presence of the biased profiles can violate the underlying principle of the recommender algorithm and affect the recommendations.
This paper proposes two metrics namely, Rating Deviation from Mean Bias (RDMB) and Compromised Item Deviation Analysis (CIDA) for identification of malicious profiles and compromised items, respectively. A framework is developed for investigating the effectiveness of the proposed metrics. Extensive evaluation on benchmark datasets has shown that the metrics due to their high Information Gain lead to more accurate detection of shilling profiles compared to the other state of the art metrics.

This document presents the required layout of articles to Medical data mining has become one of the prominent issues in the field of data mining due to the delicate lifestyle opted by the people which are leading them towards various chronicle health diseases. Heart disease is one of the conspicuous public health concern worldwide issues. Since clinical data is growing rapidly owing to deficient health awareness, various techniques and scientific methods are opted for analyzing this huge data. Several data mining techniques such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, Naïve Bayes and Artificial Neural Network (ANN) are introduced for the prediction of health disease. These techniques help to mine the relevant and useful amount of data, form the medical dataset which helps to provide beneficial information to the medical institutions. This study presents various issues related to healthcare and various machines learning algorithms which have to withstand to provide the best possible output. A comprehensive review of the literature has been summarized to put lights on the previous work done in this field.

In this article some modern techniques have been used to diagnose the oral and dental diseases. The symptoms and causes of such disease has been studied that may cases many other serious diseases .Many cases have been reviewed through patients' records, and investigation on such causes of oral and dental disease have been carried out to help design a system that helps diagnose oral and classify them, and that system was made according to the decision tree, (Id3 and J48) and artificial neural network techniques. Sample of oral and dental diseases were collected with their symptoms to become a data base so as to help construct a diagnostic system. The graphical interface were formed in C# to facilitate the use's diagnosis process where the patient chooses the symptoms through the interface which he suffered from ,and they are analyzed using the classification techniques and then re diagnosed the disease for the user.