Mohammed Hussein Naji Jabardi

Dynamic database schema for hospital management system

One of the most popular social media platforms, Twitter is used by millions of people to share information, broadcast tweets, and follow other users. Twitter is an open application programming interface and thus vulnerable to attack from fake accounts, which are primarily created for advertisement and marketing, defamation of an individual, consumer data acquisition, increase fake blog or website traffic, share disinformation, online fraud, and control. Fake accounts are harmful to both users and service providers, and thus recognizing and filtering out such content on social media is essential. This study presents a new approach to detect fake Twitter accounts using ontology and Semantic Web Rule Language (SWRL) rules. SWRL rules-based reasoner is utilized under predefined rules to infer whether the profile is trust or fake. This approach achieves a high detection accuracy of 97%. Furthermore, ontology classifier is an interpretable model that offers straightforward and human-interpretable decision rules

Abstract The Semantic Web and Machine Learning usually are seen as incompatible approaches toward Artificial Intelligence. A proposal presented for integrating the two paradigms and used data from Twitter regarding legitimate and fake accounts. Online Social Networks (OSN) such as Twitter have become a part of our lives due to their ability to connect peo-ple around the world, share documents, photos, and videos. OSN's such as Facebook, Twitter and LinkedIn have approximately 500 million users over the world; this massive population of OSN causes different kinds of problems regarding data security and privacy. Unauthorised users infringe on the privacy of legitimate users and abuse names and cre-dentials of victims by creating a fake account. We utilised Machine Learning to inductive-ly learn the rules that distinguished a phoney account from a real one. We then imple-mented those rules in a Web Ontology Language (OWL) ontology using the Semantic Web Rule Language (SWRL). This integration provides the benefits of the data-driven ML approach combined with the explicit knowledge representation and the resulting ease of explanation and maintenance of the Semantic Web paradigm.

Abstract Nowadays, Twitter has become one of the fastest-growing Online Social Networks (OSNs) for data sharing frameworks and microblogging. It attracts millions of users worldwide where subscribers communicate with each through posts and messages known as "tweets". The open structure and behaviour of Twitter cause it to be vulnerable to attacks from fake accounts and a large number of automated software, known as 'bots'. Bots are regarded to be malicious as they send spam to users of social networks over the internet. Data security and privacy are among the most critical issues of social network users, as the protection and fulfilment of these requirements strengthen the network's interest and, ultimately, its credibility. To overcome these issues, we need to build an efficient model to detect and classify fake twitter accounts. This paper presents a new approach with dual functions, namely to identify and classify the twitter bots based on ontological engineering and Semantic Web Rule Language (SWRL) rules. Web Ontology Language (OWL), Semantic Web Rule Language (SWRL) rules, and reasoners are deployed to inductively learn the rules that distinguish a fake account (bot) from a real one, as well as to classify fake accounts into fake followers or spam bot. Our approach could properly identify the false account with an accuracy of (97%) in the first stage, after which these fake accounts were classified into spam or fake follower bots with an accuracy rate of (94.9%). Furthermore, it has been found that he ontology classifier is a more interpretable model that offers straightforward and human-interpretable decision rules, as compared to other machine learning classifiers

Handwritten recognition is very powerful technology to support many applications comes in the forefront of automated sorting of letters and bank checks, and help the blind and Who have difficulty to read books and magazines, and the translation of books from one language to another, and converted to texts can store and processed in the computer. This paper is present two artificial neural network classification for handwritten digit recognition (from 0 to 9) with accuracy more than 98% by using an application of feed-forward multilayer neural network with two different classifiers (Forward Multilayer Neural Network FMNN and Binary Coding Neural Network BCNN).The highest recognition reliability and minimal error rate for the recognition of handwritten digits have been achieved. The back propagation algorithm minimizing the total error of the network over a set of training by searching of the weight value that achieves the objective. Binary coding approach is used to reducing the number of output that it leads to reducing the time that need for processing and saving the resources and finally reduce the network’s complexity

In this paper, a deep belief network (DBN) has been adopted as an efficient technique to diagnosis the Parkinson's disease (PD). This diagnosis has been established based on the speech signal of the patients. Through the distinguishing and analyzing of the speech signal, the DBN has the ability to diagnose Parkinson's disease. To realize the diagnosis of Parkinson's disease by using DBN, the proposed system has been trained and tested with voices from a number of patients and healthy people. A feature extraction process has been prepared to be inputted to the deep belief network (DBN) which is used to create a template matching of the voices. In this paper, DBN is used to classify the Parkinson's disease which composes two stacked Restricted Boltzmann Machines (RBMs) and one output layer. Two stages of learning need to be applied to optimize the networks’ parameters. The first stage is unsupervised learning which uses RBMs to overcome the problem that can cause because of the random value of the initial weights. Secondly, backpropagation algorithm is used as a supervised learning for the fine tuning. To illustrate the effectiveness of the proposed system, the experimental results are compared with different approaches and related works. The overall testing accuracy of the proposed system is 94% which is better than all of the compared methods. In short, the DBN is an effective method to diagnosis Parkinson's disease by using the speech signal.