علي حسون مهدي الفتلاوي

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.

The transition in the treatment and the hospitals’ care from a single process into continuous and recorded progress has highlighted the importance of the clinical management systems. Although, the paper-based recording operations can manage clinical data and save them they are complicated and unreliable. Therefore, computerizing these processes in a central database is crucial in order to extract beneficial information and follow the patient’s status. The difficulty in defining requirements of the medical organization previously has brought a difficulty in building a stable system with the conventional database schemas. In this regard, the proposed system challenges these difficulties through presenting a reliable and flexible solution for such organizations to manage their data efficiently by a dynamic database schema that can manage multiple hospitals centrally with high flexibility. For this purpose, Entity-Attribute-Value (EAV) modeling technique has been adopted to build a flexible database with the schema that satisfying demands of the hospitals and their ever-changing requirements. In this approach, data from unlimited number of organizations (hospitals or medical centers) can be added to the system with different entities in the same database. Moreover, centralizing the data repositories of clinical operations has a significant role in enhancing the hospitals’ care and improving the treatment’s quality in different ways. This research introduces a flexible system that is capable of overcoming the requirements’ variation and supporting the research field via building a practical and central data repository for the researchers.

Technological advances and the enormous flood of papers have motivated many researchers and companies to innovate new technologies. In particular, handwriting recognition is a very useful technology to support applications like electronic books (eBooks), post code readers (that sort mails in post offices), and some bank applications. This paper proposes three systems to discriminate handwritten graffiti digits (0 to 9) and some commands with different architectures and abilities. It introduces three classifiers, namely single neural network (SNN) classifier, parallel neural networks (PNN) classifier and tree-structured neural network (TSNN) classifier. The three classifiers have been designed through adopting feed forward neural networks. In order to optimize the network parameters (connection weights), the back-propagation algorithm has been used. Several architectures are applied and examined to present a comparative study about these three systems from different perspectives. The research focuses on examining their accuracy, flexibility and scalability. The paper presents an analytical study about the impacts of three factors on the accuracy of the systems and behavior of the neural networks in terms of the number of the hidden neurons, the model of the activation functions and the learning rate. Therefore, future directions have been considered significantly in this paper through designing particularly flexible systems that allow adding many more classes in the future without retraining the current neural networks.