Yazar "Albawi, Saad" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    Intelligent database interface techniques using semantic coordination
    (Institute of Electrical and Electronics Engineers Inc., 2018) Mohammed, Tareq Abed; Alhayli, Shaymaa; Albawi, Saad; Duru, Adil Deniz
    More and more the use of artificial intelligence and data mining techniques established in many fields to solve the problem of classification. This paper consider most new database applications request smart interface to improve effective collaborations in the middle of database and the clients. The most open interfaces for databases must be clever and ready to comprehend characteristic dialect expressions. The overall aim of this study is to look at the importance of using data mining techniques with artificial intelligence in algorithms and applications. We propose a general design for an intelligent database interface. Furthermore, a genuine usage of such a framework which can be connected to any database. One of the fundamental attributes of this interface is space, freedom, which implies that this interface can be utilized with any database. Another aspect of this framework is that it is easy setup. The intelligent interface utilizes semantic coordinating procedure to change natural language query to Structured Query Language (SQL) by depending lexicon and set of creation guidelines. The lexicon comprises semantics sets for tables and sections. The query model is executed and the outcomes are introduced to the client. This interface was initially tested utilizing Supplier-Parts database by using JAVA and the result proves the efficiency of the proposed method in intelligent database system. © 2018 IEEE.
  • Küçük Resim
    Öğe
    Social touch gesture recognition using convolutional neural network
    (Hindawi Ltd, 2018) Albawi, Saad; Bayat, Oğuz; Al-Azawi, Saad; Uçan, Osman Nuri
    Recently, social touch gesture recognition has been considered an important topic for touch modality, which can lead to highly efficient and realistic human-robot interaction. In this paper, a deep convolutional neural network is selected to implement a social touch recognition system for raw input samples (sensor data) only. The touch gesture recognition is performed using a dataset previously measured with numerous subjects that perform varying social gestures. This dataset is dubbed as the corpus of social touch, where touch was performed on a mannequin arm. A leave-one-subject-out cross-validation method is used to evaluate system performance. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (the average range of frame length was from 0.2% to 4.19% from the original frame lengths) with a classification accuracy of 63.7%. The achieved classification accuracy is competitive in terms of the performance of existing algorithms. Furthermore, the proposed system outperforms other classification algorithms in terms of classification ratio and touch recognition time without data preprocessing for the same dataset.
  • Küçük Resim
    Öğe
    Understanding of a convolutional neural network
    (Ieee, 2017) Albawi, Saad; Mohammed, Tareq Abed; Al-Zawi, Saad
    The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to surpass classical methods performance in different fields; especially in pattern recognition. One of the most popular deep neural networks is the Convolutional Neural Network (CNN). It take this name from mathematical linear operation between matrixes called convolution. CNN have multiple layers; including convolutional layer, non-linearity layer, pooling layer and fully-connected layer. The convolutional and fully-connected layers have parameters but pooling and non-linearity layers don't have parameters. The CNN has an excellent performance in machine learning problems. Specially the applications that deal with image data, such as largest image classification data set (Image Net), computer vision, and in natural language processing (NLP) and the results achieved were very amazing. In this paper we will explain and define all the elements and important issues related to CNN, and how these elements work. In addition, we will also state the parameters that effect CNN efficiency. This paper assumes that the readers have adequate knowledge about both machine learning and artificial neural network.