ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 2021-10-20T07:55:22+00:00 Dr. Dilan M. Rostam Open Journal Systems <p>ARO (Today in Hewramí Kurdish) is a&nbsp;scientific&nbsp;journal published by Koya University with&nbsp;e-ISSN: 2307-549X, p-ISSN: 2410-9355, and DOI: 10.14500/2307-549X.&nbsp;ARO is a journal of research articles, review articles, and letters to editor. ARO is a peer-reviewed, open access journal that publishes original works in areas of Science and Engineering. ARO has been indexed by <a href="">DOAJ</a> and got <a href="">DOAJ Seal</a>. ARO has been accepted for indexing in the Emerging Sources Citation Index (ESCI), a new edition of Web of Science™ - <a href=";Word=aro" target="_self">Clarivate&nbsp;Analytics</a> (Thomson Reuters)&nbsp;since Feb 2016.</p> Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network 2021-10-20T07:55:22+00:00 Sami F. Mirza Abdulbasit K. Al-Talabani <p>Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time.</p> 2021-10-13T08:29:35+00:00 Copyright (c) 2021 Sami F. Mirza, Abdulbasit K. Al-Talabani Kurdish Dialect Recognition using 1D CNN 2021-10-20T07:55:20+00:00 Karzan J. Ghafoor Karwan M. Hama Rawf Ayub O. Abdulrahman Sarkhel H. Taher <p>Dialect recognition is one of the most attentive topics in the speech analysis area. Machine learning algorithms have been widely used to identify dialects. In this paper, a model that based on three&nbsp;different 1D Convolutional Neural Network (CNN) structures&nbsp;is developed for Kurdish dialect recognition. This model is evaluated, and CNN structures are compared to each other. The result shows that the proposed model has outperformed the state of the art. The model is evaluated on the experimental data that have been collected by the staff of department of computer science at the University of Halabja. Three dialects are involved in the dataset as the Kurdish language&nbsp;consists of three major dialects, namely Northern Kurdish (Badini variant), Central Kurdish (Sorani variant), and Hawrami.&nbsp;The advantage of the CNN model is not required to concern handcraft as the CNN model is featureless. According to the results, the 1 D CNN method can make predictions with an average accuracy of 95.53% on the Kurdish dialect classification. In this study, a new method is proposed to interpret the closeness of the Kurdish dialects by using a confusion matrix and a non-metric multi-dimensional visualization technique. The outcome demonstrates that it is straightforward to cluster given Kurdish dialects and linearly isolated from the neighboring dialects.</p> 2021-10-15T10:44:53+00:00 Copyright (c) 2021 Karzan J. Ghafoor, Karwan M. Hama Rawf, Ayub O. Abdulrahman, Sarkhel H. Taher Withania Somnifera: Correlation of Phytoconstituents with Hypolipidemic and Cardioprotective Activities 2021-10-20T07:55:17+00:00 Subasini Uthirapathy Tara F. Tahir <p><em>Withania somnifera</em> (WS) (Dunal) or Ashwagandha is a well-known hypolipidemic herb and antioxidant. In this study, 75% ethanolic extract of WS is attempted to evaluate the cardioprotective activity of isoproterenol-induced cardiotoxicity and hypolipidemic activity in Triton WR 1339-induced hyperlipidemia. In addition, <br>phytochemical evaluation of the same extracts analyzed by gas chromatography–mass spectrometer (GC–MS). This study found that 7 days of therapy with WS extracts at 1000 mg/kg b.wt. reduced cholesterol by 76%, low-density lipoprotein (LDL) by 71%, and TAG by 12% (P &lt; 0.05). Furthermore, it can significantly reduce cholesterol and LDL levels (P &lt; 0.05). Similarly, the use of 50 mg/kg b.wt. of WS extract showed a cardioprotective effect against isoproterenol-induced cardiac toxic rats. The antioxidants glutathione, glutathione peroxidase, and catalase are increased in WS extract (P &lt; 0.05), whereas the release of cardiac indicators in heart tissue is reduced (P &lt; 0.05). Furthermore, a 30-day treatment with WS also reduced triacylglycerol in isoprenaline-induced cardiotoxic rats. GC–MS analysis of the methanol fraction of the Ashwagandha 70% ethanolic extract showed the presence of higher concentrations of fatty acids. In conclusion, WS showed hypolipidemic and cardioprotective activities in diseased animals induced by isoproterenol and Triton WR 1339.</p> 2021-10-15T10:45:25+00:00 Copyright (c) 2021 Subasini Uthirapathy, Tara F. Tahir