ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY https://aro.koyauniversity.org/index.php/aro <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="https://doaj.org/">DOAJ</a> and got <a href="https://docs.google.com/spreadsheets/d/183mRBRqs2jOyP0qZWXN8dUd02D4vL0Mov_kgYF8HORM/edit#gid=0">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="http://ip-science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&amp;Word=aro" target="_self">Clarivate&nbsp;Analytics</a> (Thomson Reuters)&nbsp;since Feb 2016.</p> en-US <p>Authors who publish with this journal agree to the following terms:</p> <ol type="a"> <li class="show">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a&nbsp;Creative Commons Attribution License [<a href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a>]&nbsp;that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li> <li class="show">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li> <li class="show">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See&nbsp;<a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li> </ol> aro.journal@koyauniversity.org (Dr. Dilan M. Rostam) salah.ismaeel@koyauniversity.org (Prof. Dr. Salah I. Yahya) Fri, 15 Oct 2021 14:02:53 +0000 OJS 3.1.2.4 http://blogs.law.harvard.edu/tech/rss 60 Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network https://aro.koyauniversity.org/index.php/aro/article/view/827 <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> Sami F. Mirza, Abdulbasit K. Al-Talabani Copyright (c) 2021 Sami F. Mirza, Abdulbasit K. Al-Talabani https://creativecommons.org/licenses/by-nc-sa/4.0 https://aro.koyauniversity.org/index.php/aro/article/view/827 Wed, 13 Oct 2021 08:29:35 +0000 Kurdish Dialect Recognition using 1D CNN https://aro.koyauniversity.org/index.php/aro/article/view/837 <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> Karzan J. Ghafoor, Karwan M. Hama Rawf, Ayub O. Abdulrahman, Sarkhel H. Taher Copyright (c) 2021 Karzan J. Ghafoor, Karwan M. Hama Rawf, Ayub O. Abdulrahman, Sarkhel H. Taher https://creativecommons.org/licenses/by-nc-sa/4.0 https://aro.koyauniversity.org/index.php/aro/article/view/837 Fri, 15 Oct 2021 10:44:53 +0000 Withania Somnifera: Correlation of Phytoconstituents with Hypolipidemic and Cardioprotective Activities https://aro.koyauniversity.org/index.php/aro/article/view/844 <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> Subasini Uthirapathy, Tara F. Tahir Copyright (c) 2021 Subasini Uthirapathy, Tara F. Tahir https://creativecommons.org/licenses/by-nc-sa/4.0 https://aro.koyauniversity.org/index.php/aro/article/view/844 Fri, 15 Oct 2021 10:45:25 +0000