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Linguistic Errors in second language learning through Error Analysis theory: هه‌ڵه‌ زمانییه‌كان له‌ فێربوونی زمانی دووه‌مدا (له‌ ڕوانگه‌ی تیۆری شیكاری هه‌ڵه‌ییه‌وه‌)
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Second language learner may commit many mistakes in the process of second language learning. Throughout the Error Analysis Theory, the present study discusses the problems faced by second language learners whose Kurdish is their native language. At the very stages of language learning, second language learners will recognize the errors committed, yet they would not identify the type, the stage and error type shift in the process of language learning. Depending on their educational background of English as basic module, English department students at the university stage would make phonological, morphological, syntactic, semantic and lexical as well as speech errors. The main cause behind such errors goes back to the cultural differences of the language learners. Other errors go back either to the spoken form of the second language itself or to the teacher teaching the second language.       

لە فێربوونی زمانی دووەمدا  فێرخوازی زمانی دووەم دووچاری هەڵەی جۆراوجۆر دەبنەوە، بۆ ئەم مەبەستە (لە ڕوانگەی  تیۆری شكاری هەڵەییەوە) لە هەڵەكانی فێرخوازی زمانی دووەم( ئینگلیزی) دەدوێین، كە زمانی یەكەمیان زمانی كوردییە. فێرخوازان لە سەرەتای فێربوونی زمانی دووەمدا درك بە هەڵەی فێربوونی زمانەكەیان دەكەن، بەڵام  درك بە جۆر و قۆناغ و  گۆڕانی جۆری هەڵەكان ناكەن. لە پڕۆسەی فێربوونی زمانی دووەمدا  فێرخوازان لە قۆناغەكانی خوێندنی زانكۆدا بەتایبەتی لەبەشی زمانی ئینگلیزیدا بە پشتبەستن بە پاشخانی چەند ساڵی ڕابردوویان، كە زمانی ئینگلیزیان وەكو بابەتێكی سەرەكی خوێندووە، ئەوا شێوازی هەڵەی تریان تیدا بەدیدەكرێت، بەتایبەتی لە هەڵەی فۆنەتیكی و مۆرفۆلۆژی و سینتاكسی و واتا سازی و فەرهەنگی، هەروەها لە دركاندنیشدا هەڵەیان هەیە. سەرچاوەی ئەم هەڵەكردنانەش  بۆ كاریگەری زمانی یەكەم، بۆ هه‌ڵه‌ پێشكه‌وتووه‌كان، كه‌  له‌ خودی زمانی دووه‌م به‌رهه‌م دێت، ئه‌و هه‌ڵانه‌ی سه‌رچاوه‌كه‌ی بۆ سروشتی زمانی زاره‌كی، ئه‌و هه‌ڵانه‌ی له‌ فێركاره‌وه‌ ڕووده‌ده‌ن ده‌گه‌ڕێته‌وه.‌       

 

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Publication Date
Thu Mar 02 2023
Journal Name
Applied Sciences
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The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approach

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Sat Jul 18 2020
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Density functional theory study of molecular structure, Electronic properties, UV–Vis spectra on coumarin102.
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Publication Date
Mon Dec 28 2020
Journal Name
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Publication Date
Sat Mar 01 2008
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