Research Article Mixed Script Identification Using Automated DNN Hyperparameter Optimization Muhammad Yasir , 1 Li Chen , 1 Amna Khatoon , 2 Muhammad Amir Malik , 3 and Fazeel Abid 4 1 School of Information Science and Technology, Northwest University, Xi’an, Shaanxi, China 2 Department of Information Engineering, Chang’an University, Xi’an, Shaanxi, China 3 Department of Computer Science, Islamic International University, Islamabad, Pakistan 4 Department of Information System, University of Management and Technology, Lahore, Pakistan CorrespondenceshouldbeaddressedtoLiChen;chenli@nwu.edu.cn Received 3 October 2021; Revised 30 October 2021; Accepted 5 November 2021; Published 10 December 2021 AcademicEditor:AhmedMostafaKhalil Copyright©2021MuhammadYasiretal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Mixedscriptidentificationisahindranceforautomatednaturallanguageprocessingsystems.Mixingcursivescriptsofdifferent languagesisachallengebecauseNLPmethodslikePOStaggingandwordsensedisambiguationsufferfromnoisytext.isstudy tacklesthechallengeofmixedscriptidentificationformixed-codedatasetconsistingofRomanUrdu,Hindi,Saraiki,Bengali,and English. e language identification model is trained using word vectorization and RNN variants. Moreover, through exper- imental investigation, different architectures are optimized for the task associated with Long Short-Term Memory (LSTM), BidirectionalLSTM,GatedRecurrentUnit(GRU),andBidirectionalGatedRecurrentUnit(Bi-GRU).Experimentationachieved thehighestaccuracyof90.17forBi-GRU,applyinglearnedwordclassfeaturesalongwithembeddingwithGloVe.Moreover,this studyaddressestheissuesrelatedtomultilingualenvironments,suchasRomanwordsmergedwithEnglishcharacters,generative spellings, and phonetic typing. 1. Introduction Code-mixing is defined as “the embedding of linguistic componentssuchasphrases,words,andlexemesfromone languageintoanexpressionfromanotherlanguage.”Code- mixing refers to the use of linguistic units’ words, phrases, clausesfromdifferentlanguagesatasentencelevel.Oneor morelanguageshavebeencombinedtoformanintelligible newlanguage.ishybridlanguageisknownasafusedlect. “Code-switching” is considered as unregulated choice by linguists, and is also known as “language mixing,” or as “fused lects” in cases where grammar is rigid. Wherecode-switchingbetweentwoormorelanguagesis prevalent,termsfrombothlanguagesmaybecomecommon in sentences. Instead of switching codes at semantically or sociolinguistically significant points, this code-mixing has no particular value in the immediate context. Because they are completely grammaticalized, fused lects allow for less varietythanamixedlanguagebecauseoftheirsemanticsand pragmatics.egrammarofthefusedlectdetermineswhich source-language parts may be included in the fusion. It is observedinaninformalsetting,likesocialmediacommonly. Withtheabundanceofsocialmediaplatformsavailablefor people to communicate, the quota of code-mixed data available to us is tremendous. e content shared in social media discussions is frequently mixed with stylistic and misspelled versions of original words. POS tagging and namedentityidentificationsufferduetothenoisyinput.In addition, social media users often utilize mixed scripts of Roman text. e use of Roman script leads to the generation of in- formal mixed language amalgamation of two or more lan- guages. is phenomenon is observed on social media specifically. e multilingual users are using the roman Hindawi Computational Intelligence and Neuroscience Volume 2021, Article ID 8415333, 13 pages https://doi.org/10.1155/2021/8415333