© IJARW | ISSN (O) - 2582-1008 September 2021 | Vol. 3 Issue. 3 www.ijarw.com IJARW1525 International Journal of All Research Writings 11 SECURE INTELLIGENT ESSAY SCORING SYSTEM (SIESS) USING END-TO-END ENCRYPTION Madubuezi Christian Okoronkwo 1 , Caroline Ngozi Asogwa 2 , Nwuorgu Raymond 3 1,2 Department of Computer Science, Michael Okpara University of Agricultural, Umudike 3 Computer Science Department, University of Nigeria, Nsukka ABSTRACT The study addressed the issues associated with the conventional grading which can be biased, prone to human error and usage of inefficient scoring attributes as well as subjectivity in essays grading. The research work has been able to demonstrate the effort of Natural Language processing (NLP) in developed Secure Intelligent Essay Scoring System (SIESS). Natural language processing is concerned with utilization of knowledge of Computer Science. The essay scoring apply several methods, a Holistic, Analytic and Primary trait. The method used for this was combining the holistic and analytic method. The study made use of an online platform called kaggle.com used for a pre-build model as a python library. Also, the essay scoring system was evaluated using the confusion matrix majorly the accuracy metric which is specifically recommended for machine learning algorithm (Naïve Bayes, Linear Regression and Gradient Boosting). The Gradient Boosting was best approach essay scoring pre-built model prediction. Datasets in excel template were equally collected in the domain such as operating system, expert system, database management, data structure and algorithm. SQLite as the database management system was used for data storage. The system was tested in a local hosting architecture capable of running online too. The entire system is summarized in three concepts. Mechanism of teachers, students and automatic registration. Keyword: Cryptography, Encryption, Automated Scoring System, Natural Language Processing 1. INTRODUCTION Machine Learning has become generally embraced in an assortment of genuine applications which altogether influence individuals' lives (Guimaraes and Tofighi, 2018; Guegan and Hassani, 2018) in Automated Essay Scoring (AES) particularly when sent as a component of high stakes tests. Instructive "applications dependent on Natural Language Processing (NLP) and Automated Essay Scoring (AES) Systems frequently utilize a blend of Machine Learning calculations like straight relapse (Manvi, Mishel, and Ashwin, 2012), (Ramalingam, Pandian, Prateek, and Himanshu, 2018), counterfeit neural organization (Marwa, Rania, and Ahmed, 2020), gathering procedure (Arif, Byung-Won, Ingyu, and Gyu, 2017) in consequently reviewing understudies' essay and this have demonstrated to be a decent match or override human grader unwavering quality. An automated essay scoring (AES) program is a product that utilizes procedures from corpus and computational phonetics; and machine learning to grade essays. In schooling, composing capability is a fundamental part of correspondence and deciding if, an understudy can pass on a message or express a thought with coherence and riches. Composing capacity appraisal has progressed from understudies noting numerous decision undertakings to built reaction errands. In which understudies are needed to peruse a prompt(s), ponder significant central issues, and foster an essay; subsequently, looking at their language use, composing abilities, and basic reasoning. As far as evaluating composing capacity, there is an overall need to select and prepare human raters to allocate scores. However, this course of manual scoring has its impediments like weariness, interruption, irregularity of scoring across time, etc (Leyi, Yali, and Yan, 2019). This wasteful and thorough method of scoring has achieved the requirement for quick reaction including moment scoring and investigation of composing quality. Thus the rise of Automated essay scoring (AES) framework, to arrangement composing scores by PC programs. Some Automated Essay Systems go past their extent of offering all encompassing scores, to likewise giving reaction respect to sentence design, association and language use, etc. The inescapable reception of automated scoring systems and organization innovations, have