Zulqrnain Ali

I have fourteen years of experience in the field of computer science as a software developer and active researcher.

1. As far as the application development is concerned, I have designed many applications using PHP, mySQL/ ASPNET, SQL Server, VUEJS, NUXT JS. Currently working as Senior Software Developer at Microsysx Karachi, Pakistan, here designing multi-tenant decoupled software applications using ASPNET Core Web API with VUEJS and NUXT at front. Earlier I designed applications using PHP, mySQL, Bootstrap.
2. As an active researcher I contributed in the area of ML and DL by applying the models to extract the sentiment from the text and detect the custom objects (CNN, KNN, Decision Tree, SVM etc) using Python, Scikit-learn, keras etc. I have published two international journals papers in 2020 and 2021

I am interested in the role of Full Stack ASP.NET Core Developer at your organization. Please have a look at the resume attached with this email to get more insights of my academic and professional career

Being an ambitious computer science researcher tried to learn technologies to facilitate mankind. Demonstrated strong productive skills by designing and developing multirange software solutions using technologies like ASP.NET Core Web API, SQL Server, VueJS, Nuxt JS, PHP, mySQL, Bootstrap to automate enterprise level organizations. As for as the latest research is concerned I have published research articles in the impact factor international journals.

Area of research is Machine Learning, Deep Learning and Computer Vision. Conducted research in applied computer science like Sentiment Analysis, Opinion Mining, Cotton Curl Leaf Disease, Crop Yield Prediction, Gene Editing, and Citrus Fruit Classification. Motivated to constantly improve the skills by learning new everyday

MPhil Computer Science

Research Area: NLP

Thesis Title: SAP of Text Trend using KNN (K- Nearest Neighbor)

Abstract

Sentiment Analysis and Prediction (SAP) of Text Trend through K-Nearest Neighbor (KNN) is a three-step technique of opinion mining. At first in pre-processing, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters.

The resulting features played significant role in our proposed methodology. Finally, the trend of the input text has been predicted using machine learning algorithm (KNN) based on extracted features. The training and testing of the model has been performed on publicly available datasets of twitter and movie reviews.

Experiments results of proposed method illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI based text analysis framework has been designed to perform the text analytics.