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ANITA BAI

Associate Professor in the CSE Department

  • Hyderabad, TG
  • 14 years

Available From: Available

    Candidate's About

    I am an AI and machine learning expert with 8 years of research experience in data mining and machine learning. I have hands-on experience in machine learning, computer vision, and NLP-based real-life projects. I also have working experience with Python libraries, data visualization, data science and statistics, deep learning tools, and statistical models.

    Work & Experience

    A

    Associate Professor in the CSE Department

    Mar 21 - PRESENT

    Technology

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      Python
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      Machine Learning
    • Handle industry-scaled projects based on machine learning
    • Serve as the secretary of the Artificial Intelligence and Data Science department
    • Handle courses in machine learning, Python programming, statistics, and natural language processing

    An intelligent hybrid deep belief network model for predicting student’s employability

    Associate Professor

    In this project, a hybrid deep belief network model was developed to predict a student's employability. The approach involves using a hybrid model of deep belief network and soft max regression (DBN-SR) to make the prediction. The focus of the project was on predicting a student's employability.

     


     

     

    • Deep Learning
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    An automatic approach based on CNN architecture to detect Covid-19 disease from Chest X- ray images

    Associate Professor

    A deep learning-based approach was implemented to differentiate COVID-19 disease patients from those with viral pneumonia, bacterial pneumonia, and healthy individuals. The approach was designed to accurately classify individuals in these different categories.

    • Deep Learning
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    A Novel Map Reduced Based Parallel Feature Selection and Extreme Learning for Micro Array Cancer Data Classification

    Associate Professor

    A novel map reduced-based parallel feature selection and extreme learning approach was implemented for microarray cancer data classification. This approach was designed to efficiently process and classify large amounts of microarray cancer data.

    • Deep Learning
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    A

    Associate Professor in the CSE Department

    Apr 20 - Feb 21

    Technology

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      C++

    Tools

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      Linux
    • Handled major core courses in the CSE department
    • Hands-on experience conducting computer workshops and teaching Linux commands and C and C++ fundamentals
    • Assisted students in understanding skilling subjects with classification and clustering concepts
    • Helped students complete major projects
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    Associate Professor in the CSE Department

    Mar 19 - Mar 20
    • C programming, Data Mining, Object Oriented Programming.
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    Assistant Professor

    Jul 11 - Jun 19
    • Handled business information and database management system

    Data Mining Approaches in Itemset mining - An efficient approach based on selective partitioning for maximal frequent itemsets mining

    Associate Professor

    • Proposed a novel selective database partitioning algorithm that reduces runtime for database scans
    • Proposed a novel data format called Itemset-count tree, a compact and optimized binary representation that reduces memory requirements
    • The algorithm progressively searches for longer frequent itemsets in a depth-first manner, creating smaller partitions with fewer dimensions and unique data instances for faster support counting
    • Used various optimizations to prune the search space
    • Data Mining
    • Machine Learning
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    An Application of Factor Analysis in the Evaluation of Country Economic Rank

    Factor analysis was used to evaluate the economic rank of 20 countries based on 21 economic parameters. This involved analyzing the relevant data and using statistical techniques to determine the economic standing of each country.

    • Machine Learning
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    Estimating the Similarities of G7 Countries Using Economic Parameters

    • Estimated equality and differences between and within G7 countries (Canada, France, Germany, Italy, Japan, United Kingdom, and the United States)
    • Evaluated economic growth of G7 countries based on five parameters (GDP per capita, employment, population, general government revenue, general government total expenditure, and gross national savings) that are closely correlated with economic growth in G7 countries
    • Machine Learning
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    Machine Learning Approaches in Microarray Cancer Classification

    • Microarray cancer classification using feature extraction-based ensemble learning method
    • Proposed novel technique using support vector machine (SVM) for classification and early cancer detection
    • Used various feature selection techniques, such as PCA, chi-square, GA, and F-score, to extract information from larger dataset
    • Classified extracted information into normal and malignant classes using majority voting ensemble SVM
    • Machine Learning
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    Evaluation of Statistics models on real-life problems

    • Conducted analysis of temperature periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to identify similarities and connectivity trends between and within climatic regions
    • Tested method on voice and heart rate data, and conducted experiments on synthetic climate data for nine regions in the United States and eight regions in China to evaluate the accuracy of the method
    • Machine Learning
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    Selective Database Projections Based Approach for Mining High-Utility Itemsets

    Associate Professor

    • Created unique data instances and new projections with fewer dimensions for faster HUI mining
    • Proposed two novel data structures, the selective database projection utility list, and Tail-Count list, to prune the search space for HUI mining
    • Data Mining
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    A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms

    • Proposed an integrated approach combining Farthest First (FF) clustering and Sequential Minimal Optimization (SMO) classifier algorithms for diagnosing diabetes mellitus
    • Used clustering output as input for SVM classifier, which accurately classified patients as diabetic or non-diabetic (tested positive or negative)
    • Machine Learning
    View Details
    L

    Lecturer

    Aug 10 - May 11
    L

    Lecturer

    Jun 09 - Jul 10

    Education

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    Doctor of Philosophy

    Visveswaraya National Institute of Technology Nagpur
    2014 - 2019
    M

    Bachelor of Engineering

    M. P. Christian College of Engineering and Technology,
    2005 - 2009
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    Master of Technology

    National Institute of Technology
    2011 - 2013

    Certificates

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    5 Data Science Projects

    Data Science Bootcamp 2022
    2

    5 Machine Learning Projects

    2022 Machine Learning A To Z
    S

    Practical Machine Learning

    Scikit-Learn

    Achievements

    • Swati Hira, Anita Bai, Sanchit Hira, “An automatic approach based on CNN architecture to detect Covid-19 disease from Chest X-ray images”, Applied Intelligence, pp. 2864-2889, 2020.
    • Vishali Sivalenka and Anita Bai, “A Survey on Prognostication of Breast Cancer Utilizing Machine Learning Algorithms”, Solid State Technology, pp. 6990-6999, 2020.
    • Swati Hira, Anita Bai, “A Novel Map Reduced Based Parallel Feature Selection and Extreme Learning for Micro Array Cancer Data Classification”, Wireless Personal Communications, 123, 1483–1505 (2022).
    • Anita Bai, Meera Dhabu, Viraj Jagtap, Parag S Deshpande, “An efficient approach based on selective partitioning for maximal frequent itemsets mining”, Sadhana, 2019.
    • S Pathan, M Bhushan, Anita Bai, “A Study on Health Care using Data Mining Techniques”, Journal of Critical review, pp. 7877-7890, 2020.
    • Anita Bai, Swati Hira, “Estimating the Similarities of G7 Countries using Economic Parameters”, 3rd International Conference on Information System Design and Intelligent Applications, Advances in Intelligent Systems and Computing, Visakhapatnam, pp. 59-67, Jan 08-09, 2016.
    • Anita Bai, Swati Hira, “An intelligent hybrid deep belief network model for predicting students employability”, Soft Computing, Vol. 25, pp. 9241-9254, 2021.
    • Anita Bai, Swati Hira, “Microarray Cancer Classification using Feature Extraction based Ensemble Learning Method”, International Journal of Data Analysis Techniques and Strategies, 2021.
    • Anita Bai, Swati Hira, Parag S Deshpande, “Recurrence based Similarity Identification of Climate Data”, Discrete Dynamics in Nature and Society, 2017.
    • Anita Bai, Parag S Deshpande, Meera Dhabu, “Selective Database Projections Based Approach for Mining High-Utility Itemsets”, IEEE Access, pp. 14389-14409, 2018.
    • R Delshi Howsalya Devi, Anita Bai, N Nagarajan, “A novel hybrid approach for diagnosing diabetes mellitus using farthest first and support vector machine algorithms”, Obesity Medicine, pp. 1-9, 2020.
    • Anita Bai, Swati Hira, Parag S Deshpande, “An Application of Factor Analysis in the Evaluation of Country Economic Rank”, Procedia Computer Science, pp. 311-317, 2015.
    • Thoutireddy Shilpa, Anita Bai, “A Review on Cardiovascular Disease Detection using Machine Learning Algorithms”, Solid State Technology, pp. 8754-8768, 2020.
    • R Madana Mohana, R Delshi Howsalya Devi, Anita Bai, “Lung Cancer Detection using Nearest Neighbour Classifier”, International Journal of Recent Technology and Engineering, pp. 3641-3645, 2019.