hi , i am
kadipa
aung myo han .


An Algorithm Engineer

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About Me

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Kadipa Aung Myo Han

An Algorithm Engineer

Email

kadiparaungmyohan@gmail.com

Website

https://kadipa.github.io/

Passionate Algorithm Engineer with a Data-Driven Mindset



I am currently pursuing a Ph.D. while working as an Algorithm Engineer. This dual role allows me to combine my academic pursuits with practical experience in algorithm development and engineering.

Graduated with a Master's Degree in Computational Intelligence Systems (CIS) from King Mongkut's Institute of Technology Ladkrabang (KMITL) in July 2020, with a specialization in computer vision and deep learning. My research, conducted under the guidance and supervision of Asst Prof. Dr. Ukrit Watachareeruetai, focused on the identification of plant nutrient deficiencies using deep learning techniques.

Prior to my master's degree, I completed my bachelor's degree in Knowledge Engineering at the University of Information Technology (UIT), Yangon. During my undergraduate studies, I actively engaged in various extracurricular activities, including participation in Hackathons, Programming Contests, and volunteering. I also gained valuable industry experience through internships at Bindez and V2M. For my final year project, I collaborated with Hnin and Hsu to develop a word segmentation tool for the Myanmar language.

My research interests encompass a wide range of topics within the fields of computer vision, machine learning, deep learning, and natural language processing.

Personal Interests

As a dedicated Algorithm Engineer, I also have a vibrant personal life. Here are a few aspects that define who I am beyond my professional endeavors:

Cat Lover

I have a deep affection for cats and find joy in their companionship.

Coffee Enthusiast

Exploring different coffee blends and enjoying a cup throughout the day is one of my simple pleasures.

Active Lifestyle

Maintaining an active lifestyle is essential to me. I set a goal to walk 10,000 steps daily, promoting both physical and mental well-being.

Nature Enthusiast

Being close to nature is another passion of mine. I find solace in the beauty of the outdoors and cherish moments spent in natural surroundings.

education

Bachelor of Computer Science

(Knowledge Engineering)

December, 2012 - September, 2017.

University of Information Technology (UIT)

Yangon, Myanmar.

One of the highlights of this program has been the hands-on experience I've gained through practical projects. Building AI models, experimenting with data sets, and witnessing the tangible impact of AI applications has been incredibly rewarding. These projects have not only honed my technical abilities but have also given me a glimpse into the real-world applications of AI.

Master of Engineering in

Computational Intelligence Systems (CIS)

August, 2018 - July, 2020.

King Mongut's Institute of Technology, Ladkrabang (KMITL)

Bangkok, Thailand.

This intensive 2-year master's program is designed to equip students like me with the latest concepts and techniques in computational intelligence. It aims to empower us with the practical skills needed to tackle real-world problems effectively.

Doctor of Engineering in

Electrical Engineering (International Program)

August, 2022 - Present.

King Mongut's Institute of Technology, Ladkrabang (KMITL)

Bangkok, Thailand.

This advanced Ph.D. course in Electrical Engineering is designed to provide students with an in-depth understanding of d eep learning techniques and their applications in various aspects of computer vision.

experience

  • Algorithm Engineer

    February, 2022 - Present.

    Six Atomic Pte. Ltd.

    Bangkok, Thailand.

    Design an automatic pattern grading software incorporating mathematical modeling to reduce manual workload by 90%. Users will input patterns and Point of Measurement (POM) values to initiate the grading process.

  • Hexcode Technologies Inc.

    Yangon, Myanmar.

    I have experience as a data scientist working on two separate projects: one involving house sale prediction and the other focused on data analysis for the Moodle system.

    Data Scientist

    January, 2021 - December, 2021.

  • Intern

    May, 2017 - September, 2017.

    Vision To Motion (V2M), Co. Ltd.

    Yangon, Myanmar.

    I developed a Myanmar electronic lottery system using Ruby On Rail(ROR) language. Our project involved tasks such as gathering data, defining the scope, implementing the system, and hosting it on a dedicated domain.

  • May, 2016 - December, 2016.

    Bindez Pte, Ltd.

    Yangon, Myanmar.

    I served as a Burmese Natural Language Processing Researcher(Intern) for Thadin App.

    Researcher Intern

    May, 2016 - December, 2016.

  • Field Study

    November, 2017 - November, 2017.

    Myanmar Computer Federation (MCF)

    Yangon, Myanmar.

    Built an Unicode to ZawGyi Font converter as the field study project

Scholarship

  • KMITL DOCTORAL Scholarship

    August, 2022 - Present.

    King Mongkut's Institute of Technology Ladkrabang (KMITL).

    Bangkok, Thailand.

    The scholarships aim to produce highly qualified Ph.D. researchers of KMITL.

  • International College Graduate Scholarship

    August, 2018 - July, 2020.

    King Mongkut's Institute of Technology Ladkrabang (KMITL).

    Bangkok, Thailand.

    Fully Funded Scholarship for Graduate Research Students.

publication

  • Banana Plant Nutrient Deficiencies Identification using Deep Learning

    1 - 4 June, 2023

    9th International Conference on Engineering, Applied Sciences, and Technology (ICEAST)

    Vientiane, Lao PDR

    This paper presents nutrient deficiency multi-class classification in banana plant data sets using a deep convolutional neural network. In this paper, healthy and eight nutrient deficiency classes were studied. The performance was evaluated in different situations of two public data sets. The proposed method can provide sensitivity and specificity in Raw Images, Raw Images with combination, Augmented Images, and Augmented Images with the combination. Furthermore, nearly 88% of the F1-score was outperformed.

  • Black Gram Plant Nutrient Deficiency Classification in Combined Images Using Convolutional Neural Network

    4-6 March, 2020

    8th International Electrical Engineering Congress (iEECON)

    Chiang Mai, Thailand.

    Plant nutrient deficiency classification is vital for the agricultural industry to improve both the qualities and the quantities of crops. Computer vision and deep learning technologies, especially convolutional neural networks, perform an essential role in agricultural and biological sectors to solve the various kinds of complex problems. In this paper, we conducted the classification of the complete nutrient and six types of nutrient deficiency of black gram over the combined images of old leaf and young leaf. We found that the combined image supports more useful information than a single image. We accomplished the feature extraction process by taking the advantages of the deep pre-trained model to extract the features from the image automatically. Extracted features from ResNet50 deep pre-trained model are fed into three different classifiers as the input: (1) logistic regression, (2) support vector machine and (3) multilayer perceptron and compared the performance of these models. The multilayer perceptron models achieved superior performance than support vector machine and logistic regression by the accuracy of 88.33 %.

  • Classification of Nutrient Deficiency in Black Gram Using Deep Convolutional Neural Networks

    10-12 July, 2019

    16th International Joint Conference on Computer Science and Software Engineering (JCSSE)

    Pattaya, Thailand.

    This paper investigates the use of various deep convolutional neural networks (CNNs) with transfer learning to identify nutrient deficiencies from a leaf image. Experiments were conducted with a dataset containing 4,088 images of black gram (Vigna mungo) leaves grown under seven different treatments, i.e., complete nutrient treatment and six nutrient deficiency treatment, including calcium (Ca), iron (Fe), magnesium (Mg), nitrogen (N), potassium (K), and phosphorus (P) deficiencies. Experimental results indicate that a deep CNN model known as ResNet50 was the best among all experimented models with a test accuracy of 65.44% and a F-measure of 66.15%. In addition, We found that the ResNet50 model obviously outperformed a block-based method and the human performance reported in a literature.

Blog

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Plant Disease Classification Using Convolutional Neural Network(CNN)

ဒီပို့စ်မှာတော့ agricultural domain အတွက် plant disease classification ကို CNN သုံးပြီး ဘယ်လို လုပ်မလဲဆိုတာ ပြန်လည်မျှဝေချင်ပါတယ်။ Pretrained CNN model ကို သုံးပြီး ကိုယ့် Dataကို ဘယ်လို trainမလဲ ဆိုတာရှင်းပြချင်ပါတယ်။

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