About Me


My name is Sachit Bhardwaj, I am a third-year B.Tech undergraduate pursuing Information Technology at Manipal University Jaipur, India. I am passionate about computer science and have several publications in reputed places such as prestigious Q1 journals. My experiences includes a Summer Research Intern role at Prince Sultan University, Saudi Arabia, as well as a Project Intern role at National Informatics Centre, Government of India. I have been awarded PRAISE (Publication and Research Award Incentive for Students to Excel) by university for excellence in research. This profession is my hobby and can go out of box to explore new ways of approaching the problems. I love to explore this field’s limitless application and provide new efficient ways other than traditional methods. I am interested in the new cases and opportunities that brings development in real-world applications. I am also an active member of the debate society and confident enough to deliver my views and thoughts efficiently.

Apart from this hobby, i love to watch tv shows and movies. I am a huge fan of thriller, crime, and action genre. Whenever i am free, i like to binge watch interesting seasons or listen to relatable music albums all night. I also love to write my raw thoughts in the form of quotes and read motivational stories.

Experience

Summer Research Intern, Prince Sultan University
Riyadh, Saudi Arabia
(May-2021 to July-2021)

Performed research work on the application of artificial intelligence in healthcare field, affiliated with Prince Sultan University, Saudi Arabia.


Project Intern, National Informatics Centre, Ministry of Electronics and Information Technology, Government Of India
Delhi, India
(Feb-2021 to March-2021)

Worked upon a real time project with the integration of natural language processing and machine learning techniques under the guidance of NIC team.


Undergraduate Student Researcher, Cognitive Intelligence And Data Computing Research Lab (CIDCR Lab)
Jaipur, India
(Aug-2019 to Present)

Performing research work on the vast application of cognitive intelligence in affiliation with Manipal University at CIDCR Lab.

Skills

  • Python
  • TensorFlow
  • PyTorch
  • Keras
  • Scikit-learn
  • Statistics
  • mySQL
  • Data Visualisation
  • Model Deployment
  • C
  • Java

Publications

  1. Bhardwaj, S., Sharma, A. K., Aggarwal, G.,Hussein, J., Mahdin,H., P., Chakrabarti, T., Bhattacharya, S., Mishra, R., Das, A. (2021) "Classification of Indian Classical Music With Time-Series Matching Deep Learning Approach" IEEE Access, vol.9, pp.102041-102052, 2021, doi: 10.1109/ACCESS.2021.3093911. https://doi.org/10.1109/access.2021.3093911

  2. Bhardwaj, S., Sharma, A. K. (2022). "Speech Audio Cardinal Emotion Sentiment Detection and Prediction Using Deep Learning Approach", In: Raje R.R., Hussain F., Kannan R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_13

  3. Bhardwaj, S., Cuong, N. H. H.,Sharma, A. K., Srivastava, D. K. (2022). "Probabilistic Approaches For Minimizing The Healthcare Diagnosis Cost Through Data-Centric Operations" , Cognitive And Soft Computing Techniques Based Data-Centric Operations In The Healthcare Industry, Elsevier Series : Intelligent Data-Centric Systems (2022). https://doi.org/10.1016/B978-0-323-85751-2.00006-2

  4. Bhardwaj, S., Sharma, A. K., Srivastava, D. K. (2021). "Prediction of COVID’19 Outbreak by Using ML-Based Time-Series Forecasting Approach.", In: Singh P.K., Polkowski Z., Tanwar S., Pandey S.K., Matei G., Pirvu D. (eds) Innovations in Information and Communication Technologies (IICT-2020). Advances in Science, Technology & Innovation (IEREK Interdisciplinary Series for Sustainable Development) Springer, Cham. https://doi.org/10.1007/978-3-030-66218-9_33

Awards

Publication and Research Award Incentive for Students to Excel (PRAISE)

The objective of the Publication and Research Award Incentive for Students to Excel (PRAISE Award) given by Manipal University Jaipur is to inculcate and promote excellence of the students as well as to improve the quality and citations of student research publications, by publishing articles in Scopus indexed reputed journals. This has been awarded for publishing research work in prestigious Q1 journal.

Conferences

  • Presented poster "An Approach for Forecasting Neurodegeneration Diseases Using Deep Learning" at The 4th Macquarie Neurodegeneration Meeting (2021) organised by Macquarie University, Australia.

  • Presented paper "Speech Audio Cardinal Emotion Sentiment Detection and Prediction Using Deep Learning Approach" at 3rd International Conference On Recent Trends In Advanced Computing – Artificial Intelligence And Technology - ICRTAC-AIT(2020), Springer .

  • Presented paper "Prediction Of COVID-19 Outbreak By Using ML Based Time Series Forecasting Approach" at Innovations in Information and Communication Technologies - IICT-2020, Springer .

    • Professional Development

    Specialization Courses


    1. Machine Learning Specialization by University Of Washington

    2. Deep Learning Specialization by DeepLearning.AI

    3. TensorFlow Developer Specialization by DeepLearning.AI

    Courses


    1. Natural Language Processing in TensorFlow by DeepLearning.AI

    2. Sequences, Time Series and Prediction by DeepLearning.AI

    3. Machine Learning: Clustering & Retrieval by University Of Washington

    4. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning by DeepLearning.AI

    5. Algorithmic Toolbox by University of California San Diego, HSE University

    6. Convolutional Neural Networks in TensorFlow by DeepLearning.AI

    7. Machine Learning: Classification by University of Washington

    8. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

    9. Sequence Models by DeepLearning.AI

    10. Convolutional Neural Networks by DeepLearning.AI

    11. Discrete Math and Analyzing Social Graphs by HSE University

    12. Machine Learning: Regression by University of Washington

    13. Neural Networks and Deep Learning by DeepLearning.AI

    14. Machine Learning Foundations: A Case Study Approach by University of Washington

    15. Structuring Machine Learning Projects by DeepLearning.AI

    16. Programming for Everybody (Getting Started with Python) by University of Michigan

    17. COVID19 Data Analysis Using Python by Rhyme.com (Acquired by Coursera)

    18. Traffic Sign Classification Using Deep Learning in Python/Keras by Rhyme.com (Acquired by Coursera)

    19. Generate Synthetic Images with DCGANs in Keras by Rhyme.com (Acquired by Coursera)

    Projects

    1. EpidermisAI- An AI Web Application To Detect Epidermal Lesions

      This AI powered web application classifies Epidermial Lesion's type through images. This AI model is trained on famous vast HAM10000 dataset using EfficientNet architecture. A user can upload his/her skin problem photo and the application will detect type of lesion. Some general skin care guides and methods are provided in this application along with a planned everyday schedule and positive quotes to keep users motivated to do the required treatment in an urge to get a healthy and glowing skin.


    2. Emotion Recognition Of Students While Learning Through Raw EEG Data

      In This Project, Complex Raw EEG Data is decomposed to obtain 5 brainwaves (Delta, Theta, Alpha, Beta and Gamma). With The Help of Delta, Theta, Alpha, Beta and Gamma brainwaves, Features are obtained in terms of Valence And Arousal. With the help of features i.e Valence And Arousal, Emotions are predicted.


    3. Generate Synthetic Images with DCGANs in Keras

      In This Project, Synthetic Images are generated with DCGANs (Deep Convolutional Generative Adversarial Networks) in Keras. Two models are trained simultaneously by an adversarial process. A generator learns to create images that look real, while a discriminator learns to tell real images apart from fakes.


    4. Catastrophe Detection System Using Twitter Data

      In this project, over 11,000 tweets associated with disaster keywords are used to predict catastrophe depicting tweets through machine learning. This project consists of 6 step data pre-processing such as Removal of URL, Tags, Emoji’s, HTML Tags, Stopwords and useless characters in addition to Stemming, Lemmatization and Vectorization. This pre-processed data is trained through a variety of following machine learning algorithms- Gaussian Naive Bayes, Bernoulli Naive Bayes, Complement Naive Bayes, Multinomial Naive Bayes, RBF Kernel SVM, Linear Kernel SVM, Sigmoid Kernel SVM, Random Forest, AdaBoost, K-Nearest Neighbours, Logistic Regression and 3 Layers Deep Neural Networks.


    5. Shakespeare Text Generator Using NLP

      In this project, Shakespeare’s sonnet dataset has been used. Data preprocessing techniques-tokenization, sequencing and padding has been performed followed by a bidirectional LSTM in addition with a dropout layer and l2 regularization to prevent overfitting of the model. In this model, Adam Optimization is used with Sparse Categorical Cross Entropy Loss.


    6. Traffic Sign Classifier For Self Driving Cars Using Deep Convolutional Neural Network(CNN)

      In this project, A Traffic Sign Classifier is build in Python/Keras using Deep Convolutional Neural Network (CNN). The Dataset consisted of 43 different classes with images of 32 x 32 pixels. The images are converted into grayscale and normalized. Droupout Regularization technique is used for reducing overfitting and hence improving the accuracy. In this model, Adam Optimization is used with Sparse Categorical Cross Entropy Loss.