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Nicole T.

@tansimin2591

4.9
4
2.4
2.4
100%

Research officer, Bsc.biomedical engineering

$20 USD / Hour
・
Flag of
Singapore (2:13 AM)
・
Joined on February 16, 2021
$20 USD / Hour
・
I am a data scientist working as a research officer for many years. I have been working since after graduation on MATLAB-related projects. Mainly dealing with biomedical signals, I have also dealt with prediction, anomaly detection and text analysis project using python. I specialize in machine learning algorithm and doing prediction analysis. I have been doing data visualization and analysis for 5 years. With a diverse background in machine learning, I bring a multi-disciplinary subject together and make use of creativity, technical expertise, and a results-driven mindset to every project I have done. My specialization is in medical data and analysis and I enjoy being thrived on setting out challenging tasks. My 5 years of experience have equipped me with the ability to create unusual approaches to machine learning and predictive analysis models. Whether it's structured data or unstructured data , I've consistently delivered exceptional results by using my unique machine-learning approaches. I ensure that there is no data leakage and that desirable results are obtained for all my projects My skillset include: - Twilio -Whatapp Integration - Beautiful Soup - genism - SHAP - LIME - LDA - transformers -tkinter - Keras - PyTorch - torchvision - Bertopic - NLTK - gradio - JQuery - HTTP Server - CSS - HTML • Flask • Rasa NLU • Rasa core • RasaX • Natural Language Processing • sklearn.preprocessing • sk Learn.model_selection • sk learn. metrics • SK learn • Seaborne • Os • Scipy • pygame • Tkinter • Keras • Scipy • Matplotlib • Pandas • Numpy • SckitLearn • Skimage • Pywavelet • OpenCV • MatPlotlib • Tensorflow • Wavelet Scattering Tool • Fuzzy Logic controller • Deep Learning • Global Optimization Tool • QRS peak detection Analysis Tool • Heart Rate Variability Analysis Tool • Recurrent Quantification Analysis Tool • Point Care Plot • Wavelet Transform The language i had mastered are: • MATLAB • Jupyter • Anaconda Environment • Minitab 20 • R programming • Octave - Python -Google Colab
Verifications
On time
100%
On budget
100%
Accept rate
100%
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Portfolio

Portfolio

11599041
- Load the datatset - Clean and preprocess text - Concatenate train and test set - Tokenization - Label encode categorical data - Split ratio 8: 2 - Build DNN model - Model Evaluation - Build fuzzy logic controller. - Analyze sentiment polarity. - Design app application
Sentimental Analysis using Deep neural network and fuzzy
11598184
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
11596645
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
11595865
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
11595859
- Building of CNN-naïve baye classifier using a 2D image from feature extraction - Wavelet scattering algorithm for feature extraction and mapping to 2D images. - Extraction of CNN layer as a feature in another model classification - Denoising ECG signal - 1 min ECG, 18 NSR vs 18 sleep apnea ECG
A Deep Learning Framework for Automatic Sleep Apnea
11593505
- Allow other users to test my RASA bot and gain feedback from using RASA X - Create forms in RASA chatbot e.g., Client Particular Form, Appointment Form - integrating different field of specialization into one chatbot database, (Healthcare, work-stress, basic consultation, mental health survey, tracking your health condition and feeling) - integrate voice assistant in AI chatbot - create webpage with local host and deploy speech recognition AI chatbot - create html webpage for chatbot and deployment using jQuery. - Deploy Rasa chatbot with flask
Deployment of chatbot
11599041
- Load the datatset - Clean and preprocess text - Concatenate train and test set - Tokenization - Label encode categorical data - Split ratio 8: 2 - Build DNN model - Model Evaluation - Build fuzzy logic controller. - Analyze sentiment polarity. - Design app application
Sentimental Analysis using Deep neural network and fuzzy
11598184
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - T-SNE and UMAP embedding - Combine sentence embedding with word embedding model - Bertopic modelling - LDA modelling - BERT encoding - LIME explanation - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
semantic and text analysis with topic modelling
11596645
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
- Text Analysis with NLP and BERT - Tokenization - Cleaning and preprocessing of text - Label encoding of categorical data - Normalization of text data - Combine sentence embedding with word embedding model - BERT encoding - Smote analysis for imbalance dataset - Classification of text using transfer learning - Improve classification model using stacking classifier with XGB classifier for sentence with word embedding combination. - DNN ensemble with transfer learning method - Evaluation of model metrics
Sentiments and Text Analysis
11595865
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
- Cleaning messy and noisy data in a cancer dataset with missing value and redundant data - Data Visualization using normal distribution - Categorize the number of patients with disease and non-disease cases in the dataset - Feature Extraction using pyRQA, recurrent quantification analysis - Classification of dataset using Supervised learning classification, KNN, SVM and DT
Breast Cancer Prediction using Machine Learning Technique
11595859
- Building of CNN-naïve baye classifier using a 2D image from feature extraction - Wavelet scattering algorithm for feature extraction and mapping to 2D images. - Extraction of CNN layer as a feature in another model classification - Denoising ECG signal - 1 min ECG, 18 NSR vs 18 sleep apnea ECG
A Deep Learning Framework for Automatic Sleep Apnea
11593505
- Allow other users to test my RASA bot and gain feedback from using RASA X - Create forms in RASA chatbot e.g., Client Particular Form, Appointment Form - integrating different field of specialization into one chatbot database, (Healthcare, work-stress, basic consultation, mental health survey, tracking your health condition and feeling) - integrate voice assistant in AI chatbot - create webpage with local host and deploy speech recognition AI chatbot - create html webpage for chatbot and deployment using jQuery. - Deploy Rasa chatbot with flask
Deployment of chatbot
Changes saved
4.9 · 4 Reviews
Reviews
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Fresh Produce Detection Computer Vision Engineer • $60 USD
she is a great freelancer to work with. She responds promptly and completes the work patiently. Would highly recommend her.
Islam A.
@islamamer6
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Flag of
Aswan, Egypt
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5 months ago
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Signal Processing Code Refinement for EMG Data • €50 EUR
Excellent communication and was always eager to help. Delivered on time and within budget, making the collaboration seamless and efficient.
Athanasios A.
@athanasav
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Flag of
THESSALONIKI, Greece
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5 months ago
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Project 37671480 has been deleted • $25 USD
She was very supportive and worked well with EEG datasets.
Abdur R.
@rasol1
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Flag of
Honolulu, United States
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9 months ago
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R
Brain Tumour Detection via Machine Learning • ₹2,000 INR
I am very satisfied with the work. My work has been completed within the given time as per my requirement .
Rohan S.
@rohan072
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Flag of
Guwahati, India
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1 year ago
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Experience
Research officer
Jun, 2023 - Present
•
1 year, 7 months
National Neuroscience Institute
Jun, 2023 - Present
•
1 year, 7 months
Research on AI
Jun, 2023 - Present
•
1 year, 7 months
Laboratory Technician
Dec, 2013 - Present
•
11 years
Nanyang Technological University
Dec, 2013 - Present
•
11 years
I do laboratory operation and management on daily job basis. In addition, I have been working with my professor in school for 4 year with project related to MATLAB. I graduated with a biomedical engineering in 2020
Dec, 2013 - Present
•
11 years
Education
Singapore Institute of Management
2017 - 2022
•
5 years
Biomedical engineering ( Part-time)
Flag of
Singapore
2017 - 2022
•
5 years
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