lime-explainability

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ascender1729 / HealthFraudMLChain

M.Sc. Dissertation: Healthcare insurance fraud detection using Optuna-tuned ML ensemble (F1=0.7345) with SHA-256 blockchain audit trails and ECIES encryption. 5,410 providers, 190 features, SHAP/LIME explainability.

machine-learning thesis nextjs blockchain xgboost lightgbm ensemble-learning sha256 merkle-tree fraud-detection catboost shap optuna ecies-encryption nit-patna healthcare-fraud-detection lime-explainability

Updated Apr 9, 2026
Python

Himanshu-Laddhad / Customer-review-intelligence-nlp

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3-stage NLP pipeline on 568k Amazon reviews — VADER baseline → TF-IDF + LinearSVC → DistilBERT fine-tuning. Adds Aspect-Based Sentiment Analysis (Quality/Price/Delivery/Service) and LIME explainability. Surfaces star-rating disconnects that predict return rates.

python nlp machine-learning deep-learning sentiment-analysis text-classification transformers spacy tfidf aspect-based-sentiment-analysis huggingface streamlit distilbert retail-analytics lime-explainability

Updated Jun 17, 2026
Jupyter Notebook

bk1210 / Detecting-Sarcasm-as-Sentiment-Incongruence

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Multi-task RoBERTa for sarcasm detection via Sentiment Incongruence Auto-Labeling. Focal Loss + WeightedRandomSampler + LIME explainability. F1-Macro 0.977, AUC 0.997. Zero-shot cross-domain eval on ABSA & Amazon. IEEE paper.

nlp data-science natural-language-processing deep-learning sentiment-analysis text-classification transformers pytorch data-analytics cross-domain multi-task-learning sarcasm-detection focal-loss roberta huggingface lime-explainability implicit-sentiment

Updated Apr 22, 2026
Jupyter Notebook

Enowon / LotL-Ransomware-Detection-RandomForest-LIME

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AI-assisted detection of Living-off-the-Land ransomware attacks in Windows Sysmon logs using Random Forest and LIME explainability — MSc Cybersecurity Project, University of Roehampton

python machine-learning random-forest cybersecurity ransomware-detection windows-security lime-explainability

Updated Jul 31, 2026
Python

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