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Mental Health Sentiment Analysis from Social Media with DistilRoBERTa
An NLP study that reads social media posts to detect emotional signals linked to mental health, fine-tuning DistilBERT, DistilRoBERTa, and BETO on a public sentiment corpus across Positive, Neutral, Negative, and Irrelevant classes.
Insect Pest Classification with ViT, DeiT & Ensemble Fusion
An agriculture AI study that classifies six crop-damaging insect pests from field images, fine-tuning Vision Transformer and DeiT with early/late fusion, majority voting, and ViT-to-EfficientNet-B4 knowledge distillation.
Plant Disease Detection with Optimized YOLOv8, GhostNet & Coordinate Attention
A comparison study on real-field plant disease detection, enhancing YOLOv8n/s with GhostNet backbone compression and Coordinate Attention across multiple plant species and disease classes.
Rice Leaf Disease Classification with AlexNet Snapshot Ensemble & Hybrid ViT
A comparative study of deep learning architectures on rice leaf imagery, modified AlexNet with snapshot ensembling, Hybrid ViT, EfficientNet-B4, VGG16, and Inception V3 across ten disease and healthy classes.
Five-Class Rice Leaf Disease Classification with EfficientNet-B4 & AlexNet
A comparative study of Basic CNN, Modified AlexNet, EfficientNet-B0, and EfficientNet-B4 on a balanced five-class rice leaf dataset, Leaf Scald, Healthy, Brown Spot, Leaf Blast, and Rice Hispa, for automated paddy disease diagnosis.
Multi-Crop Plant Disease Classification with EfficientNet-B4 & AlexNet
A comparative study of Basic CNN, Modified AlexNet, EfficientNet-B0, and EfficientNet-B4 on a balanced multi-crop leaf dataset, apple, grape, corn, cherry, and blueberry across 15 disease and healthy classes.
Bangladeshi Vegetable Classification with VGG16, Random Forest & XGBoost
A comparative study of hand-crafted ML (SVM, Random Forest, XGBoost, Logistic Regression) against transfer-learning CNNs (VGG16, VGG19, ResNet50, DenseNet-121, MobileNet) on a balanced 15-class vegetable dataset for supermarket and farm automation.
Hybrid Fusion for Retail Demand Forecasting & Customer Segmentation
A hybrid machine learning framework combining LSTM, GRU, and Random Forest with early and late fusion to forecast retail demand, optimize inventory, and segment customers for personalized marketing.
Reproducing a Transformer Baseline
A faithful reproduction of a published transformer baseline with validated benchmarks.
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