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Projects

Our focus is on creating innovative AI-driven solutions that address a broad range of significant and impactful challenges.

This section showcases a curated selection of student-led research projects conducted under the academic supervision of Dr. Happy N. Monday.

The projects span diverse applications of artificial intelligence, with a particular focus on:

Medical imaging and diagnostics, Renewable energy forecasting, and Advanced neural architectures

Each project card presents the student’s name, project title, and a dedicated link to detailed documentation.

This portfolio reflects Dr. Monday’s commitment to mentoring emerging researchers and advancing impactful, AI-driven innovations.

Gao Rui (Olivia)

IABiLSTM-NET Deep Learning Model for Predicting Solar Cell Degradation using Thermal Images

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Luo Jie (Kevin)

Lightweight CNN Model for Pneumothorax Detection using Chest X-Ray Images

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Li Tong (Aaron)

Classification of Diabetic Retinopathy using Residual Learning with a Custom Balanced Softmax Loss

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Huo Xingchen (Lily)

Hybrid MobileNet-BiLSTM Model with Multi-Head Attention Mechanism for Solar Radiation Prediction

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Ma Rui (Bella)

Multi-Class Diabetic Retinopathy Classification using Self-Attention Residual Networks

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Zhao Yikai (Richard)

Residual-Based Dual Attention Ensemble Model for Diabetic Retinopathy Classification

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Chen Siyu (Carrie)

CNN-BiLSTM-Attention Solar Energy Forecasting using Cloud Motion Vectors

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Xu Danyue (Esme)

LSTM-Based Multi-Head Attention Lightweight CNN Model for Pneumothorax Classification using Chest X-Ray Images

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Yang Kaijia (Alex)

Real-Time Sky radiance Prediction for Solar Panel Angle Optimization

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Dong Linrui (Link)

Radiance Sky Image-Based Cloud Shadow Mapping for Solar Energy Forecasting

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Xiang Yucheng (Salam)

BiLSTM-Based Inception Multi-Head Attention Network for Predicting Solar Cell Degradation using Thermal imaging

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Huang Zhekai (Kevin)

Predicting the Impact of Climate Change on Solar Power Production using CNN-LSTM Models

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Cheng Yu (Leon)

Super-Resolution with RCAN for Improved Malaria Cell Classification: A Performance Evaluation

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Zhong Yi (Bryan)

Vehicle Logo Classification using Deep Learning for Traffic Monitoring Systems

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Yong Wang (Wyatt)

Deep Learning Based Heart Disease Classification Using Multimodal ECG Signal and Images

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Yaoqing Wang (Edison)

Migrating Ensemble Model with Attention for Advanced Fruit Classification

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Xiao Zhengming (Shaw)

Deep Learning-Based Access Control System Using Face Recognition Technology

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Guohao Chen (Emil)

Ensemble CNN Model for Food Image Classification on Small Datasets

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Jiaqing Xue (Henry)

DRIW-Net: Deep Learning Ensemble for Retina Disease Classification

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Chen Lihan (Daniel)

Enhanced Diabetic Retinopathy Classification through Depthwise Separable CNN

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Xian Lai (Brian)

Enhanced Depthwise Inception Model of Residual Learning for Rice Leaf Classification

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Zhao Yanrui (Chris)

Food Classification with Depthwise Separable Convolution Network

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Liu Zijun (Jonas)

Enhanced SRResNet with Attention: Optimizing Image Super-Resolution

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Xiyue Lin (Lucy)

Faster R-CNN: Deep Learning-based Mask Wearing Detection in Public Spaces

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Li Linyi (Sky)

A Separable Convolutional Neural Network Approach for Plant Disease Classification

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Liu Rui (Tevin)

Self-driving Car in Video Game using Deep Learning Approach

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Wu Yile (Haley)

A convolutional Neural Network Approach for the Recognition of Traffic Signs

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Wang Qian (Kirk)

Application of Fine-tuned Pre-trained Deep Learning Network for Traffic Sign Recognition

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Xinyi Wang (Coraline)

Flower Image Recognition based on Deep Convolutional Neural Network

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Keyang He (Carlton)

Deep Neural Network Approach for Flower Recognition

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Li Qingyu (Allen)

Recognition of Images from CIFAR-10 Dataset using Deep Learning Network

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Zhu Zihao (Chris)

Classification of Clothing Images using Convolutional Neural Network

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Ye Xiao (Christina)

Recognition of Flower Images using Deep Learning

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Haoyun Yang (Ethan)

Convolutional Neural Network-based Technique for Flower Classification

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Hongyun Zhu (Ives)

Convolutional Neural Network for the Classification of Clothing Images

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Di Cui (Lynch)

Application of Convolutional Neural Network for Image Recognition using CIFAR-10 Dataset

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Zhao Fangding (Zack)

Deep Neural Network for Clothing Image Classification

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