Minh Hoang

Minh Hoang

PhD Student in Electrical & Electronic Engineering, Lecturer in Computer Science & Machine Learning

Talks and presentations

EfficientSkinTrans: Enhancing Dermatological Classification and Analysis Through a Hybrid ViT Network

January 05, 2026

Conference Talk, 2026 IEEE 16th Annual Computing and Communication Workshop and Conference (IEEE CWCC 2026), University of Nevada, Las Vegas, USA (BEST PRESENTATION AWARD for AI AND WIRELESS COMMUNICATION at IEEE CWCC 2026)

In this talk, I introduced EfficientSkinTrans, a lightweight hybrid model designed to improve skin lesion diagnosis by combining an EfficientNet-style convolutional encoder with a compact transformer module to capture both fine-grained textures and long-range contextual cues. Evaluated on ISIC-2019, the model outperformed conventional transformer-based and hybrid approaches, while also demonstrating strong zero-shot generalization to unseen diseases such as Monkeypox from the MSLD v2.0 dataset. To support clinical trust, Grad-CAM++ visualizations confirmed that the model consistently focused on medically relevant lesion regions. Overall, EfficientSkinTrans provides a reliable, interpretable, and generalizable framework for AI-assisted dermatological diagnosis.

AI-based Energy Forecasting for Net Zero Smart Cities

November 24, 2025

Conference Talk, 2025 7th International Conference on Electrical, Control and Instrumentation engineering (IEEE ICECIE 2025), Pattaya, Thailand

In this talk, I discussed how AI-driven energy forecasting is accelerating the transition toward net-zero smart cities by enabling more accurate, adaptive, and efficient energy management. The presentation explored key AI methodologies, including advanced time-series models, deep learning strategies, and hybrid AI techniques, designed to integrate real-time IoT grid data, weather information, and historical consumption patterns. I highlighted major challenges such as data integrity, model scalability, and cybersecurity, while demonstrating through case studies how AI significantly improves energy planning, distribution optimization, and supply–demand balancing. The talk emphasized AI’s growing role as a foundational technology for achieving sustainable, net-zero urban development. The talk can be found here.

AI-Driven Adaptive Detection for Eliminating Harmonic Oscillations in Inverter-Connected Systems

November 24, 2025

Conference Talk, 2025 7th International Conference on Electrical, Control and Instrumentation engineering (IEEE ICECIE 2025), Pattaya, Thailand

In this talk, I presented an AI-driven adaptive approach for detecting and mitigating harmonic oscillations in inverter-connected power systems, an increasingly critical issue as modern grids integrate more inverter-based resources. The method leverages AI techniques, including wavelet-based neural networks, CNNs, and RNNs, to dynamically extract dominant harmonic frequencies and trigger real-time harmonic traps for suppression. Compared with traditional Fourier and wavelet-based methods, the AI-driven models achieved faster and more accurate harmonic detection, demonstrating a scalable and effective solution for enhancing power quality and ensuring stable inverter-based power system operation. The talk can be found here.

Forecasting Urban Energy for Net-Zero Smart Cities: A Hybrid Attention-based Approach

October 25, 2025

Conference Talk, 2025 1st FPT International Conference on Emerging Trends in Computing (FETC 2025), FPT University, Can Tho, Vietnam

In this talk, I presented two hybrid deep learning architectures for net-zero smart city energy forecasting, integrating CNNs for short-term feature extraction, LSTMs for long-range temporal modeling, and an Attention mechanism for dynamic weighting of time-step importance. Evaluated on the ENTSO-E dataset, the proposed models outperformed all baselines, with CNN-LSTM-Att.v2 achieving the best single-step results (RMSE 106.96 MW, MAPE 1.07%) and CNN-LSTM-Att.v1 delivering the strongest multi-step day-ahead performance (RMSE 438.11 MW, MAPE 4.18%). These findings demonstrate the effectiveness of attention-driven spatiotemporal modeling for accurate and scalable energy forecasting in sustainable smart cities. The talk can be found here.

DenseTransXR: A Hybrid Transformer-Based Approach for Chest X-ray Abnormality and Emerging Disease Detection

October 25, 2025

Conference Talk, 2025 1st FPT International Conference on Emerging Trends in Computing (FETC 2025), FPT University, Can Tho, Vietnam

In this talk, I presented DenseTransXR, a hybrid architecture that combines DenseNet-121 with Transformer encoders to address the shortage of labeled radiography data during the COVID-19 pandemic. The model achieved an AUC of 0.812 on NIH ChestX-ray14 for multi-label abnormality detection and demonstrated strong zero-shot generalization on COVIDx CXR-4 with an AUC of 0.755 and a recall of 0.919 for COVID-19 detection. These results highlight the capability of hybrid Transformer models to improve radiographic diagnosis and handle previously unseen diseases effectively.

Case Study: Utilizing Deep Learning Models for Fault Detection and Diagnosis of Photovoltaic Modules to Improve Solar Energy Constructions’ O&M Activities Quality

September 09, 2025

Guest Lecture, RMIT University Vietnam, Ho Chi Minh City, Vietnam

I delivered guest lectures at RMIT University Vietnam for Undergraduate Courses EEET2602 and EEET2623 on Deep Learning approaches for photovoltaic (PV) materials and semiconductor systems, covering model architectures, PV fault detection, and real-world applications in sustainable energy engineering. The lectures can be found here (2024) and here (2025).

Deep Learning Models for Fault Detection and Diagnosis in Photovoltaic Modules Manufacture

June 06, 2023

Conference Talk, 2023 IEEE Conference on Artificial Intelligence (IEEE CAI 2023), Santa Clara, California, USA

In this talk, I highlighted the growing need for reliable fault detection and diagnosis (FDD) in photovoltaic (PV) manufacturing as global solar adoption accelerates. Traditional FDD relies on specialized instruments and expert analysis, creating bottlenecks in quality assurance. I introduced a deep learning–based approach that leverages electroluminescence (EL) imaging to automatically identify visual defects in PV modules. The presentation covered exploratory data analysis of EL images and several supervised learning techniques designed to detect and classify module faults, demonstrating how modern DL methods can significantly enhance PV quality control and support scalable, high-precision manufacturing.