AI RESEARCH · MACHINE LEARNING · COMPUTER VISION

Hey, I'm Shaharior.

Islam Chowdhury

My research interests are at the intersection of AI, Machine Learning, Data Science, Environmental Science, Agriculture and interdisciplinary research. I am particularly keen to explore applications of AI in Environmental Science, Agriculture, Computer Vision, and other interdisciplinary domains. I am continuously developing my expertise in advanced AI/ML and research methodology while seeking opportunities to collaborate, learn, and contribute to impactful academic research.

Curriculum Vitae

Shaharior Islam Chowdhury · 2026

Selected Publications

Recent Researches

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JournalIEEE Access

Classification of Freshwater Fish Diseases in Bangladesh Using a Novel Ensemble Deep Learning Model: Enhancing Accuracy and Interpretability

Effective disease management and mitigation strategies for fish diseases depend on timely and accurate diagnosis. In recent years, artificial intelligence methods—classification algorithms in particular—have become effective instruments for automating fish disease diagnosis. This paper presents two types of ensemble models: i) the baseline averaged ensemble (AE) model and ii) the novel Performance Metric-Infused Weighted Ensemble (PMIWE) model. By leveraging pre-trained models and novel ensemble techniques, we achieve a testing accuracy of 97.53%, corresponding precision, recall, and F1-score of 97%. We also bring about enhanced interpretability and trustworthiness using the Grad-CAM (Gradient-weighted Class Activation Mapping) explainable artificial intelligence (XAI) technique.

2024DOI

ConferenceIEEE

Generative Adversarial Networks (GANs) for Crop Simulation and Enhancement: Exploring Synthetic Data for Agricultural Research

Real-world data collection in agriculture is not only highly expensive but also faces challenges due to seasonal variations and limited generalization capabilities for capturing unusual disease patterns and environmental changes, as is required for deep learning models. We propose a new GAN-based model aimed at crop simulation and disease data augmentation. A class-conditional Deep Convolutional GAN will be designed to generate high-resolution, biologically plausible crop disease images by jointly learning visual structures and class-specific disease characteristics. The real samples in our dataset will be combined with these generated synthetic samples to increase training for crop disease classification. The multi-class plant dataset available in PlantVillage is used in the experimental scenario, with classes for healthy and diseased crops. Extensive ablation experiments confirm that the classifier's performance can be enhanced by GAN-based training compared to state-of-the-art deep learning architectures such as ResNet50, DenseNet121, EfficientNetB3, and VGG16. The accuracy of the proposed model, ResNet50 + GAN, is about 98.65%. This shows not only higher precision levels but also a higher recall rate with an F1-score computed accordingly. This validates the realism of the synthetic images developed. This is an implication of the usefulness of synthetic data in addressing data scarcity and class imbalance in the domain of intelligent agriculture.

2026DOI

ConferenceIEEE

Solar PV Power Generation Forecasting Using an Attention-Based Bidirectional LSTM Network

To provide the reliability and efficiency of the solar energy system. The solar photovoltaic (PV) power generation should be predicted accurately. The generation of solar power is not a linear process and it changes with time depending on the environmental factors. Therefore, it is quite difficult to forecast solar power production properly. We, in this regard, introduce a new hybrid deep learning (DL) model, SP-BiLSTM-ATT, a composite of Bidirectional Long Short-Term Memory model and Attention mechanism to forecast the solar power generation. The model proposed is tested with the help of a solar power generation dataset that has been acquired via the Kaggle data repository. The performance of the proposed model is correlated to the performance of other well-known regression models, such as Decision Tree (dt), K-Nearest Neighbors (KNN), Random Forest (RF) and standard LSTM model. The model proposed has a much greater accuracy when compared to the conventional models with an R2 which is 0.95. The fact that the precision of the suggested model was steadily increased in all the experiments validates the reliability of the model.

2026DOI

Articles Under Review Environmental Science and Pollution Research

AttenFusion-Net: An Attention-Enhanced Deep Learning Framework for Automated Municipal Solid Waste Segregation

The growing problem of municipal solid waste (MSW) generation presents a challenge in the efficient and scalable sorting of urban solid waste. Although deep learning models hold potential for automated waste segregation, their effectiveness is limited by the large Intra-class variance, Inter-class Similarity and Class Imbalance in some real world data. To overcome these limitations, we present a deep learning model with attention mechanisms, ResNet-CBAM, combining a ResNet-50 backbone with a Convolutional Block Attention Module (CBAM) to enhance feature representation through channel and spatial awareness. The approach is tested on the open Waste Segregation Image Dataset, which includes eight classes of biodegradable and non-biodegradable waste. To overcome the class imbalance problem, a combination of data augmentation and undersampling is applied. The findings reveal that our approach can attain an accuracy of 93.09% and an F1 score of 0.9122, surpassing some baseline approaches. The use of attention mechanisms improves the model's discriminative ability, resulting in better classification performance, especially in visually diverse environments. While minor errors still remain among visually similar classes, these results show a stable performance of the proposed model for all classes. In conclusion, the ResNet-CBAM model presents a reliable and transparent solution for waste classification, which can be further integrated into smart waste management and smart cities.

DOI

DatasetMendeley Data

Student Performance Data Bangladesh

This dataset has been collected to support research on predicting the academic performance of Secondary School Certificate (SSC) and Higher Secondary Certificate (HSC) students in Bangladesh. It comprises responses from many students across various institutions in the country.

2025DOI