Ashhadul Islam

Ashhadul Islam

Harnessing AI to bridge visionary innovation with real-world impact.

Posts by Collection

portfolio

publications

Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence

Published in IEEE Access, 2020

This paper introduces the Multi-Cluster Jumping Particle Swarm Optimization (PSO) algorithm, designed to address the limitations of classical PSO by improving convergence speed and avoiding local minima, particularly in high-dimensional data spaces.

Recommended citation: Rehman, A. U., Islam, A., & Belhaouari, S. B. (2020). "Multi-Cluster Jumping Particle Swarm Optimization for Fast Convergence." IEEE Access, 8, 189382--189394. https://doi.org/10.1109/ACCESS.2020.3031003
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Class Aware Autoencoders for Better Feature Extraction

Published in 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2021

This paper introduces a modified operation of autoencoders that incorporates class labels into the learning process, resulting in improved feature extraction and higher classification accuracy across multiple datasets.

Recommended citation: Islam, A., & Belhaouari, S. B. (2021). "Class Aware Autoencoders for Better Feature Extraction." In 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (pp. 1--5). IEEE. https://doi.org/10.1109/ICECCE52056.2021.9514202
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Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept

Published in IEEE Access, 2022

This paper presents a cyber-physical system (CPS) demonstration in the mining industry, showcasing an automated shuttle-conveyor-belt operation for managing multiple stockpiles. The CPS autonomously controls inventory using mixed-integer optimization and a deep neural network, providing a proof of concept for smart manufacturing in Industry 4.0.

Recommended citation: Yaqot, M., Franzoi, R. E., Islam, A., & Menezes, B. C. (2022). "Cyber-Physical System Demonstration of an Automated Shuttle-Conveyor-Belt Operation for Inventory Control of Multiple Stockpiles: A Proof of Concept." IEEE Access, 10, 127636--127653. https://doi.org/10.1109/ACCESS.2022.3226942
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KNNOR: An Oversampling Technique for Imbalanced Datasets

Published in Applied Soft Computing, 2022

This paper introduces KNNOR, a novel oversampling technique that addresses class imbalance in datasets. The method focuses on enhancing the predictive performance of ML models by ensuring a more reliable augmentation of the minority class, overcoming issues such as within-class imbalance and the small disjunct problem.

Recommended citation: Islam, A., Belhaouari, S. B., Rehman, A. U., & Bensmail, H. (2022). "KNNOR: An Oversampling Technique for Imbalanced Datasets." Applied Soft Computing, 115, 108288. https://doi.org/10.1016/j.asoc.2021.108288
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K Nearest Neighbor OveRsampling Approach: An Open Source Python Package for Data Augmentation

Published in Software Impacts, 2022

This paper introduces the K Nearest Neighbor OveRsampling (KNNOR) algorithm, a novel data augmentation technique implemented as an open-source Python package. The algorithm addresses the challenges of imbalanced datasets by generating artificial data points that enhance classifier accuracy without adding noise.

Recommended citation: Islam, A., Belhaouari, S. B., Rehman, A. U., & Bensmail, H. (2022). "K Nearest Neighbor OveRsampling Approach: An Open Source Python Package for Data Augmentation." Software Impacts, 12, 100272. https://doi.org/10.1016/j.simpa.2022.100272
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Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights

Published in International Conference on Machine Learning, Optimization, and Data Science, 2022

This paper presents innovative methods for pruning deep neural networks, making them lighter and faster while maintaining accuracy, which is crucial for their deployment on embedded and edge devices.

Recommended citation: Islam, A., & Belhaouari, S. B. (2022). "Smart Pruning of Deep Neural Networks Using Curve Fitting and Evolution of Weights." In International Conference on Machine Learning, Optimization, and Data Science (pp. 62--76). Springer.
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Analysing Keystroke Dynamics Using Wavelet Transforms

Published in 2022 IEEE International Carnahan Conference on Security Technology (ICCST), 2022

This paper presents a novel method for analyzing keystroke dynamics using wavelet transforms, enhancing the robustness of classifiers in detecting unauthorized smartphone access and offering a potential alternative to costly authentication methods.

Recommended citation: Islam, A., & Belhaouari, S. B. (2022). "Analysing Keystroke Dynamics Using Wavelet Transforms." In 2022 IEEE International Carnahan Conference on Security Technology (ICCST) (pp. 1--5). IEEE. https://doi.org/10.1109/ICCST52959.2022.9896483
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IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)

Published in 2022 IEEE International Joint Conference on Biometrics (IJCB), 2022

This paper outlines the framework and findings of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C), which benchmarks mobile user authentication systems based on behavioral biometric traits.

Recommended citation: Stragapede, G., Vera-Rodriguez, R., Tolosana, R., Morales, A., Fierrez, J., Ortega-Garcia, J., Rasnayaka, S., Seneviratne, S., Dissanayake, V., Liebers, J., et al. (2022). "IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C)." In 2022 IEEE International Joint Conference on Biometrics (IJCB) (pp. 1--7). IEEE. https://doi.org/10.1109/IJCB54206.2022.10007985
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Framework Design for Similar Video Detection: A Graph-Based Video Clustering Approach

Published in 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2022

This paper presents a graph-based video clustering approach for detecting similar videos, offering a novel strategy to identify and manage duplicated content efficiently.

Recommended citation: Al-Thani, N. F., Islam, A., Belhaouari, S. B., & Faramarzinia, S. (2022). "Framework Design for Similar Video Detection: A Graph-Based Video Clustering Approach." In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 571--576). IEEE. https://doi.org/10.1109/ISMSIT56059.2022.9932834
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Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach

Published in IEEE Access, 2023

This paper presents a novel method for generating artificial images by combining Variational Autoencoders (VAEs) with the K-Nearest Neighbor OveRsampling (KNNOR) approach. The technique addresses common issues in Generative Adversarial Networks (GANs), such as mode collapse and distortion, producing more realistic and efficient image generation.

Recommended citation: Islam, A., & Belhaouari, S. B. (2023). "Fast and Efficient Image Generation Using Variational Autoencoders and K-Nearest Neighbor OveRsampling Approach." IEEE Access, 11, 28416--28426. https://doi.org/10.1109/ACCESS.2023.3259236
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Neural Network Optimization with Weight Evolution

Published in ICML 2023 Workshop Neural Compression: From Information Theory to Applications, 2023

This paper presents a novel approach to neural network optimization through weight evolution, achieving higher compression with minimal loss of accuracy compared to traditional magnitude pruning methods.

Recommended citation: Belhaouari, S. B., & Islam, A. (2023). "Neural Network Optimization with Weight Evolution." In ICML 2023 Workshop Neural Compression: From Information Theory to Applications.
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Pushing Boundaries: Exploring Zero-Shot Object Classification with Large Multimodal Models

Published in 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS), 2023

This paper investigates the efficacy of Large Multimodal Models (LMMs) in zero-shot object classification tasks, demonstrating their potential in achieving high accuracy across diverse datasets without fine-tuning.

Recommended citation: Islam, A., Biswas, M. R., Zaghouani, W., Belhaouari, S. B., & Shah, Z. (2023). "Pushing Boundaries: Exploring Zero-Shot Object Classification with Large Multimodal Models." In 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS) (pp. 1--5). IEEE. https://doi.org/10.1109/SNAMS60348.2023.10375440
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Oversampling Techniques for Imbalanced Data in Regression

Published in Expert Systems with Applications, 2024

This paper discusses novel oversampling techniques tailored specifically for handling imbalanced data in regression tasks. These methods improve predictive accuracy by addressing issues unique to regression models, such as skewed distributions and variance within minority data points.

Recommended citation: Belhaouari, S. B., Islam, A., Kassoul, K., Al-Fuqaha, A., & Bouzerdoum, A. (2024). "Oversampling Techniques for Imbalanced Data in Regression." Expert Systems with Applications, 252, 124118. https://doi.org/10.1016/j.eswa.2024.124118
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Introducing Novel Radon-Based Transform for Disease Detection From Chest X-Ray Images

Published in 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS), 2024

This paper presents a novel Radon-based transform, named RadEx, for enhancing feature extraction in chest X-ray images, significantly improving the accuracy of lung disease detection models.

Recommended citation: Islam, A., Mohsen, F., Shah, Z., & Belhaouari, S. B. (2024). "Introducing Novel Radon-Based Transform for Disease Detection From Chest X-Ray Images." In 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS) (pp. 1--5). IEEE. https://doi.org/10.1109/PAIS62114.2024.10541204
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talks

Pruning and Optimizing Large Language Models in an Era of GPU Scarcity

Published:

This talk, presented at the ICAI’24 as part of the 2024 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE’24), focuses on the critical issue of optimizing large language models (LLMs) during a time of GPU scarcity. The talk discusses novel pruning techniques, including “evolution of weights” and “smart pruning,” aimed at reducing the computational and environmental costs associated with training and deploying these models.

teaching

Teaching Assistant

Graduate course, Hamad Bin Khalifa University, College Of Science & Engineering, 2022

Machine Learning Course Support

Deep Fake Detection Using CNN

Personal Course, Remote, 2024

Week 1: Introduction to Deep Learning and Deepfakes

Topics:

  • Overview of deep learning, neural networks, and their applications
  • Introduction to deepfakes: history, types, and impact on society
  • How AI generates deepfakes using GANs (Generative Adversarial Networks)

Homework/Practice:

  • Watch introductory videos on deep learning
  • Read articles on GANs and deepfake ethics