Publications

2025

1. IEEE TCDS

   Delving deeper into astromorphic transformers

   Md Zesun Ahmed Mia, Malyaban Bal, and Abhronil Sengupta

   IEEE Transactions on Cognitive and Developmental Systems, 2025

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   Preliminary attempts at incorporating the critical role of astrocytes—cells that constitute more than 50% of human brain cells—in brain-inspired neuromorphic computing remain in infancy. This paper seeks to delve deeper into various key aspects of neuron-synapse-astrocyte interactions to mimic self-attention mechanisms in Transformers. The crosslayer perspective explored in this work involves bioplausible modeling of Hebbian and presynaptic plasticities in neuronastrocyte networks, incorporating effects of non-linearities and feedback along with algorithmic formulations to map the neuronastrocyte computations to self-attention mechanism and evaluating the impact of incorporating bio-realistic effects from the machine learning application side. Our analysis on sentiment and image classification tasks (IMDB and CIFAR10 datasets) highlights the advantages of Astromorphic Transformers, offering improved accuracy and learning speed. Furthermore, the model demonstrates strong natural language generation capabilities on the WikiText-2 dataset, achieving better perplexity compared to conventional models, thus showcasing enhanced generalization and stability across diverse machine learning tasks.

   @article{mia2025delving,
     title = {Delving deeper into astromorphic transformers},
     author = {Mia, Md Zesun Ahmed and Bal, Malyaban and Sengupta, Abhronil},
     journal = {IEEE Transactions on Cognitive and Developmental Systems},
     year = {2025},
     publisher = {IEEE},
     url = {https://ieeexplore.ieee.org/document/10976578/},
   }

2. arXiv

   Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning

   Md Zesun Ahmed Mia, Malyaban Bal, Siyuan Lu, and 4 more authors

   2025

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   Inspired by the brain’s hierarchical processing and energy efficiency, this paper presents a Spiking Neural Network (SNN) architecture for lifelong Network Intrusion Detection System (NIDS). The proposed system first employs an efficient static SNN to identify potential intrusions, which then activates an adaptive dynamic SNN responsible for classifying the specific attack type. Mimicking biological adaptation, the dynamic classifier utilizes Grow When Required (GWR)-inspired structural plasticity and a novel Adaptive Spike-Timing-Dependent Plasticity (Ad-STDP) learning rule. These bio-plausible mechanisms enable the network to learn new threats incrementally while preserving existing knowledge. Tested on the UNSW-NB15 benchmark in a continual learning setting, the architecture demonstrates robust adaptation, reduced catastrophic forgetting, and achieves 85.3% overall accuracy. Furthermore, simulations using the Intel Lava framework confirm high operational sparsity, highlighting the potential for low-power deployment on neuromorphic hardware.

   @misc{mia2025neuromorphic,
     title = {Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning},
     author = {Mia, Md Zesun Ahmed and Bal, Malyaban and Lu, Siyuan and Nishibuchi, Grace M. and Chelian, Srinivas and Vasan, Shantanu and Sengupta, Abhronil},
     year = {2025},
     eprint = {2508.04610},
     archiveprefix = {arXiv},
     primaryclass = {cs.CR},
     url = {https://arxiv.org/abs/2508.04610},
   }

2024

1. Matter

   Self-sensitizable neuromorphic device based on adaptive hydrogen gradient

   Tao Zhang, Mingjie Hu, Md Zesun Ahmed Mia, and 8 more authors

   Matter, 2024

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   Neuromorphic computing aims to achieve superior efficiency by simulating the human brain to surpass traditional computers. It is crucial for neuromorphic devices to possess excellent biological characteristics, offering a vital way to address existing technical bottlenecks in artificial intelligence (AI). Here, we demonstrate adaptive self-sensitization based on a hydrogen gradient in artificial neurons, empowering neural networks to effectively tackle long-standing challenges of model failure in unknown situations beyond pre-defined boundaries. This advancement propels the design of adaptive neuromorphic devices to process unforeseeable signals autonomously. It significantly promotes the development of AI capable of interacting with complex environments, enhancing capabilities to perform tasks such as autonomous navigation, disaster rescue, and outer space exploration and opening up new possibilities for self-guided cognitive systems.

   @article{zhang2024self,
     title = {Self-sensitizable neuromorphic device based on adaptive hydrogen gradient},
     author = {Zhang, Tao and Hu, Mingjie and Mia, Md Zesun Ahmed and Zhang, Hao and Mao, Wei and Fukutani, Katsuyuki and Matsuzaki, Hiroyuki and Wen, Lingzhi and Wang, Cong and Zhao, Hongbo and others},
     journal = {Matter},
     volume = {7},
     number = {5},
     pages = {1799--1816},
     year = {2024},
     publisher = {Elsevier},
     url = {https://www.cell.com/matter/fulltext/S2590-2385(24)00108-5},
   }

2. OpenReview

   RMAAT: A Bio-Inspired Approach for Efficient Long-Context Sequence Processing in Transformers

   Md Zesun Ahmed Mia, Malyaban Bal, and Abhronil Sengupta

   2024

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   Astrocytes, an essential component of the brain’s neural circuitry, demonstrate learning capabilities through bioplausible mechanisms such as presynaptic plasticity and hebbian plasticity. However, their integration into computational models remains underexplored. This paper advances astromorphic computing techniques to emulate transformer self-attention mechanisms, leveraging astrocytic nonlinearity and memory retention to improve long-range dependency processing in machine learning and natural language processing (NLP) tasks. Existing transformer models have difficulty handling lengthy contexts with thousands of tokens, even with substantial computational resources. We propose Recurrent Memory Augmented Astromorphic Transformers (RMAAT), integrating astrocytic memory and recurrent processing into self-attention, enabling longer context handling without quadratic complexity growth. Our bioplausible model has been found to outperform traditional transformers in experimental tests conducted on the Long Range Arena benchmark and IMDB dataset. Specifically, our model achieves a significant reduction in memory utilization and computational latency. This paves the way for biologically inspired AI models by illustrating how astrocytic characteristics may enhance the performance and efficiency of computational models.

   @misc{mia2024rmaat,
     title = {{RMAAT}: A Bio-Inspired Approach for Efficient Long-Context Sequence Processing in Transformers},
     author = {Mia, Md Zesun Ahmed and Bal, Malyaban and Sengupta, Abhronil},
     year = {2024},
     url = {https://openreview.net/forum?id=ikSrEv8FId},
   }

3. iCACCESS

   Ultra Low Cost, Low Power, High Speed Electronic Braille Device for Visually Impaired People

   Md Zesun Ahmed Mia and Kazi Toukir Ahmed

   In 2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS), 2024

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   Highly advanced and sophisticated braille devices are always sought after, however, the expenses associated with them surpass the advantages for the majority of individuals with vision impairments. In this paper, we design an affordable, customized, and rechargeable braille pad utilizing solenoids that have the capability to read text quickly and with precise control. The device is constructed with great precision and compactness using 3D printing methodologies. A microcontroller is precisely installed within the gadget to control the entire process. The device can be easily handled in the palm of a hand and the sensitivity may be modified as well. In addition to its mobility, this device has a weight of only 338 grams and is capable of translating text into braille from both cellular devices and laptops. This specific structure serves as a prime example in reducing electricity usage and enhancing productivity.

   @inproceedings{mia2024ultra,
     title = {Ultra Low Cost, Low Power, High Speed Electronic Braille Device for Visually Impaired People},
     author = {Mia, Md Zesun Ahmed and Ahmed, Kazi Toukir},
     booktitle = {2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)},
     pages = {1--6},
     year = {2024},
     organization = {IEEE},
     url = {https://ieeexplore.ieee.org/document/10499456/},
   }

4. ICECE

   Impact of Doping and Defects on Thermal Transport of Monolayer GaN Nanoribbons: A Molecular Dynamics Simulation Study

   Md Zesun Ahmed Mia, Nazmus Saadat As-Saquib, Mehedi Hasan Himel, and 2 more authors

   In 2024 13th International Conference on Electrical and Computer Engineering (ICECE), 2024

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   Monolayer gallium nitride (GaN), with its graphenelike honeycomb structure, has emerged as a promising material for nanoelectronics and optoelectronic applications. In this study, Equilibrium Molecular Dynamics (EMD) simulations using the Stillinger-Weber (SW) potential were employed to investigate the impact of doping as well as defects on the thermal conductivity of monolayer zigzag GaN nanoribbon (GaN-NR). The thermal transport in aluminum (Al) and indium (In) doped GaN-NR is explored, considering the substitution of both Ga and nitrogen (N) atoms by the dopants. Ga replacement with In led to an increase in thermal conductivity from 11.51 W/m−K to 15.12 W/m−K at a 1% doping concentration. However, in all other cases, whether replacing Ga or N with Al or replacing N with In, it shows an opposite trend, i.e., the thermal conductivity decreases. Furthermore, defects lead to a substantial reduction in thermal conductivity, with a decrease ranging from 68.9% to 77.4% observed at a 2% vacancy concentration across various defect shapes, from point vacancies to hexagon vacancies. We further examine the effect of defect concentrations and defect types on its thermal transport. The results provide valuable insights into the tunability of thermal conductivity in GaN nanostructures, illuminating future optimization for applications in optoelectronic and thermoelectric devices.

   @inproceedings{mia2024impact,
     title = {Impact of Doping and Defects on Thermal Transport of Monolayer GaN Nanoribbons: A Molecular Dynamics Simulation Study},
     author = {Mia, Md Zesun Ahmed and As-Saquib, Nazmus Saadat and Himel, Mehedi Hasan and Limon, Mehedi Hossen and Subrina, Samia},
     booktitle = {2024 13th International Conference on Electrical and Computer Engineering (ICECE)},
     pages = {685--690},
     year = {2024},
     organization = {IEEE},
     url = {https://ieeexplore.ieee.org/document/11024945/},
   }

2022

1. ISIEA

   Study of 3-nm Cylindrical GAAFETs with Variations in High-k Dielectric Gate-oxide Materials

   KM Ashraful Hoque Fahim, Md Jubair Hasan Khalid, Md Zesun Ahmed Mia, and 1 more author

   In 2022 IEEE Symposium on Industrial Electronics & Applications (ISIEA), 2022

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   Semiconductor devices using high k dielectric materials are widely adopted in memory and amplifier applications. Among the semiconductor devices gate all around-FET (GAAFET) is now the latest trend being used instead of other field effect transistors to serve the purpose of reducing the short channel effects (SCE). In this work, we examine the performance of a circular cross-section gate all around-field effect transistor (GAA-FET) with varying gate dielectric characteristics with high-k dielectric oxide materials (Al2O3, HfO2, HfSiO4, SiO2, Ta2O5, TiO2) across the 3-nm channel length. These simulations showed that even though the dielectric constant over the channel increases in value, both ION-IOFF ratio and transconductance upsurge. The obtained results indicated that raising the dielectric constant in a gate oxide reduces subthreshold slope (SS), increases amplification rate, and reduces threshold voltage (VTH) roll-off as well. The Silvaco TCAD ATLAS simulation was calibrated against experimental data from different works of literature. The higher the dielectric constant, the lower the SCEs. It is also found that TiO2 is dominating over the other materials selected for the simulation for a higher value of dielectric constant.

   @inproceedings{fahim2022study,
     title = {Study of 3-nm Cylindrical GAAFETs with Variations in High-k Dielectric Gate-oxide Materials},
     author = {Fahim, KM Ashraful Hoque and Khalid, Md Jubair Hasan and Mia, Md Zesun Ahmed and Rasheduzzaman, Mirza},
     booktitle = {2022 IEEE Symposium on Industrial Electronics \& Applications (ISIEA)},
     pages = {1--5},
     year = {2022},
     organization = {IEEE},
     url = {https://ieeexplore.ieee.org/document/9873651/},
   }

2. ICO

   DCNN-LSTM based audio classification combining multiple feature engineering and data augmentation techniques

   Md Moinul Islam, Monjurul Haque, Saiful Islam, and 2 more authors

   In Intelligent Computing & Optimization: Proceedings of the 4th International Conference on Intelligent Computing and Optimization 2021 (ICO2021) 3, 2022

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   Everything we know is based on our brain’s ability to process sensory data. Hearing is a crucial sense for our ability to learn. Sound is essential for a wide range of activities such as exchanging information, interacting with others, and so on. To convert the sound electrically, the role of the audio signal comes into play. Because of the countless essential applications, audio signal & their classification poses an important value. However, in this day and age, classifying audio signals remains a difficult task. To classify audio signals more accurately and effectively, we have proposed a new model. In this study, we’ve applied a brand-new method for audio classification that combines the strengths of Deep Convolutional Neural Network (DCNN) and Long-Short Term Memory (LSTM) models with a unique combination of feature engineering to get the best possible outcome. Here, we have integrated data augmentation and feature extraction together before fitting it into the model to evaluate the performance. There is a higher degree of accuracy observed after the experiment. To validate the efficacy of our model, a comparative analysis has been made with the latest conducted reference works.

   @inproceedings{islam2022dcnn,
     title = {DCNN-LSTM based audio classification combining multiple feature engineering and data augmentation techniques},
     author = {Islam, Md Moinul and Haque, Monjurul and Islam, Saiful and Mia, Md Zesun Ahmed and Rahman, SMA Mohaiminur},
     booktitle = {Intelligent Computing \& Optimization: Proceedings of the 4th International Conference on Intelligent Computing and Optimization 2021 (ICO2021) 3},
     pages = {227--236},
     year = {2022},
     organization = {Springer},
     url = {https://link.springer.com/chapter/10.1007/978-3-030-93247-3_23},
   }

2021

1. ICCIT

   Irfd: A feature engineering based ensemble classification for detecting electricity fraud in traditional meters

   Md Zesun Ahmed Mia, Md Moinul Islam, Monjurul Haque, and 2 more authors

   In 2021 24th International Conference on Computer and Information Technology (ICCIT), 2021

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   Nations have suffered significant economic losses as a result of non-technical electric losses resulting from power fraud. It is a criminal act of stealing electricity by applying various mechanisms that incorporate unauthorized tapping to the power line, bypassing the smart meter, etc. Electricity theft is a significant concern for not only developing countries but also developed countries as well. However, for most developing countries, the implications are catastrophic, given that their usage is always less than their demands. Electricity theft must be detected precisely and quickly in order to be mitigated. In our study, we have proposed a method of predictive ensemble machine learning techniques (IRFD) with a novel combination of feature distinction methods to detect electricity theft. In our proposed model, we have combined feature selection technique, Recursive Feature Elimination with Stratified 10-Fold cross-validation (RFECV) and Isolation Forest (IF), to identify and remove outliers along with several machine learning classifiers to forecast the theft of electricity. This study additionally enhances the management of highly imbalanced fraudulent data with Borderline-SMOTE with SVM (SVMSMOTE) and feature scaling with StandardScaler. Following the study, the Random Forest classifier observed a higher degree of accuracy (97.06%) with higher precision, recall, and F1-Score. To evaluate the efficacy of our proposed model, comparative analysis of the classification metrics is also assessed with several machine learning classifiers like Logistic Regression, Gradient Boosting, XGBoost, AdaBoost, KNN, ANN, along with Random Forest before and after fitting our proposed feature engineering techniques.

   @inproceedings{mia2021irfd,
     title = {Irfd: A feature engineering based ensemble classification for detecting electricity fraud in traditional meters},
     author = {Mia, Md Zesun Ahmed and Islam, Md Moinul and Haque, Monjurul and Islam, Saiful and Rahman, SMA Mohaiminur},
     booktitle = {2021 24th International Conference on Computer and Information Technology (ICCIT)},
     pages = {1--6},
     year = {2021},
     organization = {IEEE},
     url = {https://ieeexplore.ieee.org/document/9689842/},
   }