arXiv subjects

Sabbir Ahmed

Publications and source records attributed to Sabbir Ahmed.

At least 37 records · Page 2Linked to original sources

arXiv2025

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer

True Random Number Generators (TRNGs) play a fundamental role in hardware security, cryptographic systems, and data protection. In the context of Deep NeuralNetworks (DNNs), safeguarding model parameters, particularly weights, is critical to ensure the integrity, privacy, and intel-lectual property of AI systems. While software-based pseudo-random number generators are widely used, they lack the unpredictability and resilience offered by hardware-based TRNGs. In this work, we propose a novel and robust Encoding-in-Memory TRNG called EIM-TRNG that leverages the inherent physical randomness in DRAM cell behavior, particularly under RowHammer-induced disturbances, for the first time. We demonstrate how the unpredictable bit-flips generated through carefully controlled RowHammer operations can be harnessed as a reliable entropy source. Furthermore, we apply this TRNG framework to secure DNN weight data by encoding via a combination of fixed and unpredictable bit-flips. The encrypted data is later decrypted using a key derived from the probabilistic flip behavior, ensuring both data confidentiality and model authenticity. Our results validate the effectiveness of DRAM-based entropy extraction for robust, low-cost hardware security and offer a promising direction for protecting machine learning models at the hardware level.

cs.CR↗

arXiv2025

Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model

Visual hallucinations in Large Language Models (LLMs), where the model generates responses that are inconsistent with the visual input, pose a significant challenge to their reliability, particularly in contexts where precise and trustworthy outputs are critical. Current research largely emphasizes post-hoc correction or model-specific fine-tuning strategies, with limited exploration of preprocessing techniques to address hallucination issues at the input stage. This study presents a novel ensemble-based preprocessing framework that adaptively selects the most appropriate filtering approach -- noise reduced (NR), edge enhanced (EE), or unaltered input (org) based on the type of question posed, resulting into reduced hallucination without requiring any modifications to the underlying model architecture or training pipeline. Evaluated on the `HaloQuest' dataset -- a benchmark designed to test multimodal reasoning on visually complex inputs, our method achieves a 44.3% reduction in hallucination rates, as measured by Natural Language Inference (NLI) scores using SelfCheckGPT. This demonstrates that intelligent input conditioning alone can significantly enhance factual grounding in LLM responses. The findings highlight the importance of adaptive preprocessing techniques in mitigating hallucinations, paving the way for more reliable multimodal systems capable of addressing real-world challenges.

cs.CV↗

arXiv2025

Performance Analysis of Few-Shot Learning Approaches for Bangla Handwritten Character and Digit Recognition

This study investigates the performance of few-shot learning (FSL) approaches in recognizing Bangla handwritten characters and numerals using limited labeled data. It demonstrates the applicability of these methods to scripts with intricate and complex structures, where dataset scarcity is a common challenge. Given the complexity of Bangla script, we hypothesize that models performing well on these characters can generalize effectively to languages of similar or lower structural complexity. To this end, we introduce SynergiProtoNet, a hybrid network designed to improve the recognition accuracy of handwritten characters and digits. The model integrates advanced clustering techniques with a robust embedding framework to capture fine-grained details and contextual nuances. It leverages multi-level (both high- and low-level) feature extraction within a prototypical learning framework. We rigorously benchmark SynergiProtoNet against several state-of-the-art few-shot learning models: BD-CSPN, Prototypical Network, Relation Network, Matching Network, and SimpleShot, across diverse evaluation settings including Monolingual Intra-Dataset Evaluation, Monolingual Inter-Dataset Evaluation, Cross-Lingual Transfer, and Split Digit Testing. Experimental results show that SynergiProtoNet consistently outperforms existing methods, establishing a new benchmark in few-shot learning for handwritten character and digit recognition. The code is available on GitHub: https://github.com/MehediAhamed/SynergiProtoNet.

cs.CV↗

arXiv2025

MangoLeafViT: Leveraging Lightweight Vision Transformer with Runtime Augmentation for Efficient Mango Leaf Disease Classification

Ensuring food safety is critical due to its profound impact on public health, economic stability, and global supply chains. Cultivation of Mango, a major agricultural product in several South Asian countries, faces high financial losses due to different diseases, affecting various aspects of the entire supply chain. While deep learning-based methods have been explored for mango leaf disease classification, there remains a gap in designing solutions that are computationally efficient and compatible with low-end devices. In this work, we propose a lightweight Vision Transformer-based pipeline with a self-attention mechanism to classify mango leaf diseases, achieving state-of-the-art performance with minimal computational overhead. Our approach leverages global attention to capture intricate patterns among disease types and incorporates runtime augmentation for enhanced performance. Evaluation on the MangoLeafBD dataset demonstrates a 99.43% accuracy, outperforming existing methods in terms of model size, parameter count, and FLOPs count.

cs.CV↗

arXiv2025

Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains

Semi-Supervised Federated Learning (SSFL) is gaining popularity over conventional Federated Learning in many real-world applications. Due to the practical limitation of limited labeled data on the client side, SSFL considers that participating clients train with unlabeled data, and only the central server has the necessary resources to access limited labeled data, making it an ideal fit for real-world applications (e.g., healthcare). However, traditional SSFL assumes that the data distributions in the training phase and testing phase are the same. In practice, however, domain shifts frequently occur, making it essential for SSFL to incorporate generalization capabilities and enhance their practicality. The core challenge is improving model generalization to new, unseen domains while the client participate in SSFL. However, the decentralized setup of SSFL and unsupervised client training necessitates innovation to achieve improved generalization across domains. To achieve this, we propose a novel framework called the Unified Alignment Protocol (UAP), which consists of an alternating two-stage training process. The first stage involves training the server model to learn and align the features with a parametric distribution, which is subsequently communicated to clients without additional communication overhead. The second stage proposes a novel training algorithm that utilizes the server feature distribution to align client features accordingly. Our extensive experiments on standard domain generalization benchmark datasets across multiple model architectures reveal that proposed UAP successfully achieves SOTA generalization performance in SSFL setting.

cs.CV↗

arXiv2025

Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis

Understanding socio-academic and economic factors influencing students' performance is crucial for effective educational interventions. This study employs several machine learning techniques and causal analysis to predict and elucidate the impacts of these factors on academic performance. We constructed a hypothetical causal graph and collected data from 1,050 student profiles. Following meticulous data cleaning and visualization, we analyze linear relationships through correlation and variable plots, and perform causal analysis on the hypothetical graph. Regression and classification models are applied for prediction, and unsupervised causality analysis using PC, GES, ICA-LiNGAM, and GRASP algorithms is conducted. Our regression analysis shows that Ridge Regression achieve a Mean Absolute Error (MAE) of 0.12 and a Mean Squared Error (MSE) of 0.024, indicating robustness, while classification models like Random Forest achieve nearly perfect F1-scores. The causal analysis shows significant direct and indirect effects of factors such as class attendance, study hours, and group study on CGPA. These insights are validated through unsupervised causality analysis. By integrating the best regression model into a web application, we are developing a practical tool for students and educators to enhance academic outcomes based on empirical evidence.

cs.LG↗

arXiv2025

Performance Evaluation of Large Language Models in Bangla Consumer Health Query Summarization

Consumer Health Queries (CHQs) in Bengali (Bangla), a low-resource language, often contain extraneous details, complicating efficient medical responses. This study investigates the zero-shot performance of nine advanced large language models (LLMs): GPT-3.5-Turbo, GPT-4, Claude-3.5-Sonnet, Llama3-70b-Instruct, Mixtral-8x22b-Instruct, Gemini-1.5-Pro, Qwen2-72b-Instruct, Gemma-2-27b, and Athene-70B, in summarizing Bangla CHQs. Using the BanglaCHQ-Summ dataset comprising 2,350 annotated query-summary pairs, we benchmarked these LLMs using ROUGE metrics against Bangla T5, a fine-tuned state-of-the-art model. Mixtral-8x22b-Instruct emerged as the top performing model in ROUGE-1 and ROUGE-L, while Bangla T5 excelled in ROUGE-2. The results demonstrate that zero-shot LLMs can rival fine-tuned models, achieving high-quality summaries even without task-specific training. This work underscores the potential of LLMs in addressing challenges in low-resource languages, providing scalable solutions for healthcare query summarization.

cs.CL↗

arXiv2025

FUSED-Net: Detecting Traffic Signs with Limited Data

Automatic Traffic Sign Recognition is paramount in modern transportation systems, motivating several research endeavors to focus on performance improvement by utilizing large-scale datasets. As the appearance of traffic signs varies across countries, curating large-scale datasets is often impractical; and requires efficient models that can produce satisfactory performance using limited data. In this connection, we present 'FUSED-Net', built-upon Faster RCNN for traffic sign detection, enhanced by Unfrozen Parameters, Pseudo-Support Sets, Embedding Normalization, and Domain Adaptation while reducing data requirement. Unlike traditional approaches, we keep all parameters unfrozen during training, enabling FUSED-Net to learn from limited samples. The generation of a Pseudo-Support Set through data augmentation further enhances performance by compensating for the scarcity of target domain data. Additionally, Embedding Normalization is incorporated to reduce intra-class variance, standardizing feature representation. Domain Adaptation, achieved by pre-training on a diverse traffic sign dataset distinct from the target domain, improves model generalization. Evaluating FUSED-Net on the BDTSD dataset, we achieved 2.4x, 2.2x, 1.5x, and 1.3x improvements of mAP in 1-shot, 3-shot, 5-shot, and 10-shot scenarios, respectively compared to the state-of-the-art Few-Shot Object Detection (FSOD) models. Additionally, we outperform state-of-the-art works on the cross-domain FSOD benchmark under several scenarios.

cs.CV↗

arXiv2024

Compromising the Intelligence of Modern DNNs: On the Effectiveness of Targeted RowPress

Recent advancements in side-channel attacks have revealed the vulnerability of modern Deep Neural Networks (DNNs) to malicious adversarial weight attacks. The well-studied RowHammer attack has effectively compromised DNN performance by inducing precise and deterministic bit-flips in the main memory (e.g., DRAM). Similarly, RowPress has emerged as another effective strategy for flipping targeted bits in DRAM. However, the impact of RowPress on deep learning applications has yet to be explored in the existing literature, leaving a fundamental research question unanswered: How does RowPress compare to RowHammer in leveraging bit-flip attacks to compromise DNN performance? This paper is the first to address this question and evaluate the impact of RowPress on DNN applications. We conduct a comparative analysis utilizing a novel DRAM-profile-aware attack designed to capture the distinct bit-flip patterns caused by RowHammer and RowPress. Eleven widely-used DNN architectures trained on different benchmark datasets deployed on a Samsung DRAM chip conclusively demonstrate that they suffer from a drastically more rapid performance degradation under the RowPress attack compared to RowHammer. The difference in the underlying attack mechanism of RowHammer and RowPress also renders existing RowHammer mitigation mechanisms ineffective under RowPress. As a result, RowPress introduces a new vulnerability paradigm for DNN compute platforms and unveils the urgent need for corresponding protective measures.

cs.AR↗

arXiv2024

Precision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI

Gene expression analysis is a critical method for cancer classification, enabling precise diagnoses through the identification of unique molecular signatures associated with various tumors. Identifying cancer-specific genes from gene expression values enables a more tailored and personalized treatment approach. However, the high dimensionality of mRNA gene expression data poses challenges for analysis and data extraction. This research presents a comprehensive pipeline designed to accurately identify 33 distinct cancer types and their corresponding gene sets. It incorporates a combination of normalization and feature selection techniques to reduce dataset dimensionality effectively while ensuring high performance. Notably, our pipeline successfully identifies a substantial number of cancer-specific genes using a reduced feature set of just 500, in contrast to using the full dataset comprising 19,238 features. By employing an ensemble approach that combines three top-performing classifiers, a classification accuracy of 96.61% was achieved. Furthermore, we leverage Explainable AI to elucidate the biological significance of the identified cancer-specific genes, employing Differential Gene Expression (DGE) analysis.

q-bio.QM↗

arXiv2024

DNN-Defender: A Victim-Focused In-DRAM Defense Mechanism for Taming Adversarial Weight Attack on DNNs

With deep learning deployed in many security-sensitive areas, machine learning security is becoming progressively important. Recent studies demonstrate attackers can exploit system-level techniques exploiting the RowHammer vulnerability of DRAM to deterministically and precisely flip bits in Deep Neural Networks (DNN) model weights to affect inference accuracy. The existing defense mechanisms are software-based, such as weight reconstruction requiring expensive training overhead or performance degradation. On the other hand, generic hardware-based victim-/aggressor-focused mechanisms impose expensive hardware overheads and preserve the spatial connection between victim and aggressor rows. In this paper, we present the first DRAM-based victim-focused defense mechanism tailored for quantized DNNs, named DNN-Defender that leverages the potential of in-DRAM swapping to withstand the targeted bit-flip attacks with a priority protection mechanism. Our results indicate that DNN-Defender can deliver a high level of protection downgrading the performance of targeted RowHammer attacks to a random attack level. In addition, the proposed defense has no accuracy drop on CIFAR-10 and ImageNet datasets without requiring any software training or incurring hardware overhead.

cs.CR↗

arXiv2024

DRAM-Profiler: An Experimental DRAM RowHammer Vulnerability Profiling Mechanism

RowHammer stands out as a prominent example, potentially the pioneering one, showcasing how a failure mechanism at the circuit level can give rise to a significant and pervasive security vulnerability within systems. Prior research has approached RowHammer attacks within a static threat model framework. Nonetheless, it warrants consideration within a more nuanced and dynamic model. This paper presents a low-overhead DRAM RowHammer vulnerability profiling technique termed DRAM-Profiler, which utilizes innovative test vectors for categorizing memory cells into distinct security levels. The proposed test vectors intentionally weaken the spatial correlation between the aggressors and victim rows before an attack for evaluation, thus aiding designers in mitigating RowHammer vulnerabilities in the mapping phase. While there has been no previous research showcasing the impact of such profiling to our knowledge, our study methodically assesses 128 commercial DDR4 DRAM products. The results uncover the significant variability among chips from different manufacturers in the type and quantity of RowHammer attacks that can be exploited by adversaries.

cs.CR↗

arXiv2023

DRAM-Locker: A General-Purpose DRAM Protection Mechanism against Adversarial DNN Weight Attacks

In this work, we propose DRAM-Locker as a robust general-purpose defense mechanism that can protect DRAM against various adversarial Deep Neural Network (DNN) weight attacks affecting data or page tables. DRAM-Locker harnesses the capabilities of in-DRAM swapping combined with a lock-table to prevent attackers from singling out specific DRAM rows to safeguard DNN's weight parameters. Our results indicate that DRAM-Locker can deliver a high level of protection downgrading the performance of targeted weight attacks to a random attack level. Furthermore, the proposed defense mechanism demonstrates no reduction in accuracy when applied to CIFAR-10 and CIFAR-100. Importantly, DRAM-Locker does not necessitate any software retraining or result in extra hardware burden.

cs.AR↗

arXiv2023

FASTEN: Towards a FAult-tolerant and STorage EfficieNt Cloud: Balancing Between Replication and Deduplication

With the surge in cloud storage adoption, enterprises face challenges managing data duplication and exponential data growth. Deduplication mitigates redundancy, yet maintaining redundancy ensures high availability, incurring storage costs. Balancing these aspects is a significant research concern. We propose FASTEN, a distributed cloud storage scheme ensuring efficiency, security, and high availability. FASTEN achieves fault tolerance by dispersing data subsets optimally across servers and maintains redundancy for high availability. Experimental results show FASTEN's effectiveness in fault tolerance, cost reduction, batch auditing, and file and block-level deduplication. It outperforms existing systems with low time complexity, strong fault tolerance, and commendable deduplication performance.

cs.DC↗

arXiv2023

Threshold Breaker: Can Counter-Based RowHammer Prevention Mechanisms Truly Safeguard DRAM?

This paper challenges the existing victim-focused counter-based RowHammer detection mechanisms by experimentally demonstrating a novel multi-sided fault injection attack technique called Threshold Breaker. This mechanism can effectively bypass the most advanced counter-based defense mechanisms by soft-attacking the rows at a farther physical distance from the target rows. While no prior work has demonstrated the effect of such an attack, our work closes this gap by systematically testing 128 real commercial DDR4 DRAM products and reveals that the Threshold Breaker affects various chips from major DRAM manufacturers. As a case study, we compare the performance efficiency between our mechanism and a well-known double-sided attack by performing adversarial weight attacks on a modern Deep Neural Network (DNN). The results demonstrate that the Threshold Breaker can deliberately deplete the intelligence of the targeted DNN system while DRAM is fully protected.

cs.AR↗

arXiv2023

MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning

Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on mhealth. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F1 score. Our research indicated a promising future in mhealth being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.

cs.LG↗

arXiv2023

An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.

cs.CV↗

arXiv2022

Fruit Quality Assessment with Densely Connected Convolutional Neural Network

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.

cs.CV↗