arXiv subjects

Md Sultanul Islam Ovi

Publications and source records attributed to Md Sultanul Islam Ovi.

7 recordsLinked to original sources

arXiv2026

SkySeaLand: A Wide-Format Satellite Transportation Benchmark with an Ultra-Lightweight Detection Baseline

Satellite object detection is challenged by small targets and wide-format scenes that lose detail under standard square-input resizing. We introduce SkySeaLand, a public dataset of 1,307 high-resolution satellite images and 19,101 verified bounding boxes across airplane, boat, car, and ship classes in terrestrial and maritime scenes. Native COCO and YOLO annotations are provided. The collection is dominated by large source images and wide scene geometry: 84.5 percent exceed 3,836 pixels on the longest side and 73.1 percent are near a 3:1 aspect ratio. We evaluate twelve detectors from the YOLO, RT-DETR, DETR, and Faster R-CNN families using a common split and COCO metrics. The tested YOLO and RT-DETR variants obtain 84.4--88.2 mAP50, with no consistent accuracy gain from larger parameter counts under the reported model-specific recipes. We also report SkyDet, a 1.22 M parameter anchor-free baseline that obtains 60.5 mAP50 and 24.32 mAP50-95 in a 4.90 MB footprint, with 13.74 ms latency (72.8 FPS) on a Tesla T4. SkySeaLand provides a compact benchmark for mixed land--maritime transportation detection, while SkyDet establishes a documented low-footprint reference rather than a state-of-the-art accuracy claim.

cs.CV↗

arXiv2026

A Dual Pipeline Machine Learning Framework for Automated Multi Class Sleep Disorder Screening Using Hybrid Resampling and Ensemble Learning

Accurate classification of sleep disorders, particularly insomnia and sleep apnea, is important for reducing long term health risks and improving patient quality of life. However, clinical sleep studies are resource intensive and are difficult to scale for population level screening. This paper presents a Dual Pipeline Machine Learning Framework for multi class sleep disorder screening using the Sleep Health and Lifestyle dataset. The framework consists of two parallel processing streams: a statistical pipeline that targets linear separability using Mutual Information and Linear Discriminant Analysis, and a wrapper based pipeline that applies Boruta feature selection with an autoencoder for non linear representation learning. To address class imbalance, we use the hybrid SMOTETomek resampling strategy. In experiments, Extra Trees and K Nearest Neighbors achieved an accuracy of 98.67%, outperforming recent baselines on the same dataset. Statistical testing using the Wilcoxon Signed Rank Test indicates that the improvement over baseline configurations is significant, and inference latency remains below 400 milliseconds. These results suggest that the proposed dual pipeline design supports accurate and efficient automated screening for non invasive sleep disorder risk stratification.

cs.LG↗

arXiv2026

Performance Characterization of Distributed Deep Learning Strategies: A Quantitative Evaluation of DDP, FSDP, and Parameter Server Architectures on GPU Clusters

Efficiently scaling deep neural networks across GPU clusters requires navigating complex trade-offs between computational throughput, memory utilization, and synchronization overhead. This paper presents a unified empirical evaluation of three dominant distributed training paradigms: Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), and the Parameter Server (PS) architecture. We conduct side-by-side benchmarking on both high-performance (NVIDIA A100) and commodity-class (NVIDIA A10G) clusters to isolate the impact of communication bandwidth and gang-scheduling dependencies. Our results indicate that while DDP achieves a 2-3x speedup in training throughput for standard architectures, FSDP demonstrates a 4-6x reduction in peak memory usage, validating its utility for memory-constrained environments despite higher communication latency. Furthermore, we evaluate the elasticity of the Parameter Server architecture; while Asynchronous PS reduced training time by up to 28% compared to synchronous approaches, it incurred significant accuracy penalties (ranging from 4% to 17%) due to gradient staleness. We also analyze a modified, staleness-mitigating asynchronous protocol, which we found introduced synchronization overheads that negated throughput gains. These findings provide a decision framework for system designers, highlighting that while DDP remains optimal for homogeneous, gang-scheduled clusters, FSDP and PS offer critical alternatives for memory-bound and heterogeneous environments respectively.

cs.DC↗

arXiv2025

Quantitative Currency Evaluation in Low-Resource Settings through Pattern Analysis to Assist Visually Impaired Users

Currency recognition systems often overlook usability and authenticity assessment, especially in low-resource environments where visually impaired users and offline validation are common. While existing methods focus on denomination classification, they typically ignore physical degradation and forgery, limiting their applicability in real-world conditions. This paper presents a unified framework for currency evaluation that integrates three modules: denomination classification using lightweight CNN models, damage quantification through a novel Unified Currency Damage Index (UCDI), and counterfeit detection using feature-based template matching. The dataset consists of over 82,000 annotated images spanning clean, damaged, and counterfeit notes. Our Custom_CNN model achieves high classification performance with low parameter count. The UCDI metric provides a continuous usability score based on binary mask loss, chromatic distortion, and structural feature loss. The counterfeit detection module demonstrates reliable identification of forged notes across varied imaging conditions. The framework supports real-time, on-device inference and addresses key deployment challenges in constrained environments. Results show that accurate, interpretable, and compact solutions can support inclusive currency evaluation in practical settings.

cs.CV↗

arXiv2025

Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring

Student mental health is an increasing concern in academic institutions, where stress can severely impact well-being and academic performance. Traditional assessment methods rely on subjective surveys and periodic evaluations, offering limited value for timely intervention. This paper introduces a context-aware machine learning framework for classifying student stress using two complementary survey-based datasets covering psychological, academic, environmental, and social factors. The framework follows a six-stage pipeline involving preprocessing, feature selection (SelectKBest, RFECV), dimensionality reduction (PCA), and training with six base classifiers: SVM, Random Forest, Gradient Boosting, XGBoost, AdaBoost, and Bagging. To enhance performance, we implement ensemble strategies, including hard voting, soft voting, weighted voting, and stacking. Our best models achieve 93.09% accuracy with weighted hard voting on the Student Stress Factors dataset and 99.53% with stacking on the Stress and Well-being dataset, surpassing previous benchmarks. These results highlight the potential of context-integrated, data-driven systems for early stress detection and underscore their applicability in real-world academic settings to support student well-being.

cs.LG↗

arXiv2024

Benchmarking ChatGPT, Codeium, and GitHub Copilot: A Comparative Study of AI-Driven Programming and Debugging Assistants

With the increasing adoption of AI-driven tools in software development, large language models (LLMs) have become essential for tasks like code generation, bug fixing, and optimization. Tools like ChatGPT, GitHub Copilot, and Codeium provide valuable assistance in solving programming challenges, yet their effectiveness remains underexplored. This paper presents a comparative study of ChatGPT, Codeium, and GitHub Copilot, evaluating their performance on LeetCode problems across varying difficulty levels and categories. Key metrics such as success rates, runtime efficiency, memory usage, and error-handling capabilities are assessed. GitHub Copilot showed superior performance on easier and medium tasks, while ChatGPT excelled in memory efficiency and debugging. Codeium, though promising, struggled with more complex problems. Despite their strengths, all tools faced challenges in handling harder problems. These insights provide a deeper understanding of each tool's capabilities and limitations, offering guidance for developers and researchers seeking to optimize AI integration in coding workflows.

cs.SE↗

arXiv2024

PhishGuard: A Multi-Layered Ensemble Model for Optimal Phishing Website Detection

Phishing attacks are a growing cybersecurity threat, leveraging deceptive techniques to steal sensitive information through malicious websites. To combat these attacks, this paper introduces PhishGuard, an optimal custom ensemble model designed to improve phishing site detection. The model combines multiple machine learning classifiers, including Random Forest, Gradient Boosting, CatBoost, and XGBoost, to enhance detection accuracy. Through advanced feature selection methods such as SelectKBest and RFECV, and optimizations like hyperparameter tuning and data balancing, the model was trained and evaluated on four publicly available datasets. PhishGuard outperformed state-of-the-art models, achieving a detection accuracy of 99.05% on one of the datasets, with similarly high results across other datasets. This research demonstrates that optimization methods in conjunction with ensemble learning greatly improve phishing detection performance.

cs.CR↗