arXiv subjects

Ratul Ali

Publications and source records attributed to Ratul Ali.

5 recordsLinked to original sources

arXiv2026

Operationalising Information Security Management: A Procedural Framework Analysis of ISO/IEC 27001:2022 Implementation in a Financial-Technology Organisation

Organisations operating within information-intensive environments face intensifying pressure to formalise the governance of information security. The ISO/IEC 27001:2022 standard provides a globally recognised framework for establishing, implementing, maintaining, and continually improving an Information Security Management System (ISMS). This article analyses the procedural architecture deployed in a financial-technology organisation's ISMS, examining eight core operational procedures: IT Risk Assessment and Treatment, User Code of Conduct, Password Policy, Access Control, Internet Access, Physical Security, Backup and Restore Management, and Nonconformity Root Cause Analysis and Corrective Action. Drawing on documented internal training materials, the article investigates how each procedure operationalises the requirements of Annex~A controls and Clauses~6--10 of ISO~27001:2022. The paper evaluates the CIA Triad as a unifying evaluation criterion, the twelve-step risk assessment methodology, role-based responsibility allocation, and the interplay between corrective action governance and continual improvement. The findings suggest that a tightly integrated, multi-layered procedural hierarchy, supported by clear accountability structures and measurable risk metrics, constitutes the foundation of an effective ISMS implementation in financial-technology operating environments.

cs.SE↗

arXiv2026

When Scaling Fails: Network and Fabric Effects on Distributed GPU Training Performance

Scaling distributed GPU training is commonly assumed to yield predictable performance gains as additional nodes are added. In practice, many large-scale deployments encounter diminishing returns and unstable behavior well before theoretical limits are reached. This paper examines why scaling fails in real systems, with a focus on the role of network and fabric effects that are often overlooked by higher-level training frameworks. We present an empirical study of distributed GPU training performance across multiple production-scale clusters. Our results show that network topology, congestion dynamics, collective synchronization behavior, and GPU locality frequently dominate end-to-end training performance once workloads move beyond a small number of nodes. Identical models and software stacks can exhibit sharply different scaling characteristics depending on fabric design and runtime communication patterns. We identify recurring failure modes that emerge as training transitions from single-node to multi-node execution, including synchronization amplification, topology-induced contention, and locality-driven performance variance. These effects are often invisible to standard profiling tools and are therefore misdiagnosed as framework or model-level inefficiencies. Based on these findings, we outline practical diagnostic principles that system builders can apply to understand scaling limits, improve predictability, and reduce the cost of large-scale distributed training.

cs.NI↗

arXiv2026

HQP: Sensitivity-Aware Hybrid Quantization and Pruning for Ultra-Low-Latency Edge AI Inference

The escalating demand for high-fidelity, real-time inference in distributed edge-cloud environments necessitates aggressive model optimization to counteract severe latency and energy constraints. This paper introduces the Hybrid Quantization and Pruning (HQP) framework, a novel, integrated methodology designed to achieve synergistic model acceleration while adhering to strict quality guarantees. We detail a sensitivity-aware structural pruning algorithm that employs a dynamic weight sensitivity metric, derived from a highly efficient approximation of the Fisher Information Matrix (FIM), to guide the iterative removal of redundant filters. This pruning is strictly conditional, enforcing an adherence to a maximum permissible accuracy drop (Delta ax) before the model proceeds to 8-bit post-training quantization. This rigorous coordination is critical, as it ensures the resultant sparse model structure is maximally robust to quantization error and hardware-specific kernel optimization. Exhaustive evaluation across heterogeneous NVIDIA Jetson edge platforms, utilizing resource-efficient architectures like MobileNetV3 and ResNet-18, demonstrates that the HQP framework achieves a peak performance gain of 3.12 times inference speedup and a 55 percent model size reduction, while rigorously containing the accuracy drop below the 1.5 percent constraint. A comprehensive comparative analysis against conventional single-objective compression techniques validates the HQP framework as a superior, hardware-agnostic solution for deploying ultra-low-latency AI in resource-limited edge infrastructures.

cs.DC↗

arXiv2026

Scalable and Secure AI Inference in Healthcare: A Comparative Benchmarking of FastAPI and Triton Inference Server on Kubernetes

Efficient and scalable deployment of machine learning (ML) models is a prerequisite for modern production environments, particularly within regulated domains such as healthcare and pharmaceuticals. In these settings, systems must balance competing requirements, including minimizing inference latency for real-time clinical decision support, maximizing throughput for batch processing of medical records, and ensuring strict adherence to data privacy standards such as HIPAA. This paper presents a rigorous benchmarking analysis comparing two prominent deployment paradigms: a lightweight, Python-based REST service using FastAPI, and a specialized, high-performance serving engine, NVIDIA Triton Inference Server. Leveraging a reference architecture for healthcare AI, we deployed a DistilBERT sentiment analysis model on Kubernetes to measure median (p50) and tail (p95) latency, as well as throughput, under controlled experimental conditions. Our results indicate a distinct trade-off. While FastAPI provides lower overhead for single-request workloads with a p50 latency of 22 ms, Triton achieves superior scalability through dynamic batching, delivering a throughput of 780 requests per second on a single NVIDIA T4 GPU, nearly double that of the baseline. Furthermore, we evaluate a hybrid architectural approach that utilizes FastAPI as a secure gateway for protected health information de-identification and Triton for backend inference. This study validates the hybrid model as a best practice for enterprise clinical AI and offers a blueprint for secure, high-availability deployments.

cs.AI↗

arXiv2023

ML-ASPA: A Contemplation of Machine Learning-based Acoustic Signal Processing Analysis for Sounds, & Strains Emerging Technology

Acoustic data serves as a fundamental cornerstone in advancing scientific and engineering understanding across diverse disciplines, spanning biology, communications, and ocean and Earth science. This inquiry meticulously explores recent advancements and transformative potential within the domain of acoustics, specifically focusing on machine learning (ML) and deep learning. ML, comprising an extensive array of statistical techniques, proves indispensable for autonomously discerning and leveraging patterns within data. In contrast to traditional acoustics and signal processing, ML adopts a data-driven approach, unveiling intricate relationships between features and desired labels or actions, as well as among features themselves, given ample training data. The application of ML to expansive sets of training data facilitates the discovery of models elucidating complex acoustic phenomena such as human speech and reverberation. The dynamic evolution of ML in acoustics yields compelling results and holds substantial promise for the future. The advent of electronic stethoscopes and analogous recording and data logging devices has expanded the application of acoustic signal processing concepts to the analysis of bowel sounds. This paper critically reviews existing literature on acoustic signal processing for bowel sound analysis, outlining fundamental approaches and applicable machine learning principles. It chronicles historical progress in signal processing techniques that have facilitated the extraction of valuable information from bowel sounds, emphasizing advancements in noise reduction, segmentation, signal enhancement, feature extraction, sound localization, and machine learning techniques...

cs.SD↗