Document classification is an essential process of natural language processing (NLP) which presupposes assigning textual documents to predefined categories depending on their contents. As the amount of digital text that needs to be classified has grown exponentially through the sources of social media, scholarly repositories, law archives, news portals and enterprise document management systems, effective and correct document classification has become more important. Most of the common machine learning models such as Naïve Bayes, Support Vector Machines, and k-Nearest Neighbors have proven to be of acceptable performance but they heavily depend on manually crafted features and are not as accurate in detecting semantic and contextual information in text. The latest technology in deep learning greatly altered the methods of document classification as it allowed extracting features and learning representations based on the context. Convolutional neural networks (CNNs) models, recurrent neural networks (RNNs), Long Short Memory networks (LSTMs), Gated Recurrent Units (GRUs) and transformer-based have been used to set the state of the art on benchmark datasets. Such models employ dense word encodings, attention, and hierarchical models to represent document-level semantic structures on both local and global levels. In the present paper, the systematic investigation of the document classification frameworks using deep learning models is offered. It analyzes background information, architectural design, learning process and optimization plans. Moreover, it evaluates the advantages and weaknesses of the different deep learning methods in processing long texts, multi-label classification, domain adaptation, and scalability. The socio-economic metrics are also standard performance metrics, and a single approach to the methodology is suggested that incorporates preprocessing, embedding learning, model training, and evaluation using these metrics. The results of the experiment when exploring representative datasets are addressed to emphasize the trends of the comparative performance. The paper will end by presenting some of the current challenges and the direction of future research and stressing the aspects of explainability, efficiency, and domain robustness.
S. Rahman· International Journal of App...· 0 citations
The integration of machine learning (ML) models into distributed systems has become pivotal for applications requiring real-time data processing and decision-making. This paper investigates methodologies to enhance distributed architectures for the efficient deployment and management of ML models in real-time environments. We explore the challenges associated with latency, scalability, and fault tolerance, and propose solutions leveraging edge computing, federated learning, and dynamic orchestration. Through empirical evaluations, we demonstrate the efficacy of the proposed approaches in optimizing real-time ML workflows.
S. Rahman· International Journal of Art...· 0 citations
Digital transformation has become essential for organizations competing in a data-driven economy, driven largely by the integration of Artificial Intelligence (AI) and advanced data platforms. These technologies enable smart automation, predictive analytics, and real-time decision-making. This paper presents digital transformation as a multi-dimensional process involving organizational culture, business processes, and technological infrastructure, with AI-powered data platforms at its core. It reviews key technological developments prior to 2019, including cloud computing, big data frameworks like Hadoop and Spark, and early enterprise AI adoption. Current research emphasizes the importance of data governance, scalability, and interoperability. The paper proposes a structured implementation approach covering data collection, preprocessing, model development, deployment, and continuous optimization, supported by a flow-based architecture. Findings show that organizations adopting AI-enabled platforms achieve up to 45% improvement in operational efficiency and a 35% reduction in decision-making delays. The study concludes by stressing the need to align AI initiatives with business goals and highlights future directions such as autonomous systems and ethical AI practices.
S. Rahman· International Journal of Art...· 0 citations
Software architectural drift arises when a software system’s implementation gradually diverges from its intended architectural design, leading to reduced maintainability, increased technical debt, and higher evolution costs. Traditional drift detection methods rely heavily on manual analysis, rule-based constraints, static metrics, or architectural conformance checking, all of which struggle to capture complex and evolving structural dependencies. This paper proposes a Graph Neural Network (GNN-based) predictive framework for modeling software architecture as heterogeneous dependency graphs and identifying potential drift before it manifests in code-level violations. The approach integrates structural features, semantic code embeddings, version-history evolution patterns, and architectural constraints into a unified graph learning model. Experimental evaluation on open-source and industrial projects demonstrates that the proposed GNN model outperforms conventional static analysis and machine learning baselines in predicting architectural deviations, detecting anomalous dependency formations, and flagging early indicators of structural degradation. The study contributes a scalable, learning-based methodology for proactive architectural governance and long-term software quality preservation.
S. Rahman· International Journal of Art...· 0 citations
The analysis of performance has shown that the proposed solution is much better in terms of throughput, latency and fault isolation than the conventional architectures, proving that microservices architecture with proper design presents a solid base of scalable data-intensive systems.
S. Rahman· International Journal of App...· 0 citations
This paper explores various deployment models of edge AI tailored for real-time industrial automation feedback systems, and analyses on-device, edge gateway, and hybrid edge-cloud approaches, discussing their architectures, benefits, limitations, and real-world applicability.
Fatima Noor, S. Rahman· International Journal of Mac...· 0 citations
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