Journal of Advances in Shell Programming
Volume 12, Issue 2 (2025)
Table of contents
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Role of Beowulf Clusters in Next-Generation Military Applications: A Comprehensive Study
Abstract: Beowulf clusters, which utilize cost-effective commodity hardware combined with open-source software for parallel computing, have emerged as a viable and efficient solution for high-performance computing needs. This paper explores their growing relevance and practical applications in modern and future military technologies. Contemporary military operations increasingly rely on rapid data processing, real-time intelligence, high-fidelity simulations, and autonomous decision-making systems. Beowulf clusters offer scalable and adaptable computational power that supports these demands …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 01–07 Read article
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Critical Review of Impact of UNIX in Development of Military Computing Paradigms
Abstract: The evolution of military computing systems has been profoundly influenced by the integration and continuous advancement of UNIX operating systems since their inception in the early 1970s. This paper presents a comprehensive review of the pivotal role UNIX has played in shaping the foundational architecture of modern military computing—spanning areas such as command-and-control infrastructures, real-time data processing, mission-critical software, and cybersecurity protocols. By tracing the historical development and technical progression …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 08–16 Read article
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Machine Learning Pipelines: A Survey on Automation, Scalability, and Deployment Strategies
Abstract: Machine learning (ML) has become a critical enabler of intelligent applications across domains, requiring robust, efficient, and scalable deployment workflows. This review paper provides an in-depth overview of machine learning pipelines, emphasizing three key dimensions: automation, scalability, and deployment methodologies. It begins by exploring automation techniques that reduce manual effort in data ingestion, preprocessing, model selection, and hyperparameter tuning. Tools such as AutoML, TFX, and workflow orchestration platforms are examined …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 17–28 Read article
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Harnessing Machine Learning for Stock Movement Prediction: A Review of Current Approaches
Abstract: Stock price prediction is a crucial task in financial analysis, aiding investors and traders in making informed decisions. This study investigates the use of deep learning methods, particularly Long Short-Term Memory (LSTM) networks, for predicting stock prices based on historical market data. The dataset, sourced from Yahoo Finance, consists of time-series stock price data, which is preprocessed, feature-engineered, and visualized to improve prediction accuracy. The model's performance is assessed using …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 29–40 Read article
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Shell Programming with Sensor Systems and Applications for Human Cognition
Abstract: Shell programming provides a flexible interface to sensor systems, enabling robust scripting for real-time data acquisition and processing, as well as integration with perception components. Such sensors have human analogs—tactile, physiological, and behavioral—and are increasingly embedded within health, mobile, and smart environments to capture fine-grained details of human cognition. Using shell scripts, the system can automatically collect sensor data, fuse multimodal data, and perform adaptive behavioral monitoring, allowing researchers and …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 41–45 Read article