ITEJ (Information Technology Engineering Journals)

Development of an Operating System Supporting Intelligent Predictions and Recommendations

Rakhmadi Rahman — 2024-07-31

This Study discusses the development of an intelligent operating system feature that supports smart prediction and recommendations using artificial intelligence (AI) capabilities within the Linux operating system. The study aims to integrate AI-driven features into Linux to enhance user productivity and efficiency by providing relevant application recommendations based on user behavior patterns. The implementation involves data collection of application usage, training machine learning models for application recommendations, and integrating these features into the Linux environment. The project utilizes Python for scripting, employing libraries such as psutil, pandas, scikit-learn, and joblib for data handling and machine learning tasks. The results demonstrate successful implementation of the AI-driven recommendation system, enhancing user interaction and productivity within the Linux operating system

Machine Learning for Predictive Maintenance to Enhance Energy Efficiency in Industrial Operations

Juan Carlos Cruz — 2024-07-28

In the realm of industrial operations, optimizing energy usage is critical for both economic and environmental sustainability. Traditional approaches to maintenance often rely on fixed schedules or reactive responses to equipment failures, leading to inefficiencies and higher energy consumption. Predictive maintenance (PdM) offers a proactive solution by leveraging machine learning algorithms to predict equipment failures before they occur. This approach not only reduces downtime but also facilitates energy-efficient practices by enabling timely interventions and optimized operational strategies. This study explores the application of machine learning techniques for predictive maintenance in a manufacturing setting. Historical operational data, including equipment performance metrics and environmental conditions, are collected and preprocessed. Various machine learning models, such as support vector machines (SVM), random forests, and neural networks, are trained on this dataset to predict equipment failures and maintenance needs. Feature engineering and model selection processes are detailed to highlight the steps taken to enhance prediction accuracy and reliability. Through rigorous experimentation and validation, our approach demonstrates significant improvements in energy efficiency within industrial operations. By predicting maintenance needs in advance, downtime is minimized, and energy-intensive emergency repairs are avoided. Furthermore, the implementation of optimized maintenance schedules and operational strategies based on machine learning predictions leads to substantial reductions in overall energy consumption. Case studies and quantitative analyses underscore the efficacy of our methodology in enhancing both operational efficiency and energy sustainability in industrial settings.

AI-Driven Urban Planning: Enhancing Efficiency and Sustainability in Smart Cities

Thomas Hadiyana — 2024-07-30

Urban planning in smart cities is increasingly leveraging artificial intelligence (AI) to enhance efficiency and sustainability. This article explores the integration of AI-driven technologies to optimize various aspects of urban development and management. Smart cities are characterized by their use of advanced technologies to improve quality of life, resource management, and infrastructure efficiency. Traditional urban planning methods often face challenges in adapting to rapid urbanization and dynamic environmental changes. AI presents opportunities to address these challenges by providing data-driven insights and predictive capabilities. This research employs a case study approach, analyzing the implementation of AI in urban planning processes across several smart cities globally. Key methodologies include data analytics, machine learning algorithms, and predictive modeling techniques applied to diverse urban datasets. The study evaluates how AI-driven decision support systems aid in infrastructure planning, traffic management, energy consumption optimization, and environmental sustainability. The findings demonstrate that AI-enabled urban planning significantly enhances efficiency and sustainability in smart cities. AI algorithms optimize traffic flow, reduce energy consumption through predictive maintenance of infrastructure, and facilitate adaptive urban design based on real-time data analytics. Moreover, AI-driven approaches improve decision-making processes by providing stakeholders with actionable insights for informed policy formulation and resource allocation. This article contributes to the evolving field of smart city technologies by showcasing the transformative potential of AI in urban planning. By harnessing AI capabilities, cities can effectively address complex urban challenges and pave the way for more resilient and sustainable urban environments.

Integration of Information Technology and Machine Learning to Improve the Efficiency of IoT-Based Logistics Systems

Maya Amelia — 2024-07-30

In today's digital era, efficiency in supply chain management and logistics is the main key to maintaining business competitiveness. This article discusses the integration of Information Technology (IT) and Machine Learning (ML) in Internet of Things (IoT)-based logistics systems to improve operational efficiency. By leveraging IoT sensors for real-time data collection and ML algorithms for predictive analysis, the system is able to optimize inventory management, route planning, and preventive maintenance. The case studies discussed in this article show that the use of ML in IoT-based logistics systems can reduce delivery times, lower operational costs, and increase responsiveness to changes in market demand. The results of this study are expected to provide insight for system developers and logistics managers in implementing advanced technologies to address challenges in the modern logistics industry.

From Battlefield to Border: The Evolving Use of Drones in Surveillance Operations

Zhang Hua — 2024-07-31

In recent decades, drone technology has undergone rapid advancements, making it a vital tool in various surveillance operations. Initially limited to the battlefield, the use of drones has now expanded to various civilian applications, including border monitoring, law enforcement, and environmental surveillance. This shift is driven by enhancements in drone capabilities, including extended range, endurance, and sensor technology. This study employs a qualitative approach using case studies to analyze the evolution of drone usage in surveillance operations. Data were collected through literature reviews, interviews with industry experts, and analysis of reports from security and defense agencies. The study also compares the effectiveness of drone-based surveillance operations with traditional methods through statistical analysis and field operational evaluations. The findings indicate that drone usage significantly enhances the efficiency and effectiveness of surveillance operations. Drones enable wider area coverage at lower costs and reduced risks compared to conventional methods. Additionally, drones equipped with advanced sensors facilitate more accurate and real-time data collection, which is crucial in critical security situations. The study also identifies major challenges in drone usage, including regulatory issues, privacy concerns, and the integration of technology with existing systems. The evolving use of drones in surveillance operations shows great potential in enhancing security and efficiency across various sectors. However, a balanced approach between technological innovation and regulatory frameworks is necessary to address existing challenges and maximize the benefits of this technology.