International Journal of Computer Science and Data Engineering

Service Quality Assessment in the Airline Industry: A TOPSIS-Based Analysis of Hawaiian Airlines and Global Competitors

2025-03-27T05:41:19+00:00

This study examines crisis management strategies used in the aviation industry and creates a set of internationally relevant evaluation standards for the Greek aviation industry. The research employs a multi-criteria analysis in an ambiguous setting because of market uncertainty. The criteria are weighted according to news analysis and worldwide literature. Departure (partial or total operational shutdown), innovation (strategic organizational renewal), sustainability (maintaining the status quo), and reduction (cutting costs and assets for immediate survival) were some of the strategies the aviation industry employed to deal with the COVID-19 crisis. The study looks at various strategies used by airports, airlines, civil aviation firms, and aircraft manufacturers. The TOPSIS (Technique for Priority Sorting by Similarity, Similarity to Ideal Solution) method, which finds solutions that are closest to the ideal across a number of criteria, is the analysis method used in this study. This method works well in ambiguous circumstances, particularly when handling linguistic metrics and measurement uncertainty. In order to objectively evaluate competitiveness and handle heterogeneity with suitable tactics, the approach integrates the entropy-tips technique. Aegean Airlines, Alaska Airlines, Delta Airlines, Hawaiian Airlines, and Philippine Airlines are among the airlines that are examined in this analysis. They are assessed based on factors including responsiveness, empathy, and dependability. Hawaiian Airlines does well by facilitating penetration in unexplored areas through smart fleet expansion, even in the face of natural events like tropical storms. The report also looks at the U.S. aviation industry's sustainability initiatives, which are aimed at reaching net-zero greenhouse gas emissions by 2050. It highlights issues including the fact that sustainable aviation fuels (SAF) are far more expensive than conventional jet fuel. With comparatively low investments, fleet modernization is becoming a more affordable option than buying new airplanes and has positive environmental effects. Stakeholders in the aviation sector can benefit greatly from this thorough analysis's insightful advice on how to overcome obstacles and implement sustainable growth plans.

Evolution and Impact of Data Warehousing in Modern Business and Decision Support Systems

2025-03-03T06:38:51+00:00

Data warehousing has become an essential tool in modern organizations driven by increasing business complexity and technological advancements. Organizations collect vast amounts of data from multiple sources that require efficient storage and analysis solutions. This research paper examines the role of data warehousing in decision making, its integration with emerging technologies, and its growing impact on various industries.

Research significance: This research is significant as it highlights the transformative role of data warehousing in decision-making across industries. By improving data quality, accessibility, and integration, data warehouses enhance business intelligence and operational efficiency. The study provides valuable insights into leveraging data warehousing technologies, addressing challenges, and fostering innovation in data-driven environments.

Methology: Alternatives: Secure Data Pool Engine, Privacy-Aware Data Warehouse, Intelligent Resource Cloud for Privacy, Confidential Data Processing Hub and Adaptive Privacy Compute Engine.

Evaluation Parameters: Wind resources,Construction and maintenance conditions,nautical environmental influence and Provincial financial subsidies.

Result: The results show that Intelligent Resource Cloud for Privacy received the highest ranking, whereas Secure Data Pool Engine received the lowest ranking.

Conclusion: Intelligent Resource Cloud for Privacy has the highest value for artificial intelligence and medicine according to the WSM approach.

Optimizing Autonomous Systems through Reinforcement Learning: The Role of Linear Regression, Random Forest, and Support Vector Machines in Decision Making

2025-03-10T05:23:13+00:00

Reinforcement Learning (RL), a powerful paradigm for decision-making in autonomous systems, has enabled agents to learn the optimum policies through trial and error in dynamic environments. This paper explores the integration of RL within autonomous systems, emphasizing how various machine learning algorithms Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Machines (SVM) can assist in predicting agent performance and optimizing training processes. These algorithms are employed to analyze key input parameters, including sensor accuracy (%), processing power (GHz), and the number of training episodes (#), to determine their influence on the agent’s learning efficiency and overall performance. The effectiveness of an RL agent is often measured using an evaluation metric such as the average reward (R), which quantifies the long-term benefits obtained by following a learned policy. By leveraging predictive modeling techniques, we aim to establish correlations between input parameters and RL performance, helping to refine system design and resource allocation. Sensor accuracy has a direct influence on decision-making processes and is essential in assessing the dependability of state information.. Processing power influences the speed and complexity of model updates, affecting convergence rates. The number of training episodes determines the agent’s exposure to various environmental states, influencing its ability to generalize learned behaviors.

This study employs a hybrid approach where RL agents are trained in simulated environments, and machine learning models are used to analyze performance trends. LR provides a simple yet interpretable linear relationship between parameters and average reward, while RFR captures complex nonlinear interactions and enhances prediction robustness. SVM, known for its strong generalization capabilities, further refines decision boundaries in high-dimensional spaces. By comparing these approaches, we derive insights into which factors most significantly impact RL performance and how predictive models can be leveraged to improve autonomous system efficiency. The findings show that the average reward is significantly impacted nonlinearly by sensor accuracy., highlighting the need for high-fidelity sensing in autonomous applications. Processing power influences real-time adaptability, while an optimal number of training episodes ensures sufficient learning without excessive computational overhead. The findings demonstrate that integrating supervised learning techniques with RL not only aids in understanding system behavior but also provides a foundation for adaptive optimization strategies in real-world applications. Future research will concentrate on extending these techniques to more complex, multi-agent environments and exploring meta-learning approaches for enhanced adaptability.

Performance Analysis of Machine Learning Algorithms in SAP Extended Warehouse Management Using ARAS Methodology

2025-03-02T06:11:27+00:00

This research explores a standardized approach to improve ticket classification performance in SAP Application Management Services (AMS) using cloud-based Machine learning, especially emphasis SAP Extended Warehouse Management. The study investigates a number of machine learning methods, such as support vector machines, random forests,logistic regression, decision trees, and neural networks. automatic classification of event tickets. While SAP offers solutions such as Service Ticket Intelligence in SAP Cloud BTP, many organizations face challenges in adopting these cloud-based solutions. This research emphasizes integration modern technologies with warehouse management systems, highlighting the critical role of data processing and analytics in improving supply chain operations. The study also incorporates the ARAS (Associative Ratio Assessment) methodology with multi-criteria decision-making approaches to assess system performance and improve operational efficiency. The investigation includes material flow systems, accuracy metrics, and F1-scores to assess how well different machine learning models perform.

This methodology combines theoretical framework analysis with practical implementation strategies, addressing the technical and organizational aspects of SAP ERP implementation. The research examines the challenges of digital transformation in business process management, including the automation of workflows and the adaptation of organizational structures. Special attention is paid to integrating enterprise resource planning systems with strategic enterprise management across production units. The findings highlight the importance of balancing technical capabilities with organizational and human factors in successful system implementation. While acknowledging the challenges of system integration and data management, this study contributes to understanding how machine learning technologies can improve warehouse management performance. The research provides valuable insights for organizations looking to improve their warehouse management processes with advanced technology solutions while maintaining operational efficiency and customer satisfaction.