Journal of Data Analytics and Engineering Decision Making(JDAEDM)

Aims and Scope

Journal of Data Analytics and Engineering Decision Making is a pioneering journal dedicated to advancing the understanding and application of data analytics techniques in engineering decision-making processes. Our aim is to serve as a leading platform for researchers, practitioners, and professionals to exchange knowledge, methodologies, and insights, driving innovation and excellence in this dynamic field.

Our Scope:

Integration of Data Analytics Techniques: We explore the integration of various data analytics methodologies, including machine learning, data mining, and predictive analytics, into traditional engineering disciplines such as civil, mechanical, electrical, industrial, and environmental engineering.

Engineering Decision-Making Processes: Our journal delves into how data-driven approaches can enhance decision-making processes across various stages of engineering projects, from initial planning and design to execution, optimization, and evaluation.

Applications in Real-World Engineering: We showcase practical applications of data analytics methodologies through in-depth case studies, highlighting successful implementations, challenges faced, and lessons learned across different industries and domains.

Ethical Considerations and Implications: We address the ethical and societal implications of employing data analytics in engineering decision making, including issues related to privacy, bias, transparency, and social responsibility.

Interdisciplinary Perspectives: We encourage interdisciplinary research that fosters collaboration between data scientists, engineers, domain experts, and stakeholders, aiming to bridge the gap between theory and practice and drive innovation in the field.

Emphasizing Innovation and Excellence: Journal of Data Analytics and Engineering Decision Making is committed to promoting excellence in research and innovation, providing a platform for high-quality contributions that push the boundaries of knowledge and contribute to the development of sustainable and efficient solutions to complex engineering challenges.

The journal welcomes contributions from engineers, researchers, and professionals dedicated to advancing the understanding and application in diverse settings.

Some of the subject areas that the journal focuses on include:

Data Analytics Techniques: unsupervised learning, supervised learning, deep learning, data visualization, pattern recognition, anomaly detection, clustering, classification, regression, feature engineering, natural language processing, reinforcement learning, neural networks, support vector machines, decision trees, ensemble methods, dimensionality reduction, time series analysis.

Engineering Disciplines: structural engineering, transportation engineering, materials engineering, aerospace engineering, chemical engineering, biomedical engineering, geotechnical engineering, water resources engineering, renewable energy engineering, systems engineering, control systems engineering, robotics, mechatronics.

Big Data and Analytics: data storage architectures, data lakes, data warehouses, data streaming, batch processing, real-time processing, data integration, data federation, data governance, data quality, data security, data privacy, data ethics, data-driven decision-making, data-driven innovation.

Decision Support Systems: optimization algorithms, linear programming, integer programming, dynamic programming, simulation optimization, goal programming, Pareto optimization, sensitivity analysis, uncertainty analysis, decision trees, Monte Carlo simulation, Markov chains, game theory.

Performance Optimization: lean manufacturing, six sigma, total quality management, continuous improvement, process reengineering, operations research, resource management, capacity planning, inventory management, logistics optimization, supply chain optimization, demand forecasting, production scheduling.

Case Studies and Applications: smart cities, predictive maintenance, predictive analytics in healthcare, predictive analytics in finance, predictive analytics in marketing, predictive analytics in energy, predictive analytics in agriculture, predictive analytics in transportation, predictive analytics in retail, predictive analytics in manufacturing, predictive analytics in telecommunications, predictive analytics in e-commerce.