Journal of Data Analytics and Engineering Decision Making(JDAEDM)

Journal of Data Analytics and Engineering Decision Making is a peer-reviewed, open-access journal dedicated to advancing knowledge and fostering innovation in the application of data analytics techniques to engineering domains. Our journal serves as a platform for researchers, practitioners, and professionals to share their latest findings, methodologies, and insights, contributing to the advancement of both theory and practice in this rapidly evolving field.

Journal Aim:

Our journal covers a wide range of topics, including but not limited to:

• Integration of data analytics techniques in engineering domains such as civil, mechanical, electrical, and industrial engineering.
• Utilization of big data, machine learning, artificial intelligence, and statistical analysis in engineering decision-making processes.
• Applications of data-driven approaches in optimizing engineering processes, resource allocation, risk assessment, and performance evaluation.
• Development and validation of computational models and algorithms for engineering decision support systems.
• Case studies showcasing successful implementation of data analytics methodologies in real-world engineering scenarios.
• Ethical considerations and challenges in the application of data analytics in engineering decision-making processes.

We welcome original research articles, reviews, case studies, and technical notes that contribute to advancing our understanding of these topics. By fostering interdisciplinary research and collaboration, our journal aims to drive innovation and address complex challenges in the field of data-driven engineering decision making.

Editorial Board: Our journal is supported by a distinguished editorial board comprised of experts in the fields of data analytics and engineering. Our board members bring a wealth of knowledge and experience, ensuring the quality and integrity of the research published in our journal.

Submit Your Work: Are you conducting groundbreaking research in data analytics applied to engineering decision- making processes? We invite you to submit your manuscript to "Data Analytics and Engineering Decision Making." Our streamlined online submission system makes it easy to share your work with our global audience of researchers and practitioners.

Stay Updated: Stay informed about the latest research and developments in data analytics and engineering decision making by subscribing to our journal. Sign up for email alerts to receive notifications about new articles, special issues, and other important announcements.

Coverage: We strive to cover a broad spectrum of topics and areas within the realm of data analytics applied to engineering decision-making processes. Our journal provides in-depth coverage of the following key areas:

Integration of Data Analytics Techniques: Data mining; Predictive analytics; Prescriptive analytics; Machine learning algorithms; Artificial intelligence.

Engineering Domains: Civil engineering; Mechanical engineering; Electrical engineering; Industrial engineering; Environmental engineering.

Big Data Applications: Handling large-scale datasets; Data preprocessing and cleansing; Real-time data analytics; Distributed computing frameworks; Scalable data storage solutions.

Decision Support Systems: Development of decision models; Optimization techniques; Simulation and modeling; Risk analysis and mitigation; Multi-criteria decision-making

Performance Optimization: Process optimization; Resource allocation; Energy efficiency; Supply chain management; Quality control.

Case Studies and Applications: Real-world applications of data analytics in engineering; Success stories and best practices; Lessons learned from practical implementations; Industry-specific case studies -

Ethical and Societal Implications: Privacy and data protection; Ethical considerations in data collection and analysis; Social responsibility in engineering decision making; Equity and fairness in algorithmic decision making

Interdisciplinary Research: Bridging the gap between data science and engineering disciplines; Collaboration between researchers from diverse backgrounds; Integration of domain knowledge with data analytics techniques Our comprehensive coverage ensures that researchers, practitioners, and professionals from various fields find valuable insights and knowledge to enhance their understanding and contribute to the advancement of data-driven engineering decision making. Submit Your Manuscript: Ready to contribute to the Data Analytics and Engineering Decision Making? Submit your manuscript at https://www.opastpublishers.com/journal/journal-of-data-analytics-and-engineering-decision-making/manuscript-submission

Topics of Interest:

Topics of interest for submissions include, but are not limited to: