Integrated Computer-Aided Engineering

Top Research Topics at Integrated Computer-aided Engineering?

Integrated Computer-aided Engineering mainly tackles studies in Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Distributed computing. While Integrated Computer-aided Engineering focused on Artificial intelligence, it was also able to explore topics like Machine learning and Natural language processing.

• Artificial intelligence (32.96%)
• Computer vision (12.11%)
• Algorithm (7.32%)

What are the most cited papers published in the journal?

• Holonic manufacturing systems (221 citations)
• Image recognition with deep neural networks in presence of noise – Dealing with and taking advantage of distortions (100 citations)
• A manufacturing paradigm toward the 21st century (84 citations)

Research areas of the most cited articles at Integrated Computer-aided Engineering:

The journal papers focus on Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Simulation. The most cited articles facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Machine learning and Identification (information). Soundness, Architectural pattern and Trajectory are some topics wherein Simulation research discussed in the most cited papers has an impact.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Operating system
• Machine learning

The previous edition focused in particular on these issues:

Integrated Computer-aided Engineering primarily tackles Artificial intelligence, Machine learning, Multi-objective optimization, Pattern recognition and Engineering ethics. The studies on Artificial intelligence discussed can also contribute to research in the domains of Point (geometry) and Decoding methods. Machine learning research featured in Integrated Computer-aided Engineering incorporates concerns from various other topics such as GRASP and Recognition algorithm.

It holds forums on Multi-objective optimization that merges themes from other disciplines such as Transfer of learning, Smoothness (probability theory), Loop subdivision, Evolutionary algorithm and Knowledge-based engineering. While Pattern recognition is the focus of Integrated Computer-aided Engineering, it also provided insights into the studies of Backpropagation, Autoencoder, Series (mathematics) and Re identification. The work on Mobile robot tackled in it brings together disciplines like Modularity, Natural computing, Membrane computing, Classifier (UML) and Control theory.

The most cited articles from the last journal are:

• Exploiting higher-order patterns for community detection in attributed graphs (5 citations)
• An improved Loop subdivision to coordinate the smoothness and the number of faces via multi-objective optimization (2 citations)
• Human-robot interaction in Industry 4.0 based on an Internet of Things real-time gesture control system (2 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Integrated Computer-aided Engineering (based on the number of publications) are:

• Ferrante Neri (8 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Fazhi He (7 papers) published 1 paper at the last edition the same number as at the previous edition,
• Ezequiel López-Rubio (6 papers) absent at the last edition,
• Gexiang Zhang (5 papers) published 1 paper at the last edition the same number as at the previous edition,
• Arturo de la Escalera (4 papers) published 2 papers at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Integrated Computer-aided Engineering (based on the number of publications) are:

• Wuhan University (8 papers) published 1 paper at the last edition,
• Technical University of Madrid (8 papers) published 1 paper at the last edition the same number as at the previous edition,
• De Montfort University (6 papers) absent at the last edition,
• University of Calgary (5 papers) absent at the last edition,
• Katholieke Universiteit Leuven (5 papers) absent at the last edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 52.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 50.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.33% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 0.00% of all publications and 41.67% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities

The vast world of Integrated Computer-aided Engineering presents numerous career opportunities to those interested in Artificial Intelligence, Machine Learning, Computer Vision, and related domains. One such promising career pathway is becoming an elementary school teacher who focuses on introducing young minds to these very subjects.

For those passionate about education and keen on shaping the future of AI and machine learning, this role presents an excellent opportunity. It allows one to cultivate the interests of kids in the realm of computer-aided engineering from an early stage. Not only does it offer a chance to play a meaningful role in society but it also builds a strong foundation for the next generation's innovation.

If you're interested in learning more about what this path entails, specifically in the state of New Jersey, you can read more about the elementary school teacher requirements in New Jersey.

Understanding this vital role and its requirements can also help those involved in machine learning and AI research devise better, more effective educational resources and tools for teachers.

.