Applied Soft Computing

Top Research Topics at Applied Soft Computing?

The main research concerns discussed in the journal are Artificial intelligence, Mathematical optimization, Algorithm, Fuzzy logic and Pattern recognition. Applied Soft Computing connects the study in Artificial intelligence with the closely related area of Machine learning. Many of the studies tackled connect Mathematical optimization with a similar field of study like Benchmark (computing).

It is focused mainly on Algorithm, particularly Metaheuristic. The study on Fuzzy logic featured in the journal expounds on the topic of Fuzzy set in particular. The study on Pattern recognition presented is investigated in conjunction with research in Feature (computer vision).

Cluster analysis research discussed connects with the study of Data mining.

• Artificial intelligence (29.47%)
• Mathematical optimization (22.12%)
• Algorithm (15.55%)

What are the most cited papers published in the journal?

• A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection (456 citations)
• A survey on deep learning techniques for image and video semantic segmentation (375 citations)
• Support vector machine applications in the field of hydrology: A review (307 citations)

Research areas of the most cited articles at Applied Soft Computing:

The most cited papers generally zeroe in on subjects such as Artificial intelligence, Mathematical optimization, Algorithm, Fuzzy logic and Machine learning. In addition to Artificial intelligence research, the most cited publications aim to explore topics under Data mining and Pattern recognition. The most cited articles focus on Mathematical optimization as well as the interrelated topics of Benchmark (computing).

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

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

The objective of the journal is to combine knowledge in the areas of Artificial intelligence, Mathematical optimization, Pattern recognition, Algorithm and Machine learning. It focuses on different Artificial intelligence studies like Deep learning, Artificial neural network, Convolutional neural network, Feature (computer vision) and Support vector machine. The journal investigates Mathematical optimization research which frequently intersects with Benchmark (computing).

Segmentation is a major topic of Pattern recognition research presented in it. Algorithm research is the primary subject tackled in the journal with a focus on Particle swarm optimization. Machine learning research is concerned with Feature (machine learning) in particular.

The most cited articles from the last journal are:

• A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer (95 citations)
• Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem (91 citations)
• AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system. (81 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 Applied Soft Computing (based on the number of publications) are:

• Witold Pedrycz (34 papers) published 9 papers at the last edition, 4 more than at the previous edition,
• Zeshui Xu (23 papers) published 2 papers at the last edition, 6 less than at the previous edition,
• Enrique Herrera-Viedma (18 papers) published 6 papers at the last edition, 2 more than at the previous edition,
• Liang Gao (17 papers) published 6 papers at the last edition, 3 more than at the previous edition,
• Huchang Liao (16 papers) published 4 papers at the last edition, 2 less than at the previous 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 Applied Soft Computing (based on the number of publications) are:

• Islamic Azad University (80 papers) published 18 papers at the last edition, 1 more than at the previous edition,
• Xidian University (62 papers) published 17 papers at the last edition, 9 more than at the previous edition,
• Huazhong University of Science and Technology (59 papers) published 13 papers at the last edition the same number as at the previous edition,
• Sichuan University (58 papers) published 15 papers at the last edition, 4 less than at the previous edition,
• Nanyang Technological University (54 papers) published 15 papers at the last edition, 5 more than at the previous 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, 8.29% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 14.29% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.56% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 13.45% of all publications and 64.71% 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 in Applied Soft Computing

One interesting facet of applied soft computing that was not covered in this article is the vast career opportunities that this field offers. The expanding fields of Artificial Intelligence (AI) and machine learning provide ample professional opportunities for research specialists from various backgrounds, including those from non-computational sciences like Mathematics and Physics. For instance, applied soft computing plays a crucial role in the educational sector, particularly for those who are interested in teaching English. One example is the role of an English teacher in Mississippi. Being an English teacher in this region may require a solid understanding of soft computing applications in teaching methodologies, curriculum design, and educational assessments. The curriculum could involve integrating technology into lesson plans to improve student engagement and learning outcomes, requiring knowledge of AI and machine learning. For more on this, check out our comprehensive guide on how to become an english teacher in mississippi. Another example of a career path in applied soft computing is as a data analyst or scientist, a role that involves heavy use of machine learning and algorithmic processes. Experts in this field can use their skills to detect patterns, build predictive models, and derive valuable insights from complex datasets. Such expertise is essential across sectors, including business, healthcare, finance, and government. These examples highlight the diversity and depth of career opportunities available in the field of applied soft computing, pointing to its significance in present and future job markets.