International Journal of Machine Learning and Cybernetics

Top Research Topics at International Journal of Machine Learning and Cybernetics?

The topics of Computational intelligence, Artificial intelligence, Pattern recognition (psychology), Pattern recognition and Complex system are the focal point of discussions in the journal. International Journal of Machine Learning and Cybernetics holds forums on Computational intelligence that merges themes from other disciplines such as Data mining, Artificial neural network, Mathematical optimization, Fuzzy logic and Algorithm. The journal facilitated presentations on Fuzzy logic research, particularly Fuzzy set, Fuzzy number and Fuzzy set operations.

Machine learning and Computer vision are some topics wherein Artificial intelligence research discussed in the journal have an impact. The majority of Machine learning studies presented zero in on Extreme learning machine. The studies in Pattern recognition (psychology) featured incorporate elements of Context (language use), Feature (computer vision), Rough set, Deep learning and Benchmark (computing).

Discussions in the journal are anchored in the subject of Pattern recognition and the similar topic of Facial recognition system. In addition to Complex system research, the journal aims to explore topics under Theoretical computer science and Control theory. Fuzzy clustering and Correlation clustering studies are all carried out as a component of the study in Cluster analysis presented.

• Computational intelligence (62.92%)
• Artificial intelligence (45.83%)
• Pattern recognition (psychology) (40.89%)

What are the most cited papers published in the journal?

• Extreme learning machines: a survey (1414 citations)
• Understanding bag-of-words model: A statistical framework (466 citations)
• Multiple classifier systems for robust classifier design in adversarial environments (156 citations)

Research areas of the most cited articles at International Journal of Machine Learning and Cybernetics:

The journal articles primarily focus on research topics in Computational intelligence, Artificial intelligence, Pattern recognition (psychology), Pattern recognition and Data mining. The most cited papers facilitate discussions on Computational intelligence that incorporate concepts from other fields like Artificial neural network, Mathematical optimization, Fuzzy logic, Complex system and Algorithm. The most cited papers dive deep in exploring the relationship between the study of Artificial intelligence and Machine learning.

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

• Artificial intelligence
• Machine learning
• Statistics

The previous edition focused in particular on these issues:

The concepts of Computational intelligence, Pattern recognition (psychology), Artificial intelligence, Pattern recognition and Algorithm are tackled in the journal. Topics in Computational intelligence explored in International Journal of Machine Learning and Cybernetics were investigated in conjunction with research in Artificial neural network, Cluster analysis, Fuzzy logic, Complex system and Robustness (computer science). The work on Cluster analysis tackled in the journal brings together disciplines like Data point and Similarity (network science).

It explores issues in Pattern recognition (psychology) which can be linked to other research areas like Context (language use), Data mining, Benchmark (computing), Feature extraction and Feature selection. Artificial intelligence study tackled is connected to the field of Machine learning. The journal facilitates discussions on Pattern recognition that incorporate concepts from other fields like Transfer of learning, Feature (machine learning) and Representation (mathematics).

The most cited articles from the last journal are:

• EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization (31 citations)
• Attention-based context aggregation network for monocular depth estimation (15 citations)
• IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification (14 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 International Journal of Machine Learning and Cybernetics (based on the number of publications) are:

• Shifei Ding (16 papers) published 5 papers at the last edition, 4 more than at the previous edition,
• Zeshui Xu (16 papers) published 4 papers at the last edition, 1 more than at the previous edition,
• William Zhu (15 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Jiye Liang (13 papers) published 4 papers at the last edition,
• Jinhai Li (12 papers) published 1 paper at the last edition, 1 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 International Journal of Machine Learning and Cybernetics (based on the number of publications) are:

• Shenzhen University (45 papers) published 7 papers at the last edition, 6 less than at the previous edition,
• Chinese Academy of Sciences (39 papers) published 5 papers at the last edition the same number as at the previous edition,
• Harbin Institute of Technology (33 papers) published 6 papers at the last edition, 2 more than at the previous edition,
• Shanxi University (30 papers) published 8 papers at the last edition, 4 more than at the previous edition,
• South China University of Technology (28 papers) published 5 papers at the last edition, 2 less 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, 4.69% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 18.94% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.95% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 22.73% of all publications and 50.38% 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.

Application of Machine Learning in Special Education

The application of Machine Learning and Cybernetics principles can be found in various fields, including special education. Advances in technology have resulted in the development of education systems that cater to students with special needs, making use of machine learning and artificial intelligence principles. Here, machine learning algorithms are used to understand individual student data and tailor a learning process that suits their specific needs. This could involve adjusting class materials, introducing assistive technology, and monitoring student development closely.

One of the critical aspects involved in these applications is understanding the requirements for becoming a specialized education teacher. If you're based in Texas, make sure to familiarize yourself with special ed teacher requirements by visiting this {anchor}.

The International Journal of Machine Learning and Cybernetics often publishes articles that investigate this particular use of computational intelligence in the field of special education, building up an insightful repository of resources. These articles delve into the intricacies of designing assistive learning systems that can adapt and learn from student data, creating a more inclusive education system.