Journal of Experimental and Theoretical Artificial Intelligence

Top Research Topics at Journal of Experimental and Theoretical Artificial Intelligence?

The journal was organized to reinforce research efforts on Artificial intelligence, Machine learning, Mathematical optimization, Cognitive science and Algorithm. In it, Pattern recognition, Computer vision and Natural language processing are investigated in conjunction with one another to address concerns in Artificial intelligence research. Cognitive science research discussed connects with the study of Cognition.

• Artificial intelligence (47.99%)
• Machine learning (10.64%)
• Mathematical optimization (10.55%)

What are the most cited papers published in the journal?

• Experiences with an architecture for intelligent, reactive agents (560 citations)
• Computation and cognition (498 citations)
• Comparing performance of algorithms for generating concept lattices (447 citations)

Research areas of the most cited articles at Journal of Experimental and Theoretical Artificial Intelligence:

The published papers tackle a plethora of topics, such as Artificial intelligence, Cognitive science, Algorithm, Machine learning and Mathematical optimization. The most cited articles facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Natural language processing, Human–computer interaction and Pattern recognition. The study of Cognitive science in the journal papers encompasses disciplines such as Cognition, as well as fields such as Dynamical systems theory, all of which overlap with one another.

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 objective of the journal is to combine knowledge in the areas of Artificial intelligence, Artificial neural network, Machine learning, Fuzzy logic and Deep learning. The concepts on Artificial intelligence presented in Journal of Experimental and Theoretical Artificial Intelligence can also apply to other research fields, including Field (computer science), Computer vision and Pattern recognition. While the journal focused on Artificial neural network, it was also able to explore topics like Stability (learning theory), Exponential stability, Control theory and Nonlinear system.

Exponential stability research presented in Journal of Experimental and Theoretical Artificial Intelligence encompasses a variety of subjects, including Class (set theory) and Applied mathematics. Studies on Control theory discussed in it link to the field of Inertial frame of reference. The Machine learning study tackled is a key component of adjacent topics in the area of Oversampling.

The most cited articles from the last journal are:

• Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images (33 citations)
• A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification (15 citations)
• Discrete wavelet transform-based freezing of gait detection in Parkinson’s disease (13 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 Journal of Experimental and Theoretical Artificial Intelligence (based on the number of publications) are:

• Eric Dietrich (15 papers) absent at the last edition,
• Chris Fields (12 papers) absent at the last edition,
• Selmer Bringsjord (9 papers) absent at the last edition,
• Henry Hexmoor (7 papers) absent at the last edition,
• Eliezer L. Lozinskii (7 papers) absent 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 Journal of Experimental and Theoretical Artificial Intelligence (based on the number of publications) are:

• Islamic Azad University (32 papers) published 5 papers at the last edition, 2 more than at the previous edition,
• Rensselaer Polytechnic Institute (12 papers) absent at the last edition,
• Amirkabir University of Technology (10 papers) published 2 papers at the last edition,
• Binghamton University (10 papers) absent at the last edition,
• University of Maryland, College Park (9 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, 11.58% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 9.52% were posted by at least one author from the top 10 institutions publishing in the journal. Another 3.57% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.48% of all publications and 71.43% 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.

Practical Applications of AI and Machine Learning in Education

While this journal does a comprehensive job at rendering the theoretical and in-depth aspects of artificial intelligence and machine learning, its applications in real-world scenarios, specifically the education sector, seems largely untouched. AI's potential in shaping education holds immense transformative potential. Application areas span across personalized learning, intelligent tutoring, learning analytics, student engagement, and admission procedures. One such fascinating realm is the utilization of AI in language learning that poses an interesting question – can a machine teach a language? If you’re specifically curious about a practical application, see how to use AI tools in education such as Natural Language Processing (NLP) and machine learning algorithms to teach high school English. For example, computer algorithms can identify a student's weak grammar constructs and subsequently provide personalized exercises to enhance their learning. They can even help in designing lesson plans. Don’t believe it? Learn more on how to become a high school English teacher in Florida and the role AI can play in improving your teaching methods. Furthermore, AI in adaptive learning can offer a more personalized experience to learners by adjusting curriculum content according to individual need, while predictive analytics can help teachers and administrators identify at-risk students and even predict future performance. Machine Learning can be employed to automate administrative tasks, making the education system more efficient and giving educators more time to spend with students. To better grasp this subject, this whole area needs further cross-disciplinary research and collaboration, bringing together researchers from AI, education, cognitive science and psychology. Twining this research, practice and policy, we can safely say that AI and ML in education is a promising and under-explored path waiting to be unravelled.