Computers

Top Research Topics at The first computers?

The objective of The first computers is to combine knowledge in the areas of Artificial intelligence, Computer network, Machine learning, Computer security and Pattern recognition. It dives deep in exploring the relationship between the study of Artificial intelligence and Computer vision. The majority of Computer network studies presented zero in on Network packet.

• Artificial intelligence (22.46%)
• Computer network (6.45%)
• Machine learning (6.45%)

What are the most cited papers published in the journal?

• A Review on Video-Based Human Activity Recognition (259 citations)
• Cloud Computing Security: A Survey (130 citations)
• Wearable Food Intake Monitoring Technologies: A Comprehensive Review (79 citations)

Research areas of the most cited articles at The first computers:

The journal articles are organized to address concerns in the fields of Artificial intelligence, Augmented reality, Cloud computing, Human–computer interaction and Robot. Issues in Artificial intelligence were discussed in the journal papers, taking into consideration concepts from other disciplines like Genetic algorithm, Machine learning, Computer vision and Pattern recognition. The journal papers tackle studies in Computer security and the interrelated subject of Security convergence, Security through obscurity, Analytics and Communications protocol to gain insights into Cloud computing.

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

• Artificial intelligence
• Operating system
• The Internet

The previous edition focused in particular on these issues:

The first computers focuses largely on the fields of Artificial intelligence, Machine learning, Virtual reality, Field (computer science) and Data science. The concepts on Artificial intelligence presented in The first computers can also apply to other research fields, including Set (abstract data type), Computer vision and Pattern recognition. The journal focuses on Machine learning research which is adjacent to topics in Anomaly detection.

It tackles topics on Virtual reality, which can potentially contribute to the wider field of Human–computer interaction.

The most cited articles from the last journal are:

• A Review of Agent-Based Programming for Multi-Agent Systems (5 citations)
• Recent Advancement of the Sensors for Monitoring the Water Quality Parameters in Smart Fisheries Farming (4 citations)
• Automated machine learning for healthcare and clinical notes analysis (4 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 The first computers (based on the number of publications) are:

• Robertas Damaševičius (5 papers) absent at the last edition,
• Ivan Miguel Pires (5 papers) published 2 papers at the last edition, 1 less than at the previous edition,
• Abdellah Touhafi (4 papers) absent at the last edition,
• Rytis Maskeliūnas (4 papers) absent at the last edition,
• Eftim Zdravevski (4 papers) published 2 papers at the last edition the same number as 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 The first computers (based on the number of publications) are:

• Glasgow Caledonian University (2 papers) absent at the last edition,
• University of Tartu (2 papers) absent at the last edition,
• University of Essex (2 papers) absent at the last edition,
• Manipal Institute of Technology (2 papers) published 1 paper at the last edition the same number as at the previous edition,
• University of St Andrews (1 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, 96.85% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 25.00% were posted by at least one author from the top 10 institutions publishing in the journal. Another 25.00% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 50.00% of all publications and 0.00% 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.

Educational Pathways in Computer Sciences

The article could benefit from a section discussing possible educational pathways for interested readers. Given the depth of the subjects studied within the journal, it's essential to highlight potential educational routes for those who may be inspired by the articles to pursue a career in one of these fields, such as becoming an English teacher. Here's a draft of this section: **Pathways to Becoming an Expert in Computer Sciences** Given the extensive research areas covered in various editions of the journal, it is evident that building a successful career in any of the highlighted fields requires a fair deal of learning and experience. For enthusiastic readers who might be keen on turning their interests into a fulfilling career, pursuing specialized education and gaining related work experience is crucial. If you are specifically interested in combining technology with teaching, becoming an English teacher equipped with IT knowledge could be an intriguing possibility. Teaching English can be enriched by the knowledge of IT skills, offering a more engaging and interactive learning experience to students. Specialized studies in this domain could prepare you for a range of teaching roles across different grades and learning codecs. A brilliant way of understanding more about the profession would be to explore {anchor}, which is a comprehensive resource giving insights into the profession and the steps to get started. Once you've gone through the academic route, it's a good practice to keep learning and stay updated with the latest advancements in the field. Engaging with research journals on the topic, such as "The First Computers," would not only boost your knowledge but also keep you abreast of the ongoing trends in the domain. Remember, the journey to becoming an expert in your field is not always easy, but it's indeed enriching and rewarding. With hard work, passion, and determination, you can carve your niche in any of the fields examined within our journal.