World's Best Scientists 2026 revealed!
Mobile Information Systems
H-index 17

Mobile Information Systems

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 393 60 65 15
Electronics and Electrical Engineering 477 12 13 4

Additional Metrics

Number of Best Scientists*: 108
Documents by Best Scientists*: 120
Top 100 Ranked Scientists*: 1
SCIMAGO H-index:
SCIMAGO SJR:
Impact Factor: N/A

Overview

Top Research Topics at Mobile Information Systems?

The primary areas of discussion in the journal are Computer network, Artificial intelligence, Distributed computing, Mobile device and Wireless. While the journal focused on Computer network, it was also able to explore topics like Wireless network and Wireless ad hoc network. The work on Artificial intelligence tackled in the journal brings together disciplines like Machine learning, Computer vision and Pattern recognition.

The research on Wireless discussed in the journal draws on the closely related field of Real-time computing.

  • Computer network (26.99%)
  • Artificial intelligence (12.41%)
  • Distributed computing (9.75%)

What are the most cited papers published in the journal?

  • Improving Indoor Localization Using Bluetooth Low Energy Beacons (148 citations)
  • A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms (103 citations)
  • A survey of software infrastructures and frameworks for ubiquitous computing (78 citations)

Research areas of the most cited articles at Mobile Information Systems:

The journal publications are organized to address concerns in the fields of Computer network, Distributed computing, Wireless, Computer security and Real-time computing. Issues in Computer network were discussed in the most cited publications, taking into consideration concepts from other disciplines like Wireless network and Wireless ad hoc network. The study of Computer security in the most cited articles encompasses disciplines such as The Internet, as well as fields such as Heterogeneous network, all of which overlap with one another.

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

  • Artificial intelligence
  • Computer network
  • Operating system

The previous edition focused in particular on these issues:

Mobile Information Systems was organized to reinforce research efforts on Artificial intelligence, Big data, The Internet, Algorithm and Multimedia. It explores topics in Artificial intelligence which can be helpful for research in disciplines like Field (computer science) and Machine learning. Topics in Big data were tackled in line with various other fields like Information system and Data science.

The most cited articles from the last journal are:

  • Multiscale Dense Cross-Attention Mechanism with Covariance Pooling for Hyperspectral Image Scene Classification (23 citations)
  • Multimodal Data Guided Spatial Feature Fusion and Grouping Strategy for E-Commerce Commodity Demand Forecasting (11 citations)
  • Multimedia Interaction-Based Computer-Aided Translation Technology in Applied English Teaching (5 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.

The top authors publishing in Mobile Information Systems (based on the number of publications) are:

  • Leonard Barolli (18 papers) absent at the last edition,
  • Ilsun You (10 papers) published 1 paper at the last edition the same number as at the previous edition,
  • Arjan Durresi (9 papers) absent at the last edition,
  • Fatos Xhafa (9 papers) absent at the last edition,
  • Sungwook Kim (9 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.

Only papers with recognized affiliations are considered

The top affiliations publishing in Mobile Information Systems (based on the number of publications) are:

  • Beijing University of Posts and Telecommunications (27 papers) published 1 paper at the last edition, 2 less than at the previous edition,
  • University of Electronic Science and Technology of China (18 papers) absent at the last edition,
  • Fukuoka Institute of Technology (16 papers) absent at the last edition,
  • Xidian University (13 papers) published 4 papers at the last edition,
  • China University of Mining and Technology (13 papers) published 5 papers at the last edition, 2 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, 28.33% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 5.74% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.77% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 12.50% of all publications and 73.99% 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.

Section Title: Application of Research in Education

While the application of the research topics in various technological fields may seem apparent, it's important to consider their influence on other areas like education. For instance, let's explore how the advancements in Artificial Intelligence could revolutionize teaching methodologies and curriculum design, specifically within the area of History teaching.

Machine learning algorithms can help to create a more personalized and efficient teaching and learning process. Implementing AI in classrooms could lead to interactive learning apps that adapt to a student's pace or simulations that allow the immersion in the studied historical period. Furthermore, advancements in computer vision can assist with creating captivating materials and resources for a better understanding of historical events.

For those interested in applying these research advancements in the classroom, understanding how to effectively integrate such technology in the learning process is crucial. To ensure that prospective educators are equipped with the knowledge to adapt to evolving teaching methods, certain requirements must be met. Visit this page to learn about the history teacher requirements in Idaho, where the incorporation of technology in the classroom is becoming increasingly common.

This practical perspective on the application of advanced research topics reveals how influential these studies can be across various aspects of our society, even beyond the direct technical fields.

Top Publications

  • Vision Navigator: A Smart and Intelligent Obstacle Recognition Model for Visually Impaired Users

    (2022)
    84 Citations
  • Propose a New Quality Model for M-Learning Application in Light of COVID-19

    (2022)
    30 Citations
  • Computer Vision-Enabled Character Recognition of Hand Gestures for Patients with Hearing and Speaking Disability

    (2021)
    28 Citations
  • A Generative Adversarial Network Model Based on Intelligent Data Analytics for Music Emotion Recognition under IoT

    I.-Sheng Huang;Yu-Hsuan Lu;Muhammad Shafiq;Asif Ali Laghari

    (2021)
    24 Citations
  • Cloud Computing-Based Medical Health Monitoring IoT System Design

    (2021)
    22 Citations
  • Design of Graph-Based Layered Learning-Driven Model for Anomaly Detection in Distributed Cloud IoT Network

    (2022)
    22 Citations
  • Rules of Smart IoT Networks within Smart Cities towards Blockchain Standardization

    (2022)
    21 Citations
  • Modeling Method of Autonomous Robot Manipulator Based on D-H Algorithm

    (2021)
    20 Citations
  • A Lightweight Location-Aware Fog Framework (LAFF) for QoS in Internet of Things Paradigm

    Qaisar Shaheen;Muhammad Shiraz;Muhammad Usman Hashmi;Danish Mahmood

    (2020)
    19 Citations
  • Efficient Multitask Scheduling for Completion Time Minimization in UAV-Assisted Mobile Edge Computing

    Bingxin Zhang;Guopeng Zhang;Shuai Ma;Kun Yang

    (2020)
    18 Citations

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens up numerous opportunities, but exploring related online degrees can broaden your skills and career prospects. For example, pursuing an online mechanical engineering degree can complement your technical expertise and enhance your problem-solving abilities in both software and hardware domains.

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Lastly, combining Computer Science with an best online electrical engineering programs USA degree can be particularly potent for careers in embedded systems, robotics, and IoT technology development.

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