Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Artificial Intelligence and Complex System
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence and Complex System

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 19986

Special Issue Editor

Prof. Dr. Mayank Kejriwal

E-Mail Website
Guest Editor
Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina Del Rey, CA 90292, USA
Interests: artificial intelligence; knowledge graphs; AI for social good; complex systems; network science; social media analytics

Special Issue Information

Dear Colleagues,

The late Stephen Hawking referred to the 21st century as the 'century of complexity'. Today, due to the rapid advent of large datasets published on the Web and the development of methodologically rigorous tools, such as network analytics and mixed models, it has become possible to conduct deep studies of complex systems across the natural and social sciences. At the same time, the rise of artificial intelligence methods and architectures such as deep neural networks and transformer-based language models allow us to not only model and describe complex systems, but to predict their evolution and prescribe appropriate interventions (e.g., recommending products in e-commerce customer networks, or combating misinformation by exposing the right information to a user at the right time).

In this Special Issue, we are seeking approaches that explore the intersection of AI and Complex Systems. We are looking both for AI approaches with prescriptive and predictive power in a range of domains (social, commercial, and natural), but we are also looking for innovative use of AI (such as evolutionary computing) in building richer models of complex systems more efficiently and accurately. Beyond regular research papers, we will also consider:

Applications, case studies, and systems: Can a unified treatment of AI and complex systems research lead to novel systems and applications that have so far proven difficult or intractable to tackle?

Novel modeling techniques: Traditionally, modeling any system (including a complex system) has relied on a combination of intuition, domain expertise, and luck that, when framed rigorously, can be empirically evaluated on real data. Can we use AI to develop or discover innovative models for explaining the richness of complex systems? Can we reverse-engineer such models to distill a human-understandable theory?

This Special Issue welcomes diverse types of articles including original research, reviews, and perspective papers (upon consultation with the Editors).

Prof. Dr. Mayank Kejriwal
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • complex systems
  • network science
  • applications
  • network analytics
  • social systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Other

3 pages, 190 KiB  
Editorial
Special Issue on Artificial Intelligence and Complex Systems
by Mayank Kejriwal
Appl. Sci. 2023, 13(20), 11153; https://doi.org/10.3390/app132011153 - 11 Oct 2023
Viewed by 928
Abstract
The late Stephen Hawking referred to our current century as the ‘century of complexity’ [...] Full article
(This article belongs to the Special Issue Artificial Intelligence and Complex System)

Research

Jump to: Editorial, Other

16 pages, 6500 KiB  
Article
Pedestrian Counting Based on Piezoelectric Vibration Sensor
by Yang Yu, Xiangju Qin, Shabir Hussain, Weiyan Hou and Torben Weis
Appl. Sci. 2022, 12(4), 1920; https://doi.org/10.3390/app12041920 - 12 Feb 2022
Cited by 12 | Viewed by 3430
Abstract
Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. [...] Read more.
Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods. Full article
(This article belongs to the Special Issue Artificial Intelligence and Complex System)
►▼ Show Figures

Figure 1

22 pages, 2843 KiB  
Article
Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information
by Xi Chen, Hao Ding, Shaofen Fang and Wei Chen
Appl. Sci. 2022, 12(3), 1572; https://doi.org/10.3390/app12031572 - 1 Feb 2022
Cited by 6 | Viewed by 2493
Abstract
This study explored how the success of project crowdfunding can be predicted based on the texts of Internet social welfare crowdfunding projects. Through a calculation of the quantity of information and a mining of the sentimental value of the text, how the text [...] Read more.
This study explored how the success of project crowdfunding can be predicted based on the texts of Internet social welfare crowdfunding projects. Through a calculation of the quantity of information and a mining of the sentimental value of the text, how the text information of the interconnected social welfare crowdfunding project affects the success of the project was studied. To this aim, a sentimental dictionary of Chinese Internet social welfare crowdfunding texts was constructed, and information entropy was used to calculate the quantity of information in the text. It was found that, compared with the information presented in the text, the fundraiser’s social network factors are key in improving the success of fundraising. The sentimental value of the text positively affects the success of fundraising, while the influence of the quantity of information is represented as an inverted, U-shaped relationship. The non-ideal R-squared indices reflected that the multiple linear regression models do not perform well regarding this prediction. Furthermore, this paper validated and analyzed the prediction efficiency of four machine-learning models, including a multiple regression model, a decision tree regression model, a random forest regression model, and an AdaBoost regression model, and the AdaBoost regressor showed the best efficiency, with an accuracy R2 of up to 97.7%. This study provides methods for the quantified processing of information contained in social welfare crowdfunding texts, as well as effective prediction on social welfare crowdfunding, and also seeks to raise the success rate of crowdfunding and thus features commercial and social value. Full article
(This article belongs to the Special Issue Artificial Intelligence and Complex System)
►▼ Show Figures

Figure 1

17 pages, 6193 KiB  
Article
A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning
by Mostafa Zafari, Abolghasem Sadeghi-Niaraki, Soo-Mi Choi and Ali Esmaeily
Appl. Sci. 2021, 11(23), 11534; https://doi.org/10.3390/app112311534 - 6 Dec 2021
Cited by 18 | Viewed by 5728
Abstract
The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is [...] Read more.
The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education. Full article
(This article belongs to the Special Issue Artificial Intelligence and Complex System)
►▼ Show Figures

Figure 1

17 pages, 1239 KiB  
Article
Boosting Fraud Detection in Mobile Payment with Prior Knowledge
by Quan Sun, Tao Tang, Hongfeng Chai, Jie Wu and Yang Chen
Appl. Sci. 2021, 11(10), 4347; https://doi.org/10.3390/app11104347 - 11 May 2021
Cited by 7 | Viewed by 2892
Abstract
With the prevalence of mobile e-commerce, fraudulent transactions conducted by robots are becoming increasingly common in mobile payments, which is severely undermining market fairness and resulting in financial losses. It has become a difficult problem for mobile applications to identify robotic automation accurately [...] Read more.
With the prevalence of mobile e-commerce, fraudulent transactions conducted by robots are becoming increasingly common in mobile payments, which is severely undermining market fairness and resulting in financial losses. It has become a difficult problem for mobile applications to identify robotic automation accurately and efficiently from a massive number of transactions. The current research does not propose any effective method or engineering implementation. In this article, an extension to boost algorithms is presented that permits the incorporation of prior human knowledge as a means of compensating for a training data shortage and improving prediction results. Prior human knowledge is accumulated from historical fraud transactions or transferred from different domains in the form of expert rules and blacklists. The knowledge is applied to extract risk features from transaction data, risk features together with normal features are input into the boosting algorithm to perform training, and therefore we incorporate boosting algorithm with prior human knowledge to improve the performance of the model. For the first time we verified the effectiveness of the method via a widely deployed mobile APP with 150+ million users, and by taking experiments on a certain dataset, the extended boosting model shows an accuracy increase from 0.9825 to 0.9871 and a recall rate increase from 0.888 to 0.948. We also investigated feature differences between robots and normal users and we discovered the behavior patterns of robotic automation that include less spatial motion detected by device sensors (1/10 of normal user pattern), higher IP group-clustering ratio (60% in robots vs. 15% in normal users), higher jailbroken device rate (92.47% vs. 4.64%), more irregular device names and fewer IP address changes. The quantitative analysis result is helpful for APP developers and service providers to understand and prevent fraudulent transactions from robotic automation.This article proposed an optimized boosting model, which has better use in the field of robotic automation detection of mobile phones. By combining prior knowledge and feature importance analysis, the model is more robust when the actual dataset is unbalanced or with few-short samples. The model is also more explainable as feature analysis is available which can be used for generating disposal rules in the actual fake mobile user blocking systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Complex System)
►▼ Show Figures

Figure 1

Other

Jump to: Editorial, Research

15 pages, 355 KiB  
Perspective
Essential Features in a Theory of Context for Enabling Artificial General Intelligence
by Mayank Kejriwal
Appl. Sci. 2021, 11(24), 11991; https://doi.org/10.3390/app112411991 - 16 Dec 2021
Cited by 3 | Viewed by 2935
Abstract
Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be [...] Read more.
Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI. Full article
(This article belongs to the Special Issue Artificial Intelligence and Complex System)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop