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Computers
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AI in Its Ecosystem
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AI in Its Ecosystem

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "ICT Infrastructures for Cybersecurity".

Deadline for manuscript submissions: 1 August 2025 | Viewed by 7991

Special Issue Editors

Dr. Behrouz Zolfaghari

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Miami University, Oxford, OH 45056, USA
Interests: cybersecurity; high-performance computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Amin Ramezani

E-Mail Website
Guest Editor
Health Lab (AIH-Lab), Baylor College of Medicine, Houston, TX 77021, USA
Interests: generative AI and large language models (LLMs) in healthcare; AI-driven image processing for medical diagnostics; federated learning for multi-institutional healthcare collaboration; AI-based causal inference and predictive modeling in healthcare; deep natural language processing (NLP) for precision medicine
Dr. Mohsen Hadian

E-Mail Website
Guest Editor
Toronto Western Hospital, University Health Network, Toronto, ON M5G 2C4, Canada
Interests: machine learning and deep learning; reinforcement learning; natural language processing (NLP); computer vision; AI for healthcare; AI in ecosystems; robust AI systems; AI-driven optimization
Dr. Firooz Saghezchi

E-Mail Website
Guest Editor
Distributed Signal Processing, RWTH Aachen University, Kopernikusstraße 16, 52074 Aachen, Germany
Interests: artificial intelligence; machine learning; large language models; wireless communications; MIMO signal processing; radio resource allocation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computers is running this Special Issue entitled “AI in Its Ecosystem.” Like any other branch of science or technology, AI is living in an ecosystem. The ecosystem of AI consists of the following items:

  • Computational Models and Environments: Including the platforms, platforms, environments, or computing models on top of which AI applications can run. To mention a few, parallel and distributed computing, networking environments, Cloud computing, Edge computing, Fog computing, quantum computing, and the IoT lie in this category.
  • Enablers: Including theories, sciences, techniques, and technologies that support the implementation of AI models. Among these enablers, one may refer to optimization, data sciences, hardware and software technologies, etc.
  • Applications: Including the scenarios, environments, and areas where AI is used. Healthcare and medical technology, biology, medical sciences, security, social sciences, physical sciences, engineering, and technology are good examples.

This Special Issue accepts papers related to the intersection of AI and its ecosystem. Areas of interest include, but are not limited to, the following:

  • The application of optimization, data sciences, hardware, and software technologies, etc., in the design and implementation of AI models.
  • AI over parallel and distributed systems, networks and the internet, Cloud computing systems, Edge computing systems, Fog computing systems, quantum computing, and the IoT.
  • The application of AI in healthcare and medical technology, biology, medical sciences, security, social sciences, and physical sciences, as well as engineering and technology.

Dr. Behrouz Zolfaghari
Dr. Amin Ramezani
Dr. Mohsen Hadian
Dr. Firooz Saghezchi
Guest Editors

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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • AI-assisted security
  • secure AI
  • AI-assisted medicine

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

Published Papers (7 papers)

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Research

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23 pages, 1956 KiB  
Article
Artificial Intelligence in Neoplasticism: Aesthetic Evaluation and Creative Potential
by Su Jin Mun and Won Ho Choi
Computers 2025, 14(4), 130; https://doi.org/10.3390/computers14040130 - 2 Apr 2025
Viewed by 662
Abstract
This research investigates the aesthetic evaluation of AI-generated neoplasticist artworks, exploring how well artificial intelligence systems, specifically Midjourney, replicate the core principles of neoplasticism, such as geometric forms, balance, and color harmony. The background of this study stems from ongoing debates about the [...] Read more.
This research investigates the aesthetic evaluation of AI-generated neoplasticist artworks, exploring how well artificial intelligence systems, specifically Midjourney, replicate the core principles of neoplasticism, such as geometric forms, balance, and color harmony. The background of this study stems from ongoing debates about the legitimacy of AI-generated art and how these systems engage with established artistic movements. The purpose of the research is to assess whether AI can produce artworks that meet aesthetic standards comparable to human-created works. The research utilized Monroe C. Beardsley’s aesthetic emotion criteria and Noël Carroll’s aesthetic experience criteria as a framework for evaluating the artworks. A logistic regression analysis was conducted to identify key compositional elements in AI-generated neoplasticist works. The findings revealed that AI systems excelled in areas such as unity, color diversity, and overall artistic appeal but showed limitations in handling monochromatic elements. The implications of this research suggest that while AI can produce high-quality art, further refinement is needed for more subtle aspects of design. This study contributes to understanding the potential of AI as a tool in the creative process, offering insights for both artists and AI developers. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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21 pages, 2585 KiB  
Article
“Optimizing the Optimization”: A Hybrid Evolutionary-Based AI Scheme for Optimal Performance
by Agathoklis A. Krimpenis and Loukas Athanasakos
Computers 2025, 14(3), 97; https://doi.org/10.3390/computers14030097 - 8 Mar 2025
Viewed by 464
Abstract
Optimization algorithms for solving technological and scientific problems often face long convergence times and high computational costs due to numerous input/output parameters and complex calculations. This study focuses on proposing a method for minimizing response times for such algorithms across various scientific fields, [...] Read more.
Optimization algorithms for solving technological and scientific problems often face long convergence times and high computational costs due to numerous input/output parameters and complex calculations. This study focuses on proposing a method for minimizing response times for such algorithms across various scientific fields, including the design and manufacturing of high-performance, high-quality components. It introduces an innovative mixed-scheme optimization algorithm aimed at effective optimization with minimal objective function evaluations. Indicative key optimization algorithms—namely, the Genetic Algorithm, Firefly Algorithm, Harmony Search Algorithm, and Black Hole Algorithm—were analyzed as paradigms to standardize parameters for integration into the mixed scheme. The proposed scheme designates one algorithm as a “leader” to initiate optimization, guiding others in iterative evaluations and enforcing intermediate solution exchanges. This collaborative process seeks to achieve optimal solutions at reduced convergence costs. This mixed scheme was tested on challenging benchmark functions, demonstrating convergence speeds that were at least three times faster than the best-performing standalone algorithms while maintaining solution quality. These results highlight its potential as an efficient optimization approach for computationally intensive problems, regardless of the included algorithms and their standalone performance. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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25 pages, 2529 KiB  
Article
Beyond Snippet Assistance: A Workflow-Centric Framework for End-to-End AI-Driven Code Generation
by Vladimir Sonkin and Cătălin Tudose
Computers 2025, 14(3), 94; https://doi.org/10.3390/computers14030094 - 6 Mar 2025
Viewed by 782
Abstract
Recent AI-assisted coding tools, such as GitHub Copilot and Cursor, have enhanced developer productivity through real-time snippet suggestions. However, these tools primarily assist with isolated coding tasks and lack a structured approach to automating complex, multi-step software development workflows. This paper introduces a [...] Read more.
Recent AI-assisted coding tools, such as GitHub Copilot and Cursor, have enhanced developer productivity through real-time snippet suggestions. However, these tools primarily assist with isolated coding tasks and lack a structured approach to automating complex, multi-step software development workflows. This paper introduces a workflow-centric AI framework for end-to-end automation, from requirements gathering to code generation, validation, and integration, while maintaining developer oversight. Key innovations include automatic context discovery, which selects relevant codebase elements to improve LLM accuracy; a structured execution pipeline using Prompt Pipeline Language (PPL) for iterative code refinement; self-healing mechanisms that generate tests, detect errors, trigger rollbacks, and regenerate faulty code; and AI-assisted code merging, which preserves manual modifications while integrating AI-generated updates. These capabilities enable efficient automation of repetitive tasks, enforcement of coding standards, and streamlined development workflows. This approach lays the groundwork for AI-driven development that remains adaptable as LLM models advance, progressively reducing the need for human intervention while ensuring code reliability. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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19 pages, 6430 KiB  
Article
Improving Road Safety with AI: Automated Detection of Signs and Surface Damage
by Davide Merolla, Vittorio Latorre, Antonio Salis and Gianluca Boanelli
Computers 2025, 14(3), 91; https://doi.org/10.3390/computers14030091 - 4 Mar 2025
Viewed by 870
Abstract
Public transportation plays a crucial role in our lives, and the road network is a vital component in the implementation of smart cities. Recent advancements in AI have enabled the development of advanced monitoring systems capable of detecting anomalies in road surfaces and [...] Read more.
Public transportation plays a crucial role in our lives, and the road network is a vital component in the implementation of smart cities. Recent advancements in AI have enabled the development of advanced monitoring systems capable of detecting anomalies in road surfaces and road signs, which can lead to serious accidents. This paper presents an innovative approach to enhance road safety through the detection and classification of traffic signs and road surface damage using advanced deep learning techniques (CNN), achieving over 90% precision and accuracy in both detection and classification of traffic signs and road surface damage. This integrated approach supports proactive maintenance strategies, improving road safety and resource allocation for the Molise region and the city of Campobasso. The resulting system, developed as part of the CTE Molise research project funded by the Italian Minister of Economic Growth (MIMIT), leverages cutting-edge technologies such as cloud computing and High-Performance Computing with GPU utilization. It serves as a valuable tool for municipalities, for the quick detection of anomalies and the prompt organization of maintenance operations. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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16 pages, 48485 KiB  
Article
Detection of Surgical Instruments Based on Synthetic Training Data
by Leon Wiese, Lennart Hinz, Eduard Reithmeier, Philippe Korn and Michael Neuhaus
Computers 2025, 14(2), 69; https://doi.org/10.3390/computers14020069 - 15 Feb 2025
Viewed by 507
Abstract
Due to a significant shortage of healthcare staff, medical facilities are increasingly challenged by the need to deploy current staff more intensively, which can lead to significant complications for patients and staff. Digital surgical assistance systems that track all instruments used in procedures [...] Read more.
Due to a significant shortage of healthcare staff, medical facilities are increasingly challenged by the need to deploy current staff more intensively, which can lead to significant complications for patients and staff. Digital surgical assistance systems that track all instruments used in procedures can make a significant contribution to relieving the load on staff, increasing efficiency, avoiding errors and improving hygiene. Due to data safety concerns, laborious data annotation and the complexity of the scenes, as well as to increase prediction accuracy, the provision of synthetic data is key to enabling the wide use of artificial intelligence for object recognition and tracking in OR settings. In this study, a synthetic data generation pipeline is introduced for the detection of eight surgical instruments during open surgery. Using 3D models of the instruments, synthetic datasets consisting of color images and annotations were created. These datasets were used to train common object detection networks (YOLOv8) and compared against networks solely trained on real data. The comparison, conducted on two real image datasets with varying complexity, revealed that networks trained on synthetic data demonstrated better generalization capabilities. A sensitivity analysis showed that synthetic data-trained networks could detect surgical instruments even at higher occlusion levels than real data-trained networks. Additionally, 1920 datasets were generated using different parameter combinations to evaluate the impact of various settings on detection performance. Key findings include the importance of object visibility, occlusion, and the inclusion of occlusion objects in improving detection accuracy. The results highlight the potential of synthetic datasets to simulate real-world conditions, enhance network generalization, and address data shortages in specialized domains like surgical instrument detection. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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14 pages, 3057 KiB  
Article
Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality
by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu and Nitesh Dhar Badgayan
Computers 2025, 14(2), 32; https://doi.org/10.3390/computers14020032 - 22 Jan 2025
Viewed by 825
Abstract
The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides [...] Read more.
The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides a measurable framework for evaluating the overall customer satisfaction. This study endeavors to ascertain whether all SERVQUAL dimensions carry equal weight in their effect on the overall service quality and to estimate the service quality based on various input features. To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. This study revealed that both CatBoost and Azure AutoML identified the frequency of hotel stays and age group as the dominant predictors of service quality. Additionally, Azure AutoML highlighted the marital status as a more significant factor, suggesting its potential influence on customer preferences. The comparative modeling results demonstrated a strong alignment between the feature importance derived from CatBoost and Azure AutoML, enabling decision-makers to identify which dimensions are influenced by specific predictors and focus on targeted improvements. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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Review

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24 pages, 3170 KiB  
Review
Leveraging Artificial Intelligence in Project Management: A Systematic Review of Applications, Challenges, and Future Directions
by Dorothea S. Adamantiadou and Loukas Tsironis
Computers 2025, 14(2), 66; https://doi.org/10.3390/computers14020066 - 13 Feb 2025
Viewed by 4183
Abstract
This article presents a systematic literature review exploring the integration of Artificial Intelligence (AI) methodologies in project management (PM). Key applications include cost estimation, duration forecasting, and risk assessment, which are critical factors for project success. This review synthesizes findings from 97 peer-reviewed [...] Read more.
This article presents a systematic literature review exploring the integration of Artificial Intelligence (AI) methodologies in project management (PM). Key applications include cost estimation, duration forecasting, and risk assessment, which are critical factors for project success. This review synthesizes findings from 97 peer-reviewed studies published between 2011 and 2024, using the PRISMA methodology to ensure rigor and transparency. AI techniques such as machine learning, deep learning, and hybrid models have exhibited their potential to enhance PM techniques across projects’ phases, including planning, execution, and monitoring. Decision trees are created to represent the application of AI methodologies in various PM stages and tasks to facilitate understanding and real-world implementation. Among these are hybrid AI models that enhance risk assessment, duration forecasting, and cost estimation, as well as categorization based on project phases to optimize AI integration. Despite these advancements, there are still gaps in addressing dynamic project environments, validating AI models with real-world data, and expanding research into underexplored phases like project closure. Full article
(This article belongs to the Special Issue AI in Its Ecosystem)
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