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Computers
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AI for Humans and Humans for AI (AI4HnH4AI)
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AI for Humans and Humans for AI (AI4HnH4AI)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 9326

Special Issue Editors

Prof. Dr. Amit Kumar Mishra

E-Mail Website
Guest Editor
National Spectrum Centre, Aberystwyth University, Aberystwyth SY23 3AH, UK
Interests: radar; signal processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Deepak Puthal

E-Mail Website
Guest Editor
Department of Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
Interests: cyber security; blockchain; edge computing; the Internet of Things; generative adversarial networks

Special Issue Information

Dear Colleagues,

We have entered an interesting time, which many experts claim to be the onset of singularity. The rate of technological progress in artificial intelligence (AI) is outpacing societal understanding of its implications. It is now broadly accepted that AI is unlike many of the other innovations that have preceded it. There is a need to understand ways in which AI innovations can be fine-tuned to match the needs of humans. At the same time, there is a need to understand how to refine human constructs (like regulations, pedagogical styles, etc.) to match the environment where AI will be a strong (if not major) influencer.

In this Special Issue, we invite work in this broad domain of AI for humans (AI4H) and humans for AI (H4AI). Researchers are encouraged to submit their work where they have tried to design AI processes and/or algorithms to meet humanitarian needs. At the same time, we encourage researchers to submit work related to the redesign of human constructs (such as pedagogical styles) to match the changes brought forth by AI.

Prof. Dr. Amit Kumar Mishra
Dr. Deepak Puthal
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 250 words) can be sent to the Editorial Office for assessment.

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
  • innovation
  • learning
  • influence
  • humans

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

Published Papers (4 papers)

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Research

17 pages, 1542 KB  
Article
Classification of Drowsiness and Alertness States Using EEG Signals to Enhance Road Safety: A Comparative Analysis of Machine Learning Algorithms and Ensemble Techniques
by Masoud Sistaninezhad, Saman Rajebi, Siamak Pedrammehr, Arian Shajari, Hussain Mohammed Dipu Kabir, Thuong Hoang, Stefan Greuter and Houshyar Asadi
Computers 2025, 14(12), 509; https://doi.org/10.3390/computers14120509 - 24 Nov 2025
Viewed by 931
Abstract
Drowsy driving is a major contributor to road accidents, as reduced vigilance degrades situational awareness and reaction control. Reliable assessment of alertness versus drowsiness can therefore support accident prevention. Key gaps remain in physiology-based detection, including robust identification of microsleep and transient vigilance [...] Read more.
Drowsy driving is a major contributor to road accidents, as reduced vigilance degrades situational awareness and reaction control. Reliable assessment of alertness versus drowsiness can therefore support accident prevention. Key gaps remain in physiology-based detection, including robust identification of microsleep and transient vigilance shifts, sensitivity to fatigue-related changes, and resilience to motion-related signal artifacts; practical sensing solutions are also needed. Using Electroencephalogram (EEG) recordings from the MIT-BIH Polysomnography Database (18 records; >80 h of clinically annotated data), we framed wakefulness–drowsiness discrimination as a binary classification task. From each 30 s segment, we extracted 61 handcrafted features spanning linear, nonlinear, and frequency descriptors designed to be largely robust to signal-quality variations. Three classifiers were evaluated—k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT)—alongside a DT-based bagging ensemble. KNN achieved 99% training and 80.4% test accuracy; SVM reached 80.0% and 78.8%; and DT obtained 79.8% and 78.3%. Data standardization did not improve performance. The ensemble attained 100% training and 84.7% test accuracy. While these results indicate strong discriminative capability, the training–test gap suggests overfitting and underscores the need for validation on larger, more diverse cohorts to ensure generalizability. Overall, the findings demonstrate the potential of machine learning to identify vigilance states from EEG. We present an interpretable EEG-based classifier built on clinically scored polysomnography and discuss translation considerations; external validation in driving contexts is reserved for future work. Full article
(This article belongs to the Special Issue AI for Humans and Humans for AI (AI4HnH4AI))
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43 pages, 20477 KB  
Article
Investigation of Cybersecurity Bottlenecks of AI Agents in Industrial Automation
by Sami Shrestha, Chipiliro Banda, Amit Kumar Mishra, Fatiha Djebbar and Deepak Puthal
Computers 2025, 14(11), 456; https://doi.org/10.3390/computers14110456 - 23 Oct 2025
Cited by 3 | Viewed by 3462
Abstract
The growth of Agentic AI systems in Industrial Automation has brought forth new cybersecurity issues which in turn put at risk the reliability and integrity of these systems. In this study we look at the cybersecurity issues in industrial automation in terms of [...] Read more.
The growth of Agentic AI systems in Industrial Automation has brought forth new cybersecurity issues which in turn put at risk the reliability and integrity of these systems. In this study we look at the cybersecurity issues in industrial automation in terms of the threats, risks, and vulnerabilities related to Agentic AI. We conducted a systematic literature review to report on the present day practices in terms of cybersecurity for industrial automation and Agentic AI. Also we used a simulation based approach to study the security issues and their impact on industrial automation systems. Our study results identify the key areas of focus and what mitigation strategies may be put in place to secure the integration of Agentic AI in industrial automation. Our research brings to the table results which will play a role in the development of more secure and reliable industrial automation systems, which in the end will improve the overall cybersecurity of these systems. Full article
(This article belongs to the Special Issue AI for Humans and Humans for AI (AI4HnH4AI))
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19 pages, 5755 KB  
Article
A Context-Aware Doorway Alignment and Depth Estimation Algorithm for Assistive Wheelchairs
by Shanelle Tennekoon, Nushara Wedasingha, Anuradhi Welhenge, Nimsiri Abhayasinghe and Iain Murray
Computers 2025, 14(7), 284; https://doi.org/10.3390/computers14070284 - 17 Jul 2025
Viewed by 1343
Abstract
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the [...] Read more.
Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users. Full article
(This article belongs to the Special Issue AI for Humans and Humans for AI (AI4HnH4AI))
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23 pages, 1779 KB  
Article
AI_TAF: A Human-Centric Trustworthiness Risk Assessment Framework for AI Systems
by Eleni Seralidou, Kitty Kioskli, Theofanis Fotis and Nineta Polemi
Computers 2025, 14(7), 243; https://doi.org/10.3390/computers14070243 - 22 Jun 2025
Cited by 2 | Viewed by 2725
Abstract
This paper presents the AI Trustworthiness Assessment Framework (AI_TAF), a comprehensive methodology for evaluating and mitigating trustworthiness risks across all stages of an AI system’s lifecycle. The framework accounts for the criticality of the system based on its intended application, the maturity level [...] Read more.
This paper presents the AI Trustworthiness Assessment Framework (AI_TAF), a comprehensive methodology for evaluating and mitigating trustworthiness risks across all stages of an AI system’s lifecycle. The framework accounts for the criticality of the system based on its intended application, the maturity level of the AI teams responsible for ensuring trust, and the organisation’s risk tolerance regarding trustworthiness. By integrating both technical safeguards and sociopsychological considerations, AI_TAF adopts a human-centric approach to risk management, supporting the development of trustworthy AI systems across diverse organisational contexts and at varying levels of human–AI maturity. Crucially, the framework underscores that achieving trust in AI requires a rigorous assessment and advancement of the trustworthiness maturity of the human actors involved in the AI lifecycle. Only through this human-centric enhancement can AI teams be adequately prepared to provide effective oversight of AI systems. Full article
(This article belongs to the Special Issue AI for Humans and Humans for AI (AI4HnH4AI))
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