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AI-Driven Innovations: Emerging Trends, Security, and Industrial Solutions
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AI-Driven Innovations: Emerging Trends, Security, and Industrial Solutions

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1312

Special Issue Editors

Dr. Mohammed Abdulhakim Al-Absi

E-Mail Website
Guest Editor
Department of Smart Computing, Kyungdong University, Gosung 24764, Republic of Korea
Interests: IoT; VANET; UAV; AI; cryptology; network security; side-channel attack; big data; deep learning; cloud computing; computer networks; digital communications
Dr. Ahmed A. Abdulhakim Al-Absi

E-Mail Website
Guest Editor
Department of Smart Computing, Kyungdong University, Gosung 24764, Republic of Korea
Interests: database systems; big data; hadoop; cloud computing; distributed systems; parallel computing; high-performance computing; VANET; bioinformatics
Dr. Nadhem Ebrahim

E-Mail Website
Guest Editor
Department of Computer Science, College of Engineering and Polymer Science, University of Akron Ohio, Akron, OH 44325, USA
Interests: AI; machine learning; software security; IoT

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative potential of artificial intelligence (AI) across a wide range of sectors, focusing on recent advances, security challenges, and practical solutions in industry.

This Special Issue concentrates on AI-driven innovations that are reshaping industries and pushing the boundaries of what is theoretically and practically possible. We invite research papers that highlight the role of AI in developing new technologies, improving decision-making processes, and creating safer, smarter systems.

This Special Issue welcomes contributions from a variety of fields, including healthcare, autonomous systems, robotics, software design, and cybersecurity. The aim is to cover the latest trends, breakthrough safety solutions, and cross-industry solutions of AI and provide a comprehensive overview of the current state and future directions of AI-powered technologies.

This Special Issue will serve as a platform for sharing new insights and research findings that bridge the gap between AI theory and industry practice. We aim to address the pressing safety concerns arising from the use of AI technologies while exploring how these innovations are impacting operational efficiency and decision-making in various industries.

While there is a growing body of literature on AI and its solutions, this Special Issue is intended to complement existing work by providing a focused discussion of emerging trends and safety implications. This Special Issue is an important resource for understanding how AI is being used in industry and what safety measures are needed to ensure safe and robust deployment. By compiling the latest research, we aim to provide new perspectives and comprehensive overviews that will enrich the existing academic and industry discourse.

We invite researchers and practitioners from academia and industry to contribute original research articles and reviews. Submissions related to the following topics are especially welcome:

  • AI in industry applications (healthcare, finance, manufacturing, IoT, etc.);
  • Security measures for AI systems;
  • AI for autonomous systems (drones and autonomous vehicles);
  • AI in robotics;
  • AI and software design innovations;
  • AI-driven healthcare solutions;
  • Cybersecurity and AI;
  • Ethical AI and responsible deployment;
  • AI for operational efficiency and decision-making.

We look forward to receiving your submissions.

Dr. Mohammed Abdulhakim Al-Absi
Dr. Ahmed A. Abdulhakim Al-Absi
Dr. Nadhem Ebrahim
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. AI 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 1600 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 in industry applications (healthcare, finance, manufacturing, IoT, etc.)
  • security measures for AI systems
  • AI for autonomous systems (drones and autonomous vehicles)
  • AI in robotics
  • AI and software design innovations
  • AI-driven healthcare solutions
  • cybersecurity and AI
  • ethical AI and responsible deployment
  • AI for operational efficiency and decision-making

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

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29 pages, 1051 KiB  
Article
Urdu Toxicity Detection: A Multi-Stage and Multi-Label Classification Approach
by Ayesha Rashid, Sajid Mahmood, Usman Inayat and Muhammad Fahad Zia
AI 2025, 6(8), 194; https://doi.org/10.3390/ai6080194 - 21 Aug 2025
Viewed by 87
Abstract
Social media empowers freedom of expression but is often misused for abuse and hate. The detection of such content is crucial, especially in under-resourced languages like Urdu. To address this challenge, this paper designed a comprehensive multilabel dataset, the Urdu toxicity corpus (UTC). [...] Read more.
Social media empowers freedom of expression but is often misused for abuse and hate. The detection of such content is crucial, especially in under-resourced languages like Urdu. To address this challenge, this paper designed a comprehensive multilabel dataset, the Urdu toxicity corpus (UTC). Second, the Urdu toxicity detection model is developed, which detects toxic content from an Urdu dataset presented in Nastaliq Font. The proposed framework initially processed the gathered data and then applied feature engineering using term frequency-inverse document frequency, bag-of-words, and N-gram techniques. Subsequently, the synthetic minority over-sampling technique is used to address the data imbalance problem, and manual data annotation is performed to ensure label accuracy. Four machine learning models, namely logistic regression, support vector machine, random forest, and gradient boosting, are applied to preprocessed data. The results indicate that the RF outperformed all evaluation metrics. Deep learning algorithms, including long short-term memory (LSTM), Bidirectional LSTM, and gated recurrent unit, have also been applied to UTC for classification purposes. Random forest outperforms the other models, achieving a precision, recall, F1-score, and accuracy of 0.97, 0.99, 0.98, and 0.99, respectively. The proposed model demonstrates a strong potential to detect rude, offensive, abusive, and hate speech content from user comments in Urdu Nastaliq. Full article
(This article belongs to the Special Issue AI-Driven Innovations: Emerging Trends, Security, and Industrial Solutions)
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18 pages, 1554 KiB  
Article
ChatCVD: A Retrieval-Augmented Chatbot for Personalized Cardiovascular Risk Assessment with a Comparison of Medical-Specific and General-Purpose LLMs
by Wafa Lakhdhar, Maryam Arabi, Ahmed Ibrahim, Abdulrahman Arabi and Ahmed Serag
AI 2025, 6(8), 163; https://doi.org/10.3390/ai6080163 - 22 Jul 2025
Viewed by 587
Abstract
Large language models (LLMs) are increasingly being applied to clinical tasks, but it remains unclear whether medical-specific models consistently outperform smaller, generalpurpose ones. This study investigates that assumption in the context of cardiovascular disease (CVD) risk assessment. We fine-tuned eight LLMs—both general-purpose and [...] Read more.
Large language models (LLMs) are increasingly being applied to clinical tasks, but it remains unclear whether medical-specific models consistently outperform smaller, generalpurpose ones. This study investigates that assumption in the context of cardiovascular disease (CVD) risk assessment. We fine-tuned eight LLMs—both general-purpose and medical-specific—using textualized data from the Behavioral Risk Factor Surveillance System (BRFSS) to classify individuals as “High Risk” or “Low Risk”. To provide actionable insights, we integrated a Retrieval-Augmented Generation (RAG) framework for personalized recommendation generation and deployed the system within an interactive chatbot interface. Notably, Gemma2, a compact 2B-parameter general-purpose model, achieved a high recall (0.907) and F1-score (0.770), performing on par with larger or medical-specialized models such as Med42 and BioBERT. These findings challenge the common assumption that larger or specialized models always yield superior results, and highlight the potential of lightweight, efficiently fine-tuned LLMs for clinical decision support—especially in resource-constrained settings. Overall, our results demonstrate that general-purpose models, when fine-tuned appropriately, can offer interpretable, high-performing, and accessible solutions for CVD risk assessment and personalized healthcare delivery. Full article
(This article belongs to the Special Issue AI-Driven Innovations: Emerging Trends, Security, and Industrial Solutions)
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