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Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 7028

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Editors

Dr. Christos Troussas

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Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: personalization; human–computer interaction; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Akrivi Krouska

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Guest Editor

Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: social network analysis; multimedia applications; artificial intelligence
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Dr. Phivos Mylonas

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Guest Editor

Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: semantic analysis; multimedia applications; artificial intelligence
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Prof. Dr. Cleo Sgouropoulou

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Guest Editor

Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, Greece
Interests: software engineering; educational technology; artificial intelligence
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Special Issue Information

Dear Colleagues,

This Special Issue invites submissions of extended and significantly revised versions of high-quality papers presented at the 5th International Conference on Novel & Intelligent Digital Systems (NiDS 2025). Additionally, the Special Issue enthusiastically welcomes new and original contributions from researchers, academics, and industry practitioners who did not participate in the conference but whose work aligns with its thematic scope.

The Special Issue is dedicated to advancing research and innovation in the design, development, and deployment of intelligent digital systems. It seeks to foster the integration of intelligent techniques into modern software and system architectures, addressing both theoretical foundations and practical applications. Contributions exploring emerging trends, challenges, and future directions in intelligent system design and security are particularly encouraged.

Submissions may cover, but are not limited to, the following topics:

AI-enhanced security mechanisms for digital systems;
Privacy-preserving techniques in data-driven environments;
Human-centered AI interfaces and interactive systems;
Cybersecurity strategies for smart, interconnected environments;
Synergies between AI, IoT, and big data analytics;
Personalized and adaptive systems for diverse application domains;
Affective computing and emotion-aware intelligent systems;
Secure data processing and trustworthy AI frameworks;
Intelligent solutions for sustainable and resilient smart cities;
The ethical, legal, and social implications of intelligent digital systems.

Particular emphasis will be placed on works related to the main tracks of NiDS 2025, including:

Intelligent Multimedia Applications and Human-Centered Technologies;
AI-Driven Transformation for Industry and Society;
Data Intelligence and Ubiquitous Computing Solutions.

However, submissions are not restricted to these areas. High-quality, innovative works that contribute to the broader vision of intelligent and secure digital ecosystems are strongly encouraged, even if they fall outside the direct scope of the conference topics.

This Special Issue provides a valuable platform for scholars and practitioners to disseminate novel insights, groundbreaking methodologies, and impactful applications that are shaping the future of secure, adaptive, and intelligent digital environments.

Dr. Christos Troussas
Dr. Akrivi Krouska
Dr. Phivos Mylonas
Prof. Dr. Cleo Sgouropoulou
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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
database security
cybersecurity in AI systems
human-centered technologies
intelligent multimedia applications
smart environments and IoT
data privacy and protection
big data analytics
ubiquitous computing
secure cloud and distributed systems

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Published Papers (8 papers)

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Research

30 pages, 4954 KB

Open AccessArticle

A Fuzzy Logic-Driven System for Interpretable and Behavior-Aware Student Assessment: E-Teacher Assistant Case Study

by Eleni Papachristou, Christos Troussas, Akrivi Krouska and Cleo Sgouropoulou

Electronics 2026, 15(12), 2671; https://doi.org/10.3390/electronics15122671 - 16 Jun 2026

Viewed by 180

Abstract

This study presents an adaptive learning framework that integrates fuzzy logic and learning analytics to support personalized education and multi-factor student assessment. The proposed system combines cognitive and behavioral indicators to provide an interpretable representation of the learner’s state within a dynamic digital learning environment. The architecture is based on adaptive learner modeling and classroom-level monitoring mechanisms, enabling personalized guidance, adaptive content sequencing, and continuous performance monitoring at both individual and classroom levels. A core contribution of the approach is a fuzzy logic-based evaluation mechanism that aggregates multiple signals, including quiz performance, time spent on theory, help-seeking behavior, and system interaction patterns. These inputs are transformed into fuzzy sets and combined through inference rules to produce interpretable learning level estimates aligned with Bloom’s taxonomy. The approach is grounded in Vygotsky’s Zone of Proximal Development, supporting adaptive scaffolding and targeted instructional interventions. The evaluation results demonstrate a strong correlation between the model outputs and conventional exam performance (r ≈ 0.91), while exhibiting reduced variability (SD ≈ 0.15 compared to SD ≈ 0.20), indicating a more stable representation of learner performance. Furthermore, statistical analysis confirms that the differences between traditional and model-based scores are significant (p < 0.01), suggesting that the proposed approach captures additional dimensions of learner behavior beyond conventional grading metrics. Overall, the findings indicate that integrating fuzzy reasoning with behavioral analytics enables a more interpretable, stable, and pedagogically grounded approach to learner assessment, supporting adaptive and interpretable personalized learning. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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23 pages, 3023 KB

Open AccessArticle

Design of an Adaptive Augmented Reality Guidance System for Mechanical Assembly

by Aleeha Zafar and Magesh Chandramouli

Electronics 2026, 15(11), 2478; https://doi.org/10.3390/electronics15112478 - 4 Jun 2026

Viewed by 341

Abstract

This paper presents the design and development of an adaptive augmented reality (AR) assistance system for complex mechanical assembly tasks. Integrating a wrist-worn optical heart rate sensor to evaluate the user’s cognitive state, the system is intended to run as a standalone application on the Meta Quest 3 headset. The system displays instructions and visual cues directly overlaid on the user’s physical workspace and constantly monitors their heart rate variability through the sensor as an estimate of their cognitive load. When the system detects an overload, it dynamically adjusts the presentation of information—for example, it slows down pacing, simplifies instructions, or switches to a different interaction modality (audio)—as an attempt to reduce the overload. The paper makes three contributions: first, it provides a documented standalone integration of physiological sensing with adaptive interface logic on a mixed reality headset without external compute infrastructure; second, it provides a systematic characterization of platform-specific tracking incompatibilities on the Meta Quest 3, documenting the progression through four spatial registration strategies and the specific failure condition that triggered each transition; third, it reports spatial interface design observations from iterative developer testing in the current prototype configuration, including panel height ranges not previously reported in the AR interface literature at this level of specificity. The paper also discusses the within-subjects evaluation protocol that is planned for final system testing with actual users. The work is intended as an engineering and design contribution that establishes the foundation for subsequent empirical evaluation of adaptive AR guidance in industrial assembly contexts. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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16 pages, 2294 KB

Open AccessArticle

A Quantitative Evaluation of Gradient-Based Visual Explainability Methods Across Convolutional and Transformer-Based Vision Models

by Angelos Tzirtis, Christos Troussas, Akrivi Krouska, Phivos Mylonas and Cleo Sgouropoulou

Electronics 2026, 15(11), 2241; https://doi.org/10.3390/electronics15112241 - 22 May 2026

Viewed by 302

Abstract

Explainable Artificial Intelligence (XAI) has become a critical requirement for the responsible deployment of deep learning systems in safety-critical and regulated domains, particularly in medical imaging. In computer vision, gradient-based explanation methods such as Saliency Maps and Gradient-weighted Class Activation Mapping (Grad-CAM) are widely used for interpreting convolutional neural networks (CNNs). However, the increasing adoption of Vision Transformers (ViTs) introduces structural differences in internal representations that challenge the direct transfer of convolutional explainability mechanisms. This study presents a systematic, quantitative, and statistically validated evaluation of gradient-based visual explainability across CNN architectures (VGG16 and ResNet50) and a Vision Transformer (ViT-B/16), using both a domain-specific medical imaging dataset (brain MRI, tumor vs. non-tumor classification). Beyond qualitative heatmap inspection, we conduct deletion-based faithfulness analysis, sensitivity-to-noise evaluation, feature masking validation, and statistical hypothesis testing over 30 independent runs. All models achieve strong predictive performance on the domain dataset (mean accuracy ≈ 0.99), enabling a fair and meaningful comparison of explanation methods across architectures. Results demonstrate that explanation reliability is highly method- and architecture-dependent. Sensitivity differences are consistently statistically significant, whereas deletion-based faithfulness does not always yield equally strong separation under the adopted masking protocol. Masking-based analysis reveals substantial false-positive rates in certain configurations, indicating that visually plausible heatmaps do not necessarily isolate decision-necessary evidence. These findings underscore the importance of coupling visual explanations with behavioral validation metrics, particularly in high-risk domains governed by emerging regulatory frameworks such as the EU AI Act. Overall, the study advocates for empirically validated, architecture-aware, and statistically grounded approaches to medical XAI. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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23 pages, 1714 KB

Open AccessArticle

Enhancing Korean-Accented English ASR with Transliteration-Based Data Synthesis

by Hana Jang, Taehwa Kim, Hyungwoo Choi and Youngbeom Jung

Electronics 2026, 15(7), 1380; https://doi.org/10.3390/electronics15071380 - 26 Mar 2026

Viewed by 793

Abstract

Despite recent advances in automatic speech recognition (ASR), performance remains limited for Korean-accented English due to the limited availability of accent-specific speech data, including pronunciation and prosodic variations. To address this limitation, we propose a synthetic data generation framework for improving Whisper-based ASR performance. Synthetic speech is generated by converting English text into Hangul-based phonetic transcriptions using an intermediate IPA representation to reflect the phonological characteristics of Korean-accented English. The ASR model is fine-tuned using Low-Rank Adaptation with a mixture of synthetic and authentic speech data. Experimental results demonstrate relative reductions of up to 16.40% in the character error rate, 14.93% in the word error rate, and 14.81% in the phoneme error rate compared to the pretrained baseline. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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16 pages, 3321 KB

Open AccessArticle

Evaluating the X2000: A Novel Integrated Platform for Rapid ADAS Development

by Michael Giuliani and George Pappas

Electronics 2026, 15(3), 679; https://doi.org/10.3390/electronics15030679 - 4 Feb 2026

Viewed by 886

Abstract

In this work, we present the design and evaluation of the X2000, a new development kit created to simplify and accelerate research for advanced driver-assistance systems (ADAS). The X2000 is a complete ADAS development kit for the Ford Mach-E. It includes a forward-facing vehicle-mounted camera, vehicle-mounted AI computer, controller area network flexible data-rate (CAN-FD) and 12 V power connections, and a CAN-FD interface to the vehicle’s forward radar. Central to the kit is a novel ADAS software architecture designed for readability and extensibility. Included in the design are software modules for the following: (1) camera and radar interfacing; (2) image processing; (3) AI model inference; (4) data logging; (5) steering and velocity planning; (6) low-level vehicle controls for steering, acceleration, and braking; (7) lane centering visualization to the car’s 17-inch touchscreen. To build on a proven system, the X2000 integrates the AI model, planner, low-level controls, and radar interfacing software from Openpilot. We build on the excellent work of the Openpilot team while creating a highly simplified system. Openpilot features 17 software processes and 77 inter-process messages, while the X2000 uses 6 processes and 7 inter-process messages. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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29 pages, 818 KB

Open AccessArticle

Templated and Overlay HW/SW Co-Optimization for Crossbar-Free P4 Deparser FPGA Architectures

by Parisa Mashreghi-Moghadam, Tarek Ould-Bachir and Yvon Savaria

Electronics 2025, 14(24), 4850; https://doi.org/10.3390/electronics14244850 - 10 Dec 2025

Viewed by 634

Abstract

The deparser stage in the Protocol-Independent Switch Architecture (PISA) is often overshadowed by parser and match-action optimizations. Yet, it remains a critical performance bottleneck in P4-programmable FPGA data planes. Challenges associated with the deparser stem from dynamic header layouts, variable emission orders, and alignment constraints, which often necessitate resource-intensive designs, such as wide, dynamic crossbar routing. While compile-time specialization techniques can reduce logic usage, they sacrifice runtime adaptability: any change to the protocol graph, including adding, removing, or reordering headers, requires full hardware resynthesis and re-implementation, limiting their practicality for evolving or multi-tenant workloads. This work presents a unified FPGA-targeted deparser architecture that merges templated and overlay concepts within a hardware–software co-design framework. At design time, template parameters define upper bounds on protocol complexity, enabling resource-efficient synthesis tailored to specific workloads. Within these bounds, runtime reconfiguration is supported through overlay control tables derived from static deparser DAG analysis, which capture the per-path emission order, header alignments, and offsets. These tables drive protocol-agnostic, chunk-based emission blocks that eliminate the overhead of crossbar interconnects, thereby significantly reducing complexity and resource usage. The proposed design sustains high throughput while preserving the flexibility needed for in-field updates and long-term protocol evolution. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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21 pages, 1073 KB

Open AccessArticle

A Graph Neural Network Model Incorporating Spatial and Temporal Information for Next-Location Prediction

by Yue-Shi Lee, Show-Jane Yen and Ren-He Wang

Electronics 2025, 14(23), 4657; https://doi.org/10.3390/electronics14234657 - 26 Nov 2025

Viewed by 1290

Abstract

With the rapid growth of smart devices and positioning technologies, spatiotemporal data has become essential for predicting user behavior. However, many existing next-location prediction models employ oversimplified temporal modeling, neglect spatial structure and semantic relationships, and fail to capture complex location interaction patterns. This study proposes a graph neural network model that integrates spatiotemporal features to enhance next-location prediction. There are three components in the proposed method. The first is location feature representation which combines geocodes and location category embeddings to construct semantically enriched node representations. The second is temporal modeling which computes temporal similarity between historical trajectories and current behaviors to generate time-decay weights, thereby capturing behavioral periodicity and preference shifts. The third is preference integration which long-term historical preferences and short-term current preferences are modeled using a long short-term memory (LSTM) network and subsequently fused with spatial preferences to generate a comprehensive semantic representation encompassing both user preferences and location characteristics. Experiments on real-world trajectory datasets demonstrate that our proposed model achieves superior accuracy compared to state-of-the-art approaches in next-location prediction. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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18 pages, 1828 KB

Open AccessArticle

A Hybrid Global-Split WGAN-GP Framework for Addressing Class Imbalance in IDS Datasets

by Jisoo Jang, Taesu Kim, Hyoseng Park and Dongkyoo Shin

Electronics 2025, 14(20), 4068; https://doi.org/10.3390/electronics14204068 - 16 Oct 2025

Cited by 1 | Viewed by 1242

Abstract

The continuously evolving cyber threat landscape necessitates not only resilient defense mechanisms but also the sustained capacity development of security personnel. However, conventional training pipelines are predominantly dependent on static real-world datasets, which fail to adequately reflect the diversity and dynamics of emerging attack tactics. To address these limitations, this study employs a Wasserstein GAN with Gradient Penalty (WGAN-GP) to synthesize realistic network traffic that preserves both temporal and statistical characteristics. Using the CIC-IDS-2017 dataset, which encompasses diverse attack scenarios including brute-force, Heartbleed, botnet, DoS/DDoS, web, and infiltration attacks, two training methodologies are proposed. The first trains a single conditional WGAN-GP on the entire dataset to capture the global distribution. The second employs multiple generators tailored to individual attack types, while sharing a discriminator pretrained on the complete traffic set, thereby ensuring consistent decision boundaries across classes. The quality of the generated traffic was evaluated using a Train on Synthetic, Test on Real (TSTR) protocol with LSTM and Random Forest classifiers, along with distribution similarity measures in the embedding space. The proposed approach achieved a classification accuracy of 97.88% and a Fréchet Inception Distance (FID) score of 3.05, surpassing baseline methods by more than one percentage point. These results demonstrate that the proposed synthetic traffic generation strategy provides advantages in scalability, diversity, and privacy, thereby enriching cyber range training scenarios and supporting the development of adaptive intrusion detection systems that generalize more effectively to evolving threats. Full article

(This article belongs to the Special Issue Advancing Intelligent Digital Systems: Theory, Practice, and Security Frontiers)

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