Research Article
AI-Enabled Interoperability in Nigeria’s Public Sector: Evaluating the Role of X-Road Digital Infrastructure
Ololade Oluwatosin Adesuyi*
Issue:
Volume 10, Issue 2, December 2026
Pages:
179-188
Received:
4 May 2026
Accepted:
22 May 2026
Published:
30 June 2026
Abstract: Despite the efforts being made towards digital transformation, the continued disintegration of digital systems within the public sector and the lack of interoperability frameworks are continuing to hinder effective governance, coordinated service delivery and evidence-informed policy-making in Nigeria. The study focuses on the use of the X-Road digital infrastructure model for enhancing public-sector governance in Nigeria by leveraging the artificial intelligence (AI) capabilities of the interoperability model. Specifically, the study attempts to answer four research questions: How do interoperability challenges impact governance outcomes in Nigeria? How can AI improve data exchange and decision-making? Is X-Road adaptable to Nigeria's governance environment? What institutional conditions are required for successful implementation? The study followed the qualitative document analysis and comparative desk-based research design, based on secondary data regarding Estonia's X-Road framework obtained from policy documents, institutional reports, academic literature and case materials. The results show that interoperability, powered by AI, can be highly beneficial for data integration, administrative automation, transparency, and efficient delivery of public services by enabling real-time information sharing among governments. The study also revealed that digital infrastructure, institutional coordination, technical capacity, legal frameworks and cyber security issues are still significant challenges to implementation in Nigeria. The study finds that technological innovation is not enough for achieving interoperability if institutions and policies are not strong. It therefore calls for gradual and locally tailored adoption of X-Road-like systems, adoption of a comprehensive data protection and information sharing law, investments in digital infrastructure and human capacity development, and a single coordinating body for the implementation of interoperability standards and implementation in public institutions.
Abstract: Despite the efforts being made towards digital transformation, the continued disintegration of digital systems within the public sector and the lack of interoperability frameworks are continuing to hinder effective governance, coordinated service delivery and evidence-informed policy-making in Nigeria. The study focuses on the use of the X-Road dig...
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Research Article
Verification Challenges of AI-Generated Identity Documents: Blockchain Technology as a Trust Layer and AI as a Supporting Signal
Tapendra Baduwal*
Issue:
Volume 10, Issue 2, December 2026
Pages:
189-197
Received:
14 May 2026
Accepted:
25 May 2026
Published:
2 July 2026
Abstract: The rapid advancement of generative AI has revolutionized digitalization, while simultaneously introducing new security challenges. As AI models are increasingly integrated into cameras to enhance or modify images, a fundamental question arises for verification systems: whether captured images retain authentic camera fingerprints, such as Photo-Response Non-Uniformity (PRNU), Color Filter Array (CFA) patterns, physically random sensor noise, and lens distortions, or are heavily altered or fully generated by AI. Modern generative AI models create images that are highly similar to those produced by cameras, increasing the risk of document forgery and verification challenges. To address these challenges, this research proposes blockchain technology as a foundational trust layer for digital identity, enabling secure and tamper-proof evidence recording through an immutable ledger and cryptographic mechanisms. The proposed system integrates blockchain with a layered microservices architecture, separating user management, blockchain interaction, and audit logging into independent services. Communication between services uses gRPC with clearly defined Protocol Buffer schemas for efficient communication. The API layer is implemented using FastAPI for authentication, authorization, and request routing with high performance and automatic documentation. Data is stored in MongoDB, including user profiles, authentication records, verification results, and audit logs, which ensures flexibility and high availability. AI is used as a supporting signal rather than a definitive decision-maker. Experimental evaluation was conducted on 4,550 handwritten signatures, created using real ink pens but not belonging to any specific individual, and 4,550 AI-generated signatures were created using OpenAI's GPT image models, Nano Banana 2, and Qwen image generation models. ResNet50 was used to compute the signal score and achieved an F1 score of 0.996 on the classification task. The proposed method is designed to generalize well across a wide range of document and image domains.
Abstract: The rapid advancement of generative AI has revolutionized digitalization, while simultaneously introducing new security challenges. As AI models are increasingly integrated into cameras to enhance or modify images, a fundamental question arises for verification systems: whether captured images retain authentic camera fingerprints, such as Photo-Res...
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Review Article
A Systematic Review of Emotion Recognition: From Unimodal Signals to Multimodal Integration
Bilkisu Muhammad Bashir*,
Zayyanu Yunusa
Issue:
Volume 10, Issue 2, December 2026
Pages:
198-208
Received:
30 June 2026
Accepted:
9 July 2026
Published:
11 August 2026
DOI:
10.11648/j.ajai.20261002.13
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Abstract: Emotion recognition is a core component of affective computing, enabling intelligent systems to interpret human emotional states across critical applications such as healthcare, online education, and human–computer interaction. Early unimodal approaches relying solely on facial expressions, speech, or text have proven insufficient due to noise, cultural variability, and signal ambiguity, prompting a decisive shift toward multimodal integration. This systematic review, conducted following the PRISMA framework, examines this transition by analyzing 89 peer-reviewed studies selected from an initial pool of 160. The objective is to synthesize current methodologies, compare performance across modalities, and identify persistent technical and ethical barriers. Our findings reveal that multimodal systems, which fuse visual, acoustic, linguistic, and physiological signals, consistently outperform unimodal counterparts, achieving accuracy levels above 85% on benchmark datasets. Deep learning architectures particularly convolutional networks for spatial features, recurrent networks for temporal dependencies, and transformer-based models enhanced with attention mechanisms dominate the field, enabling effective dynamic weighting and fusion of heterogeneous data streams. Despite these advances, several challenges impede real-world deployment. Cross-subject and cross-session variability degrades generalizability, while data scarcity and the lack of large-scale, annotated multimodal corpora constrain model training. Computational complexity, especially in transformer-based fusion, limits edge-device feasibility, and ethical concerns surrounding privacy, demographic bias, and model interpretability remain unresolved. Future research must prioritize scalable and lightweight architectures, inclusive and culturally diverse dataset curation, and explainable AI frameworks that build user trust. Ultimately, transitioning these systems from laboratory prototypes to ethically sound, practical applications will require close interdisciplinary collaboration among computer scientists, psychologists, and ethicists, ensuring that emotion recognition technologies are not only accurate but also fair, transparent, and accessible across diverse real-world settings.
Abstract: Emotion recognition is a core component of affective computing, enabling intelligent systems to interpret human emotional states across critical applications such as healthcare, online education, and human–computer interaction. Early unimodal approaches relying solely on facial expressions, speech, or text have proven insufficient due to noise, cul...
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