Articles

Quantum-Safe Artificial Intelligence: A Systematic Review of Post-Quantum Cryptography Applications

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Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography.

Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed.

Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study.

Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.

Transforming the Accounting Profession in the Era of Artificial Intelligence: A Comprehensive Analysis of Challenges, Opportunities, and Competency Roadmaps for Indonesian Accounting Graduates

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Objective: This study aims to analyze the impact of Artificial Intelligence (AI) integration on the accounting profession in Indonesia, with a focus on identifying adaptation challenges, emerging career opportunities, and developing competency recommendations for preparing accounting graduates.

Method: This study uses a systematic literature review with a descriptive-analytical approach. Primary data sources consist of 15 research articles, institutional publications (such as the Indonesian Institute of Accountants), and current media analysis (2024-2025) discussing accounting, AI, and the future of work [1, 2, 3, 4, 5, 6].

Key Findings: The analysis shows that AI automates routine accounting tasks [4], but paradoxically opens up new strategic roles such as financial data analysts, cyber auditors, and sustainability consultants [2, 3]. The main challenge lies in the digital competency gap [5]. This study identifies a critical need for a hybrid curriculum that combines traditional accounting skills, data literacy (such as the use of Power BI, basic Python), and soft skills such as critical thinking and ethics [1, 6, 7, 8].

Conclusion: The future of the accounting profession is not replacement by machines, but rather an evolution towards human-AI collaboration [9]. The success of accounting graduates is determined by adaptability, continuous learning, and mastery of a unique combination of technical, digital, and strategic competencies [10, 14].

A Review of AI-powered Diagnosis of Rare Diseases

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The diagnosis of rare diseases presents significant challenges due to their low prevalence, complex symptomatology, and the scarcity of specialized knowledge. However, advancements in Artificial Intelligence (AI) offer promising solutions to these challenges. This review explores the current state of AI-powered diagnostic tools for rare diseases, focusing on the methodologies, algorithms, and platforms utilized in this emerging field. We examine how AI technologies, such as machine learning, deep learning, and natural language processing, are being integrated into clinical practice to enhance diagnostic accuracy and speed. The research also provides the examples that highlight the successes and limitations of AI in this domain, providing insights into how AI can be harnessed to improve patient outcomes in rare disease diagnosis and management.

The Role of AI in Customer Sentiment Analysis for Strategic Business Decisions

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Customer sentiment analysis has become a vital tool for businesses seeking to understand consumer emotions, preferences, and feedback in real-time. Traditional sentiment analysis methods often struggle with scalability, contextual interpretation, and processing unstructured data from diverse sources such as social media, customer reviews, and survey responses. Artificial Intelligence (AI) has revolutionized this domain by leveraging advanced Natural Language Processing (NLP) techniques, including transformer-based models (e.g., BERT, GPT), recurrent neural networks (RNNs), and sentiment-aware embeddings, to extract nuanced insights with higher accuracy and efficiency. AI-driven sentiment analysis enhances customer experience, optimizes marketing strategies, and informs strategic business decisions in areas such as product development and risk management. However, challenges such as algorithmic bias, data privacy concerns, and model interpretability remain critical hurdles. This paper explores these challenges while discussing potential solutions, such as debiasing techniques, federated learning for privacy-preserving sentiment analysis, and explainable AI approaches. Furthermore, it highlights future advancements that could improve the accuracy, reliability, and ethical application of AI in sentiment analysis, ultimately strengthening data-driven decision-making for businesses in dynamic market environments.

Revolutionizing DevOps Security: AI and ML-Enabled Automated Testing Approaches

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In modern DevOps environments, the integration of security practices poses significant challenges due to the fast-paced nature of Continuous Integration/Continuous Deployment (CI/CD) pipelines. Traditional security testing methods are usually too slow and reactive to address vulnerabilities effectively in such dynamic settings. To overcome these challenges, organizations are increasingly adopting automated security testing solutions that leverage Artificial Intelligence (AI) and Machine Learning (ML). This paper discusses AI and ML capabilities in automating security testing during DevOps. It talks about how these technologies can improve security by enabling real-time threat detection, reducing false positives, and adapting to new vulnerabilities through continuous learning. Key AI/ML-based tools and techniques, along with their integration into DevOps workflows, are also discussed in detail. It also covers the integration challenges and the potential of AI/ ML in security testing in the coming years.

Optimizing AI-Integrated Creative Process in Advertising Industry through KBPMS Approach

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Background – The demand for content across various rising digital media platforms pushes the advertising secotr to adopt Artificial Intelligence (AI) automation to improve creativity, speed, and efficiency, especially in areas like art direction, copywriting, and graphic design. While AI offers solutions to improve efficiency and support creative processes, advertising agency stakeholders start to see the urgency in assessing how AI can work alongside human creativity to produce essential quality content for the client’s value creation as the industry moves forward for a sustainable business growth.

Methodology – This research uses mixed-method; Quantitative method to measure AI integration within advertising agencies and assess audience reactions to AI-generated ads, establishing a link between AI usage and audience behavior; Qualitative method through In-Depth Interviews to identify the underlying insights from the advertising professionals’ perspective in integrating AI on daily basis. The findings are processed for the development of a Performance Management System (PMS) using AHP scored by industry experts as the basis to prioritize the Key Performance Indicators (KPIs)

Practical implications – This PMS framework is designed for macro-level advertising agencies to monitor and optimize the use of AI tools effectively through weighted KPIs and strategic AI investments.

Originality/value – This study contributes to the existing industry study by introducing a performance measurement and addresses a theoretical gap between AI-driven creative process and its impact to the industry’s value creation.

The Importance of Studying Spontaneous Speech in Computational Linguistics

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This scientific work provides information on the importance of studying spontaneous speech in computational linguistics. Studying spontaneous speech has numerous practical implications. The ramifications of spontaneous speech analysis are extensive, ranging from improving voice assistants and speech-to-text systems to enhancing human-computer interaction. An examination of spontaneous speech in computational linguistics offers a more authentic depiction of language usage, poses difficulties for current models, and opens up fresh opportunities for enhancing the precision and adaptability of language processing systems. The integration of spontaneous speech analysis will be crucial in developing the discipline of computational linguistics as technology progresses.