Editorial: Frontiers in information technology, electronics, and management innovation

Rapid advances in information technology are reshaping how complex systems are designed, implemented, analyzed, secured, and managed. Artificial intelligence (AI), machine learning (ML), model-driven approaches, advanced data analytics, and emerging digital infrastructures increasingly interact rather than evolve as isolated technological areas. Their adoption also creates challenges related to reliability, interoperability, cybersecurity, explainability, data quality, and the transition from experimental solutions to methods applicable in operational environments.

The Research Topic Frontiers in information technology, electronics, and management innovation brings together research addressing these challenges from methodological, technological, and application-oriented perspectives. The contributing articles cover AI-supported prediction and automated engineering, cybersecurity, knowledge management, remote sensing, digital manufacturing, biometrics, and economic forecasting. Despite this diversity, several common themes emerge: increasing automation of complex tasks, integration of heterogeneous information, development of reliable computational methods, and the need to balance technological performance with practical applicability.

A prominent theme is the continuing development of AI and ML methods for prediction, classification, and generation tasks. A novel lightweight deep learning model for early prediction of cardiovascular disease investigates machine and deep learning models for clinical risk prediction, emphasizing robust validation and statistical reliability in addition to predictive accuracy (Gul et al.). Enhanced SQL injection detection using chi-square feature selection and machine learning classifiers demonstrates the importance of systematic data preparation and feature selection, showing performance improvements across several classifiers (Casmiry et al.). Together, these contributions illustrate a transition from simply applying learning algorithms toward constructing and evaluating complete analytical workflows.

AI-based methods are also extending automation into domains traditionally dependent on human expertise. Automating the creation of fashion patterns using deep learning algorithms combines visual and textual information to generate structured, CAD-compatible garment patterns, connecting multimodal AI with manufacturing requirements (Alsabhi). From another application perspective, Image processing and AI techniques for climate change detection using remote sensing: a comprehensive review synthesizes developments in image processing, machine learning, deep learning, spatio-temporal modeling, and multi-sensor fusion (Agarwal et al.). It demonstrates both the growing capabilities of AI-supported Earth observation and persistent challenges involving generalization, labeled data, and heterogeneous sensor information.

Cybersecurity constitutes another major theme. Explainable hybrid intrusion detection for SCADA/ICS: a review and research agenda examines trade-offs between signature-based and anomaly-based intrusion detection and highlights explainability, process awareness, false-positive control, and deployment complexity as important research directions (Skrodelis and Romanovs). A capability driven automated cybersecurity monitoring and response system approaches cybersecurity from an organizational and architectural perspective, using capability modeling to align monitoring and automated response mechanisms with organizational requirements while incorporating machine learning for threat detection (Minkevics and Grabis).

At the network level, Anomaly detection in netflow traffic: workflow for dataset preparation and analysis combines clustering and time-series forecasting to support anomaly detection and reliability assessment (Roponena et al.). At the IoT level, Enhancing IoT security through blockchain integration reviews blockchain-based approaches to data integrity, authentication, and access control while identifying scalability, energy efficiency, and privacy as continuing challenges (Shujaa et al.). These studies demonstrate that cybersecurity increasingly requires combinations of analytical techniques, architectural mechanisms, organizational requirements, and methods for establishing confidence in automated decisions.

The scope of digital trust is broadened by Detecting freebooted content in social media ads: multimodal provenance and e-commerce implications (Weinlich and Semeradova). Combining perceptual hashing, audio fingerprinting, vision embeddings, natural-language processing, and network analysis, the study connects technical provenance detection with deceptive advertising, intellectual property, platform governance, and consumer protection. Similarly, Implementation of selected ISO/IEC 29794-5 measures and proposing alternatives addresses quality in biometric systems through face image quality assessment and comparison of standardized measures with alternative contemporary solutions (Otlik and Wodo). Both contributions illustrate how technical quality and security mechanisms need to be considered within their operational and socio-technical contexts.

A further strand concerns explicit representations of knowledge, system structure, and transformation logic. From software architecture models to pipelines: a conceptual framework for model transformation in DevOps investigates the integration of model-driven engineering with continuous integration and continuous delivery practices (Karlovs-Karlovskis et al.). By conceptualizing transformations from software architecture models toward DevOps pipeline models and resulting code, the study points toward greater reuse, traceability, and automation in software delivery.

A related emphasis on explicit knowledge representation appears in LLM-supported collaborative ontology design for data and knowledge management platforms (Kampars et al.). The study extends an established ontology engineering methodology with large language model support while retaining domain experts in the validation loop. Its application to hydrogen-material interaction research and integration with a Data and Knowledge Management Platform illustrate how AI can complement structured semantic technologies. This combination is particularly relevant for multidisciplinary environments in which interoperability, FAIR data principles, and domain expertise must coexist.

Finally, the Research Topic demonstrates that methodological innovation remains important alongside developments in AI. The smooth transition autoregressive models for the unemployment rate of latvia compares linear and nonlinear time-series models and introduces modifications of existing R functionality for several smooth transition autoregressive models (Pavlenko). The distinction between models providing the best in-sample fit and those performing better in out-of-sample forecasting highlights the importance of rigorous model comparison and application-specific evaluation.

Taken together, the articles reflect several directions shaping current information technology research. Automation is increasingly moving beyond individual algorithms toward integrated workflows and engineering processes. AI is being combined with established approaches such as model-driven engineering, ontology engineering, statistical modeling, cybersecurity architectures, and domain-specific standards. At the same time, the contributions demonstrate that predictive or computational performance alone is insufficient: generalizability, explainability, interoperability, reliability, reproducibility, and operational applicability are becoming equally important considerations.

The diversity of application areas represented here, from healthcare and climate monitoring to software engineering, cybersecurity, manufacturing, biometrics, digital commerce, materials science, and economic forecasting, demonstrates the increasingly interdisciplinary character of information technology innovation. Collectively, these contributions provide a perspective on how intelligent, data-driven, model-based, and secure approaches can be developed and combined to address complex problems across scientific and industrial domains.

Generative AI statement

The author(s) declared that Generative AI was used in the creation of this manuscript. Generative AI was used to assist with language editing, text structuring, and refinement of the manuscript. The author(s) reviewed and approved the final content and take full responsibility for the manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

Edited and reviewed by: Chintha Tellambura, University of Alberta, Canada