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J. Theor. Appl. Electron. Commer. Res., Volume 20, Issue 4 (December 2025) – 2 articles

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Editorial
Blockchain Technology and Decentralized Applications: CBDC, Healthcare, and Not-for-Profit Organizations
by Rand Kwong Yew Low and Terry Marsh
J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 254; https://doi.org/10.3390/jtaer20040254 (registering DOI) - 24 Sep 2025
Abstract
We discuss three applications of blockchain data technology that illustrate its considerable problem-solving potential in: (i) Centralized Bank Digital Currencies (CBDC); (ii) Healthcare (HC); and (iii) Non-Profit Organizations (NPOs). Key solution features include security and immutability, along with authentication in a decentralized network [...] Read more.
We discuss three applications of blockchain data technology that illustrate its considerable problem-solving potential in: (i) Centralized Bank Digital Currencies (CBDC); (ii) Healthcare (HC); and (iii) Non-Profit Organizations (NPOs). Key solution features include security and immutability, along with authentication in a decentralized network that can yield the same consensus solution as a single centralized computer would. But notwithstanding the strength of blockchain’s security, vulnerabilities in the wider infrastructure of the applications we considered. We discuss real-world vulnerabilities in error correction and smart contract code, and the integration of blockchain data and infrastructure that is essential in day-to-day operation. Further, the decentralization in this (Web 2.0) network infrastructure is, if not the proverbial “bug”, a weakness and decidedly not a feature. Full article
(This article belongs to the Special Issue Blockchain Business Applications and the Metaverse)
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Article
Scalable Gender Profiling from Turkish Texts Using Deep Embeddings and Meta-Heuristic Feature Selection
by Hakan Gunduz
J. Theor. Appl. Electron. Commer. Res. 2025, 20(4), 253; https://doi.org/10.3390/jtaer20040253 (registering DOI) - 24 Sep 2025
Abstract
Accurate gender identification from written text is critical for author profiling, recommendation systems, and demographic analytics in digital ecosystems. This study introduces a scalable framework for gender classification in Turkish, combining contextualized BERTurk and subword-aware FastText embeddings with three meta-heuristic feature selection algorithms: [...] Read more.
Accurate gender identification from written text is critical for author profiling, recommendation systems, and demographic analytics in digital ecosystems. This study introduces a scalable framework for gender classification in Turkish, combining contextualized BERTurk and subword-aware FastText embeddings with three meta-heuristic feature selection algorithms: Genetic Algorithm (GA), Jaya and Artificial Rabbit Optimization (ARO). Evaluated on the IAG-TNKU corpus of 43,292 balanced Turkish news articles, the best-performing model—BERTurk+GA+LSTM—achieves 89.7% accuracy, while ARO reduces feature dimensionality by 90% with minimal performance loss. Beyond in-domain results, exploratory zero-shot and few-shot adaptation experiments on Turkish e-commerce product reviews demonstrate the framework’s transferability: while zero-shot performance dropped to 59.8%, few-shot adaptation with only 200–400 labeled samples raised accuracy to 69.6–72.3%. These findings highlight both the limitations of training exclusively on news articles and the practical feasibility of adapting the framework to consumer-generated content with minimal supervision. In addition to technical outcomes, we critically examine ethical considerations in gender inference, including fairness, representation, and the binary nature of current datasets. This work contributes a reproducible and linguistically informed baseline for gender profiling in morphologically rich, low-resource languages, with demonstrated potential for adaptation across domains such as social media and e-commerce personalization. Full article
(This article belongs to the Special Issue Human–Technology Synergies in AI-Driven E-Commerce Environments)
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