<?xml version="1.0" encoding="UTF-8" ?><rss version="2.0">
<channel>
<title>Asian Journal of Mathematics &amp; Statistics - Current Issue</title>
<link>https://scialert.net</link>
<description>Asian Journal of Mathematics &amp; Statistics</description>
<language>en-us</language>
<copyright>Science Alert</copyright>
<pubDate>Thu, 09 Apr 2026 18:11:57 +0200</pubDate>
<lastBuildDate>Thu, 09 Apr 2026 18:14:14 +0200</lastBuildDate>
<generator>RssPublisher 0.2.0 beta</generator>
<image>
<url>https://scialert.net/images/logo.gif</url>
<title>Asian Journal of Mathematics &amp; Statistics - Current Issue</title>
<link>https://scialert.net</link>
<height>41</height>
<width>233</width>
<description>Asian Journal of Mathematics &amp; Statistics</description>
</image>
<item>
Admissible hom-Mock Lie Algebras: Dual Representations, Matched Pairs, Manin Triples and Bialgebras<title><![CDATA[Admissible hom-Mock Lie Algebras: Dual Representations, Matched Pairs, Manin Triples and Bialgebras]]></title> 
<description><![CDATA[This work addresses the admissible representations of hom-Mock Lie algebras by studying their dual representations. The analysis establishes the conditions for admissibility, under which admissible hom-Mock Lie algebras are defined via their adjoint representations. These conditions allow for a systematic study of the structural properties and internal symmetries of such algebras. Matched pairs of admissible hom-Mock Lie algebras are constructed, providing a framework to understand the interaction between two compatible algebraic structures. In particular, the properties of adjoint and coadjoint representations of regular admissible hom-Mock Lie algebras are examined, revealing key insights into their representation theory. Furthermore, admissible hom-Mock Lie bialgebras are developed based on the compatibility of dual structures. Their equivalence to matched pairs and Manin triples is demonstrated using a standard symmetric bilinear form defined on the direct sum of a hom-Mock Lie algebra and its dual. These findings contribute to the ongoing development of hom-type algebras and extend classical Lie theoretic ideas to more generalized algebraic systems.]]></description>
<link>https://scialert.net/abstract/?doi=ajms.2025.1.7</link> 
<pubDate>09 April, 2026</pubDate>
</item>
<item>
Multiclass Detection of e-Wallet Fraud Transactions Using Deep Learning Techniques<title><![CDATA[Multiclass Detection of e-Wallet Fraud Transactions Using Deep Learning Techniques]]></title> 
<description><![CDATA[The widespread adoption of digital payments through Electronic Wallets (e-Wallets) has significantly increased the exposure of financial systems to sophisticated fraud schemes and abnormal transactional behaviors. This situation raises a critical question: How can abnormal, potentially fraudulent, transactional behaviors be detected in real time and with high reliability within massive, sequential streams of heterogeneous data? To address this challenge, this study proposes a hybrid approach combining a Long Short-Term Memory (LSTM) recurrent neural network capable of capturing the temporal dimension of user behaviors with a multinomial logistic regression (MLR) classification layer to discriminate between behavioral classes. Using a simulated dataset of 1,000 transactions from 100 users, where each transaction was enriched with contextual variables (device_score, frequency_score, timestamp, amount, location, transaction_type), the model classified behaviors into three categories: Normal, Suspicious and Fraudulent. The hybrid model demonstrated strong overall performance, achieving an average accuracy of 77.3%. It exhibited excellent recall for the Normal class (91%), acceptable performance on Suspicious transactions (73% recall) and a robust ability to detect fraud (76% recall), while reducing false positives by 35% compared to a standalone static classification. The temporal integration enabled by the LSTM significantly improved the detection of gradual behavioral drifts, particularly in cases where fraud leveraged historically trustworthy devices. This work highlights the value of a sequential and adaptive approach to enhancing transactional cybersecurity in environments characterized by high behavioral variability.]]></description>
<link>https://scialert.net/abstract/?doi=ajms.2025.8.28</link> 
<pubDate>09 April, 2026</pubDate>
</item>
</channel>
</rss>