ItemArticle

GEMS: Gas-Enhanced Marine Search for Optimizing Fusion Mamba-Attention Networks for Fake Review Classification

Title
GEMS: Gas-Enhanced Marine Search for Optimizing Fusion Mamba-Attention Networks for Fake Review Classification
Creator
Priya, C. Sharon Roji; Perumalsamy, Deepalakshmi; Thinakaran, Rajermani
Description
The rise of fake reviews has become a major problem for trust in e-commerce sites. As for traditional machine learning solutions, they fail to capture the nuanced language that separates real reviews from fake reviews. In this work, we introduce a new hybrid metaheuristic algorithm that optimizes the Fusion Mamba-Attention Network (FMA-Net) for fake review detection, called GEMS (Gas-Enhanced Marine Search). GEMS is a unique combination of the exploration capabilities of the Enhanced Marine Predators Algorithm and the exploitation process of Henry Gas Solubility Optimization, offering a dual-phase optimization design for high-dimensional, asymmetric, metaheuristic-configured GEMS-optimized FMA-Net. Geometric enhancement of GEMS optimization provides GEMS-optimized FMA-Net with an accuracy of 96.8%, F1-score of 95.4%, and AUC-ROC of 97.2%, marking 37% improvement over the current best models for fake review detection on the Yelp, Amazon, and Google Reviews datasets. We lower the average time of hyperparameter optimization using GEMS with FMA-Net to achieve 68% reduction in overall time spent in grid search and 42% lower for complexity in comparison to genetic algorithms. The contributions of this work are the first hybrid metaheuristic for transformers, a mathematically formulated GEMS algorithm, and an extensive empirical study for proving multi-dimensional metric plausibility. 2026 by the authors.
Source
Future Internet;Volume;18;Issue;3;Article No.;132;
Date
01-01-2026
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Subject
deep learning; fake review detection; henry gas solubility optimization; mamba-attention networks; marine predators algorithm; metaheuristic optimization; sentiment analysis
Coverage
Priya C.S.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University Bangalore, Bangalore, 560074, India; Perumalsamy D., Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626128, India; Thinakaran R., Faculty of Data Science and Information Technology, INTI International University, Nilai, 71800, Malaysia
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 19995903;
Format
online
Language
English
Type
Article

Citation

Priya, C. Sharon Roji; Perumalsamy, Deepalakshmi; Thinakaran, Rajermani, “GEMS: Gas-Enhanced Marine Search for Optimizing Fusion Mamba-Attention Networks for Fake Review Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23527.

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