Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Perumalsamy, Deepalakshmi; Cornelius, Sharon Roji Priya; Thinakaran, Rajermani Title A name given to the resource AdaptiveNet: A Novel Architecture for Reducing Computation Complexity to Fake Review Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Information (Switzerland);Volume;17;Issue;4;Article No.;388; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/info17040388" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/info17040388</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037265369?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037265369?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Perumalsamy D., Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, India; Cornelius S.R.P., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bangalore, 560074, India; Thinakaran R., Faculty of Data Science and Information Technology, INTI International University, Negeri Sembilan, 71800, Malaysia Description An account of the resource The exponential rise of e-commerce platforms has resulted in a dramatic increase in online reviews, which creates a challenge in distinguishing fake reviews that erode consumer confidence and harm commerce ecosystems. Traditional approaches for fake review detection employ computationally expensive deep learning networks which are resource-intensive and difficult to use in practice. In this paper, we describe AdaptiveNet, a new lightweight neural architecture that achieves fake review detection with much lower computational resources while maintaining a higher detection and classification precision. The model proposed in this paper is based on three original innovations: a Multi-Scale Semantic Fusion (MSSF) layer for hierarchical feature extraction, Dynamic Attention Scaling (DAS) with complexity measure attention, and Adaptive Parameter Sharing (APS) context-gated networks. With thorough evaluation on Amazon, Yelp, and TripAdvisor datasets of reviews totalling 1.2 million reviews, AdaptiveNet attains 94.8% accuracy while achieving 65% computational overhead in comparison to traditional models. The architecture outperformed all other state-of-the-art models, BERT-base (92.1%), RoBERTa (91.8%), and other more recent efficient models, requiring 70% lower parameters and 60% lower energy consumption. This work markedly advances the other efficient deep learning architectures for text classification and allows for the practical implementation of fake review detection systems in resource-limited settings as process innovation. 2026 by the authors. Subject The topic of the resource computational efficiency; dynamic attention; fake review detection; lightweight neural networks; multi-scale feature fusion; natural language processing; parameter sharing; text classification Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Relation A related resource ISSN: 20782489; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online