Literature & Cultural StudiesResearch ArticleVol. 1, Issue 4, 2026
Saddam Mollah
Registration ID: MERIT-2026-0016Pages: 14-25DOI: 10.66727/merit.26.0016 (Submitted to Crossref)
Published originally in Bengali as Ghare Baire (1916) against the turbulent backdrop of the anti-colonial Swadeshi movement, Rabindranath Tagore’s The Home and the World serves as a profound critique of aggressive, exclusionary nationalism. This paper examines how Tagore utilizes the domestic sphere as an allegorical battleground to explore the ideological fractures of early twentieth-century Bengal. Through a close textual reading of the novel’s polyphonic structure—comprising the diary entries of Nikhil, Sandip, and Bimala—this study analyzes how the transition of the female protagonist across the domestic threshold (purdah) mimics the nation’s chaotic entry into political modernity. By interrogating the intersections of gender agency, subaltern realities, psychoanalytic desires, and geopolitical identity, this paper argues that Tagore offers an ethical framework of "rooted cosmopolitanism" as an alternative to the destructive, hyper-masculine deification of the nation-state.
Artificial Intelligence & Machine LearningSystematic ReviewVol. 1, Issue 4, 2026
Nasrin khatun, Narju (gt), hasib (gt)
Registration ID: MERIT-2026-0017Pages: 26-38DOI: 10.66727/merit.26.0017 (Submitted to Crossref)
Artificial Neural Networks (ANNs) have evolved from a simple mathematical abstraction of biological neurons into one of the most influential computational paradigms in modern engineering. This review synthesises the mathematical foundations, architectural evolution, and cross-disciplinary applications of ANNs, drawing on peer-reviewed literature indexed in Scopus, Web of Science, and IEEE Xplore. We first revisit the mathematical formalism of the artificial neuron, activation functions, loss functions, and gradient-based optimisation, before examining the theoretical guarantees offered by the Universal Approximation Theorem (Hornik, Stinchcombe, & White, 1989). We then present a comparative taxonomy of feedforward, convolutional, recurrent, graph-based, and physics-informed architectures, and review their reported performance across civil, mechanical, electrical, electronics, chemical, and biomedical engineering domains. A quantitative synthesis of accuracy, error, and reliability metrics extracted from more than thirty recent studies is presented in tabular form to illustrate the comparative maturity of each application area. The review finds that convolutional architectures dominate vision-based inspection tasks with reported accuracies frequently exceeding 94–99%, recurrent and hybrid CNN–LSTM architectures are preferred for temporal forecasting problems such as load and traffic prediction, and physics-informed neural networks are an emerging solution for data-scarce, physics-governed problems. Persistent challenges include data quality and availability, interpretability, computational cost, and the integration of domain knowledge. The review concludes by outlining future research directions, including explainable AI, digital twins, federated learning, and hybrid physics–data models, that are likely to shape the next generation of ANN-enabled engineering systems.
Edge Computing & CybersecurityComprehensive SurveyVol. 1, Issue 4, 2026
dR. Soumen Pore, Dr. Debashree Chakraborty
Registration ID: MERIT-2026-0020Pages: 1-13DOI: 10.66727/merit.26.0020 (Submitted to Crossref)
A large number of devices connected to the Internet, cloud technology, and digital communication methods have contributed to a greater incidence of risk events in the field of digital security. Existing detection methods using fingerprints, signatures, and rules cannot always necessarily identify so-called zero-day attacks and the new methods of penetration of businesses. Therefore, the need for ML-based detection emerges because of the new possibilities for detection of hidden deviations in the behavior of networks. The aim of this article is to provide a comprehensive overview of the existing ML-based intrusion detection systems (IDS). In the course of the work the analysis of standard chains of operations, assessment of the theory behind most widely used IDS, classification of various environments of evaluation using NSL-KDD, CICIDS2017, and UNSW-NB15 benchmarks, and development of current indexes of efficiency are performed. In addition, the most important problems such as the problem of imbalance of data, the problem of attacks against static models, and the problem of interpretability of models have been discussed.
