Design and Development of an AI-Driven Framework to Secure IoT Networks
The rapid proliferation of Internet of Things (IoT) devices across industrial, domestic, and critical-infrastructure environments has created a correspondingly rapid expansion of cyber-attack surfaces, while the heterogeneity, resource constraints, and high-volume dynamic traffic of IoT networks render conventional signature- and rule-based security mechanisms increasingly inadequate. This article presents a comprehensive review of twelve recent studies (2020–2026) on artificial intelligence (AI)-driven approaches to securing IoT networks, spanning machine learning and deep learning intrusion detection systems (IDS), federated and blockchain-assisted learning frameworks, 5G software-defined network (SDN) security architectures, service-oriented edge-layer architectures, and large-scale systematic and taxonomic reviews. Reported detection accuracies across the empirical studies range from approximately 82% to 98.25%, with federated and hybrid blockchain–AI approaches offering the strongest privacy–accuracy trade-offs and 5G-SDN frameworks achieving the lowest detection latencies (as low as 15 milliseconds). Despite these advances, the literature consistently reports unresolved research gaps, including limited adaptability to evolving traffic patterns, elevated false-positive rates, insufficient attention to the computational and energy constraints of IoT endpoints, dependence on single machine learning or deep learning algorithms rather than integrated pipelines, and an absence of unified, lightweight, and quantum-resistant frameworks validated on real hardware. Building on this synthesis, the review articulates a research problem, four research objectives, and a proposed seven-stage AI-driven security framework — spanning data collection, preprocessing, feature extraction and selection, AI-based threat detection, attack classification, adaptive security response, and continuous monitoring — intended for validation on benchmark datasets (CICIoT2023, TON_IoT, BoT-IoT/N-BaIoT) and on constrained hardware platforms (Raspberry Pi, ESP32). The proposed research direction aims to deliver a lightweight, adaptive, and empirically validated AI-driven security solution suited to resource-constrained IoT deployments.
Published by: Afza Firdous, Dr. Md. Faizan Farooqui
Author: Afza Firdous
Paper ID: V12I5-1221
Paper Status: published
Published: October 21, 2026
A Bibliometric Review of Portfolio Optimization Models: An Operations Research Perspective
This study presents a bibliometric review of portfolio optimization research from an Operations Research perspective, covering literature published between 2015 and 2026. A final corpus of 708 documents was identified through a PRISMA-based selection process using Scopus. Biblioshiny and Microsoft Excel were used for data extraction, analysis, and visualization. The study examines publication and citation trends, source performance, author productivity, institutional and country-level output, international collaboration, keyword patterns, conceptual clusters, and thematic evolution. Four research questions address publication and citation activity; prominent journals, authors, institutions, and countries; the field’s main conceptual and methodological approaches; and changes in themes between earlier and later periods. The results show that portfolio optimization and portfolio selection remain central to the literature. Robust optimization, robust portfolio optimization, genetic algorithms, asset allocation, machine learning, portfolio management, multi-objective optimization, and Conditional Value-at-Risk also appear among frequently observed concepts. Publication output is concentrated in a relatively small group of sources, while researchers and countries contribute across a broad international base. China, the United States, and India are major contributors, and the collaboration analysis shows links among leading research-producing regions. The thematic analysis indicates continuity in the field’s core portfolio optimization theme alongside increasing specialization in uncertainty modelling, advanced risk measures, metaheuristics, machine-learning applications, and dynamic stochastic models. The review identifies research opportunities at the intersection of robust or distributionally robust optimization and machine-learning-integrated decision models. It highlights the need to compare these techniques with classical mean-variance models using consistent datasets, realistic constraints, and common evaluation standards. Future research directions include out-of-sample testing, method benchmarking, transparent data definitions, ESG integration, emerging computational approaches, and broader geographic collaboration. Findings remain bounded by the Scopus-based search strategy and the 2015–2026 observation window; citation-age effects and the partial nature
Published by: Dr. Richa Kalpesh Saxena, Aryan More, Dev Darira, Devansh Bhathena, Dhairya Patel, Disha Tiwari
Author: Dr. Richa Kalpesh Saxena
Paper ID: V12I5-1213
Paper Status: published
Published: October 20, 2026
Applications of OR in Portfolio Optimization and Risk Management
This paper presents a bibliometric analysis of the applications of Operations Research (OR) in portfolio optimization and risk management. The data were retrieved from Elsevier’s Scopus database and comprise 1,304 documents published between 2016 and 2026 in 619 sources, together with a corpus of 48 review papers. Science mapping was carried out with Bibliometrix and its Biblioshiny interface to examine the growth, authorship and sources of the field and to build a thematic map of its keywords. The number of publications rose from 51 in 2016 to 244 in 2026, an average annual growth rate of 16.95%. Machine learning and risk analysis emerge as the motor themes, risk management and project portfolios form the basic themes, reinforcement learning is a niche theme, and classical optimization and uncertainty modelling are emerging or declining. The review identifies gaps in validation, integration across methods, and consistent terminology, and uses them to propose a research agenda.
Published by: Dr Richa Kalpesh Saxena, Shiv Shankar, Shreyansh Agrawal, Richitha Settipalli, Shaurya Jaiswal
Author: Dr Richa Kalpesh Saxena
Paper ID: V12I5-1219
Paper Status: published
Published: October 20, 2026
Operations Research in Public Transit and Urban Mobility: A Comparative Review of Eighteen Studies
Public transit is a vast, interconnected systems design problem: coordinating numerous routes, frequencies , vehicles, operators, signals and passengers with uncertain demand and travel times. Operations Research (OR) brings a rich and mature arsenal of tools to tackle these issues. This report brings together 18 published and preprint papers which we have reviewed individually in six student-focussed sections that all bring OR to design of bus routes, timing of signals, quality of service, scheduling of vehicles and crews, demand-responsive transit, gig transportation, bike sharing, and electric vehicle fleet management. We cluster the papers into five categories and compare the OR techniques, which include calculus-based optimisation, mixed-integer, and set-partitioning programming, column-generation, and decomposition, genetic, and large-neighbourhood-search heuristics, robust optimisation, queueing, and stochastic-process models, Markov decision processes, and simulation. Three broad insights stand out. The first is the near-exclusive use of large-scale heuristics for bigger problems versus exact algorithms for smaller problems. The second is the treatment of demand and supply uncertainties as part of the decision making rather than as a post-hoc model refinement. The third is the real improvements, which are measured relative to different baselines on different case studies. We conclude by addressing limitations in the literature as well as in our review.
Published by: Shreyas Surendranath, Pranav prabhu, Tanvi Sarda, Aujaswie Jain, Neel Vipul Jain, Samar Zaidi
Author: Shreyas Surendranath
Paper ID: V12I5-1214
Paper Status: published
Published: October 19, 2026
Future Directions & Innovation in Hospitality and Tourism Start-ups and Innovation in Sustainable Tourism
The hospitality and tourism industry is experiencing a profound transformation shaped by sustainability concerns, rapid technological advancements, and evolving consumer expectations. This paper explores the future trajectory of the sector with a particular emphasis on the role of start-ups in fostering sustainable tourism innovation. It examines how emerging technologies, novel business models, and environmentally responsible practices are redefining traditional tourism frameworks. The study integrates theoretical perspectives with practical insights derived from Indian start-up case studies to illustrate how innovation can drive sustainability. The findings indicate that start-ups are critical enablers of change, promoting resource efficiency, community engagement, and experiential tourism. The paper concludes by outlining strategic recommendations for stakeholders to achieve long-term resilience and sustainability in hospitality and tourism.
Published by: Sameer Dighe
Author: Sameer Dighe
Paper ID: V12I5-1211
Paper Status: published
Published: October 16, 2026
Testing Subscription Inertia: Evidence from India’s 2021 Recurring-Payment Rules by RBI
Did the RBI's 2021 recurring-payment rules, which required customers to re-authorise auto-debits, reveal how much subscription retention depended on consumer inertia rather than preference? The study considers the transition period from October 2021 to be a regulatory shock and compares the activity in the months leading up to the shift with the activity in the months following, both in terms of card-based online payments and UPI payments, as well as between credit- and debit-card transactions, by applying an event-study and difference-in-differences framework. RBI card-payment data from public reports and NPCI UPI transaction data for the year 2021 and RBI e-commerce observations of cards made available through May 2022. Online card transactions are a proxy for subscription-level churns, as these aren't publicly known and are interpreted with caution. There was no evidence of a sudden drop in card activity at the moment reported to happen by the inertia hypothesis. Debit-card transactions grew by 1.81% in the first half of the year as compared to the second half, and credit-card transactions grew by 0.79% in the second half when compared to the first half. The difference between the card and UPI was statistically significant, but the pre-existing divergent trends violated the parallel trends assumption. But the evidence does not allow concluding that there is a causal effect of subscription inertia, but merely at the level of the available proxy to payment.
Published by: Nevaan Vijayvergia
Author: Nevaan Vijayvergia
Paper ID: V12I5-1200
Paper Status: published
Published: October 16, 2026
