arXiv subjects

Shrinivass Arunachalam Balasubramanian

Publications and source records attributed to Shrinivass Arunachalam Balasubramanian.

4 recordsLinked to original sources

arXiv2025

Enhanced Web User Interface Design Via Cross-Device Responsiveness Assessment Using An Improved HCI-INTEGRATED DL Schemes

User Interface (UI) optimization is essential in the digital era to enhance user satisfaction in web environments. Nevertheless, the existing UI optimization models had overlooked the Cross-Responsiveness (CR) assessment, affecting the user interaction efficiency. Consequently, this article proposes a dynamic web UI optimization through CR assessment using Finite Exponential Continuous State Machine (FECSM) and Quokka Nonlinear Difference Swarm Optimization Algorithm (QNDSOA). Initially, the design and user interaction related information is collected as well as pre-processed for min-max normalization. Next, the Human-Computer Interaction (HCI)-based features are extracted, followed by user behaviour pattern grouping. Meanwhile, the CR assessment is done using FECSM. Then, the proposed Bidirectional Gated Luong and Mish Recurrent Unit (BiGLMRU) is used to classify the User eXperience (UX) change type, which is labelled based on the User Interface Change Prediction Index (UICPI). Lastly, a novel QNDSOA is utilized to optimize the UI design with an average fitness of 98.5632%. Feedback monitoring is done after optimal deployment.

cs.HC↗

arXiv2025

ng-reactive-lint: Smarter Linting for Angular Apps

Reactivity is central to Angular applications, yet subtle misuse of Observables, Signals, and change-detection often leads to performance regressions that are difficult to diagnose. Although Angular 17 introduced a unified, signal-first model, most enterprise codebases still rely heavily on legacy RxJS patterns that create unpredictable update flows, memory leaks, and excessive change cycles. To address these issues, we developed ng-reactive-lint, a deterministic static analysis tool that understands Angular's component semantics, lifecycle hooks, template bindings, and reactivity patterns. Unlike generic ESLint or RxJS plugins, ng-reactive-lint performs framework-aware analysis to detect high-impact anti-patterns and provide actionable, context-specific fixes. Evaluation across five large real-world projects showed reductions of up to threefold in unnecessary change detection cycles and up to 75% lower peak memory usage. The tool offers a practical, automated path to adopting modern Angular reactivity at scale.

cs.SE↗

arXiv2025

Adaptive Hybrid Sort: Dynamic Strategy Selection for Optimal Sorting Across Diverse Data Distributions

Sorting is an essential operation in computer science with direct consequences on the performance of large scale data systems, real-time systems, and embedded computation. However, no sorting algorithm is optimal under all distributions of data. The new adaptive hybrid sorting paradigm proposed in this paper is the paradigm that automatically selects the most effective sorting algorithm Counting Sort, Radix Sort, or QuickSort based on real-time monitoring of patterns in input data. The architecture begins by having a feature extraction module to compute significant parameters such as data volume, value range and entropy. These parameters are sent to a decision engine involving Finite State Machine and XGBoost classifier to aid smart and effective in choosing the optimal sorting strategy. It implements Counting Sort on small key ranges, Radix Sort on large range structured input with low-entropy keys and QuickSort on general purpose sorting. The experimental findings of both synthetic and real life dataset confirm that the proposed solution is actually inclined to excel significantly by comparison in execution time, flexibility and the efficiency of conventional static sorting algorithms. The proposed framework provides a scalable, high perhaps and applicable to a wide range of data processing operations like big data analytics, edge computing, and systems with hardware limitations.

cs.DS↗

arXiv2025

Signal-First Architectures: Rethinking Front-End Reactivity

Modern front-end frameworks face escalating reactivity management challenges, including performance degradation from complex observable chains and unpredictable re-renders. This paper introduces Signal-First Architecture--a novel paradigm where granular, dependency-tracked signals are the atomic unit of reactivity. Unlike traditional RxJS or NgRx patterns, Signal-First enforces reactive flows from explicit signal declarations, with derived values via computed() and side effects scoped to effect(). This model ensures deterministic behavior by eliminating implicit subscriptions and optimizing reactive graph evaluation. We present a comparative analysis of three Angular reactivity models: RxJS service-based, NgRx global stores, and pure Signal-First implementations. Through controlled benchmarking, including Chrome DevTools performance tracing, memory heap snapshots, and Lighthouse audits, this study quantifies Signal-First advantages.

cs.SE↗