International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
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459 research outputs found
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Edge-Computing Assisted Robotic Vision Systems: Test Automation, Fault Prediction & Recovery
Full text linkThis dissertation investigates the integration of edge-computing technologies into robotic vision systems, focusing on enhancing test automation, fault prediction, and recovery processes. The research articulates the critical gap in operational efficiency and reliability within existing robotic vision systems due to delayed data processing and insufficient fault management strategies. Through a comprehensive analysis of real-time performance metrics, fault occurrence logs, and corresponding recovery times, the findings demonstrate a significant reduction in system downtime and an increase in fault detection accuracy, thereby optimizing the functionality of robotic vision applications. The key results reveal that implementing edge-computing not only facilitates immediate data analysis and decision-making but also substantially improves the predictive capabilities for system failures, leading to more resilient automation strategies. These advancements hold considerable significance in the healthcare sector, where robotic vision systems are increasingly deployed for surgical assistance and diagnostics, enhancing patient safety and operational workflow. The broader implications of this study suggest that by fostering robust edge-computing frameworks, healthcare institutions can leverage improved robotic systems to enhance clinical outcomes, reduce costs associated with system failures, and ultimately support the transition towards more intelligent and responsive healthcare environments. This research contributes to the ongoing dialogue regarding the adoption of innovative technologies in healthcare, positing edge-computing as a pivotal element in the future development of reliable and efficient robotic solutions
Safe Observability: A Framework for Automated PII Redaction from LLM Prompts in OpenTelemetry Pipelines
Full text linkThe proliferation of Large Language Models (LLMs) within enterprise applications has introduced a critical conflict between the goals of modern observability and the mandates of data privacy. While observability platforms provide essential, deep visibility into complex distributed systems, they inadvertently become repositories for Personally Identifiable Information (PII) when they ingest the unstructured, information-rich prompts users submit to LLMs. This leakage of sensitive data into telemetry pipelines constitutes a significant security liability and a compliance risk under regulations such as GDPR and CCPA. This paper introduces the concept of "Safe Observability," a paradigm that reconciles the need for comprehensive system insight with robust privacy protection. I propose a novel framework for achieving this through the automated redaction of PII within the OpenTelemetry (OTel) ecosystem. The core of this framework is a custom, configurable PII-Redaction Processor for the OpenTelemetry Collector, designed to act as a strategic control plane for sanitizing telemetry data in-transit. The architecture employs a hybrid PII detection methodology, combining the speed of regular expressions with the contextual accuracy of Named Entity Recognition (NER) models, implemented as a decoupled microservice. This paper details the architectural design, provides a comprehensive implementation guide for building and deploying the custom processor using the OpenTelemetry Collector Builder (OCB) and Go, and presents a rigorous evaluation of its efficacy and performance impact. The findings demonstrate that this approach offers a practically viable and architecturally sound solution for preventing PII leakage, enabling organizations to leverage the power of observability and LLMs without compromising user privacy or regulatory compliance
The Evolution from Interactive Voice Response (IVR) Systems to Intelligent Conversational AI Voicebots
Full text linkThe paper explores the structural and procedural reorientation from conventional Interactive Voice Response (IVR) mechanisms toward advanced conversational AI voicebots within the sphere of customer support, where the impetus for the study is rooted in the escalating influence of speech-based automation tools on international communicative practices and operational models in business. The research presents an integrative overview of scholarly insights and empirical findings, which collectively illustrate the way artificial intelligence, semantic interpretation of natural language, and vocal interface technologies reconfigure user engagement patterns and transform service interactions. This investigation retraces the transformation trajectory of IVR infrastructures, pinpoints the underlying stimuli for implementing AI-centric tools, and examines jurisdictional and territorial distinctions that emerge in diverse rollout strategies across regions. A focused exploration is conducted into how contemporary voicebots incorporate machine learning techniques, utilize data-driven analytical frameworks, and operate through dynamically adjustable dialogue systems capable of adapting to user behavior and intent. The primary objective is to dissect prevailing patterns, assess functional advantages, and uncover technological constraints tied to this shift, employing comparative evaluation, critical examination of existing literature, and interpretation of practice-oriented documentation. The concluding section presents reflections on the operational viability, encountered complexities, and overarching global ramifications of embedding AI-powered speech agents into digital service ecosystems, offering a valuable foundation for academics, system architects, and industry professionals working at the intersection of artificial intelligence and service infrastructure optimization
Principles of Designing Monetization Systems in Free Mobile Games
Full text linkThe article presents a comprehensive analysis of the principles underlying the design of monetization systems in free-to-play mobile games as a distinctive phenomenon of the digital economy, where behavioral psychology, game design, and user interaction ethics converge. The relevance of the topic lies in the fact that monetization in the free-to-play format has evolved beyond a purely technical function; it has become an independent design domain that shapes the longevity and reputation of gaming ecosystems. Despite a considerable body of research, there remain substantial discrepancies in the assessment of the ethical acceptability of microtransactions, the role of artificial intelligence in offer personalization, and the balance between commercial benefit and player autonomy. The purpose of this study is to identify the key patterns behind the creation of sustainable monetization systems that maintain harmony between the developer’s economic interests and the player’s psychological comfort. Drawing on academic literature, statistical data, and the author’s own experience in designing F2P projects, a set of principles is formulated to support the development of transparent and non-intrusive monetization models. The article demonstrates that the effectiveness of such systems depends not on the number of transactions but on the quality of the trust-based relationship established between the player and the game. The author’s contribution lies in the systematization of interdisciplinary approaches—economic, cognitive, and design-oriented—and in emphasizing the importance of long-term audience retention and the integration of commercial mechanisms into the game narrative. The conclusions are of particular interest to game designers, user behavior analysts, and researchers of digital markets
Evolution of User Interface in Mobile Applications
Full text linkThe evolution of user interfaces (UI) in mobile applications is a crucial aspect of modern digital interaction, influencing both user experience and functionality. This paper explores the historical development of mobile UI, from early monochrome displays to augmented reality (AR) and artificial intelligence (AI)-driven interfaces. A comparative analysis of iOS and Android UI paradigms highlights key differences and commonalities. The study provides a systematic overview of UI advancements, emphasizing the transition from button-based navigation to gesture-based and voice-controlled interfaces. The findings offer insights into emerging trends that will shape the future of mobile UI design
Modern Approaches to Risk Management in the Use of Big Data in the Financial Sector
Full text linkThis study analyzes modern approaches to financial risk management using Big Data, artificial intelligence (AI/ML), and generative AI. A comprehensive literature review has been conducted, examining the advantages of traditional methods such as MCMC, as well as the potential of machine learning algorithms and generative models for data synthesis and stress testing. A scientific gap has been identified, highlighting the lack of integrated methodologies that combine statistical modeling, AI/ML, and generative AI into a unified risk management system. The objective of this study is to explore the specific features of contemporary approaches used in risk management processes involving Big Data in the financial sector. The study\u27s scientific novelty lies in analyzing the feasibility of forming a unified integrative system capable of synthesizing synthetic data for extreme scenario modeling, as well as automating risk monitoring and analysis processes through cloud computing platforms and RegTech solutions. The findings presented in this study may be of interest to researchers, postgraduate students, and professionals in finance and risk management. Additionally, the data outlined in this research may be valuable for specialists seeking interdisciplinary synergy between financial engineering, information technology, and statistical methods to optimize managerial decision-making in the era of digital transformation
The Transformation of the Labor Market under the Influence of Artificial Intelligence: Automation of Professions, the Emergence of New Specialties, and Changes in Career Trajectories
Full text linkThis study examines the impact of artificial intelligence on the labor market. Neural networks have become a scientific breakthrough, driving changes across various fields of human activity. The study lists professions that have already undergone automation and describes the process. Additionally, it explores new professions that have emerged as a result of artificial intelligence implementation in many companies. The relevance of the study is driven by the widespread adoption of AI. The findings suggest that similar processes have occurred in the past and that AI development creates new opportunities rather than eliminating them. Technologies are designed to simplify human life, including professional activities. It is also argued that not all professions can be replaced by artificial intelligence; however, neural networks can facilitate the work of professionals, including doctors and teachers, if integrated effectively. The study hypothesizes that in the near future, large companies will need to implement artificial intelligence to maintain their competitiveness
Strategies for Consolidating Disparate Mobile Applications into a Unified Digital Platform
Full text linkThis paper examines the challenges of developing and implementing strategic approaches for consolidating disparate mobile applications into a unified digital platform. In the modern corporate landscape, where mobile technologies have evolved from a mere supplement to an integral component of business processes, many organizations face the significant challenge of fragmented digital assets. This phenomenon, marked by the proliferation of separate applications developed for different departments and purposes, inevitably leads to substantial operational and financial overheads, a considerable decline in security, and a degraded user experience. This article provides a comprehensive study of strategies aimed at consolidating these fragmented mobile solutions into a single, centralized digital platform. The author focuses on analyzing the fundamental issues caused by fragmentation, outlining the conceptual and architectural foundations for building an integrated ecosystem, and proposing specific strategic actions for implementation. Particular attention is given to analyzing current global trends, substantiated by data from leading analytical agencies—Forrester, Gartner, and Deloitte Insights. The paper concludes with a practical case study from the author\u27s personal experience, demonstrating the successful application of the described principles within a Big Four company
Comparative Analysis of Quantitative and Qualitative Research Methods in Digital Product Design: Metrics, Data Validity, and Impact on Product Decisions
Full text linkThe article is dedicated to examining how quantitative and qualitative research methods shape product decisions in digital design. The relevance lies in the growing pressure on teams to justify choices with evidence while navigating an abundance of data that often obscures user motivations. The novelty comes from treating these methods not as opposing paradigms but as interconnected ways of understanding experience, validity, and decision impact. The work describes how quantitative techniques frame behavior through measurable patterns and how qualitative approaches uncover interpretive depth, studied across multiple stages of the product lifecycle. Special attention is paid to the differences in how each method conceptualizes evidence and its uneven influence on strategic and operational choices. The work sets itself the task of clarifying their complementarities and the conditions under which they lead to more grounded decisions. Analytical and comparative methods are used to pursue this goal. A broad set of academic sources has been studied to reveal methodological contrasts and synthesis. The conclusion describes the benefits and limitations of integrating both approaches. The article will be useful for researchers, product designers, UX specialists, and analytics teams seeking more balanced methodological reasoning
Innovative Solutions for Enhancing System Performance on the .net Platform
Full text linkThis study examines architectural solutions and scaling approaches on the .NET platform in the development of distributed web applications. The impact of microservices and ASP.NET Core MVC on response speed, system resilience, and resource efficiency is analyzed. Methods of automation aimed at monitoring metrics and dynamically managing computing power are discussed. A review of research detailing codebase organization, testing, and cloud service deployment is presented. Special attention is given to DevOps practices that facilitate seamless functionality updates and reduce the risk of overloads during peak traffic periods. It is noted that collaboration among specialists from different fields accelerates product releases and simplifies task management of varying complexity. Additionally, conclusions are drawn on containerization strategies that streamline component integration and optimize communication in scalable projects. Approaches to service orchestration and automated quality control are also explored, extending CI/CD capabilities and increasing process transparency in software development. The systematization of collected data contributes to a deeper understanding of the principles behind building high-load solutions on the .NET platform. Future research perspectives are also considered. This study will be valuable for web development specialists, DevOps engineers, and IT team leaders