International Journal of Computer (IJC)
type: channel
description: The International Journal of Computer (IJC) is an open access International Journal for scientists and researchers to publish their scientific papers in Computer Science related fields. IJC plays its role as a refereed international journal to publish research results conducted by researchers. This journal accepts scientific papers for publication after passing the journal's double peer review process (within 4 weeks). For detailed information about the journal kindly check About the Journal page. All IJC published papers in Computer Science will be available for scientific readers for free; no fees are required to download published papers in this international journal.
publisher: Mohammad Nassar for Researches (MNFR)
language: en
publication name: International Journal of Computer (IJC)
issn: 2307-4523
copyright: Authors who submit papers with this journal agree to the following terms.
items: Third-Party Verification Badging for Consumer Trust: Case Study from Health and Nutrition Categories
items: Scaling MLOps in Pharma: Automating Model Deployment and Monitoring with DataRobot on AWS
items: Unified Benchmark for Evaluating Performance, Bias, and Consistency in LLM Binary Question Answering
items: Enhancing Healthcare Data Interoperability with AI-Driven Synthetic Datasets Using FHIR Standards
items: Specifics of Software Quality Assurance in High-Frequency Trading (HFT) Systems
items: Strategies for Database Performance Optimization in High-Load Systems: A Review of PostgreSQL and Redis Use Cases
items: Comparative Analysis of Quantitative and Qualitative Research Methods in Digital Product Design: Metrics, Data Validity, and Impact on Product Decisions
items: Interrogating the preparedness of Uganda’s Higher Education and the Role of Information and Communication Technology in the era of COVID-19 Pandemic and its Aftermath
items: The Impact of Cross-industry Collaboration on the Effectiveness of Devops Practices
items: Machine learning applications for event routing in streaming systems
items: Polycrystalline Organic Semiconductors for Low-Power AI-Integrated Devices: Fabrication and Characterization
items: Lean MVP Development Via SQL Prototypes: Fast ETL Pipelines, Temporary DWHs, and Accelerated Validation of Product Hypotheses
items: Artificial Intelligence in Design Systems: Advancing Scalable and Adaptive User Interface Frameworks
items: Managed File Transfer Solutions: Security and Scalability with AWS Transfer Family
items: Leveraging Big Data Analytics for Combating Fake News: A Supervised Learning Approach to Identifying Misinformation on Social Media
items: Infrastructure Deployment Patterns for AI Services on the Claude Platform
items: PostgreSQL JSONB-based vs. Typed-column Indexing: A Benchmark for Read Queries
items: Cloud Security Architecture for the Automotive Industry: A Framework for Secure Multi-Cloud Deployment
items: Federated Learning Approaches for Privacy-Preserving Conversational AI in Dental Informatics
items: Paradigms of ELT Data Pipeline Architectures for LLM Training
items: Edge-Computing Assisted Robotic Vision Systems: Test Automation, Fault Prediction & Recovery
items: A Review of Event-Driven Architecture Patterns Using Message Brokers in .NET
items: AI Approaches to Software Quality Assessment: From Defect Prediction to Test Coverage Optimization
items: Safe Observability: A Framework for Automated PII Redaction from LLM Prompts in OpenTelemetry Pipelines
items: Strategies for Consolidating Disparate Mobile Applications into a Unified Digital Platform
items: Serial Communication Inter-IC Bus, Interface and Protocol Using BeagleBone
items: The Evolution from Interactive Voice Response (IVR) Systems to Intelligent Conversational AI Voicebots
items: Principles of Designing Monetization Systems in Free Mobile Games
items: AI-Driven Capacity Forecasting for Last-Mile Logistics
items: Maturity Metrics for Engineering Teams in AI-First Application-Development Projects

Third-Party Verification Badging for Consumer Trust: Case Study from Health and Nutrition Categories
type: item
description: The article examines the role of independent certification and trust-badge systems as an institutional mechanism for reducing information asymmetry in the health and nutrition segments. The relevance of the study is driven by the rapid growth of dietary supplements and functional products, accompanied by escalating consumer distrust and the rise of label skeptics. Under such conditions, external verification functions not as an optional marketing attribute but as a key quality signal that bridges costly auditing with the consumer’s simplified cognitive processing of information. The work aims to analyze the theoretical foundations and practical cases of independent badges in healthcare and nutraceuticals, as well as to assess their capacity to generate a durable trust premium. The novelty of the study lies in a comprehensive examination of certification through the lens of the economics of trust and cognitive psychology: the badge is interpreted simultaneously as a costly signal of a manufacturer’s probity and as a visual hash that facilitates consumer choice. The main results show that the display of an outward sign not only raises the subjective feeling of safety and lowers perceived risk but also translates into financial benefits, from increased readiness to pay to boosted loyalty and keeping a price premium. At the same time, the system’s weakness is revealed: the doubting of one mark can throw doubt on a whole setup, making protocol openness and reputation risk control key parts of certification strategy. The article will be helpful to researchers in the economics of trust, marketers, experts in health and nutrition, and practitioners engaged in designing and implementing independent certification systems.
subject: Articles
  topic: section
creator: Shweta Fnu
rights: Copyright (c) 2025 Shweta Fnu https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-19
publication date: 2025-12-19
starting page: 241
ending page: 251

Scaling MLOps in Pharma: Automating Model Deployment and Monitoring with DataRobot on AWS
type: item
description: This article discusses the MLOps scaling challenge amidst an increase in medical data and growing regulatory demands, which pharmaceutical companies are facing. On the AWS infrastructure, implementing DataRobot will make it possible to automate model deployment as well as monitoring and maintenance of models — without manual oversight; this traditional bottleneck usually creates another risk avenue for non-compliance with good manufacturing practice requirements. Such solution relevance is driven by an acute need for algorithm reproducibility and traceability under circumstances where pharmaceutical companies concurrently operate dozens of models. A lapse in validation or documentation can lead to clinical program delays and increased costs. The novelty here is a unified automated model registry plus deployment pipelines integrated with built-in drift/accuracy tracking and regulatorily significant audit systems. The major findings are that by automating the lifecycle, artificial intelligence stops being an artisanal collection of disconnected experiments and becomes a manageable production process. It shows how running DataRobot on AWS not only speeds up getting algorithms to the clinic but also makes sure strong FDA and GxP rules are followed with built-in version control, legally applicable report making, and data encryption. Such a strong setup where scientific change and rule order do not fight but instead help each other. The article will be of great use to drug makers, data engineers, MLOps workers, and rule managers who want to see the real use of auto ways for model work.
subject: Articles
  topic: section
subject: MLOps
  topic: keywords
subject: pharmaceuticals
  topic: keywords
subject: DataRobot
  topic: keywords
subject: AWS
  topic: keywords
subject: automation
  topic: keywords
subject: model monitoring
  topic: keywords
subject: regulatory requirements
  topic: keywords
creator: Nivedha Sampath
rights: Copyright (c) 2025 Nivedha Sampath https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 309
ending page: 318

Unified Benchmark for Evaluating Performance, Bias, and Consistency in LLM Binary Question Answering
type: item
description: Binary question answering is central to many real-world applications of large language models (LLMs), such as fact-checking or decision-making support. Yet, despite its prevalence and the high stakes of getting a binary judgment wrong (where an error yields the exact opposite outcome), there are no recent comprehensive benchmarks dedicated to evaluating LLM behavior on this task. To address this gap, we introduce a unified benchmark for assessing binary QA across three dimensions: performance, bias, and consistency. The benchmark is supported by a five-domain dataset augmented with new controlled reformulations of each question, including paraphrases, negations, and answer option variations. Across fifteen state-of-the-art LLMs, we find strong overall performance on the task, with larger and reasoning-optimized models showing better results than the smaller variants. At the same time, we observe pervasive No-leaning bias, universally weak consistency when handling semantically opposite questions, and substantial cross-domain variation. Reading comprehension and multi-hop reasoning topics are handled reliably, whereas numerical reasoning, ethical judgment, and, especially, translation evaluation remain challenging. These findings reveal both the strengths and shortcomings of current LLMs on binary QA, providing researchers with a basis for targeted future improvements while also helping practitioners make informed choices when deploying the models in binary decision contexts.
subject: Articles
  topic: section
subject: Benchmarking
  topic: keywords
subject: Bias
  topic: keywords
subject: Binary Question Answering
  topic: keywords
subject: Consistency
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Performance Evaluation
  topic: keywords
creator: Olesia Khrapunova
rights: Copyright (c) 2025 Olesia Khrapunova https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-27
publication date: 2025-12-27
starting page: 319
ending page: 338

Enhancing Healthcare Data Interoperability with AI-Driven Synthetic Datasets Using FHIR Standards
type: item
description: This article addresses how one can combine AI-generated synthetic medical datasets as well as FHIR standard semantic standards and APIs to better enable cross-system interoperability between health care providers. In order to create data sets with the desired level of analytical utility, the author have as a study objective to demonstrate how the generation of synthetics in the context of FHIR resource and transport via the R5 mechanisms of profiles, subscriptions and asynchronous export to NDJSON removes both legal and technical barriers to accessing clinical data. Because data-sharing is complicated by the risk of regulatory noncompliance; a significant amount of clinical data is contained in unstructured documents; and clinical data grows at an exponential rate, the need for this type of solution has increased. This work is innovative in that it brings together three families of generative models: sequential LLM-like models, GANs and diffusion networks. Additionally, the author use a direct mapping pipeline from generative models to FHIR resources and validate profile IG and automated REST tests using built-in systems. To train the generative models on rare nosologies, and to perform load-testing, the author describe an architecture that enables private, secure access to multiple, representative cohorts, which are used to generate the synthetic data. Key Findings: (1) The use of synthetics inside the FHIR shell ensures compatibility with the current infrastructure while reducing legal barriers. (2) They also allow risk-free testing of extreme scenarios and (3) they also reduce sample bias. A rational and practical strategy for accelerating the implementation of analytics in clinics would include a strategy of generating synthetics in a continuous integration/continuous delivery (CI/CD) environment and (2) implementing mandatory validation through profile management. The author believe that the article will provide valuable assistance to medical AI developers, int…
subject: Articles
  topic: section
subject: synthetic data
  topic: keywords
subject: FHIR R5
  topic: keywords
subject: interoperability
  topic: keywords
subject: privacy
  topic: keywords
subject: generative models
  topic: keywords
subject: load testing
  topic: keywords
subject: profile validation
  topic: keywords
subject: asynchronous export
  topic: keywords
creator: Vamshi Paili
rights: Copyright (c) 2025 Vamshi Paili https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 297
ending page: 308

Specifics of Software Quality Assurance in High-Frequency Trading (HFT) Systems
type: item
description: This article addresses how one can combine AI-generated synthetic medical datasets as well as FHIR standard semantic standards and APIs to better enable cross-system interoperability between health care providers. In order to create data sets with the desired level of analytical utility, the author have as a study objective to demonstrate how the generation of synthetics in the context of FHIR resource and transport via the R5 mechanisms of profiles, subscriptions and asynchronous export to NDJSON removes both legal and technical barriers to accessing clinical data. Because data-sharing is complicated by the risk of regulatory noncompliance; a significant amount of clinical data is contained in unstructured documents; and clinical data grows at an exponential rate, the need for this type of solution has increased. This work is innovative in that it brings together three families of generative models: sequential LLM-like models, GANs and diffusion networks. Additionally, the author use a direct mapping pipeline from generative models to FHIR resources and validate profile IG and automated REST tests using built-in systems. To train the generative models on rare nosologies, and to perform load-testing, the author describe an architecture that enables private, secure access to multiple, representative cohorts, which are used to generate the synthetic data. Key Findings: (1) The use of synthetics inside the FHIR shell ensures compatibility with the current infrastructure while reducing legal barriers. (2) They also allow risk-free testing of extreme scenarios and (3) they also reduce sample bias. A rational and practical strategy for accelerating the implementation of analytics in clinics would include a strategy of generating synthetics in a continuous integration/continuous delivery (CI/CD) environment and (2) implementing mandatory validation through profile management. The author believe that the article will provide valuable assistance to medical AI developers, int…
subject: Articles
  topic: section
subject: high-frequency trading
  topic: keywords
subject: quality assurance
  topic: keywords
subject: FPGA verification
  topic: keywords
subject: low latency
  topic: keywords
subject: algorithmic trading
  topic: keywords
subject: performance testing
  topic: keywords
subject: jitter
  topic: keywords
creator: Khapankou Anton
rights: Copyright (c) 2025 Khapankou Anton https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 290
ending page: 296

Strategies for Database Performance Optimization in High-Load Systems: A Review of PostgreSQL and Redis Use Cases
type: item
description: The article presents an expanded analysis of performance-optimization strategies for database systems operating under high load, using PostgreSQL and Redis as representative examples. The study is based on comparing architectural principles of storage systems, query-planning models, mechanisms for handling concurrent operations, and the characteristics of in-memory data processing. It examines differences in DBMS behavior as workload profiles change—from read-dominant scenarios to intensive write patterns and mixed workloads. Particular attention is given to how PostgreSQL’s internal mechanisms, including cardinality estimation, plan selection, and concurrency management, respond to data-volume growth and increasing query complexity. A substantial part of the study focuses on the influence of infrastructural decisions—containerization, automatic scaling, load balancing, distributed queues, and fault-tolerance mechanisms—on the real performance of DBMSs under load. The analysis demonstrates that the stability of data-processing pipelines depends on the coherence between database-level optimizations and cloud-environment parameters, enabling the reduction of latency, improved resilience to failures, and effective operation during peak demand. The practical contribution of the work lies in identifying strategies that allow engineers to design databases and supporting infrastructure as a unified optimization system. The article will be useful for database administrators, developers of high-performance services, distributed-systems architects, cloud-platform engineers, and researchers studying data-processing mechanisms in highly loaded computational environments.
subject: Articles
  topic: section
subject: databases
  topic: keywords
subject: high load
  topic: keywords
subject: system performance
  topic: keywords
subject: query processing
  topic: keywords
subject: data storage
  topic: keywords
subject: distributed solutions
  topic: keywords
subject: scaling
  topic: keywords
creator: Vadym Shevchenko
rights: Copyright (c) 2025 Vadym Shevchenko https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 279
ending page: 289

Comparative Analysis of Quantitative and Qualitative Research Methods in Digital Product Design: Metrics, Data Validity, and Impact on Product Decisions
type: item
description: The 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.
subject: Articles
  topic: section
subject: quantitative research
  topic: keywords
subject: qualitative research
  topic: keywords
subject: digital product design
  topic: keywords
subject: user behavior
  topic: keywords
subject: data validity
  topic: keywords
subject: mixed methods
  topic: keywords
subject: decision-making
  topic: keywords
subject: UX research
  topic: keywords
subject: evidence-based design
  topic: keywords
subject: product analytics
  topic: keywords
creator: Volkodav Vladyslav
rights: Copyright (c) 2025 Volkodav Vladyslav https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-01-03
publication date: 2025-01-03
starting page: 309
ending page: 320

Interrogating the preparedness of Uganda’s Higher Education and the Role of Information and Communication Technology in the era of COVID-19 Pandemic and its Aftermath
type: item
description: This article is based on a qualitative study conducted to establish the preparedness of Uganda’s Higher Education and the role of Information and Communication Technology (ICT) during COVID-19 pandemic and its aftermath. Coincidentally, the dynamics of COVID-19 pandemic predicated the need for urgent adoption and scale up of ICT in education. However, universities in Uganda were strategically ill-prepared to adopt and sustain to digital and online methods of engagement, sighting problems ranging from policy and curriculum deficit, to lack of staff and student preparedness. Ideally, the inability for universities to adopt and sustain to digital and virtual mode of operations demonstrated a strategic irregularity between ICT and higher education systems. In response, this study was intended to address the salient gap by identifying the key pedagogical challenges encountered and recommend appropriate strategic options based on the global best practice in ICT and higher education. Procedurally, the study adopted qualitative study approach, using scoping literature review techniques and content analysis methods of extracting evidence. By way of reflective analysis and scrutiny, the study sought to establish how various universities coped with COVID-19 lockdown, while identifying the relevant information in the domain of ICT and higher education systems. Hence, data analysis followed a descriptive approach, linking the identified gaps with the global best practices. Subsequently, the general implication of the study finding is that there was a strategic mismatch between ICT and higher education systems. Thus, the key challenges identified include lack of pedagogical flexibility and inefficient social interaction among learners and instructors, lack of self-directed and independent learning, restricted mode of assessment, and lack of staff competence in ICT and pedagogical approaches. Hence, this study provides a strategic guidelines to higher education planners to formall…
subject: Articles
  topic: section
subject: COVID-19 lockdown
  topic: keywords
subject: Uganda’s Higher Education
  topic: keywords
subject: Information Technology
  topic: keywords
subject: Pedagogy
  topic: keywords
creator: Joe Mutebi
creator: Margaret Kareyo
creator: Paul Akampurira
creator: Brian Mugisha
creator: Watson Kabyesiza
creator: Amos Kahara
rights: Copyright (c) 2025 Joe Mutebi, Margaret Kareyo, Paul Akampurira, Brian Mugisha, Watson Kabyesiza, Amos Kahara https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 10
ending page: 31

The Impact of Cross-industry Collaboration on the Effectiveness of Devops Practices
type: item
description: This article examines the influence of interdepartmental and cross-industry collaboration on the effectiveness of DevOps practices. As the demand for faster release cycles increases, organizations face the challenge of simultaneously enhancing system stability and reducing error rates. The study is based on materials from DORA, reports by Deloitte, Atlassian, Brainhub, DevMio, and publications by Ahmad et al., Dryka et al., Offerman, and Smith. A comparative analysis is conducted on internal collaboration models involving development, operations, testing, and information security teams, as well as on knowledge exchange between IT organizations and representatives of the banking, telecommunications, and manufacturing sectors. The goal is to assess the impact of collaboration formats on release frequency, time to incident recovery, and the proportion of failed changes. The research methodology includes literature systematization, statistical analysis of performance metrics, and synthesis of empirical evidence. The results show that integrating professionals from different domains accelerates delivery cycles, lowers failure rates, and improves team satisfaction. The article is relevant to project managers, DevOps team leads, digital transformation experts, and consultants responsible for optimizing the development and operations of software solutions.
subject: Articles
  topic: section
subject: DevOps
  topic: keywords
subject: cross-industry collaboration
  topic: keywords
subject: cross-functional teams
  topic: keywords
subject: release frequency
  topic: keywords
subject: recovery time
  topic: keywords
subject: DevSecOps
  topic: keywords
subject: BizDevOps
  topic: keywords
subject: knowledge sharing
  topic: keywords
subject: digital transformation
  topic: keywords
subject: reliability
  topic: keywords
creator: Nikita Romm
rights: Copyright (c) 2025 Nikita Romm https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-25
publication date: 2025-08-25
starting page: 1
ending page: 9

Machine learning applications for event routing in streaming systems
type: item
description: The paper provides a broad overview and classification of machine learning methods used to optimize routing in distributed streaming architectures. The aim of the study is to provide a detailed analysis of existing approaches: from classical reinforcement learning algorithms to modern deep neural networks, with an assessment of their potential in various operational scenarios and identification of key limitations. The methodological basis was a systematic review of publications dealing with intelligent routing, real-time data processing, and integration of ML solutions into system pipelines. Three main classes of algorithms were identified and considered: reinforcement learning methods (including DQN and actor-critic), deep networks (CNN, RNN and their hybrids), as well as ensemble and evolutionary techniques. The advantages and disadvantages of each class are analyzed in terms of key criteria — response time to flow changes, scalability in the number of nodes, and the ability to dynamically adapt. Special attention was paid to hybrid strategies that combine several models to increase the reliability and accuracy of recommendations on event transmission routes. In conclusion, the main conclusions about the current state of research are formulated and promising areas are outlined: the development of more robust architectures with explicable decision-making logic, as well as the integration of graph neural networks for modeling complex topologies of distributed systems. The presented results will be useful for engineers developing streaming platforms, big data analysis specialists, and research groups working on information channel optimization tasks.
subject: Articles
  topic: section
subject: event routing
  topic: keywords
subject: streaming systems
  topic: keywords
subject: machine learning
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: deep learning
  topic: keywords
subject: adaptive routing
  topic: keywords
subject: streaming data processing
  topic: keywords
subject: intelligent systems
  topic: keywords
subject: performance optimization
  topic: keywords
subject: traffic management
  topic: keywords
creator: Vladyslav Vodopianov
rights: Copyright (c) 2025 Vladyslav Vodopianov https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 32
ending page: 41

Polycrystalline Organic Semiconductors for Low-Power AI-Integrated Devices: Fabrication and Characterization
type: item
description: Polycrystalline organic semiconductors constitute a promising class of functional materials, combining architectural flexibility, compatibility with low-temperature processing, and tunable electronic properties. Owing to their ability to form ordered morphologies with defined crystallinity, these materials can be seamlessly integrated into energy-efficient intelligent systems—such as neuromorphic components, sensing nodes, and photo-analytical elements. In the context of rapidly growing interest in edge computing and Internet-of-Things infrastructures, they offer advantages over silicon counterparts, including mechanical robustness, spectral sensitivity, and the capacity for on-site primary signal processing. This study reviews methods for producing polycrystalline films, examines how fabrication parameters influence morphological and electrical structure, and surveys quality-control techniques at both micro- and structural levels. Organic semiconductors thus fulfil a strategic role in the design of adaptive, miniaturized, and self-learning devices operating under unstable conditions, forming the basis for subsequent advances in intelligent organic electronics.
subject: Articles
  topic: section
subject: organic semiconductors
  topic: keywords
subject: polycrystalline films
  topic: keywords
subject: neuromorphic devices
  topic: keywords
subject: edge computing
  topic: keywords
subject: intelligent electronics
  topic: keywords
subject: sensing systems
  topic: keywords
creator: Sidra A. Shaikh
rights: Copyright (c) 2025 Sidra A. Shaikh https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-09-26
publication date: 2025-09-26
starting page: 42
ending page: 51

Lean MVP Development Via SQL Prototypes: Fast ETL Pipelines, Temporary DWHs, and Accelerated Validation of Product Hypotheses
type: item
description: This paper discusses the use of lightweight SQL prototyping for rapid ETL pipeline construction and MVPs in terms of enabling temporary data warehouses and accelerating product hypothesis validation. It lays out, formalizes, and tests aspects of a Lean Analytical Circuit Building approach based on declarative SQL language such that verifiable metrics may be available to a team without waiting for long procurement and infrastructure approval cycles. Relevance comes with high uncertainty at the beginning of both a startup and product-project development, when classic corporate data warehousing takes from six months up to two years to deploy, thus injecting great schedule and budget risk on top of this, reducing the speed of validated learning through loss of the value of data due to lack of operational feedback. It is new in the mixture of three-tier architecture (Staging, Transform, Data Mart), usage of any current DBMS or cloud engines (in temporary cluster mode, DuckDB, ClickHouse, BigQuery Sandbox), content analysis regarding availability of SQL skills and economic risk assessment together with a systematic comparative and instrumental analysis of performances of prototypes. The main finding is that the cycle from event arrival to target table update can fit within fifteen minutes which means fulfillment of a requirement that needs fast reaction for changes in user behavior and marketing campaigns while keeping the flexibility on the structure level among SQL prototypes preserving transparency and reproducibility plus automatic policies deleting obsolete data and serverless sandbox mode controlling costs. A smooth transition from the temporary solution to stable platforms, according to the Infrastructure as Code principle, minimizes operational risks and ensures continuity of metrics. The article will be helpful to startups and product teams, data engineers, and business analysts seeking to combine the speed of Lean methodology with data reliability.
subject: Articles
  topic: section
subject: Lean MVP
  topic: keywords
subject: SQL prototypes
  topic: keywords
subject: ETL pipeline
  topic: keywords
subject: temporary data warehouse
  topic: keywords
subject: validated learning
  topic: keywords
subject: product hypotheses
  topic: keywords
subject: self-service analytics
  topic: keywords
creator: Daria Bogun
rights: Copyright (c) 2025 Daria Bogun https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-11
publication date: 2025-10-11
starting page: 60
ending page: 70

Artificial Intelligence in Design Systems: Advancing Scalable and Adaptive User Interface Frameworks
type: item
description: Artificial intelligence has fundamentally reshaped design systems, establishing a new standard for modern interface development and digital product engineering. AI-powered frameworks drive efficiency by automating component creation, identifying design patterns, and monitoring accessibility compliance at scale. Through the application of neural networks, deep learning, and computer vision, these systems interpret user interactions and adjust interfaces dynamically. Evidence from testing healthcare and enterprise applications confirms that such systems consistently shorten development cycles, improve user satisfaction, and lower operational costs. Automation further enables design and engineering teams to shift focus toward higher-value innovation while ensuring uniformity across platforms. Machine learning enhances personalization, strengthens validation through automated testing, and ensures more reliable user experiences. Beyond measurable outcomes, AI integration improves accessibility compliance, decreases technical debt, and facilitates stronger collaboration across teams. Collectively, this evolution represents a decisive step forward in how organizations design, optimize, and sustain user interfaces in the digital era. This article will be especially useful for software engineers, UX designers, product managers, and academic researchers seeking to understand and implement AI-enabled design systems for scalable, adaptive, and efficient digital interfaces.
subject: Articles
  topic: section
subject: artificial intelligence
  topic: keywords
subject: design systems
  topic: keywords
subject: interface automation
  topic: keywords
subject: user experience
  topic: keywords
subject: machine learning
  topic: keywords
subject: design optimization
  topic: keywords
creator: Tamerlan Mammadzada
rights: Copyright (c) 2025 Tamerlan Mammadzada https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-11
publication date: 2025-10-11
starting page: 52
ending page: 59

Managed File Transfer Solutions: Security and Scalability with AWS Transfer Family
type: item
description: The study examines Infrastructure as Code for multi-cloud delivery with Terraform and AWS CloudFormation, focusing on conservative cross-cloud abstraction, policy-as-code enforcement, and AI-assisted configuration. Configuration analysis indicates about a 40% reduction in initial setup time and a ~50% decline in recurrent configuration defects. Economic signals show ~15% cost relief for SME tenants and ~30% faster deployment cycles for volatile workloads through pre-validated modules, drift control, and cost guardrails. The paper documents a governance model that maps automated checks to NIST 800-53 control families and integrates plan-time static analysis, secrets detection, and evidence capture. Generative AI is positioned as a CI-embedded assistant that translates natural-language intents into validated templates while remaining policy-, state-, and cost-aware. The contribution consolidates comparative tool behavior, governance placement in the pipeline, and maturity stages for AI-assisted IaC. The material addresses practitioners designing reliable and economical multi-cloud estates and researchers evaluating NL?IaC evaluation workflows.
subject: Articles
  topic: section
subject: Infrastructure as Code
  topic: keywords
subject: Terraform
  topic: keywords
subject: AWS CloudFormation
  topic: keywords
subject: Multi-cloud
  topic: keywords
subject: Policy as Code
  topic: keywords
subject: OPA/Rego
  topic: keywords
subject: NIST 800-53
  topic: keywords
subject: DevOps automation
  topic: keywords
subject: Generative AI
  topic: keywords
subject: FinOps
  topic: keywords
creator: Jakub Dunak
rights: Copyright (c) 2025 Jakub Dunak https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 231
ending page: 240

Leveraging Big Data Analytics for Combating Fake News: A Supervised Learning Approach to Identifying Misinformation on Social Media
type: item
description: The rapid rise of social media has transformed how people consume and share information but has also accelerated the spread of misinformation that undermines public trust, public health, and democratic stability. Manual fact-checking and platform moderation often lag behind the speed of misinformation, highlighting the need for scalable, automated solutions. This study develops a supervised machine learning framework supported by Big Data analytics for fake news detection. Using the ISOT Fake News Dataset of 44,898 labeled articles, we implemented a structured pipeline that included text normalization, tokenization, stopword removal, stemming, and TF-IDF vectorization, followed by training four classifiers: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Evaluation was conducted using a stratified 80/20 train-test split with 10-fold cross-validation, applying Accuracy, Precision, Recall, and F1-score as performance metrics. Results show that ensemble models, particularly XGBoost and Random Forest, consistently outperformed LR and SVM, achieving accuracies near 99% with strong precision and recall across both classes. These findings demonstrate the strength of optimized ensemble methods in detecting misinformation and their scalability for real-world application. Beyond model performance, this work proposes a distributed architecture leveraging Apache Spark for real-time deployment, providing a foundation for practical and scalable misinformation detection systems.
subject: Articles
  topic: section
subject: Fake news detection
  topic: keywords
subject: Big data analytics
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Natural language processing (NLP)
  topic: keywords
subject: Social media
  topic: keywords
subject: XGBoost
  topic: keywords
subject: Random Forest
  topic: keywords
subject: Text classification
  topic: keywords
subject: Misinformation
  topic: keywords
creator: Kehinde Racheal Ilugbiyin
creator: Damilola Nnamaka Ajobiewe
rights: Copyright (c) 2025 Kehinde Racheal Ilugbiyin, Damilola Nnamaka Ajobiewe https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-19
publication date: 2025-11-19
starting page: 82
ending page: 92

Infrastructure Deployment Patterns for AI Services on the Claude Platform
type: item
description: This article questions how to select and use infrastructure patterns in the deployment of industrial AI services on Claude due to the increased enterprise demand and broadened capabilities. This study proves relevant since, within a very short span, generative AI work has transformed from novelty experiments to mission-critical business workloads where effectiveness directly depends on architectural decisions ensuring not only low latency but also cost predictability together with the compliance of corporate security requirements. Therefore, the purpose of this paper is to identify recurring architectural patterns and their operational mechanisms that may be used in balancing response time, context size, cost, and reliability when integrating Claude into enterprise IT systems. The scientific novelty of the research is a detailed taxonomy of infrastructural solutions (serverless gateways, containers in isolated VPCs, batch queues) and how their combination begins to meet the very special challenges of Claude—challenges such as context windows up to one million tokens and loads that seem to increase sharply above 25 billion requests per month. The main findings show that the best setup comes from using small APIs with autoscaling and local cache for interactive work; containers with private links and Batch interfaces when dealing with deep analytics and big processing. The quota system and context thinning, breaking up queues by priority and budget, as well as token control and telemetry, are pieces to help give teams a p95 latency promise while staying inside financial and regulatory fences. The article will be helpful to researchers in cloud architectures, enterprise IT system architects, and practitioners deploying AI services based on Claude models.
subject: Articles
  topic: section
subject: Claude
  topic: keywords
subject: Anthropic
  topic: keywords
subject: infrastructure patterns
  topic: keywords
subject: AI services
  topic: keywords
subject: enterprise platforms
  topic: keywords
subject: containerization
  topic: keywords
subject: serverless computing
  topic: keywords
subject: token quotas
  topic: keywords
subject: scalability
  topic: keywords
subject: latency
  topic: keywords
creator: Yevhen Mykhailenko
rights: Copyright (c) 2025 Yevhen Mykhailenko https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-29
publication date: 2025-11-29
starting page: 122
ending page: 130

PostgreSQL JSONB-based vs. Typed-column Indexing: A Benchmark for Read Queries
type: item
description: A document storage pattern is used in a wide range of business applications, particularly in systems with strong requirements for immutability and auditability. Using the JSONB data type is a common approach to achieving document storage in PostgreSQL, which supports indexing for JSONB. However, building an efficient system for such structures remains a challenge in terms of read latency, mainly due to payload size, recheck cost and value extraction. In this paper, the comparative performance of regular typed-column indexes and JSONB-based indexes is evaluated across ten queries typical of a production application. Generated metrics are used to visualize latency of indexes on JSONB expressions and typed-column indexes and for a one-sided statistical test. The findings show that JSONB-based indexes are not an optimal solution in terms of read performance, and overall typed-column indexes are expected to be at least 20% faster for tables with 1 million records or more. This benchmark is important to consider when designing an efficient data storage for documents.
subject: Articles
  topic: section
subject: JSONB
  topic: keywords
subject: Indexing
  topic: keywords
subject: PostgreSQL
  topic: keywords
subject: document storage
  topic: keywords
subject: Database optimization
  topic: keywords
subject: Relational database
  topic: keywords
subject: Software Engineering
  topic: disciplines
subject: Computer science
  topic: disciplines
subject: statistics
  topic: disciplines
creator: Gennadii Turutin
creator: Mikita Puzevich
rights: Copyright (c) 2025 Gennadii Turutin, Mikita Puzevich https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-10
publication date: 2025-12-10
starting page: 179
ending page: 191

Cloud Security Architecture for the Automotive Industry: A Framework for Secure Multi-Cloud Deployment
type: item
description: This paper examines the automotive industry since it fundamentally transforms toward Software-Defined Vehicles (SDV) and exponentially increases the reliance on cloud and multi-cloud infrastructures. The attack surface expands in a dramatic way because of a shift to this model. Today cyber risks target centralized cloud backends managing entire fleets not individual vehicles. A comprehensive security architecture should be developed proactively and without delay, as potential threats may escalate, and stringent regulatory frameworks such as UNECE R155 and ISO/SAE 21434 are anticipated to come into effect. In this paper, the Automotive Multi-Cloud Security Framework (AMCSF), which is a conceptual cloud security architecture model for the automotive industry, is proposed based on a systematic literature review as well as comparative analysis of industry practices. ISO/SAE 21434 describes vehicle cybersecurity lifecycle processes the five-layer model would integrate. It works also with modern cloud-oriented technologies like Cloud Security Posture Management (CSPM) and Cloud Workload Protection Platforms (CWPP). The study primarily concludes that risk can be effectively managed in modern automotive ecosystems only if protections are applied synergistically and include both the vehicle itself and its cloud infrastructure. This paper will be helpful to cybersecurity and cloud solution engineers and architects in automotive (OEM, Tier-1/Tier-2), vSOC/DevSecOps teams, cybersecurity and compliance management specialists (CSMS/UNECE R155, ISO/SAE 21434), as well as consultants and executives responsible for designing and operating secure multi-cloud backends.
subject: Articles
  topic: section
subject: cloud security
  topic: keywords
subject: automotive industry
  topic: keywords
subject: multi-cloud environment
  topic: keywords
subject: ISO/SAE 21434
  topic: keywords
subject: UNECE R155
  topic: keywords
creator: Geol Kang
rights: Copyright (c) 2025 Geol Kang https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-19
publication date: 2025-11-19
starting page: 71
ending page: 81

Federated Learning Approaches for Privacy-Preserving Conversational AI in Dental Informatics
type: item
description: This study investigates how federated learning can enable privacy-preserving conversational AI for dental clinics while complying with GDPR and HIPAA. The main objective is to determine whether clinics can automate patient communications, call handling, reminders, and triage, without transferring raw patient data beyond local systems. The methodology combines a narrative review and comparative analysis of recent healthcare federated-learning studies with a practical deployment blueprint tailored to dental workflows. Three training paradigms are contrasted, local, centralized, and federated, to summarize evidence on model accuracy and computational cost. A layered privacy stack is presented, including differential privacy, secure aggregation, and homomorphic encryption, with guidance on when each technique is most appropriate. An end-to-end workflow is described in which clinics train lightweight local adapters on anonymized speech and text, share only encrypted model updates, and receive an aggregated global model that improves across participating sites. Findings indicate that federated models achieve accuracy comparable to centralized baselines in representative tasks (e.g., segmentation and risk prediction) while keeping patient data local. Cryptographic protections introduce overhead but remain practical when applied selectively. This study demonstrates that federated learning enables privacy-preserving conversational AI in dental informatics without compromising model performance. In practical terms, such systems can reduce missed appointments through targeted reminders, accelerate routing of urgent complaints, and support intern training with simulated dialogues, delivering the benefits of shared learning while maintaining strong confidentiality and regulatory compliance.
subject: Articles
  topic: section
subject: Federated Learning
  topic: keywords
subject: Privacy-Preserving AI
  topic: keywords
subject: Conversational AI
  topic: keywords
subject: Dental Informatics
  topic: keywords
subject: Voice AI Dentistry
  topic: keywords
subject: Differential Privacy
  topic: keywords
subject: Homomorphic Encryption
  topic: keywords
subject: Healthcare Automation
  topic: keywords
creator: Usman Tariq
rights: Copyright (c) 2025 Usman Tariq https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-26
publication date: 2025-11-26
starting page: 93
ending page: 103

Paradigms of ELT Data Pipeline Architectures for LLM Training
type: item
description: This article presents a systematic analysis of ELT pipeline architectures used in the training of large language models. The study is based on an interdisciplinary approach that integrates engineering principles of data infrastructure design, theoretical foundations of transformer architectures, and data flow automation practices under conditions of high source variability. Particular attention is given to the content analysis of scientific and applied publications addressing the role of LLMs in transformation loops, the implementation of agent-oriented solutions, and the support of multimodal adaptive pipelines. Key ELT architecture types are identified, including prompt-driven, agent-based, high-throughput, and cognitively enhanced solutions, reflecting varying levels of model involvement in data processing. The analysis shows that architectural shifts toward feedback integration and dynamic routing enable the creation of robust and adaptive solutions suited to contemporary training scenarios. Special emphasis is placed on issues related to data stream instability, the lack of benchmarks for agent-based systems, and insufficient integration of pipelines with model evaluation mechanisms. The paper proposes a conceptual classification of ELT paradigms and an outline of their adaptive evolution toward building scalable and logically coherent infrastructures. The article will be of interest to researchers in machine learning systems, LLM infrastructure developers, data platform architects, and professionals in digitalization and automation of AI training workflows.
subject: Articles
  topic: section
subject: ELT pipeline
  topic: keywords
subject: large language models
  topic: keywords
subject: data architecture
  topic: keywords
subject: data transformation
  topic: keywords
subject: agent-oriented systems
  topic: keywords
subject: LLM infrastructure
  topic: keywords
subject: multimodal data
  topic: keywords
subject: dynamic routing
  topic: keywords
subject: cognitive processing
  topic: keywords
subject: system scalability
  topic: keywords
creator: Olusesan Ogundulu
rights: Copyright (c) 2025 Olusesan Ogundulu https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-29
publication date: 2025-11-29
starting page: 112
ending page: 121

Edge-Computing Assisted Robotic Vision Systems: Test Automation, Fault Prediction & Recovery
type: item
description: This 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.
subject: Articles
  topic: section
subject: Edge Computing
  topic: keywords
subject: Test Automation
  topic: keywords
subject: Fault Prediction
  topic: keywords
subject: Fault Recovery
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Himani Singhai
rights: Copyright (c) 2025 Himani Singhai https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-04
publication date: 2025-12-04
starting page: 131
ending page: 149

A Review of Event-Driven Architecture Patterns Using Message Brokers in .NET
type: item
description: This article describes different event-driven architecture (EDA) patterns that use message brokers to implement distributed computing in .NET. This article also describes data consistency and state management techniques in a distributed system architecture. In response to the industry trend of moving from monolithic architecture to microservice architecture for scalability, agility, and resilience, this project seeks to provide a thorough basis for decision-making for the adoption of EDA patterns and message brokers (like Apache Kafka, RabbitMQ, Azure Service Bus) based on non-functional requirements and organizational maturity levels. The methodological foundation is based on a Systematic Literature Review (SLR), ensuring reproducibility, completeness, and methodological rigor of the analysis. The scientific novelty of this work lies in integrating three analytical dimensions: theoretical architectural patterns, the technological implementation of message brokers, and the practical aspects of applying them within .NET systems. The main findings emphasize that the choice of EDA patterns and broker technologies is not a search for an optimal, universal solution but a deliberate balancing of technical and organizational factors. Successful use of EDA requires maturity and experience in DevOps and observability, as well as strict adherence to idempotency principles. Therefore, the target audience for this article is software architects, developers, and researchers concerned with microservices and event-driven systems of all kinds built on .NET, as well as technical decision-makers responsible for enterprise adoption of EDA.
subject: Articles
  topic: section
subject: event-driven architecture
  topic: keywords
subject: microservices
  topic: keywords
subject: .NET
  topic: keywords
subject: message brokers
  topic: keywords
subject: Apache Kafka
  topic: keywords
subject: RabbitMQ
  topic: keywords
subject: Azure Service Bus
  topic: keywords
subject: Saga pattern
  topic: keywords
subject: Transactional Outbox pattern
  topic: keywords
subject: data consistency
  topic: keywords
subject: asynchronous communication
  topic: keywords
creator: Serhii Yakhin
rights: Copyright (c) 2025 Serhii Yakhin https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-05
publication date: 2025-12-05
starting page: 160
ending page: 178

AI Approaches to Software Quality Assessment: From Defect Prediction to Test Coverage Optimization
type: item
description: The article addresses the problem of growing inefficiency in traditional approaches to software quality assurance (QA) under accelerated cycles of continuous integration and delivery (CI/CD). The aim of the study is to present and evaluate a new integrated platform that leverages large language models (LLMs) to automate and optimize key QA processes. The methodology is based on an industrial case study of a system utilizing Claude 4 and Amazon Q CLI for semantic log analysis and predictive test prioritization. The paper presents key quantitative results, including an increase in nightly build stability from ~70% to over 90%, a one-third reduction in regression testing time, and a halving of pull request verification time. Additionally, it is emphasized that the adoption of such solutions enhances testing transparency, improves collaboration between development and QA teams, and accelerates release cycles while ensuring higher product quality. The main contribution of the article is the provision of empirical evidence from a large corporate environment, confirming that modern AI-driven approaches can significantly improve the efficiency, accuracy, and strategic value of software testing. The article will be useful for researchers, quality engineers, CI/CD architects, and DevOps practitioners interested in applying LLMs to optimize testing processes.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: large language models
  topic: keywords
subject: quality assurance
  topic: keywords
subject: test automation
  topic: keywords
subject: predictive prioritization
  topic: keywords
subject: semantic log analysis
  topic: keywords
subject: CI/CD
  topic: keywords
subject: empirical study
  topic: keywords
creator: Taras Buriak
rights: Copyright (c) 2025 Taras Buriak https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-29
publication date: 2025-11-29
starting page: 104
ending page: 111

Safe Observability: A Framework for Automated PII Redaction from LLM Prompts in OpenTelemetry Pipelines
type: item
description: The 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.
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: OpenTelemetry
  topic: keywords
subject: Safe Observability
  topic: keywords
subject: PII-Redaction
  topic: keywords
creator: Serhii Melnyk
rights: Copyright (c) 2025 S. Melnyk https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-19
publication date: 2025-12-19
starting page: 267
ending page: 278

Strategies for Consolidating Disparate Mobile Applications into a Unified Digital Platform
type: item
description: This 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's personal experience, demonstrating the successful application of the described principles within a Big Four company.
subject: Articles
  topic: section
subject: consolidation
  topic: keywords
subject: digital platform
  topic: keywords
subject: digital transformation
  topic: keywords
subject: mobile applications
  topic: keywords
subject: mobile ecosystem
  topic: keywords
creator: Maksym Yurko
rights: Copyright (c) 2025 Maksym Yurko https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 223
ending page: 230

Serial Communication Inter-IC Bus, Interface and Protocol Using BeagleBone
type: item
description: To communicate with peripherals the embedded systems mainly use the serial communication. Hence serial communication plays a vital role in designing embedded systems. The protocols used for serial communication are Universal Asynchronous Receiver Transmitter (UART), Serial Peripheral Interface (SPI), Universal Serial Bus (USB), Control Access Network (CAN) and Inter IC Protocol (I2C). The characteristics of serial communication protocol are high speed and low data loss, ensure the data transfer and simplifying the system level design. This paper provides an overview of I2C-Bus, I2C protocol and its interfacing. The paper also demonstrates the I2C protocol using BeagleBone.
subject: Articles
  topic: section
subject: Inter-IC (I2c)
  topic: keywords
subject: I2C-Bus
  topic: keywords
subject: SCL
  topic: keywords
subject: SDA
  topic: keywords
subject: BeagleBone
  topic: keywords
subject: ADXL345 3-axix accelerometer
  topic: keywords
subject: i2cdetect tool
  topic: keywords
creator: Ankur Yadav
creator: Khushboo Kumari Yadav
rights: Copyright (c) 2025 Ankur Yadav, Khushboo Kumari Yadav https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-19
publication date: 2025-12-19
starting page: 252
ending page: 266

The Evolution from Interactive Voice Response (IVR) Systems to Intelligent Conversational AI Voicebots
type: item
description: The 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.
subject: Articles
  topic: section
subject: Interactive Voice Response systems
  topic: keywords
subject: AI-driven conversational interfaces
  topic: keywords
subject: intelligent voicebots
  topic: keywords
subject: automated speech recognition
  topic: keywords
subject: semantic language processing
  topic: keywords
subject: customer interaction automation
  topic: keywords
subject: human–machine communication
  topic: keywords
creator: Udit Joshi
rights: Copyright (c) 2025 Udit Joshi https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 214
ending page: 222

Principles of Designing Monetization Systems in Free Mobile Games
type: item
description: The 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.
subject: Articles
  topic: section
subject: behavioral motivation
  topic: keywords
subject: ethical design
  topic: keywords
subject: free mobile games
  topic: keywords
subject: game design
  topic: keywords
subject: game economy
  topic: keywords
subject: in-game economy
  topic: keywords
subject: microtransactions
  topic: keywords
subject: monetization
  topic: keywords
subject: player experience
  topic: keywords
creator: Oleh Riazanov
rights: Copyright (c) 2025 Oleh Riazanov https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-05
publication date: 2025-12-05
starting page: 150
ending page: 159

AI-Driven Capacity Forecasting for Last-Mile Logistics
type: item
description: The article examines the features of throughput forecasting based on artificial intelligence in the field of last-mile logistics. The relevance of the topic is driven by the fact that, under conditions of rapid growth in e-commerce and tightening consumer expectations, last-mile logistics faces unprecedented pressure, requiring hyper-accurate forecasting of operational capacity to enable a just-in-time delivery model and meet delivery deadlines without excessive costs. Traditional planning methods demonstrate their inadequacy under conditions of high demand volatility. This work describes an artificial intelligence (AI)-based framework used for forecasting daily resource requirements (personnel, transport) in large-scale logistics networks. The framework is based on a hybrid machine learning architecture that combines regression models for baseline load forecasting and the XGBoost algorithm for detecting and quantifying abnormal demand spikes. A key element of the system is a dynamic capacity buffer algorithm that, in real time, calculates the required reserve of resources to mitigate risks associated with forecast errors. The article analyzes the architecture, implementation methodology, and empirical results, and also discusses the role of such systems as a strategic tool for demand shaping.
subject: Articles
  topic: section
subject: Last-mile logistics
  topic: keywords
subject: capacity forecasting
  topic: keywords
subject: machine learning
  topic: keywords
subject: XGBoost
  topic: keywords
subject: dynamic buffers
  topic: keywords
subject: demand shaping
  topic: keywords
subject: operational efficiency
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: supply chain management
  topic: keywords
subject: just-in-time delivery
  topic: keywords
creator: Ankur Fnu
rights: Copyright (c) 2025 Ankur Fnu https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 205
ending page: 213

Maturity Metrics for Engineering Teams in AI-First Application-Development Projects
type: item
description: This study is conducted to define a practical maturity-metrics model for AI-First engineering that links delivery and ML lifecycle to business outcomes, enabling reproducible, responsible, scalable operations across process, platform, team, and product. Comparative analysis of maturity models and delivery indicators (DORA Four Keys) combined with ML-specific metrics (retrain time, drift alerts, explainability coverage). Systematic review of MLOps practices and enterprise cases. SMART-based operationalization, automated collection via unified observability and event telemetry; explicit addition of a product plane to Microsoft’s MLOps model. Triangulation against McKinsey and DORA survey data. The model formalizes maturity across four planes—process, platform, team/culture, and product—mapped to five levels from no MLOps to fully automated operations. It integrates delivery indicators (lead time, deployment frequency, change failure rate, MTTR) with ML metrics (retrain time, drift alerts, explainability coverage) and requires SMART, automated, actionable collection. It further extends the metric set to cover generative AI and LLM-specific behavioural indicators such as factuality, instruction-following, retrieval effectiveness, safety violations and cost per useful token, and introduces repeatable proxy measures for people and culture—including knowledge diffusion, psychological safety and role-ratio tracking—so that human and organisational factors are observable and auditable. Evidence from DORA and McKinsey indicates that teams instrumenting both delivery and ML metrics achieve higher productivity, steadier SLA adherence, and reduced burnout; meanwhile, rapid AI tool uptake coexists with low trust in generated code (only 24% fully trust), underscoring an adoption–resilience gap the model addresses. Clustering metrics by change delivery, ML lifecycle, user/business value, reliability and quality, governance/ethics, and people/culture focuses investment. Operational …
subject: Articles
  topic: section
subject: maturity metrics
  topic: keywords
subject: MLOps
  topic: keywords
subject: delivery metrics
  topic: keywords
subject: drift
  topic: keywords
subject: observability
  topic: keywords
creator: Satyashil Awadhare
rights: Copyright (c) 2025 Satyashil Awadhare https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-13
publication date: 2025-12-13
starting page: 192
ending page: 204