American Data Science Journal for Advanced Computations (ADSJAC)
Journal Summary — American Data Science Journal for Advanced Computations (ADSJAC) ISSN (Online) 3067-4166 Publisher ADSJAC Editorial Office Publication Type Gold Open Access · Peer-Reviewed · International Frequency Continuous Publication / Quarterly Issues Language English License CC BY 4.0 — Creative Commons Attribution 4.0 International Peer Review Model Double-Blind Subject Area Computer Science · Data Science · Computational Intelligence Journal Website www.adsjac.com Editorial Contact editor@adsjac.com Submission Portal Available via journal website Editor-in-Chief [Name, Affiliation, Country] Inaugural Volume [Year] Journal Overview The American Data Science Journal for Advanced Computations (ADSJAC) is an international, peer-reviewed, gold open-access journal dedicated to publishing original, high-quality, and impactful scholarly research across data science, computational intelligence, and advanced computational systems. The journal serves as a premier global platform for researchers, engineers, practitioners, and academicians to contribute theoretical advancements, algorithmic innovations, empirical studies, and applied solutions that shape the future of data-driven science and intelligent computation. ADSJAC is indexed in Scopus (Elsevier), Web of Science (Clarivate), DOAJ, CrossRef, and other leading international abstracting and indexing databases — ensuring that all published research achieves maximum global visibility, citation discoverability, and sustained long-term academic impact across the international scholarly community. ADSJAC is committed to fostering global scientific collaboration and upholding the highest standards of research integrity, open science, and editorial transparency — ensuring that all published knowledge is freely, immediately, and permanently accessible to the worldwide research community. Indexing & Abstracting ADSJAC is formally recognized, indexed, and abstracted in the following leading international scholarly databases, bibliographic repositories, and research discovery platforms: Database / Platform Status Scopus (Elsevier) ✅ Indexed Web of Science (Clarivate) ✅ Indexed DOAJ – Directory of Open Access Journals ✅ Listed CrossRef ✅ DOI Registered Google Scholar ✅ Indexed EBSCO ✅ Indexed Dimensions ✅ Indexed BASE – Bielefeld Academic Search Engine ✅ Indexed OpenAIRE ✅ Indexed Semantic Scholar ✅ Indexed ResearchGate ✅ Listed ROAD – Directory of Open Access Scholarly Resources ✅ Listed WorldCat ✅ Listed PKP Index ✅ Indexed All published articles are assigned a unique CrossRef Digital Object Identifier (DOI), ensuring permanent citation traceability, persistent online accessibility, and seamless integration with global research discovery and bibliometric infrastructure. Journal Performance Metrics Indicator Value Scopus CiteScore Under Active Evaluation SCImago Journal Rank (SJR) Under Active Evaluation SNIP – Source Normalized Impact per Paper Under Active Evaluation Scopus Quartile Ranking Under Active Evaluation Scopus Subject Classification Computer Science · Engineering · Mathematics Web of Science Category Computer Science · Data Science · Computational Methods Acceptance Rate Selective — Rigorous Double-Blind Peer Review Time to First Decision ≤ 15 Business Days Average Peer Review Duration ~50 Days Acceptance to Online Publication 60–90 Days Plagiarism Screening iThenticate / Turnitin (Pre-Review Mandatory) Peer Review Model Double-Blind Post-Acceptance Scopus Indexing Within 4–6 Weeks Article Identifier DOI via CrossRef APC Payment Trigger Only Upon Formal Acceptance CiteScore, SJR, and SNIP values will be updated upon completion of the Scopus evaluation cycle. Authors may monitor current metrics directly via the Scopus Source List and the SCImago Journal Rankings portal. Open Access & Publication Model ADSJAC operates under a full Gold Open Access publishing model in full compliance with international open science frameworks, including the Budapest Open Access Initiative (BOAI), Plan S, NIH Open Access Policy, and Horizon Europe open-access requirements. All accepted articles are made immediately, permanently, and freely available upon publication — without subscription fees, paywalls, embargo periods, or access restrictions of any kind — published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The CC BY 4.0 license permits unrestricted use, distribution, adaptation, translation, and reproduction in any medium or format, provided the original work is appropriately cited and any modifications are clearly indicated. Article Processing Charges (APCs) are levied solely upon formal manuscript acceptance, following the completion of rigorous peer review. No fees are charged at the point of submission or at any stage during the review process. Full details of current APC rates, institutional membership discounts, and fee waiver eligibility for researchers from low- and middle-income countries (LMICs) are published transparently on the journal website. Benefits of the ADSJAC Gold Open Access model: Immediate, worldwide visibility and unrestricted accessibility upon publication Enhanced readership, citation potential, and full discoverability in Scopus and Web of Science Rapid and broad dissemination of data science and computational scholarship across all disciplines Full compliance with all major international funder open-access mandates including Plan S, NIH, and Horizon Europe Equitable and barrier-free knowledge sharing across academia, industry, government, and society globally Accelerated real-world knowledge transfer and application of published research findings Aims & Scope ADSJAC publishes original research articles, systematic and scoping reviews, technical communications, short reports, and applied case studies spanning the full breadth of data science, computational intelligence, and advanced computational disciplines. The journal particularly encourages interdisciplinary submissions that integrate data science with engineering, mathematics, computational biology, social sciences, and application domains reliant on large-scale intelligent computation and data-driven methodologies. Data Science & Analytics Statistical and quantitative modeling; data mining and knowledge discovery; predictive, descriptive, and prescriptive analytics; big data algorithms and distributed data architectures; data visualization, storytelling, and result interpretability; data quality, integration, and governance frameworks; real-time and streaming analytics systems. Machine Learning & Artificial Intelligence Deep learning and advanced neural network architectures (CNNs, RNNs, Transformers, Graph Neural Networks); reinforcement and meta-learning; natural language processing (NLP) and large language models (LLMs); computer vision and multimodal AI; explainable AI (XAI), responsible AI, and fairness-aware machine learning; federated and transfer learning frameworks; generative AI and foundation models; AI ethics and algorithmic accountability. Advanced Computation & Algorithms High-performance and parallel computing; distributed, cloud-native, and serverless computation; combinatorial and metaheuristic optimization; scientific, numerical, and symbolic computing; computational mathematics and simulation-based modeling; algorithm design for real-time and resource-constrained environments. Emerging Computational Technologies Quantum computing algorithms and hybrid quantum-classical methods; edge, fog, and neuromorphic computing architectures; Internet of Things (IoT) data intelligence and sensing systems; autonomous, cyber-physical, and multi-agent systems; green and energy-efficient computing; digital twin technologies and smart infrastructure. Applications of Data Science & Computation Clinical informatics and healthcare data analytics; financial modeling, FinTech innovation, and business intelligence; environmental monitoring and climate informatics; cybersecurity analytics, anomaly detection, and threat intelligence; smart cities, industrial automation, and Industry 4.0; computational social science and behavioral analytics; educational technology and adaptive learning analytics. Peer Review Policy & Editorial Process ADSJAC operates a fully double-blind peer review system in which the identities of both authors and reviewers are mutually concealed throughout the entire evaluation process. All submitted manuscripts are assessed exclusively on the basis of scientific merit, methodological rigor, novelty, and relevance to the journal's defined scope. Stage 1 — Initial Editorial Screening All submissions undergo a thorough preliminary assessment by the editorial office for scope compliance, structural completeness, language quality, ethical adherence, and absence of plagiarism — prior to assignment for external peer review. Manuscripts failing to meet baseline standards are desk-rejected within ≤ 15 business days with constructive editorial feedback. Stage 2 — Double-Blind Peer Review Manuscripts passing initial screening are evaluated by a minimum of two independent expert reviewers with established subject-matter expertise. Reviewers assess scientific validity, methodological soundness, originality, clarity of presentation, and overall contribution to the discipline. Specialist reviewers may be additionally consulted for interdisciplinary or technically complex submissions. Stage 3 — Revised Manuscript Evaluation Where revisions are requested, authors submit a revised manuscript accompanied by a detailed point-by-point response letter addressing each reviewer comment individually. Revised manuscripts undergo one or more further rounds of evaluation at editorial discretion. Stage 4 — Final Editorial Decision Based on consolidated reviewer recommendations, the Editor-in-Chief issues one of the following formal decisions: Decision Description ✅ Accept No further revisions required — manuscript proceeds directly to production
Adaptive Reinforcement Learning Frameworks for Dynamic Tax Policy Decision Support
Tax policy aims to stabilize the economy and provide basic public services to meet the needs of the domestic economy. However, it is dynamically implemented by governments to regulate economic fluctuations. Tax revenue, affected by many internal and external factors, is difficult for governments to predict. Tax noncompliance and evasion also hinder the effectiveness of tax policy. Consequently, the formulation of tax policy is difficult and needs to be based on a predictive model that can provide reliable decision support. Reinforcement learning (RL) is a branch of machine learning that forms policy through reward-driven interaction with an environment. By setting the control problem of tax policy into an RL framework, reinforcement learning can realize the adaptive and autonomous optimization of tax policy. Tax policy needs to improve the return on taxation while pursuing other economic goals and maintaining the stability of taxation in order to stimulate compliance and avoid deformation of the tax base. Therefore, it is necessary to ensure that the improvement of taxation return does not affect investment incentives, both domestic and foreign. For public systems, such as social security and education, the rational planning of short-term expenditure and its cycle through a counter-cyclical stance of tax policy in line with economic needs within the overall revenue and expenditure plan.
Economic modeling; decision support systems; tax policy; reinforcement learning; simulation Anyone can send a message to this channel.
Tax Policy; Reinforcement Learning; Economic Modeling; Decision Support Systems; Public Finance; Machine Learning
Economics; Computer Science; Public Policy; Artificial Intelligence; Public Administration
Copyright (c) 2023 American Data Science Journal for Advanced Computations (ADSJAC)
A Synthetic Data-Trained Generative Architecture for Rare-Code Coverage and Compliance-Aware Medical Bill Automation
Generative AI is emerging as a powerful enabler for intelligent and efficient medical coding to facilitate health data analytics. For predictive analytics, generative models are used alongside conventional classification approaches to predict the onset of diseases, while also serving as an internal validation tool for classifier performance. For risk stratification, generative models enhance the unsupervised stratification of patient populations. Adaptive risk-scoring systems are proposed to identify patients likely to require surgery within the next year, with analysis of state transition paths also possible. Considerations related to the privacy and security of health information in using generative models, and for computer-aided healthcare decision and workflow–support systems in general, are presented. Automated model validation frameworks for supporting ICD and CPT coding systems, along with change management in general, are outlined.
Generative Artificial Intelligence,Intelligent Medical Coding,Healthcare Analytics,Clinical Natural Language Processing,Automated ICD Coding,Electronic Health Records (EHR),Machine Learning in Healthcare,Predictive Healthcare Analytics,Medical Data Automation,AI-Driven Clinical Decision Support.
Generative Artificial Intelligence, Intelligent Medical Coding, Healthcare Analytics, Clinical Natural Language Processing, Automated ICD Coding, Electronic Health Records (EHR), Machine Learning in Healthcare, Predictive Healthcare Analytics, Medical Data Automation, AI-Driven Clinical Decision Support.
Computer Science / Artificial Intelligence, Health Informatics, Biomedical Engineering, Clinical Data Science, and Natural Language Processing.
Copyright (c) 2025 American Data Science Journal for Advanced Computations (ADSJAC)
A Unified Multi-Hazard Data Ingestion and Risk Inference Engine for Global Supply Network Stress Testing
Supply chain risk prediction supports decisions on investment, protection, and recovery against disruptions. However, the prediction of such risks at a global scale remains underdeveloped. Artificial-intelligence-enhanced data engineering frameworks can contribute by defining relevant risks, specifying architectures and components for risk-analytics drivers, and identifying metrics for predictive performance. Informed by these contributions, AI-enhanced data-engineering frameworks offer a step towards meeting the prediction challenge. A supply chain risk prediction framework elaborates general-purpose prediction pipelines using real-time data streams and multimodal data fusion for demand, supply, and logistics risks. Data pipelines for crisis risk signals incorporate geopolitical, economic, and weather-related predictors. Global pipelines remain sensitive to prediction quality for individual geographies. Hence, prediction performance metrics encompass accuracy, degree of calibration, area under the receiver operating characteristic and precision-recall curves, and risk-relevant decision-analytic metrics. Guidelines for robust, generalizable, and calibrated predictions cover stress testing, cross-domain evaluation, domain-adaptation concerns, and uncertainty quantification
Global supply chains; risk prediction; data engineering; artificial intelligence; machine learning.AI-Driven Supply Chain Analytics;Predictive Risk Modeling;Data Pipeline Automation;Machine Learning for Supply Chain Resilience;Real-Time Supply Chain Monitoring;Big Data Integration for Logistics;Intelligent Risk Forecasting;Distributed Data Engineering Architecture;Supply Chain Disruption Prediction;AI-Powered Decision Support Systems.
Supply chain risk prediction, data engineering, AI/ML-driven risk analytics, global supply networks, stress testing.
Computer Science / Data Engineering, Operations & Supply Chain Management, Artificial Intelligence and Machine Learning.
A Goal-Conditioned Multi-Agent Reasoning Architecture for Self-Optimizing Enterprise Workflow Orchestration
Enterprise Resource Planning (ERP) systems integrate essential business functions through a centralized database, but cannot respond autonomously to data updates. Optimizing these systems requires frequent, expert-level decisions. Recent advancements in AI—particularly optimization algorithms, reinforcement learning, planning, constraint satisfaction, and learning-to-plan techniques—enable autonomous, data-driven decisions. Such decisions enhance efficiency, adaptability, resilience, and profitability. AI agents can specialize in distinct aspects of ERP decision making and cooperate in planning and orchestration. These developments should reposition ERP systems as a foundation for business processes: decentralized ecosystems with emergent behavior, analogous to the transportation and aviation industries, rather than a monolithic backbone. Demand forecasting drives tailored inventory policies that determine order quantities and reorder points. Production planning incorporates capacity constraints, product sequencing preferences, lead times, resource bottlenecks, and sales forecasts to align supply with demand. Decision-makers expect immediate operational support, so these plans require timely updates based on unplanned events.
Autonomous AI Systems,AI-Driven Decision Making,Enterprise Resource Planning (ERP) Optimization,Intelligent Process Automation,Predictive Analytics in ERP,Reinforcement Learning for Business Decisions,Self-Optimizing Enterprise Systems,AI-Based Resource Allocation,Autonomous Business Process Management,Data-Driven Enterprise Decision Systems,Cognitive ERP Platforms,AI-Powered Workflow Optimization,Real-Time Enterprise Analytics,Adaptive Business Intelligence Systems,Machine Learning for ERP Efficiency.
Autonomous AI, Enterprise Resource Planning (ERP) optimization, multi-agent systems, demand forecasting, inventory optimization, production planning, decision orchestration.
Computer Science / Artificial Intelligence, Operations Research, Business Intelligence, Supply Chain Management.
Gradient Without Borders: Homomorphic Encryption-Backed Collaborative Model Training Across Sovereign Healthcare Networks
The difficulties of working in sectors with strict privacy constraints, such as healthcare, result from the fact that data is separated and distributed across multiple sources that are unable to exchange it. The solution to this dilemma is to allow multiple data owners to collaborate on a joint task by using shared/global models while keeping the data decentralized. Three paradigms of federated machine learning are proposed: horizontal federated learning, vertical federated learning, and federated transfer learning. Federated learning processes and architectures with privacy-preserving properties that also address data provenance, access control, and interoperability. Integrating heterogeneous distributed data sources while maintaining privacy is a major challenge for modern data and AI analysis. Sensitive data in particular, such as healthcare data, privacy is guaranteed by law. Therefore, many healthcare data sources are available based on these data sharing and data monetization are a difficult integration task. Multiple organizations are willing to collaborate by sharing insights from their private datasets, but without sharing the original datasets because healthcare data is highly sensitive. However, existing guidelines permit the use of healthcare data in Europe and America only for either research or health benefits, and not even for commercial use. Nevertheless, several companies are attempting to break this barrier by using fake-generated data for AI modeling purposes. As a result, three main criteria must be considered during the establishment of collaboration for multi-organization AI data modeling: privacy, security, and monitoring.
Federated Learning,Privacy-Preserving Machine Learning,Secure Multi-Party Computation (SMPC),Healthcare Data Collaboration,Distributed AI Systems,Differential Privacy,Data Governance in Healthcare,Secure Model Aggregation,Cross-Institutional Data Analysis,Homomorphic Encryption,Decentralized Machine Learning,Clinical Data Sharing Frameworks,Privacy-Aware AI Architectures,Interoperable Healthcare Systems,Trustworthy AI in Healthcare.
give the answers using the pdf Subjects Disciplines Supporting Agencies Coverage Rights Source Type Data Availability Statement
Computer Science, Artificial Intelligence, Healthcare Informatics, Data Privacy and Security, Machine Learning.
Designing Intelligent Data Fabric Systems for Multi-Hazard Emergency Response Optimization
AI-based big data systems support near real-time disaster response and resource allocation. These systems shorten reaction times by leveraging scarce resources (data, bandwidth, processing, and decision-making) more efficiently in disaster management. A layered architecture provides distributed computing, data fusion, and decision management for near real-time deployment. Future implementations will prototype alarm generation using social-media and sensor data. Large-scale disasters place severe and often insurmountable demands on emergency agencies. Under such conditions, resource allocation plays a critical role in minimizing disaster impact, but conventional methods based on historical data often cannot meet real-time requirements. The integration of geographical information with data from social networks, sensor networks, and other sources can facilitate the relevant prediction and decision-making processes, but the available data, computing, and human resources are all severely constrained—with the same scarcity of responses and alerts as for other data types. For these reasons, proposed methods shorten response times by applying AI-based big-data systems.
Big Data; Disaster; Streaming Data; Real-Time; Predictive Modelling; Resource Allocation; Command-and-Control; Decision Management; Social and Open Data; Sensor Networks; Internet of Things.
Big Data, Disaster Management, Emergency Response, Resource Allocation, Real-Time Analytics, AI-Based Systems, Predictive Modelling, Command-and-Control Systems, Decision Management.
Computer Science / Data Science, Emergency Management / Disaster Risk Reduction, Information and Communication Technologies (ICT), Geospatial Sciences, Systems Engineering.
Copyright (c) 2024 American Data Science Journal for Advanced Computations (ADSJAC)
A Real-Time Deep Learning Pipeline for Proactive Fault Isolation in Next-Generation Power Networks
Research on smart grids has drawn attention due to the increasing reliance on multiple sources of generation, such as solar and wind farms, and the decentralization of decision-making due to the increase in distributed generators. The implementation of new technologies such as machine learning (ML), available within the framework of artificial intelligence (AI), promises to increase reliability and efficiency by anticipating events and acting before they occur. AI-enabled data engineering pipelines integrate data-processing procedures—such as quality assurance, governance, ingestion, transformation, and preparation—with AI modeling techniques addressing applications such as fault detection and predictive maintenance. These pipelines embed advanced data-processing techniques and tools into a reliable management framework. Smart grids monitor power distribution, detect faults, and feed data, including that from phasor measurement units (PMUs), supervisory control and data acquisition (SCADA), and weather-related sensors. Data flowing through the pipelines is analyzed in real-time using a fixed library of ML fault-detection models. Fixed models, configured in a lawyer-and-create manner, allow some level of exploration during the training phase of the pipeline. Delays in the deployment of fixed models represent a serious concern for many businesses; the situation is more critical for companies with a mature AI practice—the latency is built into the model and processes association—and is nevertheless significant for business pipelines. Stream processing pipelines execute each processing step as soon as new data arrive for that step. However, model evaluation requires more than a supervised test data set; measures are needed to control the false-positive rate in a business pipeline and assess model robustness against rare events such as blackouts or smaller events such as terminal failures.
Smart grid · Fault detection · Data engineering · Artificial intelligence · Supervised learning · Unsupervised learning · Semi-supervised learning,AI Use academic tone with objective, evidence-based arguments, and formal structure; present clarity, rigor, and precise terminology throughout the manuscript.
Smart grid fault detection; AI-enabled data engineering pipelines; real-time deep learning for power networks.
Electrical Engineering; Computer Science / Artificial Intelligence; Data Engineering.
Goal-Seeking Support: Designing Multi-Agent LLM Pipelines for End-to-End Customer Query Resolution
Digital-platform automation holds the potential to reduce operational costs while improving service quality and availability. Nevertheless, full or near-full automation of customer support remains elusive. Current automated solutions are limited in capabilities, often providing only basic and inflexible assistance. Autonomous AI agents capable of intelligent customer-support task execution would therefore represent a major milestone. Such technology could automatically carry out common support actions and ensure a satisfactory experience for all users that require assistance. An architectural framework specifying the essential building blocks for the autonomous AI agent operation is proposed. Commonly employed data governance, privacy-preserving techniques, and deployment guidelines are also presented. Together, these concepts provide a theoretical foundation for the automated customer-support automation task in the digital platform domain and serve as guidance for future implementations.
Autonomous AI agents; customer support; digital platforms; intelligent assistance; intelligent automation; natural language processing.
Autonomous AI agents, customer support automation, multi-agent LLM pipelines, query resolution, natural language processing, data governance.
Artificial Intelligence, Computer Science, Information Systems, Digital Platform Management
Intelligent Automation: Multi-Agent AI for Enterprise IT Service Management
Development of Multi-Agent Systems capable of partially or fully automating IT incident response, service request fulfilment or routing across the agent lifecycle, for integration into existing enterprise toolchains and alignment with ITIL across both DevOps and IT Service Management seams. Active experimentation underpins performance evaluation and identifies critical non-functional dimensions: Reliability, robustness, fault tolerance. Performance matches established benchmarks where applicable; incident automation and mitigation, and service request fulfilment and routing for unsupported categories remain areas for ongoing development. Ongoing Development of a Multi-Agent System capable of autonomously handling enterprise IT service requests. Component agents operate as virtual analysts, equipped to automate service fulfilment, fulfilment-orchestrate routing, or detect & mitigate incident effects – commonly without human involvement. Continuous Improvement promulgates operational agents’ hard-won experience, simultaneously bolstering robustness, broadening capability and confirming normative behaviour for integration-testing seeker agents. Seamless ITIL alignment drives current service request fulfilment; applicable Service Transition activities support experimental automation of incident response and mitigation. Active experimentation probes critical non-functional dimensions: Reliability, robustness, fault tolerance. Continuously-evolving performance meets established benchmarks where applicable; incident automation & mitigation, and service request fulfilment for unsupported categories remain areas for ongoing development.
Multi-Agent AI Systems, Autonomous Handling, IT Service Requests, Incident Management Automation, Service Request Mitigation, Service Request Fulfillment, ITSM Automation, ITIL Autonomous Request Handling, ITSIAM, Artificial Intelligence as a Service, Non-Intrusive, Integration with ITSM Frameworks.
Multi-Agent AI Systems, IT Service Management (ITSM), Incident Management Automation, Service Request Fulfillment, ITIL Alignment, DevOps, Autonomous Handling
Computer Science / Artificial Intelligence, Information Technology, Systems Engineering, Enterprise IT Operations
Cloud-Native DevOps Frameworks for Agentic AI in Financial Automation
Conventional automated trading and risk management employ deterministic models within a well-defined decision support paradigm. Emerging DevOps-driven cloud-native architectures, however, enable scalable, resilient, operable, observant, and automated systems where models are continuously updated in response to changing real-world conditions. Recent research suggests extending agent-based reinforcement learning beyond a decision support role toward direct decision-making responsibilities, underpinned by proper control and governance principles. The combination of such agentic models within automated financial systems presents novel challenges, while fulfilling requirements defined by supervisory authorities. A suitably architected cloud-native environment thus has the potential to not only expedite the delivery of financial AI models, but also to support their increasingly frequent deployment and operation with minimum effort and oversight. The specific considerations, from foundational concepts through to development and operational constraints, then govern the underlying implementation. The ultimate goal is a DevOps-driven cloud-native implementation framework that guarantees the reliable delivery of agentic AI models into automated trading and risk management systems, quickly, frequently, and with minimal overhead. In this highly regulated domain, controls must therefore be embedded within the supporting infrastructure and processes rather than bolted on afterwards. The key metrics for measuring success then become the speed, frequency, and simplicity with which models are delivered, continuously improved, and retired, all while remaining compliant with a multitude of ever-present security, risk, governance, and operational requirements. By explicitly specifying these considerations, the intention is to present a coherent synthesis of the architectural rationale and a repeatable implementation approach.
Agentic Artificial Intelligence in Finance, Reinforcement Learning for Trading Systems, Automated Trading and Risk Management, DevOps-Driven Financial AI, Cloud-Native Financial Architectures, Continuous Model Deployment (MLOps)
Cloud-native DevOps frameworks, agentic AI, automated trading, risk management, financial automation, MLOps, regulatory compliance.
Computer Science (distributed systems, cloud architecture, DevOps), Financial Technology (FinTech), Artificial Intelligence / Machine Learning, Regulatory/Compliance Technology (SupTech).
Copyright (c) 2026 American Data Science Journal for Advanced Computations (ADSJAC)
AI-Driven Predictive Testing Framework for Advanced Semiconductor Chips
Next-generation semiconductor chips require predictive testing to assure the quality, reliability, and time-to-market of increasing complex systems-on-chip with multiple processor cores and dedicated hardware accelerators. AI-enabled methodologies are needed such that the test coverage and resource commitments are commensurate with the risk of undetected latent defects. Against this background, this work presents the architecture of a predictive testing framework that deploys AI technologies to address all main elements crucial for predictive testing—component fault prediction, test conditions that prioritize areas with elevated risk, and test vectors that consider fault interactions. Core capabilities are outlined, the integration approach specified, and the expected outcomes discussed. The proposed predictive testing framework contributes to the body of knowledge by providing AI-enabled methodologies that go beyond simple fault detection and create a predictable testing roadmap for semiconductor chips.
Predictive Semiconductor Testing, AI-Enabled Test Engineering, System-on-Chip Reliability, Component Fault Prediction, Risk-Aware Test Coverage, Intelligent Test Vector Generation, Latent Defect Detection, Hardware Accelerator Validation, Multi-Core SoC Testing, Test Resource Optimization, Fault Interaction Modeling, Predictive Quality Assurance, Silicon Yield Improvement, Time-to-Market Optimization, AI-Driven Verification Frameworks, Test Prioritization Strategies, Integrated Test Architectures, Reliability Analytics, Semiconductor Manufacturing Intelligence.
Predictive semiconductor testing, AI-enabled test engineering, fault prediction, system-on-chip (SoC) reliability, test resource optimization, and silicon yield improvement.
Semiconductor engineering, electrical/computer engineering, machine learning, statistical modeling, and VLSI testing.
Performance Optimization of Data Pipelines for Retail Demand Forecasting
A high-performance data pipeline is critical for achieving data-driven goals in retail demand forecasting and is particularly important for online algorithms. This work outlines best practices for building optimally responsive data pipelines that meet, in the least possible time, the fault-tolerance needs of the retail domain and improve forecast accuracy. Data-flow architecture, data ingestion, processing and transformation methods, storage systems, model deployment, and online inference are covered. Demand forecasting is essential to retail operations, yet accurate forecasting is challenging due to the complexity of customer behaviour and the multitude of signals that impact demand. Data-driven forecasts that harness these signals require high-performance data pipelines that smoothly ingest large volumes of data, apply complex feature engineering, and support online inference. These pipelines must balance three interrelated dimensions: scalability, latency, and reliability. Scalable architectures can grow without degradation; low-latency systems respond rapidly to the arrival of new data; and fully fault-tolerant systems provide the right answers in the face of storage and computation failures. In retail, where the cost of data storage is essential but the need for data freshness is great, certain compromises in fault tolerance can be afforded.
Retail Demand Forecasting, High-Performance Data Pipelines, Online Learning Algorithms, Real-Time Forecast Inference, Scalable Data Architectures, Low-Latency Data Processing, Fault-Tolerant Retail Systems, Feature Engineering at Scale, Data Ingestion and Transformation Frameworks, Cloud-Native Forecasting Infrastructure, Online Model Deployment, Data Freshness Optimization, Stream Processing in Retail Analytics, Reliability–Latency Trade-Offs, Data-Driven Retail Operations.