American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Journal Summary The American Online Journal of Science and Engineering (AOJSE) ISSN: 3067-1140 (Online) Publisher: AOJSE Editorial Office Publication Type: Open Access | Peer-Reviewed | International Frequency: Continuous Publication / Quarterly Issues Language: English License: Creative Commons Attribution 4.0 International (CC BY 4.0) Journal Overview The American Online Journal of Science and Engineering (AOJSE) is an international, peer-reviewed, open-access journal that publishes original research, review articles, technical reports, and short communications across the full spectrum of science, engineering, and applied technology disciplines. Since its establishment, AOJSE has served as a rigorous and inclusive scholarly platform for researchers, academicians, engineers, and industry professionals worldwide. The journal is committed to the rapid dissemination of high-quality, impactful research that advances scientific understanding and addresses real-world technological challenges. AOJSE is indexed in Scopus (Elsevier), Web of Science, DOAJ, CrossRef, and other leading international abstracting and indexing databases, providing authors with extensive global visibility and citation tracking. Indexing & Abstracting Database Status Scopus (Elsevier) ✅ Indexed Web of Science (Clarivate) ✅ Indexed DOAJ ✅ Listed CrossRef ✅ DOI Registered Google Scholar ✅ Indexed EBSCO ✅ Indexed Dimensions ✅ Indexed BASE ✅ Indexed OpenAIRE ✅ Indexed ResearchGate ✅ Listed All published articles are assigned a CrossRef DOI, ensuring permanent identification and global traceability. Key Metrics Indicator Value Scopus CiteScore Under Evaluation SCImago Journal Rank (SJR) Under Evaluation Acceptance Rate < 18% Time to First Decision 15 Days Average Peer Review Duration 50 Days Acceptance to Online Publication 60–90 Days Plagiarism Check iThenticate / Turnitin Peer Review Type Double-Blind Scope & Coverage AOJSE publishes cutting-edge research across a broad range of disciplines, including: Engineering — Electrical, Mechanical, Civil, Chemical, Industrial, Materials & Nanotechnology Computer Science — Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Cloud Computing, Software Engineering Applied Sciences — Physics, Chemistry, Environmental Science, Renewable Energy, Earth Sciences Mathematics & Modeling — Applied Mathematics, Numerical Methods, Statistical Analysis, Scientific Simulation Emerging Technologies — Robotics, IoT, Biotechnology, Smart Systems, Advanced Instrumentation Interdisciplinary research that bridges scientific principles with engineering innovation is strongly encouraged. Publication Ethics & Standards AOJSE upholds the highest standards of publication ethics in full compliance with: COPE – Committee on Publication Ethics ICMJE – International Committee of Medical Journal Editors STM Association Best Practices Elsevier Publishing Ethics Guidelines The journal operates a rigorous double-blind peer review process and enforces strict policies against plagiarism, data fabrication, authorship misconduct, and duplicate publication. Open Access Policy All content published in AOJSE is freely accessible under the CC BY 4.0 license, allowing unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This policy ensures compliance with major funder open-access mandates including Plan S, NIH, and Horizon Europe. Article Processing Charges (APCs) are applied only upon manuscript acceptance. Digital Preservation Published content is preserved through internationally recognized archiving networks: CLOCKSS — Controlled Lots of Copies Keep Stuff Safe Portico — Long-term Digital Preservation Internet Archive Contact: editor@aojse.com
Retrieval-Augmented Generation Pipelines for Context-Sensitive ICD-10 Assignment and Clinical Revenue Cycle 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 — as stated in the Keywords section.
Healthcare Informatics, Artificial Intelligence, Medical Data Science, Clinical Decision Support Systems.
Copyright (c) 2025 American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Homomorphic Encryption-Backed Collaborative Learning Pipelines for SovereignClinical Intelligence Across Hospital 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.
The paper addresses privacy-preserving machine learning in healthcare, specifically federated learning for cross-institutional clinical data collaboration.
Computer Science, Healthcare Informatics, Distributed Systems, Cryptography, Data Governance
Cognitive Automation Frameworks Using Multi-Agent Reasoning for Proactive Enterprise Operational Intelligence
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 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.
Transformer-Based Streaming Architectures for Multi-Tier Supplier Vulnerability Detection in Global Trade Ecosystems
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.
Global supply chains; risk prediction; data engineering; artificial intelligence; machine learning; supply chain disruption; real-time monitoring; predictive risk modeling.
Data Engineering, Artificial Intelligence, Machine Learning, Supply Chain Management, Risk Analytics, Operations Research.
Goal-Directed Agentic AI Frameworks for Proactive Query Resolutionand Experience Personalization in Digital Services
Nevertheless, full or near-full automation of customer support remains elusive. Current automated solutions are limited in capabilities, often providing only basic and inflexible Digital-platform automation holds the potential to reduce operational costs while improving service quality and availability. 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 Systems Enterprise Resource Planning (ERP) Optimization Multi-Agent Reasoning Cognitive Automation Proactive Operational Intelligence
Computer Science / Artificial Intelligence Business Information Systems Operations Research Supply Chain Management Data Engineering
Copyright (c) 2024 American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Scalable ETL-Driven Machine Learning Systems for Predictive Fault Localization in Smart Energy 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.
give the answers using the pdf Subjects Disciplines Supporting Agencies Coverage Rights Source Type Data Availability Statement
Electrical Engineering, Computer Science (Machine Learning / Artificial Intelligence), Data Engineering, Energy Systems.
Adaptive Machine Learning Pipelines for Instantaneous Disaster Triage and Supply Chain Coordination
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, Real-Time Analytics, Machine Learning, Resource Allocation, Supply Chain Coordination, Command-and-Control Systems, Decision Management.
Computer Science, Information Technology, Emergency Management, Data Engineering, Systems Architecture, Geospatial Informatics.
Cognitive Infrastructure Scaling: Harnessing Machine Intelligence for Enterprise Cloud Efficiency
Cloud resource consumption is vital to the operational costs of enterprise applications, and it is often a main explanatory variable driving the performance experience. Hyper-scale public cloud providers such as Amazon, Microsoft, and Google have been investing in making resource consumption simple and cost-effective across different resource layers: virtual machines, containers, serverless, and database services. However, the rich functionality, flexibility, and cloud-native advantages of enterprise applications come with complexity that transforms any cost benefits into a burden and can negatively affect resource consumption efficiency, especially at very high scale. Empirical studies have shown that a single large-scale business application deployed and operating in the cloud costs many millions of dollars every month. Addressing these cost issues and improving efficiency at such scale require AI, ML, and Data products at their core. Optimization of resource consumption under cost and performance objectives becomes essential. Solutions using AI for data-driven resource optimization, prediction, and forecasting can be utilized on their own whenever a given cost-resource dimension is analysed or combined into a single cost-performance optimization through a case study on a real-world enterprise cloud application. AI and ML solutions for cloud resource optimization have already been proposed and evaluated in situations such as demand prediction and capacity planning, autoscaling policies, performance-aware workload scheduling, workload-specific service selection, anomaly detection, predictive maintenance, and cost optimization. Promising results have been obtained in terms of cost, performance, and application experience. However, a single solution at a time is not sufficient for producing world-class cost-performance efficiency. A comprehensive AI-powered resource optimization framework leveraging internal or external data sources for the given task is needed to be analysed and deployed with each deployment at every stage of the data-life-cycle process while also considering specific planning needs
Artificial Intelligence in Cloud Computing,Cloud Resource Optimization,Machine Learning for Resource Allocation,Large-Scale Enterprise Applications,Cloud Infrastructure Management,Intelligent Workload Scheduling,Auto-Scaling and Resource Provisioning,Cloud Performance Optimization,Predictive Analytics in Cloud Systems,Distributed Cloud Computing Systems.
Artificial Intelligence in Cloud Computing, Cloud Resource Optimization, Machine Learning for Resource Allocation, Large-Scale Enterprise Applications, Cloud Infrastructure Management, Intelligent Workload Scheduling, Auto-Scaling and Resource Provisioning, Cloud Performance Optimization, Predictive Analytics in Cloud Systems, Distributed Cloud Computing Systems.
The paper spans Computer Science, Cloud Computing, Artificial Intelligence/Machine Learning, and Enterprise Systems Engineering.
Copyright (c) 2023 American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Deep Learning-Driven Predictive Models for Real-Time Urban Air Quality Assessment Using Big Data Frameworks
Abstract Environmental factors are responsible for 28% of premature deaths in the European Union, notably due to urban air pollution. INQUA, the global network of terrestrial researchers investigating the impacts of air quality, greenhouse gas emissions, and climate change on human health, responds to these threats with an open-ended challenge to develop reliable predictive models for urban air quality monitoring. Such externalities are increasingly treated as a common good. The approach promises to improve the public health decision-making process and governance by enabling predictive analytics and beyond-the-air-data-informed decision-support systems. Empirical evidence is provided by two pilot studies, one in a megacity with high-density urban cores and the other in a mid-sized city. Resistance to predictive urban air quality models stems from concerns about the accuracy of traditional regression estimates when compared with modern supervised learning. The ensemble of data from real-time sensor networks, remotely sensed earth properties, and urban activity emissions should determine both algorithms for accurate future estimates and feature-engineering procedures to identify the most effective and interpretable paths for each pollutant. Well-designed learning tasks improve predictive skills, providing a probabilistic assessment that accounts for quality uncertainty, reinforced by temporal validation tests and quantile-class predictive-distribution measures.
Artificial Intelligence; Predictive Analytics; Air Quality Monitoring; Big Data; Urban Health; Machine Learning; Decision-Making Support
Urban air quality monitoring, deep learning, predictive analytics, big data frameworks, air pollution, public health decision-making.
Environmental Science, Artificial Intelligence / Machine Learning, Urban Studies, Public Health, Data Science.
Leveraging AI and Cloud Computing for Disaster Prediction and Management
Cloud Computing and Artificial Intelligence (AI) are two of the widely used technological domains in the present world. Clouds are considered as a base for Disaster Management Support Systems. In the cloud environment, people have enormous computing, storage, and communication capabilities at ease so that they could collect large amounts of data from different sources when disaster incidents occur, and later analyze these data and build knowledge. Data and information serve as the critical backbone of Disaster Management. AI techniques and systems are used in Disaster Management area for Disaster prediction and monitoring. AI is useful for searching damaged buildings and planning in dangerous environments of rescue with machine-to-machine or human-machine collaboration. Supported by cloud environment, Disaster Management Support Systems provide different real-time data analysis services. In areas of the world that suffer from severe weather driven-hazards such as storm surges, flooding, or heavy snow and wind, a greater effort mediated by cooperatives and National Disaster Management Agencies is needed. There is an opportunity to develop Cloud Systems that bring together hydrological, meteorological, and impact models at high spatial resolution for forecast and early warning in these regions. Such a service will be essential not only for local, national, and regional receivers of the information but also for adjacent areas that may be indirectly affected by the potential trans-boundary effects of severe weather events.
Aim; cloud computing; computer science; computing; disaster management; information technology; information systems; machine learning; operational research; services; systems; technologies; artificial intelligence; big data; bioinformatics; cloud computing; data management; security; artificial intelligence; data processing; disaster prediction; disaster response; systems; services; technologies; warning; weather forecasting; cloud computing; machine learning; artificial intelligence; sensor networks; remote sensing; image processing; decision support system; object detection; deep learning; situational awareness; disaster management; science technology; education.
The paper covers AI and Cloud Computing applied to Disaster Prediction and Management, including machine learning, deep learning, hazard forecasting, flood modeling, urban search and rescue, and crisis mapping.
Computer Science, Information Technology, Artificial Intelligence, Cloud Computing, Operational Research, Bioinformatics, Remote Sensing, Data Management, Environmental Science, and Emergency/Disaster Management.
Copyright (c) 2026 American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140)
Data-Driven Process Modeling for Clinical Workflow Optimization
Clinical motivation, objectives, data-driven approach, methods, findings, and implications are synthesized. Clinical workflows govern patient management in specialty practices, creating a need for technique deployment to improve operational outcomes and resource utilization. Data Driven Process Modeling uses systematic data analysis to provide rigorous evidence and formal structure to clinical decision-making. ‡Process mining identifies the real recurrence of patient journeys; machine learning models future event occurrences to replicate desirable flows; simulation of mapped processes quantifies throughput, waits, and cycle times; and hybrid methods combine these techniques. Together, they identify bottlenecks and variations affecting performance. An Emergency Department throughput case shows how analyzed paths reveal long wait times at triage and radiology, together with extended time between physician disposition and patient departure. Measurable improvements result from targeted interventions. In a second application, development of Machine Learning algorithms for segments of an inpatient discharge planning pathway highlights excessive attorney handoffs, leading to simplified processes and faster discharge times. Analysis of quantitative results indicates throughput times, waits, and cycle times are shortened, variance reduced, and changes statistically significant. ‡Qualitative evidence complements numbers by revealing practitioner satisfaction and user acceptance.
Data-Driven Process Modeling, Clinical Workflow Optimization, Process Mining in Healthcare, Machine Learning for Clinical Operations, Healthcare Throughput Analysis, Discrete-Event Simulation in Medicine, Emergency Department Throughput, Inpatient Discharge Planning, Bottleneck Identification, Patient Flow Optimization, Clinical Decision Support Analytics, Healthcare Operations Management, Hybrid Analytics Methods, Wait Time and Cycle Time Reduction, Evidence-Based Healthcare Process Improvement.
Clinical workflow optimization, Emergency Department throughput, inpatient discharge planning, patient flow, bottleneck identification, wait time and cycle time reduction.
Healthcare Operations Management, Health Informatics, Process Mining, Machine Learning, Discrete-Event Simulation, Business Process Management, and Clinical Decision Support Analytics.
Adaptive Semiconductor Architectures Powered by Agentic AI for Future Intelligent Wireless Systems
Intelligent wireless network systems must adapt in real time to channel conditions, mobile users, and traffic demands while complying with strict performance specifications. The presented study describes a foundation for agentic AI-driven adaptive semiconductor architectures, taking into account both technology and system-level considerations. With respect to technology, it focuses on recent semiconductor architectural trends inspired by early developments in AI computing. In particular, it examines reconfigurable and programmable hardware, neuromorphic processors, ternary and binary computing, in-memory and near-memory computing, and 3D integration. From a system perspective, it identifies mechanisms supporting real-time adaptation: spectrum and mobile resource management, context-aware modulation and coding schemes, and end-to-end control loops. In addition, it discusses AI-driven development toolchains, addressing tool automation, data privacy, and security issues. Finally, it proposes metrics, benchmarks, and evaluation protocols enabling a holistic assessment of adaptive semiconductor architectures for intelligent wireless networks. Attention to both technology and systems can steer forthcoming technology development towards the ultimate intelligent wireless systems and networks targeted by 6G and beyond.
Intelligent Wireless Networks, Agentic AI, Adaptive Semiconductor Architectures, Real-Time Network Adaptation, Reconfigurable and Programmable Hardware, Neuromorphic Processors, In-Memory and Near-Memory Computing, Ternary and Binary Computing, 3D Integration, Spectrum and Mobile Resource Management, Context-Aware Modulation and Coding, End-to-End Control Loops, AI-Driven Development Toolchains, Secure Hardware–Software Co-Design, Performance Metrics and Benchmarks, Evaluation Protocols, 6G Systems, Intelligent Network Control, Hardware–AI Co-Design, Autonomous Wireless Systems.
Electrical Engineering, Computer Engineering, Wireless Communications, Semiconductor Design, Artificial Intelligence / Machine Learning.
Cloud-Enabled AI Solutions for Intelligent Transportation Safety Systems
Cloud Computing and Artificial Intelligence will enable Intelligent Transportation Safety Systems (ITSS) that ensure a reliable low accident rate in road traffic. ITSS are considered an extension of Intelligent Transportation Systems connected to Cloud Computing with integrated Artificial Intelligence and focus on traffic safety. ITSS detect critical situations, predict dangerous conditions, and inform vehicle and driver assistance systems in real time to avoid accidents, control signal flows, and manage incidents. Today’s configuration of CT and AI can only solve special tasks, for instance, the level of cloud service and the bandwidth of data exchange. However, both technologies can work on a multidisciplinary basis, increase the robustness of work, manage data from various sources, analyze them dynamically and, thus, bring traffic safety to a new level. The paper applies the original methodology developed for expanding the possible use of Cloud Computing and AI in solving applied problems. Cloud Computing allows integration of data from multiple sources in real time. AI based on interconnection and dynamic development between models of risk, collision, smart camera detection of critical mode conditions, IP-traffic incident detection and routing for traffic management on internal cloud and on-device level of connected vehicles makes it possible to predict modes of accidents with high probability and with a certain reaction time. Although the current architecture of Cloud Computing for ITSS has its limitations, it has enough potential to integrate a sufficiently rich set of modern safety-related solutions.
Intelligent Transportation Safety Systems, Cloud Computing For Traffic Safety, AI-Enabled Traffic Management, Real-Time Accident Prediction, Connected Vehicle Safety Systems, Cloud-Based Traffic Data Integration, AI Risk And Collision Models, Smart Camera Traffic Detection, Incident Detection And Management
Computer Science, Transportation Engineering, Information Technology, Systems Engineering, Data Science, Cybersecurity.