Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities DOI Creative Commons
Cosmina-Mihaela Roșca, Adrian Stancu,

Cosmin-Florinel Neculaiu

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(19), P. 8861 - 8861

Published: Oct. 2, 2024

Many countries encourage their populations to use public urban transport decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times low economic efficiency for companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm allocating fleet based on the number of passengers waiting bus considering companies. The PUTSS integrates artificial intelligence (AI) methods identify people each station through real-time image acquisition. technique presented is Azure Computer Vision. In case study, accuracy correctly identifying persons in an was computed using Microsoft Vision service. proposed also uses Google Maps Service congestion-level identification. Employing these modern tools makes improving services possible. integrated into software application developed C#, simulating real-world scenario involving two vehicles. global rate 89.81% demonstrates practical applicability product.

Language: Английский

Artificial Intelligence of Things as New Paradigm in Aviation Health Monitoring Systems DOI Creative Commons
Igor Kabashkin, Leonid Shoshin

Future Internet, Journal Year: 2024, Volume and Issue: 16(8), P. 276 - 276

Published: Aug. 2, 2024

The integration of artificial intelligence things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies reducing costs. Utilizing three-tiered cloud architecture, the AIoT system enables real-time acquisition from sensors embedded in aircraft systems, followed machine learning algorithms to analyze interpret for proactive decision-making. research examines evolution traditional AIoT-enhanced monitoring, presenting comprehensive architecture integrated satellite communication 6G technology. mathematical models quantifying benefits increased diagnostic depth through AIoT, covering aspects such as cost savings, improvements are introduced this paper. findings emphasize strategic importance investing technologies balance cost, safety, efficiency operations, marking paradigm shift management aviation.

Language: Английский

Citations

6
Artificial Intelligence and Machine Learning in Research and Development DOI
Omar Al Jadaan, Omnia Ibrahim Mohamed,

Nowar Nizar Al Ani

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 53 - 86

Published: Feb. 5, 2025

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly changing the face of Research Development (R&D). This chapter deals with a profound review current status future trends AI ML in R&D. First all, it gives an overview huge investments fast growth AI, for instance, spending on systems worldwide is projected to reach as high $110 billion by 2024. In health sector, will potentially add up $150 every year 2026. The highlights some most remarkable achievements ML, including transformer models like GPT-3 or Google's BERT, setting new benchmarks natural language processing, low-code/no-code platforms democratize AI. Finally, asserts that have potential transform R&D while insinuating such development should be responsible ethical. adopting collaborative open approaches, stakeholders could reap maximum benefits from technologies boosting innovation societal across different industries.

Language: Английский

Citations

0
Human-AI Hybrids in Safety-Critical Systems: Concept, definition and perspectives from Air Traffic Management DOI
Hasnain Ali, Duc-Thinh Pham, Sameer Alam

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103256 - 103256

Published: March 19, 2025

Language: Английский

Citations

0
Unified Technical Surveillance and Auditing Method for Global Aviation Regulations DOI
Manoj K. Paidisetty, Sanjay Singh, Om Prakash

et al.

Journal of Aerospace Sciences and Technologies, Journal Year: 2025, Volume and Issue: unknown, P. 48 - 63

Published: Feb. 18, 2025

The aviation industry’s global nature necessitates a sophisticated aircraft technical surveillance/ audit application for international regulatory bodies. Auditors face significant challenges in overseeing commercial due to its complex components, including design, manufacturing, maintenance, and operations. varied fleet sizes types operated by different airline operators further complicate the process[1]. While regulators receive comprehensive training, deploying specialized each location is impractical, leading gaps knowledge. Modern continuously evolve, requiring auditors adapt checklists changing configurations structures. process demands flexible spontaneous checks during unconventional hours. Historical data analytics can guide selecting airlines inspection, providing insights into potential compliance issues[2–6]. Identifying noncompliances within expertise of auditors, but associating these with specific regulations remains challenging. proposed surveillance will offer customized checklists, real-time alerts, guidance on violated based identified noncompliance. It leverage historical recommend organizations inspections, highlighting violations. This paper explores development generalized surveillance/audit application, detailing features capabilities addressing auditor challenges, optimizing time, enhancing quality.

Language: Английский

Citations

0
Human Factors Requirements for Human-AI Teaming in Aviation DOI Creative Commons
Barry Kirwan

Future Transportation, Journal Year: 2025, Volume and Issue: 5(2), P. 42 - 42

Published: April 5, 2025

The advent of Artificial Intelligence in the cockpit and air traffic control centre coming decade could mark a step-change improvement aviation safety, or else usher flush ‘AI-induced’ accidents. Given that contemporary AI has well-known weaknesses, from data biases edge corner effects, to outright ‘hallucinations’, mid-term will almost certainly be partnered with human expertise, its outputs monitored tempered by judgement. This is already enshrined EU Act on AI, adherence principles agency oversight required safety-critical domains such as aviation. However, sound policies are unlikely enough. Human interactions current automation tower require extensive requirements, methods, validations ensure robust (accident-free) partnership. Since inevitably push boundaries traditional human-automation interaction, there need revisit Factors meet challenges future human-AI interaction design. paper briefly reviews types ‘Intelligent Agents’ along their associated levels autonomy being considered for applications. It then evolution identify critical areas where can aid teaming performance generate detailed requirements set organised Teaming resultant comprises eight areas, Human-Centred Design Organisational Readiness, 165 been applied three AI-based Intelligent Agent prototypes (two cockpit, one tower). These early applications suggest new scalable different design maturity autonomy, acceptable an approach Human-AI teams.

Language: Английский

Citations

0
The Iceberg Model for Integrated Aircraft Health Monitoring Based on AI, Blockchain, and Data Analytics DOI Open Access
Igor Kabashkin

Electronics, Journal Year: 2024, Volume and Issue: 13(19), P. 3822 - 3822

Published: Sept. 27, 2024

The increasing complexity of modern aircraft systems necessitates advanced monitoring solutions to ensure operational safety and efficiency. Traditional health (AHMS) often rely on reactive maintenance strategies, detecting only visible faults while leaving underlying issues unaddressed. This gap can lead critical failures unplanned downtime, resulting in significant costs. To address this issue, paper proposes the integration artificial intelligence (AI) blockchain technologies within an enhanced AHMS, utilizing iceberg model as a conceptual framework illustrate both hidden defects. highlights importance addressing at earliest possible stages, ensuring that defects are identified mitigated before they evolve into failures. rationale behind approach lies need for predictive system capable identifying mitigating risks escalate. Key tasks completed study include: comparative analysis proposed with existing solutions, selection AI algorithms fault prediction, development blockchain-based infrastructure secure, transparent data sharing. evolution AHMS is discussed, emphasizing shift from traditional advanced, predictive, prescriptive approaches. integrated demonstrates potential significantly improve detection, optimize schedules, enhance security across aviation industry.

Language: Английский

Citations

3
Designing and Implementing a Public Urban Transport Scheduling System Based on Artificial Intelligence for Smart Cities DOI Creative Commons
Cosmina-Mihaela Roșca, Adrian Stancu,

Cosmin-Florinel Neculaiu

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(19), P. 8861 - 8861

Published: Oct. 2, 2024

Many countries encourage their populations to use public urban transport decrease pollution and traffic congestion. However, this can generate overcrowded routes at certain times low economic efficiency for companies when buses carry few passengers. This article proposes a Public Urban Transport Scheduling System (PUTSS) algorithm allocating fleet based on the number of passengers waiting bus considering companies. The PUTSS integrates artificial intelligence (AI) methods identify people each station through real-time image acquisition. technique presented is Azure Computer Vision. In case study, accuracy correctly identifying persons in an was computed using Microsoft Vision service. proposed also uses Google Maps Service congestion-level identification. Employing these modern tools makes improving services possible. integrated into software application developed C#, simulating real-world scenario involving two vehicles. global rate 89.81% demonstrates practical applicability product.

Language: Английский

Citations

1