Predicting the Impacts of the Environment and Severity of High-risk Burns by AIML DOI

G Divyavani,

Birlangi Usha Rani,

P Ganesh Kumar

et al.

Published: Oct. 27, 2023

A burn injury's effects might vary from mild to potentially fatal. Burn treatment depends on their degree and location. Extreme burns need medical attention, usually at specialized centres with significant follow-up treatment, whereas light may occasionally be managed home. The goal of this research is apply AI/ML predict potential complications for patients. patients who were hospitalized included in retrospective analysis. study built predicted models graft surgery, hospital stay, using data a total 10 variables. These factors things like the patient's histories laboratory findings. AI trained 65% information set, while remaining 35% was used evaluation. Precision, sensitivity, specificity, area under receiver operating characteristic curve (AUC) evaluate three machine learning (ML) techniques randomized forests, Light GBM, logistic regression. results showed that tested, model based random forests had most excellent AUC (82.2%) predicting lengthy stays $( \gt15$ days), followed by XGBoost (80.8%), GBM (80.6%). Additionally, (79.9%) shown forest requirement skin transplant, (88.3%) seen both incidence unfavourable consequences. best-performing maximum values are create integrate an prediction system into healthcare systems. use approaches has remarkable promise whether or not patient would how long they will hospitalized, likelihood other, more severe issues. Hope developing new can systems hospitals, improving clinical choices reinforcing doctor-patient conversations, stoked our study's

Language: Английский

Leveraging Advanced Technologies for (Smart) Transportation Planning: A Systematic Review DOI Open Access
Hosung Son, Jinhyeok Jang, Jihan Park

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 2245 - 2245

Published: March 5, 2025

Transportation systems worldwide are facing numerous challenges, including congestion, environmental impacts, and safety concerns. This study used a systematic literature review to investigate how advanced technologies (e.g., IoT, AI, digital twins, optimization methods) support smart transportation planning. Specifically, this examines the interrelationships between proposed solutions, enabling technologies, providing insights into these innovations mobility initiatives. A review, following PRISMA guidelines, identified 26 peer-reviewed articles published 2013 2024, studies that examined technologies. To quantitatively assess relationships among key concepts, Sentence BERT-based natural language processing approach was employed compute alignment scores technological implementation strategies. The findings highlight fact real-time data collection, predictive analytics, twin simulations significantly enhance traffic flow, safety, operational efficiency while mitigating impacts. analysis further reveals strong correlations congestion public transit optimization, reinforcing effectiveness of integrated, data-driven Additionally, IoT-based sensor networks AI-driven decision-support shown play critical role in sustainable urban by proactive management, multimodal planning, emission reduction From policy perspective, underscores need for investment urban-scale infrastructures, integration modeling long-term planning frameworks, tools with improvements foster equitable efficient mobility. These offer actionable recommendations policymakers, engineers, planners, guiding resource allocation legislative strategies sustainable, adaptive, technologically ecosystems.

Language: Английский

Citations

1
Determining the Factors Influencing Business Analytics Adoption at Organizational Level: A Systematic Literature Review DOI Creative Commons
Omar Mohammed Horani, Ali Khatibi, Anas Ratib Alsoud

et al.

Big Data and Cognitive Computing, Journal Year: 2023, Volume and Issue: 7(3), P. 125 - 125

Published: June 28, 2023

The adoption of business analytics (BA) has become increasingly important for organizations seeking to gain a competitive edge in today’s data-driven landscape. Hence, understanding the key factors influencing BA at organizational level is decisive successful implementation these technologies. This paper presents systematic literature review that utilizes PRISMA technique investigate organizational, technological, and environmental affect BA. By conducting thorough examination pertinent research, this consolidates current pinpoints essential elements shape process adoption. Out total 614 articles published between 2012 2022, 29 final were carefully chosen. findings highlight significance factors, technological shaping process. consolidating analyzing body offers valuable insights aiming adopt successfully maximize their benefits level. synthesized also contribute existing provide foundation future research field.

Language: Английский

Citations

22
Developing a novel approach in estimating urban commute traffic by integrating community detection and hypergraph representation learning DOI Creative Commons
Yuhuan Li, Shaowu Cheng, Yuxiang Feng

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 249, P. 123790 - 123790

Published: March 21, 2024

The efficiency of urban traffic management and congestion alleviation relies heavily on accurate forecasting Origin-Destination (O-D) demand matrices. Existing models primarily focus estimating O-D for various travel purposes throughout the day, which is characterised by its pulsating nature. However, these often compromise precision peak-hour forecasts, leading to unreliable dynamic control challenges in effectively reducing congestion. To tackle this challenge, paper proposes a novel method predicting commuting Our employs community detection algorithms road networks precisely partition commute regions, incorporating Points Interest (POIs). We also present spatio-temporal weighted hypergraph model that leverages partitioned time characteristics from observed trips, meteorological data improve forecasting. Comparative analyses with contemporary ablation studies indicate our significantly enhances prediction accuracy, approximately 5%. These findings imply proposed more encompasses varied during peak hours, thereby providing matrices management.

Language: Английский

Citations

4
Prediction of traffic time using XGBoost model with hyperparameter optimization DOI

Deepika,

Gitanjali Pandove

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

Language: Английский

Citations

0
Reconciling spatiotemporal conjunction with digital twin for sequential travel time prediction and intelligent routing DOI
Yi‐Ting Chen, Edward W. Sun, Yi‐Bing Lin

et al.

Annals of Operations Research, Journal Year: 2024, Volume and Issue: unknown

Published: May 11, 2024

Language: Английский

Citations

2
Reliable information system for identifying spatio-temporal continuity of kinetic deformed objects with big point cloud data DOI
Yi‐Ting Chen, Edward W. Sun, Yi‐Bing Lin

et al.

Annals of Operations Research, Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 2, 2023

Language: Английский

Citations

1
Supply chain control tower and the adoption of intelligent dock booking for improving efficiency DOI Creative Commons
Sławomir Wyciślak, Pourya Pourhejazy

Frontiers in Energy Research, Journal Year: 2023, Volume and Issue: 11

Published: Oct. 13, 2023

Poor coordination at distribution centers is a prime source of supply chain delays and energy waste that can be avoided through real-time planning enhanced visibility. As modern logistics topic with implications for transformation, Intelligent Dock Booking (IDB) coordinates the incoming outgoing shipments centers. The research on IDB early development stage. This study contributes to Supply Chain Control Tower (SCCT) by developing conceptual model IDB, identifying its implementation requirements, exploring impacts performance. causal loops stock/flow diagrams are used investigate how several efficiency indicators like number cancellations, time, utilization space loading unloading, duration processing trucks improved. Further, data integration, operational preconditions, automated scheduling, dynamic responsiveness, interdepartmental integration identified as key requirements. findings provide foundation implementing systems in SCCTs.

Language: Английский

Citations

1
Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks DOI
M. Al-Janabi, Keivan Borna, Shamsollah Ghanbari

et al.

Expert Systems, Journal Year: 2024, Volume and Issue: 42(1)

Published: March 21, 2024

Abstract Multimodal freight transport allows switching among various modes of transportation to efficiently utilize facilities. A multimodal system incorporates geographical scales from global local. Travel time estimation in a multi‐modal cargo network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel prediction great importance transportation, as it enables SC participants increase efficiency quality. It requires adequate input data, which can be generated. In recent times, the machine learning (ML) algorithm has been well‐suited resolve complex nonlinear relationships collected tracking data. This study designs deep learning‐powered networks (DLTTE‐MFTN) technique. The goal DLTTE‐MFTN technique estimate using hyperparameter‐tuned ensemble approach. To achieve this, method initially undergoes data pre‐processing convert raw into useful format. addition, singular value decomposition (SVD) model applied feature dimensionality reduction considerably improving prediction. Besides, estimates an three DL approaches including one‐dimensional convolutional neural (1D‐CNN), stacked autoencoder (SAE) attention, recurrent (RNN). Finally, hyperparameter tuning models takes place whale optimization (WOA). performance analysis Kaggle dataset. experimental results stated that attains superior over other ML models.

Language: Английский

Citations

0
Artificial Intelligence for Supply Chain Optimization DOI
Dhruv Kishore Bole, Narasimha Rao Vajjhala

Published: Dec. 1, 2024

In today's fast-paced and uncertain market, firms need a resilient supply chain to be competitive. The necessity of optimization is underscored by the fact that numerous companies have experienced loss competitive advantage due inadequate management. development artificial intelligence (AI) has created opportunities increase effectiveness chains. However, incorporating AI into operations not without its challenges. This chapter, which builds on earlier studies, assesses advantages for organizational chains offers methods integrating processes smoothly. chapter also looks at challenges organizations face when implementing in their chains, as well potential solutions implementation light increasingly complex dynamic highlights firms' leverage critical first step towards maintaining competitiveness. Effective management essential success any organization, businesses use will far more successful improving efficiency.

Language: Английский

Citations

0
Big data for logistics decarbonization DOI Creative Commons
Chun‐Hsien Chen, Gang Chen, Junliang He

et al.

Annals of Operations Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 3, 2024

Citations

0