Formal approach to model complex adaptive computing systems

Jarrar, Abdessamad; Wakrime, Abderrahim Ait; Balouki, Youssef

doi:10.1186/s40294-020-0069-7

Cited by 3 publications

(3 citation statements)

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“…In juxtaposition with the vision of smart campuses as CAS, some authors model the IoT-an enabler technology for SCs-as a complex system too [30,[33][34][35][36]. To exemplify our SC modeling approach, we consider the increase in students' comfort and energy efficiency.…”

Section: Framework-based Methodologymentioning

confidence: 99%

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A Smart Campus’ Digital Twin for Sustainable Comfort Monitoring

et al. 2020

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Interdisciplinary cross-cultural and cross-organizational research offers great opportunities for innovative breakthroughs in the field of smart cities, yet it also presents organizational and knowledge development hurdles. Smart cities must be large towns able to sustain the needs of their citizens while promoting environmental sustainability. Smart cities foment the widespread use of novel information and communication technologies (ICTs); however, experimenting with these technologies in such a large geographical area is unfeasible. Consequently, smart campuses (SCs), which are universities where technological devices and applications create new experiences or services and facilitate operational efficiency, allow experimentation on a smaller scale, the concept of SCs as a testbed for a smart city is gaining momentum in the research community. Nevertheless, while universities acknowledge the academic role of a smart and sustainable approach to higher education, campus life and other student activities remain a mystery, which have never been universally solved. This paper proposes a SC concept to investigate the integration of building information modeling tools with Internet of Things- (IoT)-based wireless sensor networks in the fields of environmental monitoring and emotion detection to provide insights into the level of comfort. Additionally, it explores the ability of universities to contribute to local sustainability projects by sharing knowledge and experience across a multi-disciplinary team. Preliminary results highlight the significance of monitoring workspaces because productivity has been proven to be directly influenced by environment parameters. The comfort-monitoring infrastructure could also be reused to monitor physical parameters from educational premises to increase energy efficiency.

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Section: Framework-based Methodologymentioning

confidence: 99%

“…In a large deployment, communication between all agents would create communication overhead. To reduce this overhead, the state of the world should be perceived within the agents' neighborhood [33].…”

Section: Framework-based Methodologymentioning

confidence: 99%

A Smart Campus’ Digital Twin for Sustainable Comfort Monitoring

et al. 2020

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“…A system, which predicts flight arrival delays and provides airport personnel, companies, passengers and aviation users with the delay before its occurrence, is needed. Several macroscopic factors impact flight delays such as bad weather conditions, severe crosswinds [18], technical problems, late and disturbing passengers, airport crowdedness, runway queues, lack in airport infrastructure, aircraft maintenance delays, flight check-list delays, etc. Our objective in this study is to identify and use new microscopic factors which participate in flight delays to develop a predictive model based on good performing machine learning classifiers.…”

Section: Research Motivationmentioning

confidence: 99%

Impact of Aircraft Performance and Time of the Day on Flight Arrival Delays Prediction in the United States: a Machine Learning Classification

2022

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The excessive growth of air traffic, with the limited airspace and airports capacity, results in a flight demand-capacity imbalance leading to air traffic delays. This paper explores the factors associated with delay in both microscopic and macroscopic ways. The aim is to develop a model which analyzes and predicts the occurrence of flight arrival delays using US domestic flight data for the year 2018. It will provide passengers, airlines and airport managers with reliable flight arrival schedules, and consequently reduce economic losses and enhance passengers trust. Beside database features, the proposed model is to the best of our knowledge the first attempt to predict flight arrival delays using three new features which are contributive factors to delays: Departure Time and Arrival Time of the day in which the flight was performed (Early morning, late morning, noon, afternoon, evening or night) and model of aircraft. Four Machine Learning classifiers namely Random Forest, Decision Trees, K-Nearest Neighbors and Naive Bayes were used. In order to find the best parameters of each algorithm, we implemented Grid Search technique. The performance of each classifier was compared in terms of hyperparameters tuning, classification metrics and features description. The experimental results showed that the proposed system was able to predict flight arrival delays with the best Random Forest accuracy of 0.9356 and a higher number of correctly classified flights. To prove the importance of our findings, we compared our model to that of existing literature studies.

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