AI for next generation computing: Emerging trends and future directions

Gill, Sukhpal Singh; Xu, Minxian; Ottaviani, Carlo; Patros, Panos; Bahsoon, Rami; Shaghaghi, Arash; Golec, Muhammed; Stankovski, Vlado; Wu, Huaming; Abraham, Ajith; Singh, Manmeet; Mehta, Harshit; Ghosh, Soumya K.; Baker, Thar; Parlikad, Ajith Kumar; Lutfiyya, Hanan; Kanhere, Salil S.; Sakellariou, Rizos; Dustdar, Schahram; Rana, Omer; Brandić, Ivona; Uhlig, Steve

doi:10.1016/j.iot.2022.100514

Cited by 344 publications

(198 citation statements)

References 115 publications

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“…The proposed system uses AI technologies and cloud services to work autonomously and cost‐effectively. AI plays a vital role in Autonomic computation, 21 getting trained for an Autonomous environment independently. This autonomous behavior can be developed using AI and ML algorithms.…”

Section: Discussionmentioning

confidence: 99%

A multimodel fire detection and alarm system for automobiles using internet of things and machine learning

Uma

Eswari

2022

Concurrency and Computation

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Fire accidents in vehicles lead to the loss of human lives. The fire detection and alarm systems are often error-prone and respond to nonactual indications of fire presence, known as false alarms. The proposed model detects the fire at the smoldering stage and buzzes an alarm if an actual fire or smoke is detected. This system can achieve this real alarm using multiple internet of things-based sensors, namely smoke/gas, flame, temperature, and a visualization camera. The visualization camera continuously captures images of the vehicle to check the existence of fire. Machine learning algorithms are executed on the sensor and image dataset to reduce false alarms and achieve high accuracy of results by using various performance metrics. K E Y W O R D Sfire detection and alarm system, flame sensor, gas sensor, internet of things, temperature sensor, visualization camera INTRODUCTIONLoss of human lives and economic losses are observed every year due to fire accidents. Many types of research were carried out to study the cause and explosion of fire and the efficient ways to control or prevent the fire outbreak in automobiles. Research findings state that most of the fire accident takes place during summer due to overheating of engine and other components. Use of old vehicle and low-maintenance vehicles is highly prone to get fire. Identified reasons for fire outbreaks are gas Leakage, electrical short circuit, electric car battery, AC problems, etc. The leakage of toxic gases due to car burning is hazardous.In recent days, the probability of fire accidents in vehicles has been higher, and it could be even more in electric cars. In fire detection, sensors are used to detect the fire; a single sensor cannot sense the fire effectively. It is high time to detect fire beforehand. Smoke is a significant sign for detecting early fire conditions. A number of fire detection systems were proposed in the past few years. Most approaches use different visual elements, including flame colors, motion, and geometrical contour. Integrated sensors work efficiently in Fire environments.These Integrated alarm systems allow fast identification, alarm alerts, and even fire extinguishing. This can be achieved by using integrated sensors.In this article, a fire detection and alarm system is proposed to detect fire in earlier stages. The proposed model uses integratedsensors and a camera for fire detection. Therefore, the proposed model avoids the rising of false alarm. The data is collected from the sensors such as smoke, temperature, and flame and are used to train the machine using the machine learning algorithms. Similarly, the image patterns of smoke and flame are also collected and used to train the machine. Therefore, when the fire is detected, the alarm will be buzzed.The remainder of the article is organized as follows: Section 2 discusses the related work. Section 3 presents the proposed system. Section 4 elucidates the components of the proposed architecture. Section 6 confers the efficiency of the proposed system and discusses the ...

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Section: Discussionmentioning

confidence: 99%

A multimodel fire detection and alarm system for automobiles using internet of things and machine learning

Uma

Eswari

2022

Concurrency and Computation

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“…Our proactive fault tolerant model of infrastructure's cloud computing can be considered as an AI/ML-integrated Next Generation Computing approach of cloud computing discussed in [55]. In the future, to improve resource autonomy, our VM controller will be expanded to the elasticity's cloud infrastructure based on the integration of AI/ML when there are changing workloads, infrastructure faults of multi-tier applications.…”

Section: Discussionmentioning

confidence: 99%

V2PFQL: A proactive fault tolerance approach for cloud‐hosted applications in cloud computing environment

Bui

Tran

Pham

2022

IET Control Theory & Appl

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Proactive Fault tolerant (FT) approaches for infrastructure's cloud computing which have been increasingly developed in research scope with the support of machine learning need consistent monitoring and highly accurate predicting systems. However, it is difficult to implement the proactive fault tolerant approach of infrastructure's cloud computing because of complex architecture, dynamic services, and many interdependent factors existing in cloud environment. To overcome this problem, a proactive FT approach for cloud‐hosted application is proposed to achieve a high degree of automation, including physical machine (PM) fault analysis and self‐learning virtual machine (VM) migration. It is based on MAPE‐K (Monitor‐Analyze‐Plan‐Execute over a shared Knowledge) loop to take into account the knowledge inside controllers in run time. To evaluate PM fault analysis model, the web e‐Commerce is deployed by OpenStack Stein while the prototype of VM migration component is built by CloudSim for VM migration solution evaluation. The effectiveness of the approach is demonstrated in experiment results.

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“…In healthcare applications, the use of AI is not trustworthy enough for healthcare professionals, as the machine learning models are essentially a black box to medical experts 8 due to the poor explainability of the working of these models. XAI helps to explain the working of machine learning models, and shows the impact of every feature or attribute used to train the model.…”

Section: Proposed Methodologymentioning

confidence: 99%

CovidXAI: explainableAIassisted web application forCOVID‐19 vaccine prioritization

Chowdhury

Poddar²,

Banarjee

et al. 2022

Internet Technology Letters

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COVID‐19 vaccines have a limited supply, and there is a huge gap between supply and demand, leading to disproportionate administration. One of the main conditions on which balanced and optimal vaccine distribution depends are the health conditions of the vaccine recipients. Vaccine administration of front‐line workers, the elderly, and those with diseases should be prioritized. To solve this problem, we proposed a novel architecture called CovidXAI, which is trained with a self‐collected dataset with 24 parameters influencing the risk group of the vaccine recipient. Then, Random Forest and XGBoost classifiers have been used to train the model—having training accuracies of 0.85 and 0.87 respectively, to predict the risk factor, classified as low, medium, and high risk. The optimal vaccine distribution can be done using the derived from the predicted risk class. A web application is developed as a user interface, and Explainable AI (XAI) has been used to demonstrate the varying dependence of the various factors used in the dataset, on the output by CovidXAI.

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AI for next generation computing: Emerging trends and future directions

Cited by 344 publications

References 115 publications

A multimodel fire detection and alarm system for automobiles using internet of things and machine learning

A multimodel fire detection and alarm system for automobiles using internet of things and machine learning

V2PFQL: A proactive fault tolerance approach for cloud‐hosted applications in cloud computing environment

CovidXAI: explainableAIassisted web application forCOVID‐19 vaccine prioritization

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