<scp>IoT‐Pi</scp>: A machine learning‐based lightweight framework for cost‐effective distributed computing using <scp>IoT</scp>

Shao, Tianchen; Chowdhury, Deepraj; Gill, Sukhpal Singh; Buyya, Rajkumar

doi:10.1002/itl2.355

Cited by 15 publications

(14 citation statements)

References 9 publications

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“…User accessibility can be improved by making a mobile application for android and iOS devices by utilizing the concept of Internet of Things (IoT). 10 …”

Section: Discussionmentioning

confidence: 99%

CovidXAI: explainableAIassisted web application forCOVID‐19 vaccine prioritization

Chowdhury

Poddar²,

Banarjee

et al. 2022

Internet Technology Letters

Self Cite

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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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“…User accessibility can be improved by making a mobile application for android and iOS devices by utilizing the concept of Internet of Things (IoT). 10 …”

Section: Discussionmentioning

confidence: 99%

CovidXAI: explainableAIassisted web application forCOVID‐19 vaccine prioritization

Chowdhury

Poddar²,

Banarjee

et al. 2022

Internet Technology Letters

Self Cite

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“…From natural language processing, face recognition, bio-medicine to autonomous driving, more and more intelligent applications are being deployed on IoT devices [201]. Due to the slow hardware development in smallsized equipment, the contradiction between the limited computing capacity of IoT devices and running complicated AI applications cannot be efficiently solved in a short time [202].…”

Section: Intelligent Thingsmentioning

confidence: 99%

AI for Next Generation Computing: Emerging Trends and Future Directions

Gill,

Xu,

Ottaviani

et al. 2022

Preprint

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Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

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“…16 So far, various methods have been introduced to identify the objects in the image and understand their meaning. 17,18 These methods can be divided into three general categories: pattern matching algorithms, 19 machine learning algorithms, 20 and object detection algorithms. 21 In particular, automatic human tracking in video has always been an interesting topic for researchers, as it is a multidisciplinary field of research with multiple applications.…”

Section: Gigabytementioning

confidence: 99%

“…Object detection systems mean the separation and labeling of the edges around objects in an image 16 . So far, various methods have been introduced to identify the objects in the image and understand their meaning 17,18 . These methods can be divided into three general categories: pattern matching algorithms, 19 machine learning algorithms, 20 and object detection algorithms 21 .…”

Section: Introductionmentioning

confidence: 99%

“…Human behavior detection is used in many applications such as video surveillance, motion detection, and human-computer interaction. 17 Extensive applications of human behavior detection have led to the development of various systems in areas such as care, control, and analysis. 24 Therefore, it is important to know the HBA and the related datasets.…”

Section: Introductionmentioning

confidence: 99%

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A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

Ding

Gan

Bahrami

2022

Trans Emerging Tel Tech

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In recent years, understanding human behavior has become one of the most important topics in the field of computer vision research. The reason for this growing attention is the wide range of applications that can benefit from the results of this field of research. The human behavior analysis (HBA) includes a wide range of research areas from the detection of human motion and action.Datasets created through actions and detection of human activities make it possible to compare different detection methods with the same input data. Data mining and big data approaches are very popular for analyzing data related to human behavior and can be used to address the challenges of fast processing. This article provides a systematic survey of data mining and big data in the HBA. We focus on current datasets and models related to the detection of human behavior patterns in the literature. The purpose of this survey is to assist researchers in select appropriate datasets and models to evaluate algorithms, as well as to identify research gaps for future work. To achieve this goal, articles published between 2010 and 2021 have been reviewed. These articles fall into five general categories in terms of dataset focus: object detection, motion, action, activity, and behavior. This article provides a summary of data mining and big data models in the HBA, as well as related datasets based on these categories, to highlight promising research avenues for future work.

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IoT‐Pi: A machine learning‐based lightweight framework for cost‐effective distributed computing using IoT

Cited by 15 publications

References 9 publications

CovidXAI: explainableAIassisted web application forCOVID‐19 vaccine prioritization

CovidXAI: explainableAIassisted web application forCOVID‐19 vaccine prioritization

AI for Next Generation Computing: Emerging Trends and Future Directions

A systematic survey of data mining and big data in human behavior analysis: Current datasets and models

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