It is possible to develop intelligent and self‐adaptive application on the edge nodes with rapid increase in computational capability of Internet of Things (IoT) devices. With the rapid growth of cloud technologies, the demand for hybrid architecture with cloud and IoT has also been boosted as well. To satisfy the critical and comprehensive requirements in the architecture evolution, we proposed a lightweight framework called IoT‐Pi to provide a 3‐phase (sample, learn, adapt) life cycle management of cloud resources with machine learning prediction working on IoT edge nodes using Raspberry Pi device. Compared to the traditional interference by human beings in the field of system administration, the accuracy rate of machine learning prediction in the proposed technique for some algorithms reached over 70%, which demonstrates the feasibility and effectiveness of running cloud resource management on an IoT devices such as Raspberry Pi.
Many patients affected by breast cancer die every year because of improper diagnosis and treatment. In recent years, applications of deep learning algorithms in the field of breast cancer detection have proved to be quite efficient. However, the application of such techniques has a lot of scope for improvement. Major works have been done in this field, however it can be made more efficient by the use of transfer learning to get impressive results. In the proposed approach, Convolutional Neural Network (CNN) is complemented with Transfer Learning for increasing the efficiency and accuracy of early detection of breast cancer for better diagnosis. The thought process involved using a pre-trained model, which already had some weights assigned rather than building the complete model from scratch. This paper mainly focuses on ResNet101 based Transfer Learning Model paired with the ImageNet dataset. The proposed framework provided us with an accuracy of 99.58%. Extensive experiments and tuning of hyperparameters have been performed to acquire the best possible results in terms of classification. The proposed frameworks aims to be an efficient tool for all doctors and society as a whole and help the user in early detection of breast cancer.
Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that Random Forest gives the best results in terms of accuracy (98.4%). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.
The application of the Internet of Things (IoT) and Artificial Intelligence (AI) in healthcare is an emerging domain. In Healthcare applications, relying on both IoT and AI requires paying attention to latency, responsiveness and management of data loads. Most of the healthcare applications are based on Cloud computing and use Cloud platforms such as Google Cloud and Microsoft Azure. With the increased adoption of IoT in various domains, the data generation rate and volume by IoT devices has tremendously increased, making the Cloud insufficient for latency sensitive healthcare applications. Fog computing, complementing the Cloud services, can be deployed close to the data source to better utilize distributed resources and meet the Quality of Service (QoS) requirements of healthcare application. In this paper, we propose a Fog-based cardiac health detection framework, called FogDLearner. FogDLearner utilizes distributed resources to diagnose cardiac health of a person without compromising QoS and accuracy. FogDLearner uses a deep learning based classifier to predict the cardiac health of the user. The performance of the proposed framework is evaluated on the PureEdgeSim simulator, in terms of resource utilization under overload and under-load scenarios, mobility support, and power consumption. The experimental results show the validity of proposed work for support of mobile applications.
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