An anomaly exposure system's foremost objective is to categorize the behavior of the system into normal and untruthful actions. To estimate the possible incidents, the administrators of smart cities have to apply anomaly detection engines to avert data from being jeopardized by errors or attacks. This article aims to propose a novel deep learning‐based framework with a dense random neural network approach for distinguishing and classifying anomaly from normal behaviors based on the type of attack in the Internet of Things. Machine learning algorithms have the improbability to explore the performance, compared with deep learning models. Distinctively, the examination of deep learning neural network architectures achieved enhanced computation performance and deliver desired results for categorical attacks. This article focuses on the complete study of experimentation performance and evaluations on deep learning neural network architecture for the recognition of seven categorical attacks found in the Distributed Smart Space Orchestration System traffic traces data set. The empirical results of the simulation model report that deep neural network architecture performs well through noticeable improvement in most of the categorical attack.
Voting is a formal expression of opinion or choice, either positive or negative, made by an individual or a group of individuals. However, conventional voting systems tend to be centralized, which are known to suffer from security and efficiency limitations. Hence, there has been a trend of moving to decentralized voting systems, such as those based on blockchain. The latter is a decentralized digital ledger in a peer-to-peer network, where a copy of the append-only ledger of digitally signed and encrypted transactions is maintained by each participant. Therefore, in this article, we perform a comprehensive review of blockchain-based voting systems and classify them based on a number of features (e.g., the types of blockchain used, the consensus approaches used, and the scale of participants). By systematically analyzing and comparing the different blockchain-based voting systems, we also identify a number of limitations and research opportunities. Hopefully, this survey will provide an in-depth insight into the potential utility of blockchain in voting systems and device future research agenda.
Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification.
Currently COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This paper provides a road-map by highlighting the IoT applications that can help to control it. This study also proposes a real time identification and monitoring of COVID-19 patients. Proposed framework consists of four components using cloud architecture: data collection of disease symptoms (using IoT based devices), health center or quarantine center (data collected using IoT devices), data warehouse (analysis using Machine Learning models) and health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT based real time data, we experimented with five machine learning models. Results reveal that Random Forest outperformed among all other models. IoT applications will help management, health professionals and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.
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