To perform the task of clustering, various data mining tools are freely available. Clustering has been used in diverse fields including Text Mining, Pattern recognition, Image analysis, Bioinformatics, Machine Learning, Voice mining, Image processing, Web cluster engines, Whether report analysis etc. Edge‐Cloud Amalgamation in the IoT‐based HealthcareĬlustering, a component of data mining is the process of grouping objects into several clusters such that objects in the same cluster have maximum similarity while the objects in different clusters has maximum dissimilarity. However, the dual‐mode edge‐cloud system provides more flexibility and capability to counter the bio‐sensor data streaming and analytics services within the constrained framework.
The implementation results show near correlation between the standalone edge and dual‐mode edge system. Firstly, a standalone IoT‐edge system is developed and later, an integrated IoT‐based edge‐cloud system is developed to compare between the effectiveness of the systems. This study aims to achieve the aforesaid goal by presenting two different experiments that deals with the real‐time pulse sensor data streaming and analytics while utilizing light‐weight IoT‐supported JavaScript frameworks that includes Node.js, Johnny‐Five, Serialport.js, Plotly client, Flot.js, jQUERYy, Express Server, and Socket.io. The objective of this article is to implement an integrated dual‐mode edge‐cloud system to serve streaming and analytics in real‐time.
Moreover, traditional solutions fail to mitigate the edge‐cloud integration within a single sub‐system under IoT periphery which lead to investigate how edge‐cloud hybridization could be realized via similar set of tools. However, existing Internet of Things (IoT) ecosystem is unable to materialize the real‐time bio‐sensor data streaming and analytics within resource constrained environment. The obtained results indicate that the proposed approach is effective in terms of feature space reduction leading to better accuracy and efficient classification model.īio‐sensor data streaming and analytics is a key component of smart e‐healthcare. While accuracy recorded with the six SBS selected features was 98.54%. The highest classification accuracy recorded was 99.03% with FFNN using the seven SFS selected features. The feature space is reduced from nine feature to seven and six features using SFS and SBS respectively. After selecting the optimal classification model, the data is divided into training set and testing set and the performance was evaluated. The learning algorithm hyper-parameters are optimized using the grid search process.
The feed forward neural network (FFNN) is used as a classification algorithm. Two wrapper-based feature selection methods, namely, sequential forward selection(SFS) and sequential backward selection (SBS) are used to identify the most discriminant features which can contribute to improve the classification performance. The main objective of this study is to propose an efficient approach to classify breast cancer tumor into either benign or malignant based on digitized image of a fine needle aspirate (FNA) of a breast mass represented by the Wisconsin Breast Cancer Dataset. Early diagnosis plays a significant role in reducing the fatality rate. It is also helpful for authorities to strategize and plan ways to reduce traffic violations among road users by studying the most common traffic violation types in an area, whether a citation, a warning, or an ESERO (Electronic Safety Equipment Repair Order).īreast cancer is commonest type of cancers among women. This paper’s results could serve as baseline results for investigations related to the classification of traffic violation types. Naïve Bayes, on the other hand, has high recall since it is a picky classifier that only performs well in a dataset that is high in precision.
The results show that the Gradient Boosted Trees and Deep Learning algorithm have the best value in accuracy and recall but low precision. The performance of all the three classification models developed in this work is measured and compared. This study is set to perform a comparative experiment to compare the performance of three classification algorithms (Naive Bayes, Gradient Boosted Trees, and Deep Learning algorithm) in classifying the traffic violation types. Traffic summons, also known as traffic tickets, is a notice issued by a law enforcement official to a motorist, who is a person who drives a car, lorry, or bus, and a person who rides a motorcycle.
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