Outlier detection is an essential task in data mining applications which include, military surveillance, tax fraud detection, telecommunication, etc. In recent years, outlier detection received significant attention compared to other problem of discoveries. The focus on this has resulted in the growth of several outlier detection algorithms, mostly concerning the strategy based on distance or density. However, each strategy has intrinsic weaknesses. The distance-based techniques have the problem of local density, while the density-based method is recognized as having an issue of a low-density pattern. Also, most of the existing outlier detection algorithms have a parameter selection problem, which leads to poor detection results. In this article, we present an unsupervised density-based outlier detection algorithm to deal with these shortcomings. The proposed algorithm uses a Natural Neighbour (NaN) concept, to obtain a parameter called Natural Value (NV) adaptively, and a Weighted Kernel Density Estimation (WKDE) method to estimate the density at the location of an object. Besides, our proposed algorithm employed two different categories of nearest neighbours, k Nearest Neighbours (kNN), and Reverse Nearest Neighbours (RNN), which make our system flexible in modelling different data patterns. A Gaussian kernel function is adopted to achieve smoothness in the measure. Further, we use an adaptive kernel width concept to enhance the discrimination power between normal and outlier samples. The formal analysis and extensive experiments carried out on both artificial and real datasets demonstrate that this technique can achieve better outlier detection performance.

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning–blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.

This article proposes a novel outlier detection algorithm called Relative Kernel Density-based Outlier Score (RKDOS) to detect local outliers. The proposed algorithm uses a weighted kernel density estimation (WKDE) method with an adaptive kernel width for density estimation at the location of an object based on its extended nearest neighbors. For density estimation, we consider both Reverse Nearest Neighbors (RNN) and k-Nearest Neighbors (kNN) of an object. To achieve smoothness in the measure, the Gaussian kernel function is adopted. Further, to improve discriminating power between normal and abnormal samples, we use an adaptive kernel width concept. Extensive experiments on both synthetic and real data sets have shown that our proposed algorithm has better detection performance over some popular existing outlier detection approaches.