A k-Nearest Neighbor Medoid-Based Outlier Detection Algorithm

The on-line estimation of some key hard-to-measure process variables by using soft-sensor technique has received extensive concern in industrial production process. The precision of on-line estimation is closely related to the accuracy of soft-sensor model, while the accuracy of soft-sensor model depends strongly on the accuracy of modeling data. Aiming at the special character of the definition for outliers in soft-sensor modeling process, an outlier detection method based on k-nearest neighbor (k-NN) is proposed in this paper. The proposed method can be realized conveniently from data without priori knowledge and assumption of the process. The simulation result and practical application show that the proposed outlier detection method based on k-NN has good detection effect and high application value.

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Improved Elastic Model Matching Algorithm for Facial Expression Recognition

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.2143 ◽

2013 ◽

Vol 397-400 ◽

pp. 2143-2147

Author(s):

Wen Dong Zhao ◽

Jie Zhang ◽

You Dong Zhang

Keyword(s):

Nearest Neighbor ◽

Detection Algorithm ◽

Gabor Wavelet ◽

Similar Degree ◽

Elastic Model ◽

Expression Recognition ◽

Model Matching ◽

K Nearest Neighbor ◽

Matching Algorithm ◽

Expression Template

Key frames detected in Video stream contain sufficient expression information. In order to classify and recognize these expression information, a new elastic model matching algorithm is proposed in this paper. Firstly, expression template is transformed by Gabor wavelet , and the detection algorithm of the key expression in the template image is used. According to the feature information of the key expression, structure expression elastic graph, then by changing the key location in the expression template graph and doing non-rigid match of the expression template and expression elastic graph which is measured, so that similar degree between them is got. Finally, by improving the K-nearest neighbor classification strategy, the effective classification and recognition of measured image expression is achieved.

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Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

Journal of Healthcare Engineering ◽

10.1155/2021/9500304 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Ijaz Ahmad ◽

Inam Ullah ◽

Wali Ullah Khan ◽

Ateeq Ur Rehman ◽

Mohmmed S. Adrees ◽

...

Keyword(s):

Artificial Intelligence ◽

Comparative Analysis ◽

Target Detection ◽

Nearest Neighbor ◽

Detection Algorithm ◽

Vital Role ◽

Computational Time ◽

K Nearest Neighbor ◽

Mri Scan ◽

Video File

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time.

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Early detection algorithm for alzheimer’s disease using autonomous learning

Revista Boletín Redipe ◽

10.36260/rbr.v10i11.1564 ◽

2021 ◽

Vol 10 (11) ◽

pp. 608-621

Author(s):

Jorge Eduardo Aguilar Obregón ◽

Octavio José Salcedo Parra ◽

Juan Pablo Rodríguez Miranda

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Nearest Neighbor ◽

Complete Information ◽

Research Problem ◽

Detection Algorithm ◽

Autonomous Learning ◽

K Nearest Neighbor ◽

Learning Techniques ◽

Information Bank

The current document describes the approach to a research problem that aims to generate an algorithm that allows detecting the probable appearance of Alzheimer’s disease in its first phase, using autonomous learning techniques or Machine Learning, more specifically KNN (K- nearest Neighbor) with which the best result was obtained. This development will be based on a complete information bank taken from ADNI (Alz- heimer’s Disease NeuroImaging Initiative), with all the necessary parameters to direct the inves- tigation to an algorithm that is as efficient as pos- sible, since it has biological, sociodemographic and medical history data, biological specimens, neural images, etc., and in this way the early de- tection of the aforementioned disease was con- figured. A complete guide to the process will be carried out to finally obtain the KNN algorithm whose efficiency is 99%, and then discuss the obtained results.

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A dynamic ensemble outlier detection model based on an adaptive k-nearest neighbor rule

Information Fusion ◽

10.1016/j.inffus.2020.05.001 ◽

2020 ◽

Vol 63 ◽

pp. 30-40

Author(s):

Biao Wang ◽

Zhizhong Mao

Keyword(s):

Outlier Detection ◽

Nearest Neighbor ◽

K Nearest Neighbor ◽

Detection Model ◽

Model Based ◽

Nearest Neighbor Rule

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A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks

Security and Communication Networks ◽

10.1155/2021/5540895 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Jeng-Shyang Pan ◽

Fang Fan ◽

Shu-Chuan Chu ◽

Hui-Qi Zhao ◽

Gao-Yuan Liu

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Intrusion Detection ◽

Nearest Neighbor ◽

Fog Computing ◽

Detection Algorithm ◽

Wireless Sensor ◽

K Nearest Neighbor ◽

Network Resources ◽

Detection Model

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

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A Noble Classification Framework for Data Glove Classification of a Large Number of Hand Movements

Journal of Electrical and Computer Engineering ◽

10.1155/2021/9472053 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yuhuang Zheng

Keyword(s):

Nearest Neighbor ◽

Short Term Memory ◽

Detection Algorithm ◽

Classification Algorithm ◽

Hand Movements ◽

Movement Detection ◽

K Nearest Neighbor ◽

Data Glove ◽

Classification Framework ◽

Movement Classification

The recognition of hand movements is an important method for human-computer interaction (HCI) technology, and it is widely used in virtual reality and other HCI areas. While many valuable efforts have been made, efficient ways to capture over 20 types of hand movements with high accuracy by one data glove are still lacking. This paper addresses a new classification framework for 52 hand movements. This classification framework includes the following two parts: the movement detection algorithm and the movement classification algorithm. The fine K-nearest neighbor (Fine KNN) is the core of the movement detection algorithm. The movement classification algorithm is composed of downsampling in data preparation and a new deep learning network named the DBDF network. Bidirectional Long Short-Term Memory (BiLSTM) is the main part of the DBDF network. The results of experiments using the Ninapro DB1 dataset demonstrate that our work can classify more types of hand movements than related algorithms with a precision of 93.15%.

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An Incremental Local Outlier Detection Method in the Data Stream

Applied Sciences ◽

10.3390/app8081248 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1248 ◽

Cited By ~ 4

Author(s):

Haiqing Yao ◽

Xiuwen Fu ◽

Yongsheng Yang ◽

Octavian Postolache

Keyword(s):

Outlier Detection ◽

Data Streams ◽

Data Stream ◽

Nearest Neighbor ◽

Nearest Neighbors ◽

Detection Accuracy ◽

K Nearest Neighbor ◽

Major Work ◽

Wide Range ◽

Local Outlier

Outlier detection has attracted a wide range of attention for its broad applications, such as fault diagnosis and intrusion detection, among which the outlier analysis in data streams with high uncertainty and infinity is more challenging. Recent major work of outlier detection has focused on principle research of the local outlier factor, and there are few studies on incremental updating strategies, which are vital to outlier detection in data streams. In this paper, a novel incremental local outlier detection approach is introduced to dynamically evaluate the local outlier in the data stream. An extended local neighborhood consisting of k nearest neighbors, reverse nearest neighbors and shared nearest neighbors is estimated for each data. The theoretical evidence of algorithm complexity for the insertion of new data and deletion of old data in the composite neighborhood shows that the amount of affected data in the incremental calculation is finite. Finally, experiments performed on both synthetic and real datasets verify its scalability and outlier detection accuracy. All results show that the proposed approach has comparable performance with state-of-the-art k nearest neighbor-based methods.

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