Fuzzy K-nearest Neighbor Algorithm Research Articles

Identification of Network Disruptions Using the Fuzzy K-Nearest Neighbor Algorithm in Case Based Reasoning at STMIK Pranata Indonesia

Identification of Network Disruptions Using the Fuzzy K-Nearest Neighbor Algorithm in Case Based Reasoning at STMIK Pranata Indonesia

Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.

Target threat estimation based on discrete dynamic Bayesian networks with small samples

The accuracy of target threat estimation has a great impact on command decision-making. The Bayesian network, as an effective way to deal with the problem of uncertainty, can be used to track the change of the target threat level. Unfortunately, the traditional discrete dynamic Bayesian network (DDBN) has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing. Considering the finiteness and discreteness of DDBN parameters, a fuzzy k-nearest neighbor (KNN) algorithm based on correlation of feature quantities (CF-FKNN) is proposed for DDBN parameter learning. Firstly, the correlation between feature quantities is calculated, and then the KNN algorithm with fuzzy weight is introduced to fill the missing data. On this basis, a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning. Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing, and improve the effect of DDBN parameter learning in the case of serious sample missing. With the proposed method, the final target threat assessment results are reasonable, which meets the needs of engineering applications.

Efficacy of Morphine Combined with Mechanical Ventilation in the Treatment of Heart Failure with Cardiac Magnetic Resonance Imaging under Artificial Intelligence Algorithms.

This study was aimed at exploring the efficacy of morphine combined with mechanical ventilation in the treatment of heart failure with artificial intelligence algorithms. The cardiac magnetic resonance imaging (MRI) under the watershed segmentation algorithm was proposed, and the local grayscale clustering watershed (LGCW) model was designed in this study. A total of 136 patients with acute left heart failure were taken as the research objects and randomly divided into the control group (conventional treatment) and the experimental group (morphine combined with mechanical ventilation), with 68 cases in each group. The left ventricular end-diastolic diameter (LVEDD), left ventricular end-systolic diameter (LVESD), left ventricular ejection fraction (LVEF), N-terminal pro-brain natriuretic peptide (NT-proBNP), arterial partial pressure of oxygen (PaO2), and arterial partial pressure of carbon dioxide (PaCO2) were observed. The results showed that the mean absolute deviation (MAD) and maximum mean absolute deviation (max-MAD) of the LGCW model were lower than those of the fuzzy k-nearest neighbor (FKNN) algorithm and local gray-scale clustering model (LGSCm). The Dice metric was also significantly higher than that of other algorithms with statistically significant differences (P < 0.05). After treatment, LVEDD, LVESD, and NT-proBNP of patients in the experimental group were significantly lower than those in the control group, and LVEF in the experimental group was higher than that in the control group (P < 0.05). PaO2 of patients in the experimental group was also significantly higher than that in the control group (P < 0.05). It suggested that the LGCW model had a better segmentation effect, and morphine combined with mechanical ventilation gave a better clinical efficacy in the treatment of acute left heart failure, improving the patients' cardiac function and arterial blood gas effectively.

Pengenalan Pola Huruf Hijaiyah dengan Metode Local Binary Pattern Menggunakan Algoritma Fuzzy K-Nearest Neighbor

A letter is a form, stroke, or symbol writing system. Any information obtained from a sentence depends on the letters are written clearly. Finding written hijaiyah letters can be recognized by humans, but will be difficult if a computer tries to recognize them. The reason system is difficult is because of the large variety of different letters. This study aims to make it easier for someone to learn to recognize hijaiyah letters by using the Local Binary Pattern method for the feature extraction process. The results of feature extraction will take the maximum value of the histogram of each letter. And results feature extraction will be carried out classification process using the Fuzzy K-Nearest Neighbor algorithm until finally hijaiyah letters can be recognized. Based on experimental results that have been carried out, the highest level of accuracy is obtained when the amount of training data is 154 data and the number of data testing is 29 data, resulting in an accuracy rate of 96.55%.

A generalized fuzzy k-nearest neighbor regression model based on Minkowski distance

The fuzzy k-nearest neighbor (FKNN) algorithm, one of the most well-known and effective supervised learning techniques, has often been used in data classification problems but rarely in regression settings. This paper introduces a new, more general fuzzy k-nearest neighbor regression model. Generalization is based on the usage of the Minkowski distance instead of the usual Euclidean distance. The Euclidean distance is often not the optimal choice for practical problems, and better results can be obtained by generalizing this. Using the Minkowski distance allows the proposed method to obtain more reasonable nearest neighbors to the target sample. Another key advantage of this method is that the nearest neighbors are weighted by fuzzy weights based on their similarity to the target sample, leading to the most accurate prediction through a weighted average. The performance of the proposed method is tested with eight real-world datasets from different fields and benchmarked to the k-nearest neighbor and three other state-of-the-art regression methods. The Manhattan distance- and Euclidean distance-based FKNNreg methods are also implemented, and the results are compared. The empirical results show that the proposed Minkowski distance-based fuzzy regression (Md-FKNNreg) method outperforms the benchmarks and can be a good algorithm for regression problems. In particular, the Md-FKNNreg model gave the significantly lowest overall average root mean square error (0.0769) of all other regression methods used. As a special case of the Minkowski distance, the Manhattan distance yielded the optimal conditions for Md-FKNNreg and achieved the best performance for most of the datasets.

Combining Fuzzy Logic and k-Nearest Neighbor Algorithm for Recommendation Systems

Recommendation systems are a type of systems that are able to help users finding relevant and personalized content in a wide variety of possibilities. To help computers perform recommendations, there are several approaches used nowadays such as the Content-based approach, the Collaborative filtering approach and the Hybrid recommendation approach. However, these approaches are sometimes inappropriate for use cases where there is no prior large datasets of users’ feedbacks or ratings needed for training Machine Learning models. Thus, in this work, we proposed a novel approach based on the combination of Fuzzy Logic and the k-Nearest neighbor algorithm (KNN). The proposed approach can be applied without any prior collected feedbacks of users and performs good recommendations. Moreover, our proposal uses Fuzzy Logic to infer values based on inputs and a set of rules. Furthermore, the KNN uses the output values of the Fuzzy Logic system to do some retrieval tasks based on existing distance measures. In order to evaluate our approach, we considered an expert system of food recommendation for people suffering from the two deadliest diseases in Cameroon: HIV/AIDS and Malaria. The obtained results are closed to the recommendation made by nutritionists. These results demonstrate how effective our approach can be used to solve a real nutrition problem for people suffering from Malaria or HIV/AIDS. Furthermore, this approach can be extended to other fields and even be used to perform any recommendation task where there is no prior collected user’s feedback or ratings by using the proposed approach as a framework.

Web-Based Rice Disease Diagnosis Expert System Using Fuzzy Tsukamoto Method and K-Nearest Neighbor Algorithm

Technology today is growing rapidly from year to year, not least started to spread to the agricultural sector. With the information technology making society more easily in search of information via the internet from your smart device. The goal of this study was made to facilitate the community, especially farmers in helping to diagnose diseases and pests in rice plants. Rice plants can be attacked by a wide variety of diseases and pests with a wide variety of symptoms experienced in rice plants. To know the kind of disease on rice plants in the era of technology, it takes an expert system that can help detect the disease in rice plants. In this study, Expert System-Based Website using Tsukamoto Fuzzy method and the Algorithm of K-Nearest Neighbor whose purpose is to help people, especially farmers in diagnosing diseases and pests in rice plants by looking at the symptoms of the attack on the rice plant. Data was obtained from the Research and the Ministry of Agriculture then taken some sample data for testing done. The results of the testing data of this expert system is the result of late diagnosis in diseases of the rice with the symptoms that already exist based on the data that have been obtained with an accuracy rate of 92,88%.

A New Algorithm for Analysis of MiRNA Expression Profiles&amp;#x2014;SVM-RFE-FKNN

Abstract Based on MicroRNA (miRNA) expression profiles, this article proposes a new algorithm—SVM-RFE-FKNN, which combines the support vector machine-recursive feature elimination (SVM-RFE) algorithm and the fuzzy K-nearest neighbor (FKNN) algorithm, to realize binary classification of tumors. First, the SVM-RFE algorithm was used to select features from the miRNA expression profile dataset to constitute feature subsets and to determine the maximum number of support vectors. Next, this maximum number was regarded as the upper limit of the parameter K in the FKNN algorithm that was then used to classify the samples to be tested. Finally, the leave-one-out cross-validation method was adopted to assess the classification performance of the proposed algorithm. Through experiments, our proposed algorithm was compared with other twelve classification methods, and the result shows that our algorithm had better classification performance. Specifically, with only a few miRNA biomarkers, the proposed algorithm could reach an accuracy of 99.46% and an area under the receiver operating characteristic curve (AUC) of 0.9874.

Intrusion Detection System Using FKNN and Improved PSO

Intrusion detection system (IDS) techniques are used in cybersecurity to protect and safeguard sensitive assets. The increasing network security risks can be mitigated by implementing effective IDS methods as a defense mechanism. The proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor (FKNN) algorithm. Using this method, two parameters, i.e., the neighborhood size (k) and fuzzy strength parameter (m) were characterized by implementing the particle swarm optimization (PSO). In addition to being used for FKNN parametric optimization, PSO is also used for selecting the conditional feature subsets for detection. To proficiently regulate the indigenous and comprehensive search skill of the PSO approach, two control parameters containing the time-varying inertia weight (TVIW) and time-varying acceleration coefficients (TVAC) were applied to the system. In addition, continuous and binary PSO algorithms were both executed on a multi-core platform. The proposed IDS model was compared with other state-of-the-art classifiers. The results of the proposed methodology are superior to the rest of the techniques in terms of the classification accuracy, precision, recall, and f-score. The results showed that the proposed methods gave the highest performance scores compared to the other conventional algorithms in detecting all the attack types in two datasets. Moreover, the proposed method was able to obtain a large number of true positives and negatives, with minimal number of false positives and negatives.

A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization

Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large number of features to attend as they play an essential role in detection efficiency. In this article, we're working on a feature selection method to e-mail spam. This approach is considered a hybrid of optimization algorithms and classifiers in machine learning. Binary Whale Optimization (BWO) and Binary Grey Wolf Optimization (BGWO) algorithms are used for feature selection and K-Nearest Neighbor (KNN) and Fuzzy K-Nearest Neighbor (FKNN) algorithms are applied as the classifiers in this research. The proposed method is tested on the SPAMBASE datasets from UCI Machine learning Repesotries and the experimental results revealed the highest accuracy of 97.61% on this dataset. The obtained results indicateed that the proposed method is suitable and capable to provide excellent performance in comparison with other methods.

An instance selection algorithm for fuzzy K-nearest neighbor

The condensed nearest neighbor (CNN) is a pioneering instance selection algorithm for 1-nearest neighbor. Many variants of CNN for K-nearest neighbor have been proposed by different researchers. However, few studies were conducted on condensed fuzzy K-nearest neighbor. In this paper, we present a condensed fuzzy K-nearest neighbor (CFKNN) algorithm that starts from an initial instance set S and iteratively selects informative instances from training set T, moving them from T to S. Specifically, CFKNN consists of three steps. First, for each instance x ∈ T, it finds the K-nearest neighbors in S and calculates the fuzzy membership degrees of the K nearest neighbors using S rather than T. Second it computes the fuzzy membership degrees of x using the fuzzy K-nearest neighbor algorithm. Finally, it calculates the information entropy of x and selects an instance according to the calculated value. Extensive experiments on 11 datasets are conducted to compare CFKNN with four state-of-the-art algorithms (CNN, edited nearest neighbor (ENN), Tomeklinks, and OneSidedSelection) regarding the number of selected instances, the testing accuracy, and the compression ratio. The experimental results show that CFKNN provides excellent performance and outperforms the other four algorithms.

Design and Evaluation of Indoor Positioning System for User Access Management in Data Center

One critical point in data center management from the physical security perspective (according to ISO 27001 standards) is the management of user access and traffic. Physical security could be improved through the implementation of indoor positioning with a real-time monitoring system. Other than security needs, the information provided could be used for emergency evacuation purposes in a disastrous situation. Presently, indoor positioning is principally based on wireless signals, such as WiFi, RFID, Zigbee, Bluetooth, etc. This study designs a user access management system in the Data Center using indoor - positioning. For the indoor positioning method, k-Nearest Neighbor (kNN) and Fuzzy k-Nearest Neighbor (FkNN) algorithms and Kalman Filter implementation are evaluated. The simulation result shows that the proposed method can improve the positioning accuracy until 8% compared to the existing method.

Identification of Fresh-Chilled and Frozen-Thawed Chicken Meat and Estimation of their Shelf Life Using an E-Nose Machine Coupled Fuzzy KNN

In this study, the capability of an electronic nose machine to identify chilled and frozen-thawed chicken meat was investigated. According to the importance of fresh chicken meat consumption in people’s daily diet, recognition of fresh chicken meat from frozen-thawed one is an essential issue. In the mentioned system, eight sensors (MOS type) were applied. The outputs of sensors were preprocessed, then features were extracted, and fuzzy K-nearest neighbors (F-KNN) algorithm was applied for classification. Chicken meats were classified in two classes (fresh and frozen-thawed). Also, each group of fresh and frozen-thawed chicken meat was classified in five classes according to their shelf life (elapsed day after slaughtering/thawing). The obtained results showed that the average amount of accuracy for fresh-chilled chicken meat classification and frozen-thawed one were 95.2 and 94.67%, respectively. Finally, the accuracy of fresh chicken meat with 95.83% demonstrated the high accuracy of it compare with frozen-thawed one. According to the results, high performance of F-KNN algorithm demonstrated that e-nose system can be applied as a rapid, accurate, and nondestructive method for online and automated identification of fresh and frozen-thawed chicken meat.

Implementation of Text Mining Classification as a Model in the Conclusion of Tafsir Bil Ma'tsur and Bil RA'YI Contents

Studies related to Tafsir Qur'an have only been carried out based on a manual system or on application-based development. The purpose of this study is to build an application which can classify type of interpretation automatically into two classes, tafsir Bil Ma'tsur and tafsir Bil Ra'yi, can provide user convenience in the Al-Qur-an. KNN algorithm is a reliable algorithm in the classification process, also has many parts of the algorithm. This study was done by applying K-Nearest Neighbor (KNN) algorithm with an accuracy of 98.12%. However KNN had been compared firstly with Modified K-Nearest Neighbor (MKNN) and Fuzzy K-Nearest Neighbor (FKNN) algorithms, where the two algorithms had 98.01% and 88.3% accuracy respectively. MKNN is the best algorithm with the highest accuracy, but also has a high error value by 4.3% which is higher than KNN, 1.9%. From the research conducted, the more text documents used in KNN modeling, the higher accuracy will be. Therefore, in the implementation of this application KNN is used as modeling in the conclusion of Tafsir Al-Qur'an. Tests performed with BlackBox Testing and User Aceptance Test reached value of 100% and 98.8%.

Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors Classifier for Big Data

One of the best-known and most effective methods in supervised classification is the k-nearest neighbors algorithm (kNN). Several approaches have been proposed to improve its accuracy, where fuzzy approaches prove to be among the most successful, highlighting the classical fuzzy k-nearest neighbors (FkNN). However, these traditional algorithms fail to tackle the large amounts of data that are available today. There are multiple alternatives to enable kNN classification in big datasets, spotlighting the approximate version of kNN called hybrid spill tree. Nevertheless, the existing proposals of FkNN for big data problems are not fully scalable, because a high computational load is required to obtain the same behavior as the original FkNN algorithm. This article proposes global approximate hybrid spill tree FkNN and local hybrid spill tree FkNN, two approximate approaches that speed up runtime without losing quality in the classification process. The experimentation compares various FkNN approaches for big data with datasets of up to 11 million instances. The results show an improvement in runtime and accuracy over literature algorithms.

PENERAPAN METODE FUZZY K-NEAREST NEIGHBOR (FK-NN) UNTUK MENENTUKAN PENYAKIT PADA TERNAK SAPI POTONG

Cattle are one of the livestock commodities that are a mainstay as a source of protein. Animal is meat that is quite well known in the community. Decent meat taken from healthy livestock and free from diseases caused by diseases suffered by cattle must be handled seriously. Beef cattle breeders in East Nusa Tenggara, especially young cattle breeders, are hard to find by medical personnel such as compilation veterinarians to find sick cattle. On the other hand, the Livestock Service Office of NTT Province annually collects cattle disease data to draw conclusions about animal diseases in the regency / city in East Nusa Tenggara. Through data from the Kupang District Animal Husbandry Service, East Nusa Tenggara with data mining techniques can predict livestock disease using the Fuzzy K-Nearest Neighbor (FK-NN) algorithm. Fuzzy K-Nearest Neighbor (FK-NN) algorithm works by receiving input of diseases as input, then it will be processed with FK-NN algorithm and the results of processing become diagnoses of diseases suffered and therapeutic suggestions for diseases in beef cattle So it can increasing the yield of beef collected from beef cattle and minimizing the costs incurred by cattle farmers to care for infected livestock to consult with veterinarians.

An Improved Fuzzy K-Nearest Neighbor Algorithm for Imbalanced Data using Adaptive Approach

ABSTRACTFuzzy classification is a widely explored research solution of objects in data sciences and engineering. With the span of time, it got new heights with significant improvements according to the needs. Still there are some issues to be discussed and solved in a fuzzy manner; fuzzy classification of imbalanced data is one of them. Consequently, the importance of fuzzy nearest neighbor came into the scenario and deployed in many applications. Various improved crisp nearest neighbor approaches are performing well on imbalanced data-sets, but not much work has done on the fuzzy nearest neighbor for imbalanced data. In this paper, we propose to find out correct memberships of test instances from imbalanced data by merging an adaptive K-nearest neighbor approach to deal with the imbalanced issue and then join it with fuzzy K-nearest neighbor.

Cavitation Damage Prediction on Dam Spillways using Fuzzy-KNN Modeling

The present paper deals with a numerical method for prediction of cavitation damage level and location on dam spillways. A method was applied to predict the intensity of cavitation damage to spillways, using the fuzzy k-nearest neighbor algorithm. Five levels of damage intensity were considered to predict cavitation damage in the spillway of Karun-1 Dam in Iran. According to the results, the proposed model could properly predict the location and intensity of damage in comparison with the actual damage reports of past floods. According to the Pearson's correlation coefficient, mean absolute error, coefficient of residual mass, and normalized root mean square error, the fuzzy k-nearest neighbor model is efficient and suitable.

An overlap-sensitive margin classifier for imbalanced and overlapping data

Classification is an important task in various areas. In many real-world applications, class imbalance and overlapping problems have been reported as major issues in the application of traditional classification algorithms. An imbalance problem occurs when training data contain considerably more representatives of one class than of other classes. Class overlap occurs when a region in the data space contains a similar number of data for each class. When a class overlap occurs in imbalanced data sets, classification becomes even more complicated. Although various approaches have been proposed to deal separately with class imbalance and overlapping problems, only a few studies have attempted to address both problems simultaneously. In this paper, we propose an overlap-sensitive margin (OSM) classifier based on a modified fuzzy support vector machine and k-nearest neighbor algorithm to address imbalanced and overlapping data sets. The main idea of the proposed OSM classifier is to separate the data space into soft- and hard-overlap regions using the modified fuzzy support vector machine algorithm. The separated spaces are then classified using the decision boundaries of the support vector machine and 1-nearest neighbor algorithms. Furthermore, by separating a data set into soft- and hard-overlap regions, one can determine which part of the data is to be examined more closely for classification in real-world situations. Experiments using synthetic and real-world data sets demonstrated that the proposed OSM classifier outperformed existing methods for imbalanced and overlapping situations.