Modified K-nearest Neighbor Research Articles

An Effective Opinion Mining-Based K-Nearest Neighbours Algorithm for Predicting Human Resource Demand in Business

The process of estimating and preparing for an association's future supply and management of human resources is known as human resource planning (HRP). It is a significant section of directorial development. This study suggested a novel technique to HRP demand forecasting using the Modified K-nearest neighbor (M-KNN) algorithm. M-KNN is machine learning (ML) algorithms that locate the k major comparable data sample to a new information sample and uses those data sample to forecast the value of the new sample. We generate an HR order estimate pointer organization consisting of variables that pressure HR order such as market share, sales volume, and economic conditions. We use M-KNN to find the k most similar data samples to each data point in the display organization and use those data samples to predict HR command. We assessed our M-KNN algorithm using data from a actual association and find that it can correctly forecast HR command with an fault speed of less than 5%. M-KNN is a talented advance to HRP command forecasting as it is easy to use, does not necessitate a lot of data, and can exactly predict HR command in the attendance of non-linearity and haziness.

An Effective Opinion Mining-Based K-Nearest Neighbours Algorithm for Predicting Human Resource Demand in Business

The process of estimating and preparing for an association's future supply and management of human resources is known as human resource planning (HRP). It is a significant section of directorial development. This study suggested a novel technique to HRP demand forecasting using the Modified K-nearest neighbor (M-KNN) algorithm. M-KNN is machine learning (ML) algorithms that locate the k major comparable data sample to a new information sample and uses those data sample to forecast the value of the new sample. We generate an HR order estimate pointer organization consisting of variables that pressure HR order such as market share, sales volume, and economic conditions. We use M-KNN to find the k most similar data samples to each data point in the display organization and use those data samples to predict HR command. We assessed our M-KNN algorithm using data from a actual association and find that it can correctly forecast HR command with an fault speed of less than 5%. M-KNN is a talented advance to HRP command forecasting as it is easy to use, does not necessitate a lot of data, and can exactly predict HR command in the attendance of non-linearity and haziness.

Kombinasi Modified K-Nearest Neighbor dan Certainty Factor untuk Peningkatan Akurasi Diagnosis Malnutrisi

Malnutrition remains a major public health challenge in low and middle-income countries. This research proposes a method for diagnosing malnutrition diseases by combining Certainty Factor (CF) and Modified K-Nearest Neighbor (MKNN). CF is used to obtain certainty values from the symptoms experienced by patients, while MKNN classifies patient symptom data into specific disease classes based on proximity to the training data. The symptom CF values are combined with the rule CFs to obtain the final CF for each disease. The patient's CF data becomes the testing data, while the disease dataset is the training data. The MKNN technique involves calculating the Euclidean distance, validity, and applying weight voting to identify the class to which the testing data belongs based on the majority class of the k nearest training data. In the test case, CF indicated a tendency towards Kwashiorkor, reinforced by MKNN with the majority of the nearest training data classified as Kwashiorkor. Cross-validation testing with 20 testing data resulted in an accuracy of 95% for the combined CF-MKNN method. The combination of the two methods mutually reinforces and increases the confidence in the diagnosis.

Classification of Heart Disease Using Modified K-Nearest Neighbor (MKNN) Method

Penyakit jantung memiliki banyak jenis dan gejala yang dialami. Penyakit jantung adalah sebuah kondisi ketika organ jantung tidak dapat bekerja sebagaimana fungsinya dengan baik. Jantung adalah organ penting dalam tubuh manusia yang dimana fungsinya adalah memompa darah ke seluruh tubuh. Karena itu dibutuhkannya diagnosa awal untuk pencegahan penyakit jantung dengan memanfaatkan system yang dapat dibuat untuk diagnosa awal pada gejala yang dialami. Yang pada penelitian ini akan menggunakan metode Modified K-Nearest Neighbor (MKNN) dalam mengklasifikasikan penyakit jantung berdasarkan kriteria atau gejala yang ada. Penelitian ini menggunakan 6 kriteria penyakit dan 3 kelas diagnosa penyakit jantung. Dengan melewati beberapa langkah pengerjaan yaitu menghitung jarak Euclidean, menghitung nilai validitas dan terakhir menghitung weight voting dengan mengandalkan nilai K yang telah ditentukan sejak awal perhitungan. Pada penelitian ini telah ditentukan nilai K=5 dan didapat hasil pengujian akurasi sebesar 85%, dengan recall 90% dan precision 85%.

Feature Selection using Information Gain on the K-Nearest Neighbor (KNN) and Modified K-Nearest Neighbor (MKNN) Methods for Chronic Kidney Disease Classification

Purpose: Kidneys has an important role in the human excretory system. Unhealthy kidneys can affect kidney function. It is important to know the symptoms of chronic kidney disease. One data mining technique that can be applied is the classification technique to determine whether a person has chronic kidney disease or not based on the symptoms (attributes) obtained from medical records. The symptoms of chronic kidney disease obtained amount to 24 symptoms or attributes,Methods/Study design/approach: In this research, the classification of chronic kidney disease is performed using the information gain feature selection method and the KNN and MKNN classification methods. The number of data used is 400 data with 2 classes, namely chronic kidney disease (CKD) and non-chronic kidney disease (non-CKD).Result/Findings: Based on the test results, it was found that the hemo (Hemoglobin) attribute has the highest information gain value, which is 0.6255. The best accuracy for the KNN classification method is 96.61%, and for the MKNN method, it is 98%. Novelty/Originality/Value: The purpose of information gain feature selection is to choose features or attributes that significantly influence chronic kidney disease. Keywords: Chronic Kidney Disease, Information Gain, KNN, MKNN

Improving sporadic demand forecasting using a modified k-nearest neighbor framework

Forecasting sporadic or intermittent demand presents significant challenges in supply chain management, primarily due to the frequent occurrence of zero demand values and the inherent difficulty in capturing underlying hidden patterns in sporadic dataset. Although various parametric and non-parametric methods are available for sporadic demand forecasting, they often fail to detect these hidden patterns, underscoring the need for artificial intelligence (AI) algorithms which are effective in identifying irregular patterns. Among AI algorithms, the k-Nearest Neighbor (k-NN) is particularly good at identifying patterns with limited data. In the past, k-NN based frameworks have been recognized for their ability to effectively forecast sporadic demand, particularly when it is anticipated that patterns will reoccur in subsequent non-zero demand values. However, this assumption does not always hold true in different real-world scenario. In sporadic time series data, zero values often comprise a significant proportion (>30%). To address this, this paper proposes a “zero-inclusive k-NN framework” that leverages both zero and non-zero demand data to identify patterns. The proposed framework offers two significant features: it enables industrial managers to utilize a large number of nearest neighbors and provides adaptability in demand-vector length. Numerical investigations with both synthetic and real datasets affirm the superior forecasting performance of the proposed k-NN method when compared to existing k-NN framework and conventional parametric benchmark methods. The implications of our findings extend to domains where sporadic or intermittent demand forecasting plays a vital role.

Klasifikasi Berita Hoax Pembagian Kuota Internet Menggunakan Algoritma Modified K-Nearest Neighbor

In this day and age, information technology is growing rapidly, it is very easy for people to access information through internet networks or social media. With easy access to the internet, it can open up opportunities for people to spread information or news on social media. But besides that, there is a lot of fake information or news that can't be accounted for by the source, so this research classifies hoax news on internet quota distribution. The hoax classification process will use several stages, namely preprocessing. The weighting of TF-IDF words, up to the classification process using the Modified K-Nearest Neighbor algorithm, in the process will use the Google Collab Platform (python). The classification results obtained are indicated by default k-values ​​with the highest accuracy being at k25 with a precision value of 1%, recall 0.4444%, and an accuracy value of 80.77%.

Sistem Klasifikasi Penyakit Jantung Menggunakan Teknik Pendekatan SMOTE Pada Algoritma Modified K-Nearest Neighbor

The heart is a vital organ that plays a crucial role in pumping oxygenated blood and nutrients throughout the body. Heart disease refers to damage to the heart that can occur in various forms, caused by infections or congenital abnormalities. The World Health Organization (WHO) reports nearly 17.9 million deaths each year due to heart disease. In Indonesia, the prevalence of heart disease is around 1.5%, meaning that in 2018, approximately 15 out of 1,000 people, or nearly 2,784,060 individuals, were affected by this disease, according to the Basic Health Research data (Riskesdas) 2018. Many people have limited knowledge about heart health, leading to a lack of awareness of their heart conditions. This can be attributed to a lack of understanding regarding the importance of medical checkups related to heart health. Modified K-Nearest Neighbors (MKNN) is one of the data mining methods applied for classifying the risk of heart disease. The research utilized data obtained from the UCI dataset repository, which consists of 918 records with 12 attributes. To balance the imbalanced dataset with minority classes, the Synthetic Minority Over-sampling Technique (SMOTE) approach was used to generate new synthetic samples from the minority class. The objective of developing a web-based system for heart disease classification is to assist the public in assessing their risk of heart disease as early as possible, enabling them to take preventive actions sooner. The accuracy results of the MKNN algorithm with a 90:10 ratio are 80.37%, while with the MKNN+SMOTE approach, the accuracy increased to 84.00%. The use of the SMOTE approach improved the accuracy of low-performing data.

Entropy estimation via uniformization

Entropy estimation is of practical importance in information theory and statistical science. Many existing entropy estimators suffer from fast growing estimation bias with respect to dimensionality, rendering them unsuitable for high-dimensional problems. In this work we propose a transform-based method for high-dimensional entropy estimation, which consists of the following two main ingredients. Firstly, we provide a modified k-nearest neighbors (k-NN) entropy estimator that can reduce estimation bias for samples closely resembling a uniform distribution. Second we design a normalizing flow based mapping that pushes samples toward the uniform distribution, and the relation between the entropy of the original samples and the transformed ones is also derived. As a result the entropy of a given set of samples is estimated by first transforming them toward the uniform distribution and then applying the proposed estimator to the transformed samples. The performance of the proposed method is compared against several existing entropy estimators, with both mathematical examples and real-world applications.

Modified K-Nearest Neighbor Web Based Chatbot as Service and Academic Information School

At a time when everything is done on an Online basis starting from office activities, teaching and learning activities especially administrative activities school. Where in the activity, aninformant needs to be can serve academic and non-academic information needs for para parents of students and other parties. Chatbot is one of the means to overcome these needs by presentingfeatures that allows for someone to ask when and where. Therefore, in the conclusion that chatbot can be a public means especially on schools - schools that can implement their uses as one school information sources other than the school's business. This Chatbot made using the Modified K-Nearest Neighbor method or can be briefed MKNN. MKNN is the latest version of the previous method, K-Nearest Neighbor where in the application MKNN method does the process additional after Euclidean Distance calculation, Weight Voting. Premise from the weight voting method itself is the biggestweight calculation, so chatbot will calculate the value of the shortest distance to the database from the chatbot then after completion, the chatbot will calculate the value weight or information from that distance so that the results that can be from this are the shortestdistance to the data class that is being addressed by carrying the weight of information biggest or most. This is used to facilitate the deep chatbot determine the category of a database so that it can provide answers that more certain and accurate.

An Innovative Human-Computer Interaction (HCI)

The interface between citizens and elegant cities is human computer interaction (HCI), a place that is crucial in bridging the application gap for information technology in contemporary cities. Hand gestures (HG) are generally recognized as a potential HCI technique, and the use of Surface Electromyograms (SEMG) to recognize Human Hand Gestures (HHG) is a significant area of study. Modern signal processing techniques, instead, are not robust in feature extraction utilizing Principal Component Analysis (PCA), using feature re-extraction, and guide respect with SEMG signals; there be motionless several technical issues that need to be resolved. The way for instance, can myoelectric control be kept available in intermittent use, as time variability has a significant negative impact on pattern recognition quality yet is unavoidable in regular use. Developing a solid HCI also requires ensuring the myoelectric control system's efficacy and dependability. In this study, Augmented Partial Swarm Optimization and Modified K-Nearest Neighbor (APSO- MKNN) are used in the HGR system that can eliminate redundant information in SEMG signals and increase the effectiveness and precision of recognition. The investigational findings help lower the time differences in Gesture Recognition (GR) based on SEMG.This study is focused on optimizing the time differences in SEMG pattern recognition. The identification approach that is proposed in this study has the possibility of increasing the long-term accuracy of the generalization of an HCI system. Additionally, the proposed framework can simplify the process of data collecting prior to having a gadget prepared and ready for usage.

The Application of Modified K-Nearest Neighbor Algorithm for Classification of Groundwater Quality Based on Image Processing and pH, TDS, and Temperature Sensors

The limited availability of water in remote areas makes rural communities pay less attention to the water quality they use. Water quality analysis is needed to determine the level of groundwater quality used using the Modified K-Nearest Neighbor Algorithm to minimize exposure to a disease. The data used in this study was images combined with sensor data obtained from pH (Potential of Hydrogen), TDS (Total Dissolved Solids) sensors and Temperature Sensors. The test used the Weight voting value as the highest class majority determination and was evaluated using the K-Fold Cross Validation and Multi Class Confusion Matrix algorithms, obtaining the highest accuracy value of 78% at K-Fold = 2, K-Fold = 9, and K- Fold = 10. Meanwhile, the results of testing the effect of the K value obtained the highest accuracy value at K = 5 of 67.90% with a precision value of 0.32, 0.37 recall, and 0.33 F1-Score. From the results of the tests carried out, it can be concluded that most of the water conditions are suitable for use.

FunFun: ITS-based functional annotator of fungal communities.

The study of individual fungi and their communities is of great interest to modern biology because they might be both producers of useful compounds, such as antibiotics and organic acids, and pathogens of various diseases. And certain features associated with the functional capabilities of fungi are determined by differences in gene content. Information about gene content is most often taken from the results of functional annotation of the whole genome. However, in practice, whole genome sequencing of fungi is rarely performed. At the same time, usually sequence amplicons of the ITS region to identify fungal taxonomy. But in the case of amplicon sequencing there is no way to perform a functional annotation. Here, we present FunFun, the instrument that allows to evaluate the gene content of an individual fungus or mycobiome from ITS sequencing data. FunFun algorithm based on a modified K-nearest neighbors algorithm. As input, the program can use ITS1, ITS2, or a full-size ITS cluster (ITS1-5.8S-ITS2). FunFun was realized as a pip-installed command line instrument and validated using a shuffle-split approach. The developed instrument can be very useful in the fungal community comparing and estimating functional capabilities of fungi under study. Also, the program can predict with high accuracy the most variable functions.

Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan

An accurate prediction of the Remaining Useful Life (RUL) of aircraft engines plays a fundamental role in the aerospace field since it is both mission and safety critical. In fact, a reliable estimate of the RUL can effectively reduce the maintenance costs while fostering safety. This paper proposes a novel data-driven method to increase accuracy of the RUL prediction for real-time prognostic systems, considering multiple degradation mechanisms and making the model easy to implement. The proposed method exploits a novel modified k-Nearest Neighbors Interpolation (kNNI) with an a posteriori Least Square Smoothing (LSS) automatically optimized to obtain the minimum prediction error. The LSS novel formulation was also generalized and proved to be equivalent to a Cumulative and Moving Average (CMA) mixture filter, which can be easily implemented online. The method was developed and validated based on a new NASA dataset generated by the dynamic model Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) with run-to-failure data related to a small fleet of aircraft engines under realistic flight conditions. Finally, a reference kNN-based method already known in the literature was compared to the novel proposed one to demonstrate the goodness of the results and the performance improvements.

Inertial Sensing for Lateral Walking Gait Detection and Application in Lateral Resistance Exoskeleton

Lateral walking gait detection is necessary for the development of wearable devices applied to side stepping. To our knowledge, rarely work has been conducted to identify lateral walking gait by wearable sensors. Based on a hip exoskeleton, we presented a method for lateral walking gait phase detection only by two IMUs mounted on the shank. Experiments were conducted to detect narrow double support, swing of the leading leg, wide double support and swing of trailing leg phases of 12 healthy subjects walking at various speeds. The performance of four different algorithms including thresholding (THR), modified k-nearest neighbor named urban buildings indexing (UBI), random forest (RF) and neural networks (NN) were evaluated. The occupation of space resources of NN is the smallest besides THR. The total recognition accuracy (mean and SEM) of the RF-based, UBI-based, and NN-based systems was 97.07 ± 0.07% (off-line), 96.64 ± 0.16% (off-line) and 95.22 ± 0.60% (real-time), respectively. The recognition time of the models based on RF, UBI, NN, and THR was 13.3 ± 1.1 ms, 5.7 ± 0.5 ms, 2.6 ± 0.2 ms and 0.8 ± 0.2 ms, respectively. The recognition accuracy (cross-subjects) of RF-based, UBI-based and NN-based systems was 91.72 ± 0.42%, 89.63 ± 0.48% and 89.60 ± 0.43%, respectively. The results demonstrated that the proposed method can be applied to lateral walking gait detection.

Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations

This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm. The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm. Compared to other active learning methods resorting to experimental designs, the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification, which is applicable for most structural reliability estimation problems. Moreover, the validity, efficiency, and accuracy of the proposed method are demonstrated numerically. In addition, the optimal value of K that maximizes the computational efficiency is studied. Finally, the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements, which further validates its practicability.

Study of selected methods for balancing independent data sets in [formula omitted]-nearest neighbors classifiers with Pawlak conflict analysis

The article is devoted to the issue of classification based on independent data sets. More specifically, the impact of using different methods for balancing data sets on the classification quality in an approach that uses Pawlak conflict analysis is investigated. The newly proposed method for classification based on independent data sets assumes applying algorithms for imbalanced classification separately to all fragmented sets. The following algorithms are considered: SMOTE, random over-sampling, TOMEK links, Near Miss, random under-sampling and a combination of SMOTE and TOMEK links. For balanced data sets — decision tables, conflict analysis is used, and coalitions of tables are created. Then the aggregated table for each coalition is defined, and a modified k-nearest neighbors algorithm is used to determine the decision vectors. The majority voting method is used to fuse decision vectors. Experimental results showed that the proposed approach, in most cases, gives much better results than without using methods for imbalanced data. In addition, the proposed approach achieves better results than other methods known from the literature applied to dispersed data. It was noticed that for dispersed and independent data, the best results are generated by the over-sampling approach, especially by the SMOTE, the random over-sampling and the SMOTE and TOMEK methods.

Modified K-Nearest Neighbour Using Proposed Similarity Fuzzy Measure for Missing Data Imputation on Medical Datasets (MKNNMBI)

Early disease diagnosis is a burning problem in health sector, medical domain and disease management. During analysis, quality of the data can be achieved only if the data is complete. Missing values reduces the efficiency of data analysis task. Researchers proposed various imputation methods but always there was a need for a better imputation method. This paper objective is to propose a method for imputation using proposed similarity fuzzy measure through which we can impute missing values by finding k similar instances called as Modified k-Nearest Neighbour for imputation of missing data (MKNNMBI). The proposed imputation method outperformed when compared with other existing imputation methods MV EM, MV BPCA, MV Ignore, MV KMeans, MV FKMeans, MV KNN, MV MC, MV WKNNimpute, MV SVDimpute, MV SVMimpute, CBC-IM-FUZZY. These imputation methods were studied on different benchmark datasets and tested for performance on different classifiers like C4.5, SVM, kNN, NB and found that the proposed method leads to accurate imputation and improves the accuracy.

Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4.5 Algorithm

To improve public services, accuracy and acceleration are needed in classifying the types of complaints so that complaints can immediately get a response from the relevant regional apparatus. This public complaint data is in text form and is not balanced in each category of regional apparatus, so we contribute to research to compare the performance of different text mining-based classification algorithms. In addition, we also tested the resampling method to overcome imbalanced data. In the final stage, testing is carried out using a multiclass confusion matrix table to show accuracy, precision, recall, and f1-score. The test results show the highest value in the Naïve Bayes algorithm with the ComplementNB model without resampling data, which is 89.58% accuracy, 86.72% precision, 82.40% recall, 84.09% f1-score. However, all scores decreased when combined with SMOTE resampling of 83.66% accuracy, 67.79% precision, 80.35% recall, 71.68% f1-score. ComplementNB can be an alternative model in the classification of public complaints with imbalanced datasets

Comparison of K-Nearest Neighbor And Modified K-Nearest Neighbor With Feature Selection Mutual Information And Gini Index In Informatics Journal Classsification

With the rapid development of informatics where thousands of informatics journals have been made, a new problem has occured where grouping these journals manually has become too difficult and expensive. The writer proposes using text classification for grouping these informatics journals. This research examines the combinations of two machine learning methods, K-Nearest Neighbors (KNN) and Modified K-Nearest Neighbors with two feature selection methods, Gini Index (GI) and Mutual Information (MI) to determine the model that produces the higherst evaluation score. The data are informatics journals stored in pdf files where they are given one of 3 designated labels: Information Retrieval, Database or Others. 252 data were collected from the websites, neliti.com and garuda.ristekbrin.go.id. This research examines and compares which of the two methods, KNN and MKNN at classifying informatics journal as well as determining which combination of parameters and feature selection that produces the best result. This research finds that the combination of method and feature selection that produces the best evaluation score is MKNN with GI as feature selection producing precision score, recall score and f1-score of 97.7%