scholarly journals k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 830
Author(s):  
Seokho Kang
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Weighting Function ◽  
High Sensitivity ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Main Challenge ◽  
Benchmark Datasets ◽  
Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

scholarly journals A Hybrid Short-term Traffic Flow Forecasting Method Based on Neural Networks Combined with K-Nearest Neighbor

2018 ◽  
Vol 30 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Zhao Liu ◽  
Jianhua Guo ◽  
Jinde Cao ◽  
Yun Wei ◽  
Wei Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Flow ◽  
Nearest Neighbor ◽  
Network Models ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Neural Network Models ◽  
Traffic Forecasting

It is critical to implement accurate short-term traffic forecasting in traffic management and control applications. This paper proposes a hybrid forecasting method based on neural networks combined with the K-nearest neighbor (K-NN) method for short-term traffic flow forecasting. The procedure of training a neural network model using existing traffic input-output data, i.e., training data, is indispensable for fine-tuning the prediction model. Based on this point, the K-NN method was employed to reconstruct the training data for neural network models while considering the similarity of traffic flow patterns. This was done through collecting the specific state vectors that were closest to the current state vectors from the historical database to enhance the relationship between the inputs and outputs for the neural network models. In this study, we selected four different neural network models, i.e., back-propagation (BP) neural network, radial basis function (RBF) neural network, generalized regression (GR) neural network, and Elman neural network, all of which have been widely applied for short-term traffic forecasting. Using real world traffic data, the  experimental results primarily show that the BP and GR neural networks combined with the K-NN method have better prediction performance, and both are sensitive to the size of the training data. Secondly, the forecast accuracies of the RBF and Elman neural networks combined with the K-NN method both remain fairly stable with the increasing size of the training data. In summary, the proposed hybrid forecasting  approach outperforms the conventional forecasting models, facilitating the implementation of short-term  traffic forecasting in traffic management and control applications.

scholarly journals Review on Techniques for Plant Leaf Classification and Recognition

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 77 ◽  
Author(s):  
Muhammad Azfar Firdaus Azlah ◽  
Lee Suan Chua ◽  
Fakhrul Razan Rahmad ◽  
Farah Izana Abdullah ◽  
Sharifah Rafidah Wan Alwi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Plant Systematics ◽  
K Nearest Neighbor ◽  
Plant Leaf

Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

scholarly journals Penerapan Metode K-Nearest Neighbors (kNN) pada Bearing

2021 ◽  
Vol 1 (1) ◽  
pp. 10-18
Author(s):  
Anggi Priliani Yulianto ◽  
Sutawanir Darwis
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Real Data ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
K Value

Abstract. Monitoring the condition of the engine is a top priority to avoid damage. To know the condition of the bearing, it is important to know the remaining useful life of the machine. In the IEEE PHM 2012 Prognostic Challenge platform provides real data related to accelerated bearing degradation carried out under constant operating conditions and online controlled variables of temperature and vibration (with horizontal and vertical accelerometers). In this platform, the data used is bearing2_3 data in the horizontal direction which has a duration of about 2 hours, calculated RMS every 1/10 second (2560 data). In this study machine learning based modeling will be done using the k-nearest neighbor (kNN) method to determine the prediction of RMS bearings. The kNN method is based on the classification of objects based on training data that is the closest distance to the object. kNN is a nonparametric machine learning algorithm which is a model that does not assume distribution. The advantage is that the class decision line produced by the model can be very flexible and very nonlinear. The smallest MSE value was obtained at k = 16 with MSE value = 0.157579. After getting the optimum k value, proceed with predicting a RMS of 97 lags and identifying bearing performance in several phases. Abstrak. Pemantauan kondisi mesin menjadi prioritas utama untuk menghindari adanya kerusakan. Untuk mengetahui kondisi bantalan, penting untuk mengetahui sisa masa manfaat dari mesin tersebut. Dalam platfrom IEEE PHM 2012 Prognostic Challenge ini menyediakan data nyata terkait dengan degradasi bantalan yang dipercepat yang dilakukan di bawah kondisi operasi konstan dan variabel yang dikendalikan secara online berupa suhu dan getaran (dengan akselerometer horizontal dan vertikal). Dalam platform ini, data yang digunakan adalah data bearing2_3 pada arah horizontal yang berdurasi sekitar 2 jam ini dihitung RMS setiap 1/10 detik (2560 data). Dalam penelitian ini akan dilakukan pemodelan berbasis machine learning menggunakan metode k-nearest neighbor (kNN) untuk mengetahui prediksi RMS bearing. Metode kNN didasarkan pada klasifikasi terhadap objek berdasarkan data pelatihan yang jaraknya paling dekat dengan objek tersebut. kNN merupakan salah satu algoritma pembelajaran mesin yang bersifat nonparametrik yakni model yang tidak mengasumsikan distribusi. Kelebihannya adalah garis keputusan kelas yang dihasilkan model tersebut bisa jadi sangat fleksibel dan sangat nonlinier. Nilai MSE terkecil diperoleh pada k = 16 dengan nilai MSE = 0,157579. Setelah mendapatkan nilai k optimum, dilanjutkan dengan memprediksi RMS sebanyak 97-lag serta mengidentifikasi performance kinerja bearing dalam beberapa fase.

scholarly journals AffinityNet: Semi-Supervised Few-Shot Learning for Disease Type Prediction

2019 ◽  
Vol 33 ◽  
pp. 1069-1076 ◽  
Author(s):  
Tianle Ma ◽  
Aidong Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Nearest Neighbor ◽  
Synthetic Data ◽  
Genomic Data ◽  
Training Data ◽  
Great Success ◽  
K Nearest Neighbor ◽  
Neural Network Models

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the “big p, small N” problem (i.e., a relatively small number of samples with highdimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data.

Music Emotion Recognition

2021 ◽  
Author(s):  
tejaswini kambaiahgari ◽  
Uma Rao K
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Ann Model ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Present World

Abstract In the present world, there are many songs over the internet. But the information retrieval on these songs can be complicated. This paper intends to classify songs based on emotions using deep learning. We propose a strategy to recognize the emotion present in a song by classifying their spectrograms, which contains both time and frequency information. According to human psychology, neurons within a sub pop- ulation of our brain did not react the same way for all the emotions.So only specific neurons need to be triggered for identifying an emotion. Dif- ferent deep learning and machine learning algorithms are implemented to build music emotion recognizer. The main objective of this study is to study about the features which are important for audio file ,to de- velop a music emotion classifier using deep learning algorithm and also to validate the model.The datasets are split into training and testing sets, models are trained with training data set. The accuracy of Artifi- cial Neural Network (ANN) model is 79.7% ,K-Nearest Neighbor (KNN) model is 78.26% and logistic regression for gender classification is 81%.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

2018 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Jerzy Leszczynski ◽  
Olexandr Isayev
Keyword(s):  
Neural Network ◽  
Molecular System ◽  
Computational Cost ◽  
Chemical Properties ◽  
The State ◽  
Molecular Properties ◽  
Training Data ◽  
Dft Methods ◽  
Benchmark Datasets ◽  
Quantum Phenomena

<p>Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.</p>

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals Intelligent Dynamic Identification Technique of Industrial Products in a Robotic Workplace

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1797
Author(s):  
Ján Vachálek ◽  
Dana Šišmišová ◽  
Pavol Vašek ◽  
Jan Rybář ◽  
Juraj Slovák ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control Charts ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Conveyor Belt ◽  
Standard Uncertainty ◽  
K Nearest Neighbor ◽  
Industrial Products ◽  
Dynamic Identification ◽  
Identification Technique

The article deals with aspects of identifying industrial products in motion based on their color. An automated robotic workplace with a conveyor belt, robot and an industrial color sensor is created for this purpose. Measured data are processed in a database and then statistically evaluated in form of type A standard uncertainty and type B standard uncertainty, in order to obtain combined standard uncertainties results. Based on the acquired data, control charts of RGB color components for identified products are created. Influence of product speed on the measuring process identification and process stability is monitored. In case of identification uncertainty i.e., measured values are outside the limits of control charts, the K-nearest neighbor machine learning algorithm is used. This algorithm, based on the Euclidean distances to the classified value, estimates its most accurate iteration. This results into the comprehensive system for identification of product moving on conveyor belt, where based on the data collection and statistical analysis using machine learning, industry usage reliability is demonstrated.

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