Evaluation of speaker normalization methods for vowel recognition using neural network and nearest‐neighbor classifiers

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.

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Semantics of Voids within Data: Ignorance-Aware Machine Learning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040246 ◽

2021 ◽

Vol 10 (4) ◽

pp. 246

Author(s):

Vagan Terziyan ◽

Anton Nikulin

Keyword(s):

Spatial Data ◽

Information Science ◽

Nearest Neighbor ◽

Geographical Information ◽

Two Dimensional ◽

Prototype Selection ◽

Data Space ◽

Important Concern ◽

Traditional Classification ◽

Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

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k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽

10.3390/math9080830 ◽

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.

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