SYNTHESIS OF NEURAL NETWORK ALGORITHMS FOR CLASSIFICATION OF MARINE OBJECTS IN LOW-FREQUENCY PASSIVE SONAR SYSTEMS

2019 ◽  
pp. 3-8
Author(s):  
O.A. Andreev ◽  
A.T. Trofimov
Keyword(s):  
Neural Network ◽  
Probabilistic Models ◽  
Low Frequency ◽  
Gaussian Mixture ◽  
Classification Systems ◽  
Classification Algorithms ◽  
Correct Classification ◽  
Passive Sonar ◽  
Sonar Systems

The paper addresses the issue of insuring the required probability of correct classification of marine objects in low-frequency passive sonar systems. The solution to the issue is sought through the application of methods for the synthesis of neural network classification algorithms using poly-Gaussian probabilistic models (Gaussian mixture models, GMM). It is shown that the use of GMM makes it possible to solve a number of problems specific to the issue; classification algorithms synthesized using mentioned methods can be implemented in the form of neural networks, which in turn can be described in C++/VHDL to create endpoint computing devices or software systems. The results of modeling of synthesized classification algorithms on experimental data are presented; it is demonstrated that such algorithms make it possible to increase the probability of correct classification of marine objects and to satisfy typical requirements for classification systems in low-frequency passive sonar systems.

scholarly journals Research on Sentiment Classification Algorithms on Online Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Ruixia Yan ◽  
Zhijie Xia ◽  
Yanxi Xie ◽  
Xiaoli Wang ◽  
Zukang Song
Keyword(s):  
Neural Network ◽  
Sentiment Classification ◽  
Online Review ◽  
Support Vector ◽  
Classification Algorithms ◽  
Evaluation Indexes ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Reinforcement Learning Models

The product online review text contains a large number of opinions and emotions. In order to identify the public’s emotional and tendentious information, we present reinforcement learning models in which sentiment classification algorithms of product online review corpus are discussed in this paper. In order to explore the classification effect of different sentiment classification algorithms, we conducted a research on Naive Bayesian algorithm, support vector machine algorithm, and neural network algorithm and carried out some comparison using a concrete example. The evaluation indexes and the three algorithms are compared in different lengths of sentence and word vector dimensions. The results present that neural network algorithm is effective in the sentiment classification of product online review corpus.

PCA–Based Human Posture Classification

2012 ◽  
Author(s):  
Nooritawati Md Tahir ◽  
Aini Hussain ◽  
Salina Abdul Samad ◽  
Hafizah Husain
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Principal Component ◽  
Optimal Choice ◽  
Correct Classification ◽  
Human Posture ◽  
Posture Classification

Kertas kerja ini membentangkan suatu mekanisme untuk pengelasan susuk tubuh manusia berdasarkan kombinasi pelbagai jelmaan ruang eigen yang dinamakan sebagai eigenposture dan Multilayer Perceptron (MLP) sebagai pengelas. Penjelmaan komponen utama telah digunakan untuk menyari sifat pada bayang-bayang bentuk badan manusia. Gabungan sarian sifat ini digunakan untuk pengelasan susuk tubuh manusia sebagai berdiri atau sebaliknya berasaskan bentuk badan yang diperoleh selepas proses peruasan. Uji kaji telah dijalankan dengan mengubah bilangan vektor eigen yang dijadikan perwakilan untuk tujuan pengelasan. Keputusan yang diperoleh menunjukkan gabungan eigenposture kedua dan keempat memberi keputusan pengelasan bentuk badan manusia yang agak baik iaitu 98% dan boleh dijadikan sebagai pilihan optimal masukan bagi tujuan pengelasan menggunakan bilangan input minima. Kata kunci: Analisa komponen utama, vektor eigen, pengelasan, rangkaian neural tiruan, susuk tubuh manusia This paper outlines a mechanism for human body posture classification based on various combination of eigenspace transform, which we named as eigenposture, and using Multilayer Perceptron (MLP) as classifier. We apply principal component transformation to extract the features from human shape silhouettes. Combinations of the extracted features were used to classify the posture of standing and non-standing based on the human shape obtained from the segmentation process. We experiment by using various combinations of eigenvectors as input representations for classification purpose. Results showed that the second and fourth eigenpostures combination gives reasonably good result with 98% correct classification of human posture and can be adopted as the optimal choice of input for classification using a minimal combination. Key words: Principal component analysis (PCA), eigenvectors, classification, artificial neural network, human posture

scholarly journals Classification of Image using Convolutional Neural Network (CNN)

2019 ◽  
pp. 13-18 ◽  
Author(s):  
Md. Anwar Hossain ◽  
Md. Shahriar Alam Sajib
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lower Layer ◽  
Classification Systems ◽  
Automatic Extraction ◽  
Discriminative Power ◽  
Single Image ◽  
Training Time ◽  
Discriminant Power

Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN’s potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.

scholarly journals Data Mining Technique for Diabetes Diagnosis using Classification Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 9044-9049
Keyword(s):  
Logistic Regression ◽  
Early Stage ◽  
Classification Algorithms ◽  
Diabetes Diagnosis ◽  
Correct Classification ◽  
Data Mining Technique ◽  
Receiver Operating Characteristic Curves ◽  
Logistic Regression Algorithm ◽  
High Level

Diabetes mellitus is defined as a one of the chronic and deadliest diseases which combined with abnormally high level of sugar (glucose) in the blood. The classification technique helps in diagnosis the symptoms at starting stages. This paper focused to prognosticate the chance of diabetes in patients with extremely correct classification of Diabetes. The classification algorithms viz., Naïve Bayes, Logistic Regression, and Decision Tree can be used to detect diabetes at an early stage. The algorithm performances are evaluated based on various measures like Recall, Precision, and F-Measure. Experiments are conducted where the time complexity of each of the algorithm is measured. Accuracy is also measured over correct classification and misclassification instances, observed that a Logistic Regression algorithm has much better performance when compared to the other type classifications. Using Receiver Operating Characteristic curves the results are verified in a systematic manner.

scholarly journals Three criteria of intrinsically theoretical categories in biological system and classification of some medical plants

2019 ◽  
Author(s):  
Huabin Zou
Keyword(s):  
Information Theory ◽  
Biological System ◽  
Classification Systems ◽  
Similarity Function ◽  
Correct Classification ◽  
Maximum Information ◽  
Medical Plants ◽  
Taxonomic Categories ◽  
Variation Model

AbstractIn classical biology, different taxonomic categories are all decided based on empirical rules established on learning knowledge. In taxonomy, different classification systems are of diversified rules. One biology is often in different categories, although the concepts related to taxonomic categories are the same to each other. Whether there exist some absolute standards to classify biology, and to give unalterable results. This is of greatly scientific meaning. Common and variation, that is heredity and variation are the most elemental information in biology, and exist at multiply biology material levels. In this paper a generalized biological heredity and variation information theory was proposed based on previous works. Three typical heredity and variation models were analyzed by using this theory. They are unique asymmetric variation model, symmetric two variation model and extreme radial variation model. In the maximum information states, two biological constants Pg1 = 0.69 and Pg2=0.61 and a boundary similarity function were obtained. These Pg and Pg function can be defined as the three theoretically taxonomic category criteria of biological system. For 29 samples belonging to four kind plants, their chemical fingerprint — infrared (IR) fingerprint spectra (FPS) were analyzed depending on the theoretical criteria. The correct classification ratio was 96.6%. The results showed these samples could be ideally classified. A suggestion was proposed that biology should be absolutely classified relying on the three intrinsic theoretical criteria.

A comparative analysis of euphemistic sentences in news using feature weight scheme and intelligent techniques

2021 ◽  
pp. 1-12
Author(s):  
K. Seethappan ◽  
K. Premalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Automatic Classification ◽  
Weighting Scheme ◽  
Feature Weighting ◽  
Classification Algorithms ◽  
Weighting Schemes

Although there have been various researches in the detection of different figurative language, there is no single work in the automatic classification of euphemisms. Our primary work is to present a system for the automatic classification of euphemistic phrases in a document. In this research, a large dataset consisting of 100,000 sentences is collected from different resources for identifying euphemism or non-euphemism utterances. In this work, several approaches are focused to improve the euphemism classification: 1. A Combination of lexical n-gram features 2.Three Feature-weighting schemes 3.Deep learning classification algorithms. In this paper, four machine learning (J48, Random Forest, Multinomial Naïve Bayes, and SVM) and three deep learning algorithms (Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory) are investigated with various combinations of features and feature weighting schemes to classify the sentences. According to our experiments, Convolutional Neural Network (CNN) achieves precision 95.43%, recall 95.06%, F-Score 95.25%, accuracy 95.26%, and Kappa 0.905 by using a combination of unigram and bigram features with TF-IDF feature weighting scheme in the classification of euphemism. These results of experiments show CNN with a strong combination of unigram and bigram features set with TF-IDF feature weighting scheme outperforms another six classification algorithms in detecting the euphemisms in our dataset.

scholarly journals Diagnosis and Classification of Alzheimer's Disease by Using a Convolution Neural Network Algorithm

2021 ◽  
Author(s):  
Mosleh Hmoud Al-Adhaileh
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Classification Systems ◽  
Detection Accuracy ◽  
Learning Approaches

Abstract Alzheimer's disease (AD) is a high-risk and atrophic neurological illness that slowly and gradually destroys brain cells (i.e. neurons). As the most common type of amentia, AD affects 60–65% of all people with amentia and poses major health dangers to middle-aged and elderly people. For classification of AD in the early stage, classification systems and computer-aided diagnostic techniques have been developed. Previously, machine learning approaches were applied to develop diagnostic systems by extracting features from neural images. Currently, deep learning approaches have been used in many real-time medical imaging applications. In this study, two deep neural network techniques, AlexNet and Restnet50, were applied for the classification and recognition of AD. The data used in this study to evaluate and test the proposed model included those from brain magnetic resonance imaging (MRI) images collected from the Kaggle website. A convolutional neural network (CNN) algorithm was applied to classify AD efficiently. CNNs were pre-trained using AlexNet and Restnet50 transfer learning models. The results of this experimentation showed that the proposed method is superior to the existing systems in terms of detection accuracy. The AlexNet model achieved outstanding performance based on five evaluation metrics (accuracy, F1 score, precision, sensitivity and specificity) for the brain MRI datasets. AlexNet displayed an accuracy of 94.53%, specificity of 98.21%, F1 score of 94.12% and sensitivity of 100%, outperforming Restnet50. The proposed method can help improve CAD methods for AD in medical investigations.

scholarly journals Data Mining Approach to Herbs Classification

2018 ◽  
Vol 12 (2) ◽  
pp. 570
Author(s):  
Adillah Dayana Ahmad Dali ◽  
Nurul Aswa Omar ◽  
Aida Mustapha
Keyword(s):  
Neural Network ◽  
Nearest Neighbors ◽  
Decision Table ◽  
Classification Algorithms ◽  
Image Capture ◽  
K Nearest Neighbors ◽  
Data Mining Approach ◽  
Tree Algorithms ◽  
Artificial Neural Network Ann

Herbs are one of the high-value products in Malaysia. The term ‘herbs’ has more than one definition. It is also demanding by multiple manifolds. Herbs are used in many sectors nowadays. The ability to identify variety herbs in the market is quite hard without the intervention of human experts. Unfortunately, human experts are prone to error. Herbs classification is able to assist human experts and at the same time minimizing the intervention. This research performs identification and classification of herbs based on image capture ad variety of classification algorithms such as an Artificial Neural Network (ANN), K-Nearest Neighbors (IBK), Decision Table (DT) and M5P Tree algorithms. The selected algorithms are implemented and evaluated to their relative performance and IBK is found to produce the highest quality outputs.

Ferroelectric Memristive Networks for Dimensionality Reduction: A Process for Effectively Classifying Cancer Datasets

2019 ◽  
Vol 201 (1) ◽  
pp. 126-141
Author(s):  
P. Michael Preetam Raj ◽  
V. Jeffry Louis ◽  
Sumit Kumar Chatterjee ◽  
Sayan Kanungo ◽  
Souvik Kundu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Power Dissipation ◽  
Principal Component ◽  
Ferroelectric Materials ◽  
Reduction Technique ◽  
Classification Systems ◽  
High Classification Accuracy ◽  
Neural Network Algorithm

In this work, a copper-doped (5%) zinc oxide (Cu:ZnO) ferroelectric materials-based memristor model was realized and it was employed to develop principal component analysis (PCA), a data dimension reduction technique. The developed PCA was utilized to efficaciously classify breast cancer datasets, which are considered as complex and big volumes of data. It was found that the controllable memristance variations were analogous to the weight modulations in the implemented neural network-based learning systems. Sanger’s rule was utilized to achieve unsupervised online learning in order to generate the principal components. On one side, the developed memristor-based PCA network was found to be effective to isolate distinct breast cancer classes with a high classification accuracy of 97.77% and the error in the classification of malignant cases as benign of 0.529%, a significantly low value. On the other side, the power dissipation was found to be 0.27 µW, which suggests the proposed memristive network is suitable for low-power applications. Further, a comparison was established with other existing non-memristor and non-PCA-based data classification systems. Furthermore, the devised less complex equations to implement PCA on this memristive crossbar array could be employed to implement any neural network algorithm.

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