A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

Download Full-text

A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8040160 ◽

2019 ◽

Vol 8 (4) ◽

pp. 160 ◽

Cited By ~ 11

Author(s):

Bingxin Liu ◽

Ying Li ◽

Guannan Li ◽

Anling Liu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector ◽

Oil Film ◽

One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

Download Full-text

Structured Pyramidal Neural Networks

International Journal of Neural Systems ◽

10.1142/s0129065717500216 ◽

2018 ◽

Vol 28 (05) ◽

pp. 1750021 ◽

Cited By ~ 3

Author(s):

Alessandra M. Soares ◽

Bruno J. T. Fernandes ◽

Carmelo J. A. Bastos-Filho

Keyword(s):

Neural Network ◽

Neural Networks ◽

Processing Time ◽

Receptive Fields ◽

Memory Capacity ◽

The Other ◽

Support Vector ◽

Proposed Model ◽

Novel Method ◽

Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

Download Full-text

Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

Sensors ◽

10.3390/s21072411 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2411

Author(s):

Davor Kolar ◽

Dragutin Lisjak ◽

Michał Pająk ◽

Mihael Gudlin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Classification Accuracy ◽

Bayesian Optimization ◽

Intelligent Fault Diagnosis ◽

Accelerometer Signal ◽

Rotary Machinery

Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

Download Full-text

Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Journal of Environmental Geography ◽

10.2478/jengeo-2020-0005 ◽

2020 ◽

Vol 13 (1-2) ◽

pp. 43-52

Author(s):

Boudewijn van Leeuwen ◽

Zalán Tobak ◽

Ferenc Kovács

Keyword(s):

Neural Network ◽

Machine Learning ◽

Land Use ◽

Land Cover ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Land Use Land Cover ◽

Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

Download Full-text

Automatic subtyping of individuals with Primary Progressive Aphasia

10.1101/2020.04.04.025593 ◽

2020 ◽

Author(s):

Charalambos Themistocleous ◽

Bronte Ficek ◽

Kimberly Webster ◽

Dirk-Bart den Ouden ◽

Argye E. Hillis ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Primary Progressive Aphasia ◽

Support Vector ◽

Progressive Aphasia ◽

Primary Progressive ◽

Machine Learning Model ◽

Machine Learning Approach ◽

Automated Machine Learning

AbstractBackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

Download Full-text

XCSR Learning from Compressed Data Acquired by Deep Neural Network

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2017.p0856 ◽

2017 ◽

Vol 21 (5) ◽

pp. 856-867 ◽

Cited By ~ 1

Author(s):

Kazuma Matsumoto ◽

Takato Tatsumi ◽

Hiroyuki Sato ◽

Tim Kovacs ◽

Keiki Takadama ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Learning Classifier System ◽

Rule Based ◽

Classifier System ◽

Learning Classifier ◽

The Neural Network ◽

Compressed Data

The correctness rate of classification of neural networks is improved by deep learning, which is machine learning of neural networks, and its accuracy is higher than the human brain in some fields. This paper proposes the hybrid system of the neural network and the Learning Classifier System (LCS). LCS is evolutionary rule-based machine learning using reinforcement learning. To increase the correctness rate of classification, we combine the neural network and the LCS. This paper conducted benchmark experiments to verify the proposed system. The experiment revealed that: 1) the correctness rate of classification of the proposed system is higher than the conventional LCS (XCSR) and normal neural network; and 2) the covering mechanism of XCSR raises the correctness rate of proposed system.

Download Full-text

Classification of non-chronic and chronic kidney disease using SVM neural networks

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.3.10669 ◽

2017 ◽

Vol 7 (1.3) ◽

pp. 191 ◽

Cited By ~ 3

Author(s):

Ravindra B.V ◽

N Sriraam ◽

M Geetha

Keyword(s):

Neural Networks ◽

Chronic Kidney Disease ◽

Kidney Disease ◽

Classification Accuracy ◽

The Body ◽

Support Vector ◽

Potential Candidate ◽

Kidney Dialysis ◽

Sensitivity Specificity

Chronic kidney disease (CKD) refers to the failure of the renal functionalities that leads to the deposition of wastes, electrolytes and other fluids in the body. It is very important to recognize the symptoms that cause the CKD and pathological blood and urine test indicates the key attributes. It is well fact that one has to undergo dialysis due to renal failure. The severity level of disease can be predicted as well as classified using appropriate computer aided quantitative tools. This specific study discusses the classification of chronic and non-chronic kidney disease NCKD using support vector machine (SVM) neural networks. The simulation study makes use of UCI repository CKD datasets with n=400. In order to train to train the attributes of kidney dialysis four cases were considered by including the nominal and numerical values. A radical basis kernel function was employed to train SVM. The performance of the proposed scheme is evaluated in terms of the sensitivity, specificity and classification accuracy. Results reveal an overall classification accuracy of 94.44% was obtained by combining 6 attributes. It can be concluded that the SVM based approach found to be a potential candidate for classification of CKD and NCKD.

Download Full-text

Automatic Subtyping of Individuals with Primary Progressive Aphasia

Journal of Alzheimer s Disease ◽

10.3233/jad-201101 ◽

2021 ◽

pp. 1-10

Author(s):

Charalambos Themistocleous ◽

Bronte Ficek ◽

Kimberly Webster ◽

Dirk-Bart den Ouden ◽

Argye E. Hillis ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Primary Progressive Aphasia ◽

Support Vector ◽

Progressive Aphasia ◽

Primary Progressive ◽

Machine Learning Model ◽

Machine Learning Approach ◽

Automated Machine Learning

Background: The classification of patients with primary progressive aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists. Objective: The aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA. Methods: In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as to expert clinicians’ classifications. Results: The DNN model outperformed the other machine learning models as well as expert clinicians’ classifications with 80% classification accuracy. Importantly, 90% of patients with nfvPPA and 95% of patients with lvPPA was identified correctly, providing reliable subtyping of these patients into their corresponding PPA variants. Conclusion: We show that the combined speech and language markers from connected speech productions can inform variant subtyping in patients with PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA.

Download Full-text

Hierarchy-guided Neural Networks for Species Classification

10.1101/2021.01.17.427006 ◽

2021 ◽

Cited By ~ 1

Author(s):

Mohannad Elhamod ◽

Kelly M. Diamond ◽

A. Murat Maga ◽

Yasin Bakis ◽

Henry L. Bart ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Classification Accuracy ◽

Training Data ◽

List Type ◽

Species Classification ◽

Phylogenetic Information ◽

Proposed Model ◽

Fish Distributions

AbstractFish species classification is an important task that is the foundation of many industrial, commercial, ecological, and scientific applications involving the study of fish distributions, dynamics, and evolution.While conventional approaches for this task use off-the-shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes.In this work, we propose infusing phylogenetic information into the model’s training to guide its structure and relationships among the extracted features. In our extensive experimental analyses, the proposed model, named Hierarchy-Guided Neural Network (HGNN), outperforms conventional ConvNet models in terms of classification accuracy under scarce training data conditions.We also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied.

Download Full-text

Identification of Baikal phytoplankton inferred from computer vision methods and machine learning

Limnology and Freshwater Biology ◽

10.31951/2658-3518-2021-a-3-1143 ◽

2021 ◽

pp. 1143-1146

Author(s):

A.V. Lysenko ◽

◽

M.S. Oznobikhin ◽

E.A. Kireev ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Computer Vision ◽

Object Recognition ◽

Light Microscope ◽

Optimal Size ◽

Optimal Parameters ◽

The Neural Network

Abstract. This study discusses the problem of phytoplankton classification using computer vision methods and convolutional neural networks. We created a system for automatic object recognition consisting of two parts: analysis and primary processing of phytoplankton images and development of the neural network based on the obtained information about the images. We developed software that can detect particular objects in images from a light microscope. We trained a convolutional neural network in transfer learning and determined optimal parameters of this neural network and the optimal size of using dataset. To increase accuracy for these groups of classes, we created three neural networks with the same structure. The obtained accuracy in the classification of Baikal phytoplankton by these neural networks was up to 80%.

Download Full-text