scholarly journals Intelligent Neural Network Schemes for Multi-Class Classification

2019 ◽  
Vol 9 (19) ◽  
pp. 4036 ◽  
Author(s):  
You ◽  
Wu ◽  
Lee ◽  
Liu
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Reduction Techniques ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Dimensionality Reduction Techniques ◽  
Multi Class Classification

Multi-class classification is a very important technique in engineering applications, e.g., mechanical systems, mechanics and design innovations, applied materials in nanotechnologies, etc. A large amount of research is done for single-label classification where objects are associated with a single category. However, in many application domains, an object can belong to two or more categories, and multi-label classification is needed. Traditionally, statistical methods were used; recently, machine learning techniques, in particular neural networks, have been proposed to solve the multi-class classification problem. In this paper, we develop radial basis function (RBF)-based neural network schemes for single-label and multi-label classification, respectively. The number of hidden nodes and the parameters involved with the basis functions are determined automatically by applying an iterative self-constructing clustering algorithm to the given training dataset, and biases and weights are derived optimally by least squares. Dimensionality reduction techniques are adopted and integrated to help reduce the overfitting problem associated with the RBF networks. Experimental results from benchmark datasets are presented to show the effectiveness of the proposed schemes.

An Evolving System in the Text Classification Problem

2013 ◽  
pp. 467-486
Author(s):  
Elias Oliveira ◽  
Patrick Marques Ciarelli ◽  
Evandro Ottoni Teatini Salles
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Probabilistic Neural Network ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Learning ◽  
Fuzzy Neural ◽  
Evolving System ◽  
Series Of Experiments

Traditional machine learning techniques have been successful in yielding good results when the data are stable along the time horizon. However, in many cases, these techniques may be inefficient for data that are constantly expanding and changing over time. To address this problem, new learning techniques have been proposed in the literature. In this chapter, the authors discuss some improvements on their technique, called Evolving Probabilistic Neural Network (ePNN), and present the aspects of this recent learning paradigm. This technique is based on the Probabilistic Neural Networks. In this chapter the authors compare their technique against two other competitive techniques that can be found in the literature: Incremental Probabilistic Neural Network (IPNN) and Evolving Fuzzy Neural Network (EFuNN). To show the better performance of their technique, the authors present and discuss a series of experiments that demonstrate the efficiency of ePNN over both the IPNN and EFuNN approaches.

In-game behaviour analysis of football players using machine learning techniques based on player statistics

2020 ◽  
pp. 174795412095976
Author(s):  
Abraham García-Aliaga ◽  
Moisés Marquina ◽  
Javier Coterón ◽  
Asier Rodríguez-González ◽  
Sergio Luengo-Sánchez
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Football Players ◽  
Machine Learning Algorithm ◽  
Reduction Techniques ◽  
Learning Techniques ◽  
Dimensionality Reduction Techniques ◽  
Player Positions

The purpose of this research was to determine the on-field playing positions of a group of football players based on their technical-tactical behaviour using machine learning algorithms. Each player was characterized according to a set of 52 non-spatiotemporal descriptors including offensive, defensive and build-up variables that were computed from OPTA’s on-ball event records of the matches for 18 national leagues between the 2012 and 2019 seasons. To test whether positions could be identified from the statistical performance of the players, the dimensionality reduction techniques were used. To better understand the differences between the player positions, the most discriminatory variables for each group were obtained as a set of rules discovered by RIPPER, a machine learning algorithm. From the combination of both techniques, we obtained useful conclusions to enhance the performance of players and to identify positions on the field. The study demonstrates the suitability and potential of artificial intelligence to characterize players' positions according to their technical-tactical behaviour, providing valuable information to the professionals of this sport.

Relevance of Machine Learning Techniques and Various Protein Features in Protein Fold Classification: A Review

2019 ◽  
Vol 14 (8) ◽  
pp. 688-697 ◽  
Author(s):  
Komal Patil ◽  
Usha Chouhan
Keyword(s):  
Secondary Structure ◽  
Tertiary Structure ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Protein Fold ◽  
Learning Techniques ◽  
Benchmark Datasets ◽  
Scoring Matrix ◽  
Feature Attribute ◽  
Fold Prediction

Background: Protein fold prediction is a fundamental step in Structural Bioinformatics. The tertiary structure of a protein determines its function and to predict its tertiary structure, fold prediction serves an important role. Protein fold is simply the arrangement of the secondary structure elements relative to each other in space. A number of studies have been carried out till date by different research groups working worldwide in this field by using the combination of different benchmark datasets, different types of descriptors, features and classification techniques. Objective: In this study, we have tried to put all these contributions together, analyze their study and to compare different techniques used by them. Methods: Different features are derived from protein sequence, its secondary structure, different physicochemical properties of amino acids, domain composition, Position Specific Scoring Matrix, profile and threading techniques. Conclusion: Combination of these different features can improve classification accuracy to a large extent. With the help of this survey, one can know the most suitable feature/attribute set and classification technique for this multi-class protein fold classification problem.

scholarly journals Practical Application Using the Clustering Algorithm

2021 ◽  
Author(s):  
Yoosoo Oh ◽  
Seonghee Min
Keyword(s):  
Particulate Matter ◽  
Clustering Algorithm ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Cluster Center ◽  
Matter Distribution ◽  
Optimal Point ◽  
Learning Techniques ◽  
Dust Distribution ◽  
Particulate Matter Distribution

This chapter will survey the clustering algorithm that is unsupervised learning among data mining and machine learning techniques. The most popular clustering algorithm is the K-means clustering algorithm; It can represent a cluster of data. The K-means clustering algorithm is an essential factor in finding an appropriate K value for distributing the training dataset. It is common to find this value experimentally. Also, it can use the elbow method, which is a heuristic approach used in determining the number of clusters. One of the present clusterings applied studies is the particulate matter concentration clustering algorithm for particulate matter distribution estimation. This algorithm divides the area of the center that the fine dust distribution using K-means clustering. It then finds the coordinates of the optimal point according to the distribution of the particulate matter values. The training dataset is the latitude, longitude of the observatory, and PM10 value obtained from the AirKorea website provided by the Korea Environment Corporation. This study performed the K-means clustering algorithm to cluster feature datasets. Furthermore, it showed an experiment on the K values to represent the cluster better. It performed clustering by changing K values from 10 to 23. Then it generated 16 labels divided into 16 cities in Korea and compared them to the clustering result. Visualizing them on the actual map confirmed whether the clusters of each city were evenly bound. Moreover, it figures out the cluster center to find the observatory location representing particulate matter distribution.

scholarly journals Auto-Colorization of Historical Images Using Deep Convolutional Neural Networks

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2258
Author(s):  
Madhab Raj Joshi ◽  
Lewis Nkenyereye ◽  
Gyanendra Prasad Joshi ◽  
S. M. Riazul Islam ◽  
Mohammad Abdullah-Al-Wadud ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
User Study ◽  
Mean Squared Error ◽  
Color Image ◽  
Machine Learning Techniques ◽  
Global Features ◽  
Black And White ◽  
Historical Images ◽  
Learning Techniques

Enhancement of Cultural Heritage such as historical images is very crucial to safeguard the diversity of cultures. Automated colorization of black and white images has been subject to extensive research through computer vision and machine learning techniques. Our research addresses the problem of generating a plausible colored photograph of ancient, historically black, and white images of Nepal using deep learning techniques without direct human intervention. Motivated by the recent success of deep learning techniques in image processing, a feed-forward, deep Convolutional Neural Network (CNN) in combination with Inception- ResnetV2 is being trained by sets of sample images using back-propagation to recognize the pattern in RGB and grayscale values. The trained neural network is then used to predict two a* and b* chroma channels given grayscale, L channel of test images. CNN vividly colorizes images with the help of the fusion layer accounting for local features as well as global features. Two objective functions, namely, Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR), are employed for objective quality assessment between the estimated color image and its ground truth. The model is trained on the dataset created by ourselves with 1.2 K historical images comprised of old and ancient photographs of Nepal, each having 256 × 256 resolution. The loss i.e., MSE, PSNR, and accuracy of the model are found to be 6.08%, 34.65 dB, and 75.23%, respectively. Other than presenting the training results, the public acceptance or subjective validation of the generated images is assessed by means of a user study where the model shows 41.71% of naturalness while evaluating colorization results.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

Autism Spectrum Disorder Identification Using Polynomial Distribution based Convolutional Neural Network

2021 ◽  
Vol 19 (2) ◽  
pp. 19-30
Author(s):  
G. Nagarajan ◽  
Dr.A. Mahabub Basha ◽  
R. Poornima
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Chicken Swarm Optimization ◽  
Sensitivity Specificity ◽  
Polynomial Distribution

One main psychiatric disorder found in humans is ASD (Autistic Spectrum Disorder). The disease manifests in a mental disorder that restricts humans from communications, language, speech in terms of their individual abilities. Even though its cure is complex and literally impossible, its early detection is required for mitigating its intensity. ASD does not have a pre-defined age for affecting humans. A system for effectively predicting ASD based on MLTs (Machine Learning Techniques) is proposed in this work. Hybrid APMs (Autism Prediction Models) combining multiple techniques like RF (Random Forest), CART (Classification and Regression Trees), RF-ID3 (RF-Iterative Dichotomiser 3) perform well, but face issues in memory usage, execution times and inadequate feature selections. Taking these issues into account, this work overcomes these hurdles in this proposed work with a hybrid technique that combines MCSO (Modified Chicken Swarm Optimization) and PDCNN (Polynomial Distribution based Convolution Neural Network) algorithms for its objective. The proposed scheme’s experimental results prove its higher levels of accuracy, precision, sensitivity, specificity, FPRs (False Positive Rates) and lowered time complexity when compared to other methods.

scholarly journals A novel reconstruction framework for an imaging calorimeter for HL-LHC

2021 ◽  
Vol 251 ◽  
pp. 03013
Author(s):  
Leonardo Cristella ◽  
Keyword(s):  
Clustering Algorithm ◽  
Particle Identification ◽  
Machine Learning Techniques ◽  
Radiation Tolerance ◽  
Reconstruction Algorithms ◽  
High Luminosity ◽  
Silicon Sensors ◽  
Learning Techniques ◽  
Recent Developments ◽  
Speed And Accuracy

To sustain the harsher conditions of the high-luminosity LHC, the CMS collaboration is designing a novel endcap calorimeter system. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly-granular information in the readout to help mitigate the effects of pileup. In regions characterised by lower radiation levels, small scintillator tiles with individual on-tile SiPM readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigate pileup in the very dense environment of HL-LHC. The inputs to the framework are clusters of energy deposited in individual calorimeter layers. Clusters are formed by a density-based algorithm. Recent developments and tunes of the clustering algorithm will be presented. To help reduce the expected pressure on the computing resources in the HL-LHC era, the algorithms and their data structures are designed to be executed on GPUs. Preliminary results will be presented on decreases in clustering time when using GPUs versus CPUs. Ideas for machine-learning techniques to further improve the speed and accuracy of reconstruction algorithms will be presented.

scholarly journals Enhancing Classifier Accuracy in Ayurvedic Medicinal Plants using WO-DNN

2019 ◽  
Vol 9 (1) ◽  
pp. 6705-6714
Keyword(s):  
Neural Network ◽  
Medicinal Plants ◽  
Leaf Shape ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Plant Identification ◽  
Learning Techniques ◽  
Whale Optimization ◽  
Medical Plant

Identification of right medicinal plants that goes in to the formation of a medicine is significant in ayurvedic medicinal industry. This paper focuses around the automatic identification proof of therapeutic plants that are regularly utilized in Ayurveda. The fundamental highlights required to distinguish a medicinal plant is its leaf shape, color and texture. In this paper, we propose efficient accurate classifier for ayurvedic medical plant identification (EAC-AMP) utilizing using hybrid optimal machine learning techniques. In EAC-AMP, image corners detect first and top, bottom leaf edges are computed by the improved edge detection algorithm. After preprocessing, the segmentation can achieve using spider optimization neural network (SONN), which segments leaf regions from an image. The time and frequency domain features are computed by the symbolic accurate approximation (SAX); other features shape features, color features and tooth features are computed by the two-dimensional binary phase encoding (2DBPE). Finally, a whale optimization with deep neural network (DNN) classifier is used to characterize the type of plants. Accuracy in identification of any ayurvedic plant leaf is achieved by understanding and extracting the plant features. The main objective of the proposed EAC-AMP approach is to increase the accuracy of classifier. MATLAB experimental analysis showed better results such as accuracy, sensitivity and specificity.

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