scholarly journals An Optimized Recursive General Regression Neural Network Oracle for the Prediction and Diagnosis of Diabetes

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Dana Bani-Hani ◽  
Pruthak Patel ◽  
Tasneem Alshaikh
Keyword(s):  
Neural Network ◽  
Performance Metrics ◽  
Prediction Models ◽  
Probabilistic Neural Network ◽  
General Regression Neural Network ◽  
Medical Decision ◽  
Support Vector ◽  
Pima Indians ◽  
K Nearest Neighbor ◽  
General Regression

Diabetes is a serious, chronic disease that has been seeing a rise in the number of cases and prevalence over the past few decades. It can lead to serious complications and can increase the overall risk of dying prematurely. Data-oriented prediction models have become effective tools that help medical decision-making and diagnoses in which the use of machine learning in medicine has increased substantially. This research introduces the Recursive General Regression Neural Network Oracle (RGRNN Oracle) and is applied on the Pima Indians Diabetes dataset for the prediction and diagnosis of diabetes. The R-GRNN Oracle (Bani-Hani, 2017) is an enhancement to the GRNN Oracle developed by Masters et al. in 1998, in which the recursive model is created of two oracles: one within the other. Several classifiers, along with the R-GRNN Oracle and the GRNN Oracle, are applied to the dataset, they are: Support Vector Machine (SVM), Multilayer Perceptron (MLP), Probabilistic Neural Network (PNN), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), and Random Forest (RF). Genetic Algorithm (GA) was used for feature selection as well as the hyperparameter optimization of SVM and MLP, and Grid Search (GS) was used to optimize the hyperparameters of KNN and RF. The performance metrics accuracy, AUC, sensitivity, and specificity were recorded for each classifier.

scholarly journals A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1692 ◽  
Author(s):  
Iván Silva ◽  
José Eugenio Naranjo
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Performance Metrics ◽  
Prediction Models ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
The Other ◽  
Support Vector ◽  
Classification Models ◽  
K Nearest Neighbor

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

Neural Network Performance Comparison in Infant Pain Expression Classifications

2013 ◽  
Vol 475-476 ◽  
pp. 1104-1109
Author(s):  
Muhammad Naufal Mansor ◽  
Mohd Nazri Rejab
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Probabilistic Neural Network ◽  
Performance Comparison ◽  
General Regression Neural Network ◽  
Pain Expression ◽  
Different Types ◽  
Infant Pain ◽  
Value Decomposition ◽  
General Regression

Infant pain is a non-stationary made by infants in response to certain situations. This infant facial expression can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of features in infant pain classification. Fast Fourier Transform (FFT), and Singular value Decomposition (SVD) features are computed at different classifier. Two different case studies such as normal and pain are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant pain. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant pain.

scholarly journals Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3670 ◽  
Author(s):  
Krzysztof Rzecki ◽  
Tomasz Sośnicki ◽  
Mateusz Baran ◽  
Michał Niedźwiecki ◽  
Małgorzata Król ◽  
...  
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Computational Intelligence ◽  
Probabilistic Neural Network ◽  
Spectral Lines ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
K Nearest Neighbor ◽  
Computational Intelligence Methods

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

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 Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Neha Sharma ◽  
Hari Om
Keyword(s):  
Neural Network ◽  
Oral Cancer ◽  
Early Detection ◽  
Clinical Symptoms ◽  
Probabilistic Neural Network ◽  
Cost Effective ◽  
General Regression Neural Network ◽  
Oral Cancers ◽  
General Regression ◽  
Early Detection And Prevention

In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

Selector: PSO as Model Selector for Dual-Stage Diabetes Network

2018 ◽  
Vol 29 (1) ◽  
pp. 475-484 ◽  
Author(s):  
Ramalingaswamy Cheruku ◽  
Damodar Reddy Edla
Keyword(s):  
Neural Network ◽  
Predictive Accuracy ◽  
State Of The Art ◽  
Probabilistic Neural Network ◽  
Support Vector ◽  
Pima Indians ◽  
Second Stage ◽  
Dataset Size ◽  
Dual Stage ◽  
Hidden Layer

Abstract Diabetes is a chronic disease caused by insulin deficiency, and it should be detected in the early stages for effective treatment. In this paper, the Diabetes-Network (Dia-Net) is proposed to increase diabetes predictive accuracy. The proposed Dia-Net is a dual-stage network. It combines both optimized probabilistic neural network (OPNN) and optimized radial basis function neural network (ORBFNN) in the first stage. Hence, Dia-Net possesses the advantages of both the models. In the second stage, the linear support vector machine is used. As the dataset size increases, both RBFNN and PNN perform better, but both suffers from complexity and computational problems. To address these problems, in this paper, particle swarm optimization-based clustering is employed for discovering centers in high-dense regions. This reduces the size of the hidden layer of both RBFNN and PNNs. Experiments are carried out on the Pima Indians Diabetes dataset. The Experimental results showed that the proposed Dia-Net model outperformed individual as well as state-of-the-art models.

Application of Least-Squares Support Vector Regression with PSO for CPU Performance Forecasting

2012 ◽  
Vol 630 ◽  
pp. 366-371 ◽  
Author(s):  
Kuo Ping Lin
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Support Vector Regression ◽  
Performance Prediction ◽  
Pso Algorithm ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Linear Kernel ◽  
Performance Forecasting ◽  
General Regression

The success of CPU performance prediction will make many benefits. This study adopts the least-squares support vector regression (LS-SVR) with particle swarm optimization (PSO) algorithm to improver accuracy of CPU performance prediction. LS-SVR with PSO, support vector regression (SVR) with PSO, general regression neural network (GRNN), radial basis neural network (RBNN), and linear regression are employed for CPU performance prediction. Empirical results indicate that the LS-SVR (Linear kernel) with PSO has better performance in terms of forecasting accuracy than the other methods. Therefore, the LS-SVR (Linear kernel) with PSO model can efficiently provide credible CPU performance estimated value.

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