Automated Identification of Optically Sensed Aphid (Homoptera: Aphidae) Wingbeat Waveforms

Partial discharge (PD) classification in power cable accessories and high voltage equipment in general is essential in evaluating the severity of the damage in the insulation. In this article, the PD classification was realised as a two-fold process. Firstly, measurements taken from a high-frequency current transformer (HFCT) sensor were represented as features by means of a transformation to the classifier and secondly, the probabilistic neural network (PNN) classifier itself was capable of effectively recognising features coming from different types of discharges. The feature that was used as a fingerprint for PD characterisation was extracted from the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through the wavelet packets transformation. The PNN classifier was used to classify the PDs and assess the suitability of this feature vector in PD classification. Four types of artificial PDs were created in a high voltage laboratory, namely corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air, at different applied voltages, and were used to train the PNN algorithm. The results obtained here (97.49, 91.9, 100 and 99.8% for the corona, the floating, the internal and the surface discharges, respectively) are very encouraging for the use of PNN in PD classification with this particular feature vector. This article suggests a feature extraction and classification algorithm for PD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, and achieved very high levels of classification.

In this paper, a synthesized algorithm for flaw classification in ultrasonic guided waves signal is presented, in which Wavelet Transform is utilized in the process of noise suppression and envelop extraction, and the Probabilistic Neural Network (PNN) is introduced for flaw classification of the ultrasonic testing signal. Besides, in the process of feature extraction, the necessity of attenuation correction and feature selection of ultrasonic signal is discussed. The comparison of the performances of PNN and BP classifier is made in the last chapter, which demonstrates that the performance of flaw classification is significantly improved by the synthesized algorithm presented in this paper.

Neural Network (NN) system is an information-processing that has characteristics similar to the neural network in living beings. A model of Neural Network is used for classification is Probabilistic Neural Network (PNN). PNN structured by four layers, the input layer, layer pattern, the summation layer and output layer. One of classification problems that can be solved by PNN is a classification of Diabetes Mellitus’s status. Diabetes mellitus is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin produced. To facilitate the classification of diabetes mellitus, it is used a software-based Graphical User Interface (GUI) of Matlab to build a software of PNN. GUI that is formed can do PNN classification and predict the status of one’s Diabetes Mellitus. PNN structure that is formed resulting the highest accuracy 0.9143548 on the training process and 0.919512 on the testing process obtained by the percentage of training data than testing data by 90%:10% with holdout accuracy evaluation method, and a smoothing value of 1. This classification resulting 23 patients were classified as negative diabetes and 18 patients were classified as positive diabetes. Keywords : Neural Network, Probabilistics Neural Network, diabetes mellitus, GUI, holdout, smoothing parameter.

Brain tumour classification using conventional techniques involving human intervention is prone to errors. Errors in feature extraction and/or classification may turn out to be fatal. Hence focus has shifted on automated techniques for the feature extraction and classification of brain tumour images. In the present work, a mechanism for the same has been proposed involving Artificial Neural Networks (ANN). Three categories have been incorporated in the present work, viz. normal brain, brain with benign tumour and brain with malignant tumour. In the present case, MRI images of the brain have been used to train an Artificial Neural Network which subsequently predicts the category of some new MRI data. The key challenge in designing such a system is attaining high accuracy of classification. This can be achieved by accurate feature extraction mechanism and then designing an appropriate Neural Network for training and testing. At the outset, segmentation has been employed to separate the region of the tumour (if any). Later the Discrete Wavelet Transform (DWT) is used as a preprocessing tool. The DWT helps in removing the non linearities of the image signal and smoothen it out. Since images exhibit abrupt changes in pixel values, conventional Fourier methods like the Fourier Transform prove to be unfit for image analysis. Subsequently, features like contrast, correlation, energy, homogeneity, mean, standard deviation, entropy, RMS value, variance, smoothness, kurtosis and skewness have been evaluated. Prior to training the Neural Network using the feature values, Principal Component Analysis (PCA) has been employed to find trends in the feature values. A Probabilistic Neural Network (PNN) has been designed and it has been trained using the feature values. I has been found that brain tumour MRI image are classified into normal, benign and malignant category using PNN classifier with an accuracy of 97% for the used data set which can be attributed to the efficacy of the proposed method. Key Words: Brain Tumor Classification, Machine Learning, Feature Extraction, Neural Networks, Confusion Matrix, Classification Accuracy.

Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait

In this paper, feature subset selection from river-ice image samples for classification of river-ice types is proposed using extreme learning machine (ELM) and differential evolution feature selection (DEFS) technique called extreme learning machine-based differential evolution feature selection (ELM-DEFS). Although DEFS is a good feature selection technique, the feature weights are selected by cross over and mutation which may be trapped. So, ELM is integrated with DEFS to overcome the difficulties of selecting the weights. Feature subset selection plays the most important role to classify the river-ice types. First-and second-order statistical texture features are extracted from real river-ice images. All the original features extracted from feature extraction methods do not help in recognition of river-ice types, so it is essential to reduce the feature subset to improve classification accuracy. In this paper, experiments are conducted on real river-ice image samples with probabilistic neural network (PNN) classifier which classifies different ice types. Performances of ELM-DEFS are compared with genetic algorithm (GA) and DEFS by means of performance evaluation metrics. Experimental results show that ELM-DEFS convergence is better than both DEFS and GA in selecting top-most feature subset; ELM-DEFS-based PNN (ELM-DEFS-PNN) classifier exhibits higher performance with an accuracy of 93% which is approximately 1% higher than GA-based PNN (GA-PNN) and DEFSbased PNN (DEFS-PNN) classification.

Coronary Artery Disease (CAD) is one of the hazardous heart disease which results in angina, Myocardial Infarction (MI) and Sudden Cardiac Death (SCD). CAD is a cardiac disorder in which a plague develops in the interior wall of the arteries resulting in blockage of blood reaching to the heart muscles. Electrocardiogram (ECG) is the cardiac signal which represents cardiac depolarisation and repolarisation regulated at the surface of the chest. The minute variations in amplitude and duration in the ECG wave specifies different pathological conditions which are tedious to interpret visually. Hence computer aided diagnostic systems are used to monitor ECG signals. In the present work, automated diagnosis of CAD is done using Discrete Wavelet Transform (DWT) and nonlinear feature extraction techniques like; Multivariate Multi-scale Entropy (MMSE), Tsallis entropy and renyi entropies. The extracted features after DWT are ranked based on t-value and fed to K Nearest Neighbour (KNN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Decision Tree (DT) classifiers for automated classification of normal and CAD classes. This technique provided the highest accuracy of 98.67% using KNN classifier. Hence, the proposed system can aid clinicians in faster and accurate diagnosis of CAD and thereby provide sufficient time for proper treatment.

Drones are becoming more prevalent across a variety of industries, including recreation, engineering, agriculture, farming, search and rescue, surveillance, and security.Aside from their advantages, drones have numerous drawbacks that come along with their rising popularity.These drawbacks include privacy concerns, safety risks, monitoring at airport and military systems, and vulnerability to hacking.In recent years, there have been numerous incidents of unauthorized drone usage at airports, causing significant disruptions to airline operations.Because they can appear identical when viewed from a distance, differentiating between drones and birds can occasionally be difficult.This paper offers a probabilistic neural network (PNN) learning-based approach for the effective detection and identification of drones as a solution to this issue.The proposed approach involves preprocessing the visible imagery to enhance relevant features and remove noise.Gabor filtering is applied to extract Gabor features from the images, which capture both spatial and frequency information.These features, along with other relevant attributes, are then fed into PNN for classification.Smaller datasets have a positive impact on PNN performance.Initial results show that the PNN algorithm, in conjunction with Gabor characteristics, is effective for classifying drones and birds.The suggested method is able to identify and separate drones from birds as well as determine whether drones are present or absent in a given area.

The energy crisis has promoted the development of solar photovoltaic power generation systems, but during the operation of solar panels, there will be hidden troubles such as ground fault, line-to-line fault, open-circuit fault, short-circuits fault and the hot spots. This will cause serious obstacles to the power generation of photovoltaic systems. Therefore, the immediate diagnosis and elimination of the fault of the photovoltaic system is the guarantee for the stable operation of the photovoltaic system. To address these issues, this paper makes contribution in the following Three aspects: (1) Building a 4×3 PV array model based on the key points and model parameters extracted from PV array by using Matlab, an efficient feature vector of five dimensions is proposed as the input of the fault diagnosis model; (2) The probabilistic neural network (PNN) is proposed as the fault classification tools, and achieving a good classification effect by using the simulated data after normalization to classify; (3) Performing the field test and inputting the experimental data into PNN for classification, with an accuracy of 97%. Both the simulation and experimental results show that the PNN can achieve high accuracy classification, provide a more favorable premise basis for the intelligent classification of faults in photovoltaic arrays.

Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.

Surrounding rock squeezing deformation is a common and prominent hazard in tunnel engineering projects, which often induces the shield jamming disaster during the TBM tunneling process. Based on the 139 groups of historical squeezing deformation cases, this study developed a hybrid PCA-IWGO-PNN model for squeezing classification. According to the influencing factors and characteristics of squeezing deformation, the strength-stress ratio, tunnel burial depth, tunnel equivalent diameter, rock mass quality index, and support stiffness were selected to establish the prediction index system of squeezing level. Because the probabilistic neural network (PNN) requires that the input variables are independent, principal component analysis (PCA) was used to preprocess the original data to eliminate the correlation between prediction indexes and achieve dimensionality reduction. The spread coefficient was the critical hyper-parameter in the PNN, and the improved gray wolf optimization (IGWO) algorithm was used to realize its efficient automatic optimization. Then, the PNN model was applied to engineering practice. Only 1 of 20 test samples was misjudged, achieving the 95% prediction accuracy. Finally, the comparison analysis with the artificial neural network (ANN) model, support vector machine (SVM) model, and random forest (RF) model was conducted. Among them, the PNN model achieved the highest prediction accuracy, followed by the artificial neural network (85%), RF (85%), and SVM (80%). In addition, the PNN model had the fastest running speed, which only consumed 5.6350 s, while the running time of ANN, SVM, and RF was 8.8340, 6.2290, and 6.9260 s, respectively. The hybrid PCA-IWGO-PNN model developed in this research provides an effective method for surrounding rock squeezing classification, and it has superiorities in both prediction accuracy and running speed.

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

High-throughput genome sequencing projects continue to churn out enormous amounts of raw sequence data. However, most of this raw sequence data is unannotated and, hence, not very useful. Among the various approaches to decipher the function of a protein, one is to determine its localization. Experimental approaches for proteome annotation including determination of a protein's subcellular localizations are very costly and labor intensive. Besides the available experimental methods, in silico methods present alternative approaches to accomplish this task. Here, we present two machine learning approaches for prediction of the subcellular localization of a protein from the primary sequence information. Two machine learning algorithms, k Nearest Neighbor (k-NN) and Probabilistic Neural Network (PNN) were used to classify an unknown protein into one of the 11 subcellular localizations. The final prediction is made on the basis of a consensus of the predictions made by two algorithms and a probability is assigned to it. The results indicate that the primary sequence derived features like amino acid composition, sequence order and physicochemical properties can be used to assign subcellular localization with a fair degree of accuracy. Moreover, with the enhanced accuracy of our approach and the definition of a prediction domain, this method can be used for proteome annotation in a high throughput manner. SubCellProt is available at www.databases.niper.ac.in/SubCellProt.

In cloud classification from satellite imagery, temporal change in the images is one of the main factors that causes degradation in the classifier performance. In this paper, a novel temporal updating approach is developed for probabilistic neural network (PNN) classifiers that can be used to track temporal changes in a sequence of images. This is done by utilizing the temporal contextual information and adjusting the PNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the PNN updated up to the last frame while at the same time, a prediction using Markov chain models is also made based on the classification results of the previous frame. The results of both the old PNN and the predictor are then compared. Depending on the outcome, either a supervised or an unsupervised updating scheme is used to update the PNN classifier. Maximum likelihood (ML) criterion is adopted in both the training and updating schemes. The proposed scheme is examined on both a simulated data set and the Geostationary Operational Environmental Satellite (GOES) 8 satellite cloud imagery data. These results indicate the improvements in the classification accuracy when the proposed scheme is used.

Category search can be supported by methods that allow intelligent selection of potentially relevant images. This paper explores the use of a nearest neighbor network in the selection process. We created a prototype that visualizes the network of images. As in the nearest neighbor network the images are connected to similar images we assume that if an image is selected or deselected, the same action can be performed on its neighbors. This results in five possible actions: selecting an image, selecting an image with its nearest neighbors, deselecting an image, deselecting an image with its nearest neighbors and growing the selection with all the nearest neighbors. Using these actions four different interaction scenarios are defined which are evaluated using experiments. Our experiments show that the nearest neighbor network can have a positive effect on the interaction effort needed to select images, compared to the baseline of sequentially selecting images. The experiments further show that the different interaction scenarios can cater for different values of allowed interaction effort with respect to the requirements on precision and recall.