Single-Channel Region-Based Speller for Controlling Home Appliances

The brain-computer interface (BCI) system uses electroencephalography (EEG) signals for correspondence between the human and the outside world. This BCI communication system does not require any muscle action; hence, it can be controlled with the help of brain activities only. Therefore, this kind of system is helpful for patients, who are completely paralyzed or suffering from diseases like ALS (Amyotrophic Lateral Sclerosis), and spinal cord injury, etc., but having a normal functioning brain. A region-based P300 speller system for controlling home electronic appliances is proposed in this article. With the help of the proposed system, users can control and use appliances like an electronic door, fan, light, system, etc., without carrying out any physical movement. The experiments are conducted for five, ten, and fifteen trails for each subject. Among all classifiers, the ANN classifier provides the best off-line experiment accuracy of the order of 80% for fifteen flashes. Moreover, for the control translation, the Arduino module is also designed which is low cost and low power-based and physically controlled a device.

Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection

With the progression of bioinformatics, applications of GE profiles on cancer diagnosis along with classification have become an intriguing subject in the bioinformatics field. It holds numerous genes with few samples that make it arduous to examine and process. A novel strategy aimed at the classification of GE dataset as well as clustering-centered feature selection is proposed in the paper. The proposed technique first preprocesses the dataset using normalization, and later, feature selection was accomplished with the assistance of feature clustering support vector machine (FCSVM). It has two phases, gene clustering and gene representation. To make the chose top-positioned features worthy for classification, feature reduction is performed by utilizing SVM-recursive feature elimination (SVM-RFE) algorithm. Finally, the feature-reduced data set was classified using artificial neural network (ANN) classifier. When compared with some recent swarm intelligence feature reduction approach, FCSVM-ANN showed an elegant performance.

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN-classifier. A method of providing the data into the classifier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.

A Novel Approach of Audio Based Feature Optimisation for Bird Classification

Bird classification using audio data can be beneficial in assisting ornithologists, bird watchers and environmentalists. However, due to the complex environment in the jungles, it is difficult to identify birds by visual inspection. Hence, identification via acoustical means may be a better option in such an environment. This study aims to classify endemic Bornean birds using their sounds. Thirty-five (35) acoustic features have been extracted from the pre-recorded soundtracks of birds. In this paper, a novel approach for selecting an optimum number of features using Linear Discriminant Analysis (LDA) has been proposed to give better classification accuracy. It is found that using a Nearest Centroid (NC) technique with LDA produces the optimum classification results of bird sounds at 96.7% accuracy with reduced computational power. The low computational complexity is an added advantage for handheld portable devices with minimal computing power, which can be used in birdwatching expeditions. Comparison results have been provided with and without LDA using NC and Artificial Neural Network (ANN) classifiers. It has been demonstrated that both classifiers with LDA outperform those without LDA. Maximum accuracies for both NC and ANN with LDA, with NC and the ANN classifiers requiring 7 and 10 LDAs to achieve the optimum accuracy, respectively, are 96.7%. However, ANN classifier with LDA is more computationally complex. Hence, this is significant as the simpler NC classifier with LDA, which does not require expensive processing power, may be used on the portable and affordable device for bird classification purposes.

Neural Networks Oblige Diagnosis of the Ischemic CVA’s by MRI

In present time the technological & computing evolution are promoted for new opportunities that will help to improve the standard of life between the new medical accomplishments, in particular and the standard of diagnostic evaluations. MRI is one of the imaging equipment for the diagnosis which has become more beneficial for technological development, because of this and due to the standard of a diagnosis manufacturer, that is one of the most engaging apparatus in the clinical application. The attentiveness in that pathology & in the general the encephalon picture analysis as the preventive diagnosis. The present research paper suggests the evaluation of the ability of ANN for the automatic identification of an ICVA by tissue images density obtained by MRI. In this examination the diagnosis and their medical reports were used to train the ANN classifier which extracted features from the given images. In this stage the ANN significantly contributes to the ICVA of MRI diagnosis aid, so since the test occurrence automatic identification of ischemic lesions that has been performed with the accuracy results that will be false positive and false negative.

Melanoma Detection from Lesion Images using Optimized Features Selected by Metaheuristic Algorithms

This paper deals with a simple but efficient method for detection of deadly malignant melanoma with optimized hand-crafted feature sets selected by three alternative metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Simulated Annealing (SA). Total 1898 number of features relating to lesion shapes, colors and textures are extracted from each of the 170 non-dermoscopy camera images of the popular MED-NODE dataset. This large feature set is then optimized and the number of features is reduced to up-to the range of single digit using metaheuristic algorithms as feature selector. Two well-known supervised classifiers, i.e. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are used to classify malignant and benign lesions. The best classification accuracy result found by this method is 87.69% with only 7 features selected by PSO using ANN classifier which is far better than the results found in the literature so far.

Artificial Neural Network-Based Method for Real-Time Estimation of Compaction Quality of Hot Asphalt Mixes

With the advancement of intelligent compaction technology, real-time quality control has been widely investigated on the subgrade, while it is insufficient on asphalt pavement. This paper aims to estimate the real-time compaction quality of hot mix asphalt (HMA) using an artificial neural network (ANN) classifier. A field experiment of HMA compaction was designed. The vibration patterns of the drum were identified by using the ANN classifier and classified based on the compaction levels. The vibration signals were collected and the degree of compaction was measured in the field experiment. The collected signals were processed and the features of vibration patterns were extracted. The processed signals were tagged with their corresponding compaction level to form the sample dataset to train the ANN models. Four ANN models with different hidden layer setups were considered to investigate the effect of hidden layer structure on performance. To test the performance of the ANN classifier, the predictions made by ANN were compared with the measuring results from a non-nuclear density gauge (NNDG). The testing results show that the ANN classifier has good performance and huge potential for estimating the compaction quality of HMA in real-time.

A Hybrid Approach for Automated Plant Leaf Recognition Using Hybrid Texture Features and Machine Learning-Based Classifiers

Automated plant recognition performs a significant role in various applications used by environmental experts, chemists, and botany experts. Humans can recognize plants manually, but it is a prolonged and low-efficiency process. This paper introduces an automated system for recognizing plant species based on leaf images. A hybrid texture and colour-based feature extraction method was applied on digital leaf images to produce robust feature, and a further classification model was developed. A combination of machine learning methods, such as SVM (support vector machine), KNN (k-nearest neighbours), and ANN (artificial neural network), was applied on dataset for plant classification. This dataset contains 32 types of leaves. The outcomes of this work proved that success rate of plant recognition can be enhanced up to 94% with ANN classifier when both shape and colour features are utilized. Automatic recognition of plants is useful for medicine, foodstuff, and reduction of chemical wastage during crop spraying. It is also useful for identification and preservation of species.