An intelligent approach for galaxies images classification

Congestive heart failure detection using random forest classifier

Automatic voice based disease detection method using one dimensional local binary pattern feature extraction network

This paper presents an automatic fruit recognition system for classifying and identifying fruit types. The work exploits the fruit shape and color, to identify each image feature. The proposed system includes three phases namely: pre-processing, feature extraction, and classification phases. In the pre-processing phase, fruit images are resized to 90 x 90 pixels in order to reduce their color index. In feature extraction phase, the proposed system uses scale invariant feature transform (SIFT) and shape and color features to generate a feature vector for each image in the dataset. For classification phase, the proposed model applies K-Nearest Neighborhood (K-NN) algorithm classification, and support vector machine (SVM) algorithm of different kinds of fruits. A series of experiments were carried out using the proposed model on a dataset of 178 fruit images. The results of carrying out these experiments demonstrate that the proposed approach is capable of automatically recognize the fruit name with a high degree of accuracy.

Emotion recognition by artificial intelligence (AI) is a challenging task. A wide variety of research has been done, which demonstrated the utility of audio, imagery, and electroencephalography (EEG) data for automatic emotion recognition. This paper presents a new automated emotion recognition framework, which utilizes electroencephalography (EEG) signals. The proposed method is lightweight, and it consists of four major phases, which include: a reprocessing phase, a feature extraction phase, a feature dimension reduction phase, and a classification phase. A discrete wavelet transforms (DWT) based noise reduction method, which is hereby named multi scale principal component analysis (MSPCA), is utilized during the pre-processing phase, where a Symlets-4 filter is utilized for noise reduction. A tunable Q wavelet transform (TQWT) is utilized as feature extractor. Six different statistical methods are used for dimension reduction. In the classification step, rotation forest ensemble (RFE) classifier is utilized with different classification algorithms such as k-Nearest Neighbor (k-NN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and four different types of the decision tree (DT) algorithms. The proposed framework achieves over 93 % classification accuracy with RFE + SVM. The results clearly show that the proposed TQWT and RFE based emotion recognition framework is an effective approach for emotion recognition using EEG signals.

Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.

We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical methods in Astronomy. The methods are illustrated using different subsets from the ESO-LV catalogue, for which both machine parameters and human classification are available. The main methods we explore are: (i) Principal Component Analysis (PCA) which tells how independent and informative the input parameters are. (ii) Encoder Neural Network which allows us to find both linear (PCA-like) and non-linear combinations of the input, illustrating an example of unsupervised ANN. (iii) Supervised ANN (using the Backpropagation or Quasi-Newton algorithms) based on a training set for which the human classification is known. Here the output for previously unclassified galaxies can be interpreted as either a continuous (analog) output (e.g. $T$-type) or a Bayesian {\it a posteriori} probability for each class. Although the ESO-LV parameters are sub-optimal, the success of the ANN in reproducing the human classification is 2 $T$-type units, similar to the degree of agreement between two human experts who classify the same galaxy images on plate material. We also examine the aspects of ANN configurations, reproducibility, scaling of input parameters and redshift information.

One of the most harmful viruses is Tomato yellow leaf curl virus (TYLCV), which is widespread over the world with tomato yellow leaf curl disease (TYLCD). It causes some symptoms to tomato leaf such as upward curling and yellowing. This paper introduces an efficient approach to detect and identify infected tomato leaves with these two viruses. The proposed approach consists of four main phases, namely pre-processing, image segmentation, feature extraction, and classification phases. Each input image is segmented and descriptor created for each segment. Some geometric measurements are employed to identify an optimal feature subset. Support vector machine (SVM) algorithm with different kernel functions is used for classification. The datasets of a total of 200 infected tomato leaf images with TSWV and TYLCV were used for both training and testing phase. N-fold cross-validation technique is used to evaluate the performance of the presented approach. Experimental results showed that the proposed classification approach obtained accuracy of 90 % in average and 92 % based on the quadratic kernel function.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

This paper presents a bio-inspired optimized classification approach for assessing water quality. As fish liver histopathology is a good biomarker for detecting water pollution, the proposed classification approach uses fish liver microscopic images in order to detect water pollution and determine water quality. The proposed approach includes three phases; preprocessing, feature extraction, and classification phases. Color histogram and Gabor wavelet transform have been utilized for feature extraction phase. The Machine Learning (ML) Support Vector Machines (SVMs) classification algorithm has been employed, along with the bio-inspired Gray Wolf Optimization (GWO) algorithm for optimizing SVMs parameters, in order to classify water pollution degree. Experimental results showed that the average accuracy achieved by the proposed GWO-SVMs classification approach exceeded 95% considering a variety of water pollutants.

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

From the Publisher: Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster method of developing systems that classify seismic events as being earthquakes or underground explosions. Farid Dowla and Leah Rogers have developed a number of ANN applications for researchers and students in hydrology and seismology. This book, complete with exercises and ANN algorithms, illustrates how ANNs can be used in solving problems in environmental engineering and the geosciences, and provides the necessary tools to get started using these elegant and efficient new techniques. Following the development of four primary ANN algorithms (backpropagation, self-organizing, radial basis functions, and hopfield networks), and a discussion of important issues in ANN formulation (generalization properties, computer generation of training sets, causes of slow training, feature extraction and preprocessing, and performance evaluation), readers are guided through a series of straightforward yet complex illustrative problems. These include groundwater remediation management, seismic discrimination between earthquakes and underground explosions, automated monitoring for acoustic and seismic sensor data, estimation of seismic sources, geospatial estimation, lithologic classification from geophysical logging, earthquake forecasting, and climate change. Each chapter contains detailed exercises often drawn from field data that use one or more of the four primary ANN algorithms presented.

The computational cost of training artificial neural network (ANN) algorithms limits the use of large systems capable of processing complex problems. Implementing ANNs on a parallel or distributed platform to improve performance is therefore desirable. This work illustrates a method to predict and evaluate the performance of distributed ANN algorithms by analyzing the performance of the comparatively simple mathematical operations, which are used to construct the ANN. The ANN algorithms are divided into simple components: matrix and vector multiplication, matrix processed through a function, competition in a matrix. These basic operational parts are examined individually and it is demonstrated that the computation processes of distributed neural networks can be derived from the composition of these basic operations. Three popular network architectures are examined: multi-layer perceptrons with back-propagation learning, self-organizing map, and radial basis functions network.

This study analyzed consumer intention to repurchase a smartphone, using an analysis of previous research and suggesting this as a research model. To this end, factors influencing “intention to repurchase” the smartphone were derived as social influence, consumer satisfaction, emotional loyalty, and habit. In addition, statistical analysis was conducted to investigate consumer repurchase intentions and the causal relationships. It is also possible to investigate whether consumer habits are linked to repurchases by analyzing their psychological inclinations; therefore, in this study, 390 people who repurchased a smartphone over the past two years were surveyed, and data were collected. This study analyzed the causal relationships among the factors using SPSS 24.0. Moreover, the causal relationship analysis was enhanced using the artificial neural network (ANN) algorithm. With multiple regression analysis and the ANN algorithm, consumer satisfaction (0.71), emotional loyalty (0.108), and social influence (0.062) were determined to affect the intention to repurchase. This means that the ANN algorithm can be used over multiple regression analysis and improve the results of the analysis. In addition, this study also provided practitioners with a way to improve their understanding of consumer behavior intention to repurchase smartphones.

“Melanoma is a serious form of skin cancer that begins in cells known as melanocytes and more dangerous due to its spreading ability to other organs more rapidly if it is not treated at an early stage”. This paper aims to propose a Melanoma detection methodology that includes four major phases: “(i) pre-processing (ii) segmentation (iii) the proposed feature extraction and (iv) classification”. Initially, pre-processing is performed, where the input image is subjected to processing like resizing and edge smoothening. Subsequently, segmentation is carried out by the Otsu thresholding process. In the feature extraction phase, the proposed Higher-Order Standardized Moment Induced-Local Binary Patterns (HOSMI-LBP)-based features are extracted. These features are then subjected to a classification process for classifying the disease. For this, it is planned to use a hybrid classification framework, where the Convolutional Neural Network (CNN) and the Neural Network (NN) are deployed. Two-phase of classification gets processed: the extracted features are subjected to NN; the input image is directly classified using an optimized CNN framework. Finally, the classified outputs from NN and optimized CNN are averaged and the final output is considered as detected output. Particularly, the weight and initial rate of CNN is optimized using the proposed algorithm known as the Sea Lion Integrated Grey Wolf Algorithm (SLI-GWO) method that hybrid the concepts of both Sea Lion Optimization (SLnO) and Grey Wolf Optimization (GWO) algorithm. At last, the proposed work performance is computed with traditional systems in terms of various measures.

This paper presents an algorithm for reducing speckle noise from optical coherence tomography (OCT) images using an artificial neural network (ANN) algorithm. The noise is modeled using Rayleigh distribution with a noise parameter, sigma, estimated by the ANN. The input to the ANN is a set of intensity and wavelet features computed from the image to be processed, and the output is an estimated sigma value. This is then used along with a numerical method to solve the inverse Rayleigh function to reduce the noise in the image. The algorithm is tested successfully on OCT images of Drosophila larvae. It is demonstrated that the signal-to-noise ratio and the contrast-to-noise ratio of the processed images are increased by the application of the ANN algorithm in comparison with the respective values of the original images.