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Sorting is an important step in processing and packing lines of pomegranate fruits. Currently pomegranates are sorted into quality categories manually. But manual sorting poses problems such as tediousness, low accuracy, subjectivity etc. Moreover, manual sorting is not recommended for export quality fruits. Hence a machine vision system is required in order to sort the pomegranate fruits. The present...
In this paper, the various technologies of data mining (DM) models for forecast of heart disease are discussed. Data mining plays an important role in building an intelligent model for medical systems to detect heart disease (HD) using data sets of the patients, which involves risk factor associated with heart disease. Medical practitioners can help the patients by predicting the heart disease before...
This article offers a general approach to developing methods of determining operation tolerances for the parameters' values of memristor-based artificial neural networks (ANNM), as a system that constitutes an united physical and informational object implemented by the hardware and software learning facilities. While looking for a solution to the issues of analysis and synthesis of this system's tolerances,...
Lung Cancer is a disease of infection in lung due to uncontrolled cell growth which affects its functionality. It is mostly incurable due to that early detection of Lung Cancer is important. Early detection and treatment may help for patient's survival. Normally, diagnosis of lung cancer includes chest X-ray, ECG, Citi scan, MRI etc. In cancer diagnosis Artificial Neural Network and Fuzzy Min-Max...
An Artificial Neural Network (ANN) is a statistical data modeling tool inspired by the functionality and the structure of the biological nervous system. An ANN consists of processing elements known as neurons that are interconnected to each other and work in unison to answer a particular problem. Neural networks can be used in places where detecting trends and extracting patterns are too complex to...
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automated based Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. In this paper the authors have employed...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
Fabric defect inspection is the pivotal part in the production of textile products. Since manual inspection is tedious and erroneous, automated fabric inspection has been topic of research for past years. Automation of fabric inspection involves two major aspects: defect detection and defect classification. We focused on classifying defects based on geometric features of defects. The features are...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
A research approach of crack detection of rotating shafts based on acoustic emission (AE) signals and machine learning is proposed in this paper. The relationship between crack intensity and domain features are investigated, and the features which could well indicate the crack condition are selected for modelling and crack prediction. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN)...
The growing interests in multi-way data analysis have made the tensor factorization and classification a crucial issue in machine learning for signal processing. Conventional neural network (NN) classifier is estimated from a set of input vectors. The multi-way data are unfolded as high-dimensional vectors for model training. The classification performance is constrained because the neighboring temporal...
Lending loans to borrowers is considered one of the main profit sources for banks and financial institutions. Thus, careful assessment and evaluation should be taken when deciding to grant credit to potential borrowers. With the rapid growth of credit industry and the massive volume of financial data, developing effective credit scoring models is very crucial. The literature in this area is very dense...
Medical diagnosis is an exciting are of research and many researchers have been working on the application of Artificial Intelligence techniques to develop disease recognition systems. They are analysing currently available information and also biochemical data collecting from clinical laboratories and experts for identifying pathological status of the patient. During the process of diagnosis, the...
In this paper, we propose a hybrid method for intrusion detection which is based on k-means, naive-bayes and back propagation neural network (KBB). Initially we apply k-means which is partition-based, unsupervised cluster analysis method. In the form of clusters, we attain the gathered data which can be easily processed and learned by any machine learning algorithm. These outcomes are provided to...
By virtue of recent developments in machine learning techniques, higher-level information can now to be extracted from big data. To analyze big data, efficient and smart representations of data achieved by using sufficiently fast algorithms, as well as highly accurate results, are important. In this paper, we focus on extracting multiple semantic relations using light-weight processing through the...
Fuzzy Grey Cognitive Map (FGCM) is an innovative soft computing technique mixing Fuzzy Cognitive Maps and Grey Systems Theory. FGCMs are supervised learning fuzzy-neural systems typically modeled with signed fuzzy grey weighted digraphs, generally involving feedbacks. It is hard to find an accurate mathematical model to describe this decision-making because it includes a high uncertainty and the factors...
Artificial neural networks have been investigated for many years as a technique for automated diagnosis of defects causing partial discharge (PD). While good levels of accuracy have been reported, disadvantages include the difficulty of explaining results, and the need to hand-craft appropriate features for standard two-layer networks. Recent advances in the design and training of deep neural networks,...
In machine learning, ensemble model is combining two or more models for obtaining the better prediction, accuracy and robustness as compared to individual model separately. Before getting ensemble model first we have to assign our training dataset into different models, after that we have to select the best model suited for our data sets. In this work we explored six machine learning parameter for...
Extreme learning machine (ELM) is an efficient learning algorithm which can be easily used with least human intervene. But when ELM is applied as multiclass classifier, the results of some classes are not satisfactory and it's hard to adjust the parameters for these classes without affecting other classes. To overcome these limitations, a novel method is proposed. In proposed approach, binary ELM...
This paper addresses the problem of recognizing handwritten numerals for Gujarati Language. Three methods are presented for feature extraction. One belongs to the spatial domain and other two belongs to the transform domain. In first technique, a new method has been proposed for spatial domain which is based on Freeman chain code. This method obtains the global direction by considering n × n neighbourhood...
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