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Neural network technique has been recently preferred in textile sector for the prediction task because the traditional mathematical and statistical methods can be inadequate to derive complex relations within textile datasets. Meanwhile ensemble learning has become a popular machine learning approach in recent years due to the high prediction performance it provides. Therefore, this study proposes...
The handwritten digit recognition problem becomes one of the most famous problems in machine learning and computer vision applications. Many machine learning techniques have been employed to solve the handwritten digit recognition problem. This paper focuses on Neural Network (NN) approaches. The most three famous NN approaches are deep neural network (DNN), deep belief network (DBN) and convolutional...
The typical method of entering a password for user authentication is vulnerable to hacking; therefore, various security technologies using bio-signals, such as iris scan, electrocardiography, electromyography (EMG), and fingerprint recognition, are being developed. In this research, an authentication algorithm using an EMG signal is proposed to supplement the weakness of personal certification techniques...
In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been...
An algorithm that can predict the review rating of a potential business with only existing information about the location and business categories would be an invaluable tool in making investment decisions. Utilizing the Yelp business dataset, we built a model, that can do as such, by classifying whether a potential business belongs to a positively-reviewed class (star ratings greater than or equal...
The arrival times of buses are often hard to predict due to variation of real time traffic conditions, deployment schedules and traffic incidents. The provision of timely arrival time information is thus vital for passengers to minimize their waiting time and improve riders' confidence in the public transportation system, directly promoting more ridership. Multiple buses are commonly observed to arrive...
Huge amount of data in today's world are stored in the form of electronic documents. Text mining is the process of extracting the information out of those textual documents. Text classification is the process of classifying text documents into fixed number of predefined classes. The application of text classification includes spam filtering, email routing, sentiment analysis, language identification...
In the Industry 4.0 era, manufacturers can establish smart factories based on the industry wireless sensor network (IWSN). And data analysis plays a vital role to realize smart manufacturing. However, data collected in the real production through IWSN generally represent features as incomplete and imbalanced resulting in incorrect or biased analysis results. Therefore, a solution is proposed to resolve...
This paper aims to investigate the neural networking system. The signals to be studied have been taken from photonic sensors. For classification, a given signal is first transformed into different feature domains and then neural network is used to train the given dataset to form the network. Wavelet transform is used to extract the signal properties-skewness, kurtosis and entropy and Fourier Transform...
Understanding temporal expressions is the important foundation of many NLP tasks. However, the varied representations of temporal expressions is difficulty in analysis and understanding. To parsing expressions, an effective classification method of temporal expressions is significant. A temporal expression may belong to one or more classes, but the classification usually requires manual annotation...
Applying highly accurate neural networks to mobile devices encounters energy problems in battery-limited mobile environments. To resolve these problems, neuromorphic hardware solutions that enable event-driven operation have been proposed. In this work, we present a novel sparse neuromorphic system that implements an E-I Net algorithm to further improve energy efficiency. We introduce a neuron clock-gating...
An ultra-low power neural spike sorting technique for implantable, multi-channel neural implant is proposed. It involves spiking neural network (SNN) with binary weights as an energy and area efficient classifier, along with a suitable frontend for spike encoding of the recorded neuro-potential. The proposed scheme employs two step training to implement supervised learning for the classifier, in order...
Chronic kidney failure (chronic kidney disease ‘CKD’) is a serious disease that related to the gradual loss of kidney function. It is considered one of the health threats in the developing and undeveloped countries At early stages, few symptoms can be detected, where the CKD may not become obvious until significant kidney function impaired occur. CKD treatment focuses on reducing the kidney damage...
With the future development of substation, the research of power fault detection algorithm has very important theoretical significance and wide application prospects. In order to improve the recognition of power line fault detection, one modeling method based on sparse self-encoding neural network is proposed. The dB3 wavelet is used to decompose the fault signal, and then the sub-band energy is calculated...
This paper discusses the application and benefits of data mining techniques to construct prediction models in the field of corporate bankruptcy. It analyzes a dataset of 120 companies using different data mining techniques. Findings show that neural network is recommended as the best model to predict corporate bankruptcy. Findings also show that the proper use and selection of data mining techniques...
Today, In the content of road vehicles, intelligent systems and autonomous vehicles, one of the important problem that should be solved is Road Terrain Classification that improves driving safety and comfort passengers. There are many studies in this area that improved the accuracy of classification. An improved classification method using color feature extraction is proposed in this paper. Color...
Classification is one of the important tasks in Data Mining or Knowledge Discovery with prolific applications. Satisfactory classification depends on characteristics of the dataset too. Numerical and nominal attributes are commonly occurred in the dataset. However, classification performance may be aided by discretization of numerical attributes. At present, several discretization methods and numerous...
The objective of article is to describe a method for signal processing and decision making process of six-legged robots. An important characteristic of six-legged robot is its ability to maintain static stability while walking through terrain with obstacles. That is accomplished by evaluation of its leg position and body state and their adjustment realised by the decision making process. Control system...
In this paper, a hybrid structure-adaptive radial basis function-extreme learning machine (HSARBF-ELM) network classifier is presented. HSARBF-ELM consists of a structure-adaptive radial basis function (SARBF) network and an extreme learning machine (ELM) network of cascade, where the output of the SARBF network hidden layer is used as the input layer of the ELM network. In the HSARBF-ELM network...
In this Research Experience for Undergraduate (REU) project, we develop and implement deep neural network algorithms for change detection of synthetic aperture radar (SAR) images. Deep neural networks represent a powerful data processing methodology that integrates recent deep learning techniques on neural network computing frameworks to undercover underlying features and structures of observational...
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