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Data transformation (normalization) is a method used in data preprocessing to scale the range of values in the data within a uniform scale to improve the quality of the data; as a result, the prediction accuracy is improved. However, some scholars have questioned the efficacy of data normalization, arguing that it can destroy the structure in the original (raw) data. To address these arguments, we...
Text-to-phoneme mapping is a very important preliminary step in any text-to-speech synthesis system. In this paper, we study the performances of the multilayer perceptron (MLP) neural network for the problem of text-to-phoneme mapping. Specifically, we study the influence of the input letter encoding in the conversion accuracy of such system. We show, that for large network complexities the orthogonal...
Online Peer-to-Peer (P2P) lending has achieved explosive development recently, which could be beneficial to both sides of individual lending. In this study, a data mining (DM) approach to predict the performance of P2P loan before funded is proposed. Using data from the Lending Club, we explore the characteristics of loan and its applicant and use random forest to do the feature selection in the modeling...
Brain machine interface (BMI) devices facilitate communication and control of computers using signals measured from within the brain of the operators. These signals are detected using electroencephalography (EEG) devices. Research in this field aims to enable victims of ‘locked-in syndrome’ as a result of amyotrophic lateral sclerosis, spinal injury, cerebral palsy, muscular dystrophies, or multiple...
A large-scale artificial neural network, a three-layer perceptron, is implemented using two phase-change memory (PCM) devices to encode the weight of each of 164,885 synapses. The PCM conductances are programmed using a crossbar-compatible pulse scheme, and the network is trained to recognize a 5000-example subset of the MNIST handwritten digit database, achieving 82.2% accuracy during training and...
Correct discrimination of essential tremor from Parkinson's tremor is a major problem in clinical neurology as minor differences in the tremor patterns are hard to distinguish. Mathematical analysis of tremor signals recorded non-invasively has been widely accepted for tremor differentiation. However, classification of tremor signals collected from electromyograph or accelerometer, based on time and...
Analog/Mixed-Signal (AMS) circuits present significant challenges to designers with the increase of design complexity and aggressive technology scaling. Design optimization techniques that account for process variation while presenting an accurate and fast design flow which can perform design optimization in reasonable time are still lacking. As a trade-off of the accuracy and speed, this paper presents...
During the last decades and recession of 2007–2009 witnessed many global financial crises. Consequently, this research represents a proactive study via introducing new modeling tool; in order to diagnose the financial distress and assess its probability of occurrence. The Neuro-Logit is a new approach for diagnosis, prediction and forecasting corporate financial distress. This tool acts as Logit (Logistic...
Architecture of neural network complex for forecasting and analysis of time series based on the neural network spectral analysis has been developed. Advantages of neural spectral analysis compared with the existing methods of singular spectral analysis have been shown. Approach to trend and other components forecasting of the time series has been proposed. Architecture improvement option has been...
Building's energy demand is influenced by many factors, such as: weather conditions, building structure and characteristics, energy consumption of components (lighting and HVAC systems), level of occupancy and user's behavior. As consequence of multi-variable impact on building's energy consumption, theoretical models based on first principles are not able to forecast actual energy demand of a generic...
Motorcycles produce sound signals with varying temporal and spectral properties under different working conditions. These sound patterns can be used as an important source of information for automatic diagnosis of faults in motorcycles. Fault localization is a process of identifying the exact source of failure from a set of observed fault indications. The work proposed in this paper demonstrates the...
Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated...
Research into deep learning has demonstrated performance competitive with humans on some visual tasks, however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus neuroevolution for deep learning is investigated in this paper. In particular,...
Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology...
Rainfall forecasting is one of the most imperative and demanding operational responsibilities carried out by meteorological services all over the world. The task is complicated since all decisions are to be taken in the visage of uncertainty. In this article, the traditional data pre-processing technique, moving average is coupled with Artificial Neural Network as MA - ANN to improve the prediction...
In this study the usability of routinely measured meteorological parameters to estimate the global solar radiation is investigated. The proposed models are in the form of polynomials. The parameters such as ratio of duration of sunshine to maximum sunshine hours, mean temperature and mean relative humidity are used. Different combinations of these parameter sets have been used in proposing the monthly...
Diagnosis is an important task in medical science because of its criticality, efficiency and accuracy in determining whether or not a patient has a particular disease. This shall further decide the most suitable line of treatment. There has been a large increase in the number of thyroid cases over the past few years. Since thyroid has a complex relation with metabolism and body weight, it is extremely...
In spite of wide use of projection-based features in handwritten character recognition of several languages, its implementation was somewhat scanty in Bangla handwritten character recognition. This paper introduces the usage of projection profile features in recognizing handwritten Bangla basic characters. Alongside it also demonstrates a qualitative and quantitative analysis to visualize the effect...
The wind turbine power curve (WTPC) shows the relationship between the wind speed and power output of the turbine. Power curves, which are provided by the manufacturers, are mainly used in planning, forecasting, performance monitoring and control of the wind turbines. Hence an accurate WTPC model is very important in predictive control and monitoring. This paper presents comparative analysis of various...
As human brain activities, represented by EEG brainwave signals, are more confidential, sensitive, and hard to steal and replicate, they hold great promise to provide a far more secure biometric approach for user identification and authentication. In this study, we present an EEG-based biometric security framework. Specifically, we propose to reduce the noise level through ensemble averaging and low-pass...
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