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The aim of this paper is to present a new method to produce a Receiver Operating Characteristic (ROC) curve from a Probabilistic Neural Network (PNN). Traditionally, an ROC curve has been used widely to report the recognition system measurements. Two main problems arise when using the PNN. Firstly, the PNN outputs are always logical (zeros and one); secondly, a PNN is considered as a multi-class classifier,...
Deep Convolutional Neural networks (ConvNets) have achieved impressive results in several applications of computer vision and speech processing. With the availability of a large training set, it is common to find that the set contains useless samples (instances), either redundant or noisy. The process of removing these instances is called instance selection in the machine learning field. This paper...
While most embedded systems are designed for real-time applications, they suffer from resource constraints. Many techniques have been proposed for real-time task scheduling to reduce energy consumption. A combination of Dynamic Voltage Scaling (DVS) and feedback scheduling can be used to scale dynamically the frequency by adjusting the operating voltage, and improve the run-time reliability of embedded...
Given an image, we propose to use the appearance of people in the scene to estimate when the picture was taken. There are a wide variety of cues that can be used to address this problem. Most previous work has focused on low-level image features, such as color and vignetting. Recent work on image dating has used more semantic cues, such as the appearance of automobiles and buildings. We extend this...
The call center provides customer services to the customer of a company. Call center agents play an important role in such services. To ensure the quality of customer service, agent training and evaluation are essential. Usually, agents are monthly evaluated by their supervisor. Nevertheless, an objective evaluation standard is desired. Twenty three quantitative indicators for call center operations...
Electricity demand forecasting is a nonlinear and complex problem. It consists of three levels, including long-term forecast for new power plant planning, medium-term forecast for maintenance scheduling and inventory of fuel, and short-term forecast for daily operations. There are many statistical forecasting techniques applied to short term load forecasting, such as Stochastic Time series, Regression...
Attention Deficit Hyperactivity Disorder (ADHD) is a mental health disorder. People diagnosed with ADHD are often inattentive (have difficulty focusing on a task for a considerable period of time), overly impulsive (make rash decisions), and are hyperactive (moving excessively, often at inappropriate times). ADHD is often diagnosed through psychiatric assessments with additional input from physical/neurological...
Apparel classification encompasses the identification of an outfit in an image. The area has its applications in social media advertising, e-commerce and criminal law. In our work, we introduce a new method for shopping apparels online. This paper describes our approach to classify images using Convolutional Neural Networks. We concentrate mainly on two aspects of apparel classification: (1) Multiclass...
This paper presents a non-linear, data driven Adaptive Network based Fuzzy Inference System (ANFIS) modeling of a Two Tanks Hydraulic System (TTHS). The paper also addresses the design of a Type 1 Fuzzy Logic Controller optimized with Genetic Algorithms (GA). The controller was designed and tested in simulation with the obtained ANFIS model and validated in real-time with the actual TTHS. Obtained...
This work aims to solve the problem of musical instrument identification in monophonic audio samples. The instruments chosen for this work were piano, flute, violin, drums and guitar. The audio data were sampled into frames of fixed size & then MFCC and few other TIMBRAL features were extracted from them. These features were used for training and testing the network. But instead of selecting one...
Fault prediction techniques aim to predict faulty module in order to reduce the effort to be applied in later phase of software development. Majority of the approaches available in literature for fault prediction are based on regression analysis and neural network techniques. It is observed that numerous software metrics are also being used as input for fault prediction. In this paper, a cost evaluation...
In this paper, we introduce a new public image dataset for Devanagari script: Devanagari Handwritten Character Dataset (DHCD). Our dataset consists of 92 thousand images of 46 different classes of characters of Devanagari script segmented from handwritten documents. We also explore the challenges in recognition of Devanagari characters. Along with the dataset, we also propose a deep learning architecture...
Traditional pattern recognition systems were implemented using neural network systems in order to recognize partial prints ranging from 100%, 75%, 50%, 40% and 30% of the whole fingerprint image. The uniqueness of the patterns of each individual fingerprint served as the motivation in pursuing this research in identifying partial fingerprints where the minutiae pattern identification method cannot...
On the basis of depth study of commercial bank credit risk control model literature, this paper introduced the concepts of credit risk and credit risk control. We research the main influencing factors of commercial bank credit risk control scientifically by artificial neural network theory, and then set a commercial bank credit risk control index system which contains 3 levels of 27 indexes. Improved...
Shunt active power filter, when used with non linear loads, provides an elegant solution to reactive power compensation as well as harmonic mitigation, leading to improvement in power quality. The present study deals with Neuro based controllers viz. neural network (NN) control and neuro fuzzy (NF) control for shunt active power filter. Their superiority over PI type of controller is demonstrated...
User Identification and User Verification are the primary problems in the area of Keystroke Dynamics. In the last decade there has been massive research in User Verification, and lesser research in User Identification. Both approaches take a username and a passphrase as input. In this paper, we introduce this problem of replacing authentication systems with the passphrase alone. This is done by using...
This paper highlights the importance of using student data to drive improvement in education planning. It then presents techniques of how to obtain knowledge from databases such as large arrays of student data from academic Institution databases. Further, it describes the development of a tool that will enable faculty members to identify, predict and classify students based on academic performance...
We proposed a deep Convolutional Neural Network (CNN) approach and a Multi-View Stacking Ensemble (MVSE) method in Ali Mobile Recommendation Algorithm competition Season 1 and Season 2, respectively. Specifically, we treat the recommendation task as a classical binary classification problem. We thereby designed a large amount of indicative features based on the logic of mobile business, and grouped...
A new learning framework is proposed for multivariate chaotic system modeling. In order to construct suitable input variables, we put forward a scheme of input variable selection based on nonuniform state space reconstruction. A new criteria based on low dimensional approximation of joint mutual information is derived, which is solved by evolutionary computation approach efficiently with low computation...
In this work, an approach for Arabic handwriting word segmentation is proposed. In this approach words are over-segmented and the segmentation points (SPs) are then validated. As the validation stage accuracy controls the whole system accuracy, an improved validation approach is proposed to alleviate other approaches' limitations and enhances the accuracy. In this validation approach, a set of zoning...
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