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Cost-performance trade off is one of the critical challenges in cloud computing environments. Predictive auto-scaling systems mitigate this issue by scaling in/out system automatically based on performance prediction results. The goal of this research is to investigate the impact of different prediction results on the scaling actions generated by predictive auto-scaling systems. In this study, predictive...
This paper presents a study aimed to assess applicability of artificial neural networks (ANNs) in human activity recognition from simple features derived from accelerometric signals. Secondary goal was to select the most descriptive signal features and sensor locations to be used as inputs to ANNs. Five triaxial accelerometers were attached to human body in the following places: one at back, two at...
This paper investigates the accuracy of predictive auto-scaling systems in the Infrastructure as a Service (IaaS) layer of cloud computing. The hypothesis in this research is that prediction accuracy of auto-scaling systems can be increased by choosing an appropriate time-series prediction algorithm based on the performance pattern over time. To prove this hypothesis, an experiment has been conducted...
This paper gives a new approach for recognition of handwritten Devanagari characters. Twenty handwritten characters from 100 people resulting 2000 characters are used for the experimentation. The handwritten characters written of paper is scanned, preprocessed and on every individual characters wavelet transform is applied so as to get decomposed images of characters. Statistical parameters are computed...
Training Artificial Neural Networks (ANN) is relatively slow compared to many other machine learning algorithms. In this study, we focus on instance selection to improve training speed. We first evaluate the effectiveness of instance selection algorithms for k-nearest neighbor algorithms with ANN. We then analyze factors in accuracy -- distance from decision boundary, dense regions, and class distributions,...
Breast cancer is one of fatal disease in women, which is better curable if detected at an early stage. This paper presents a neural network based diagnosis system for breast cancer. Neural network system has the ability to be trained by large data and hidden information or features in the samples. Thus exhaustive case studies of a specialized doctor can be used to train a neural network which results...
In recent years, progress in the field of artificial neural networks provides a very important tool for complex problems in pattern recognition, data mining and medical diagnosis. The training algorithms of neural networks play an important role for adjustment the network parameters. Different algorithms have been presented for training neural networks; the most common one is the use of gradient descent...
Gaze tracking is the process of measuring gaze point or the motion of an eye relative to the head. Gaze tracking technique provides us a brand new way of human computer interaction. In addition, eye gaze tracking can be also applied to support seriously disabled people in using computer. In this paper, we explored the use of eye gaze tracking technology on a tablet device, designed and implemented...
Skin color is a robust cue in human skin detection. It has been widely used in various human-related image processing applications. Although many researches have been carried out for skin color detection, there is no consensus on which color space is the most appropriate for skin color detection because many researchers do not provide strict justification of their color space choice. In this paper,...
Speeding up the design optimization process of Analog/Mixed-Signal circuits has been a subject of active research. Techniques such as metamodeling, artificial neural networks, and optimization over SPICE netlists have been used. While the results are accurate and promising, the effects of process variation on design space exploration still persist. Metamodels created by existing techniques are still...
Can we reliably predict human behavior using Artificial Intelligence? Can traditional methods of Ethology, such as an ethogramm, be implted to achieve this goal ? This is the aim of this study which used observational data collected from ambulatory addictology patients of a district general hospital, using traditional methodological tools of Ethology (ethograms). Observed versus predicted data after...
In this paper, we present a model for Turkish speech recognition. The model is syllable-based, where the recognition is performed through syllables as speech recognition units. The main goal of the model is to recognize as much as possible of a given continuous speech by identifying only a small set of syllables in the language. For that purpose, only the syllable types with a higher frequency are...
A vehicle-mounted GPS receiver used for localization can suffer from signal blockage. To remedy to this problem, GPS/ INS integration can be considered as a solution in some cases. However, in the case of urban areas where there are severe multipath conditions, the performance degrades considerably. The last decade have seen many proposals of techniques aiming at improving the accuracy of GPS positions...
This paper regards the exploitation of RSS in localization techniques within UWB networks. Both fingerprinting and model based approaches are studied and evaluated using a real UWB measurement campaign. As for fingerprinting approach, SVM, KNN, and ANN techniques are proposed and compared. As for model based approach, refined RSS models are proposed in order to better characterize the RSSI-distance...
Wireless Sensor Networks (WSNs) are being deployed in a variety of location-aware applications, where the measurement of data is meaningless without accurate location. Many localization algorithms have been proposed in the literature. However, localization in threedimensional (3D) space has not been studied sufficiently. Also, artificial neural networks are not commonly used in localization. In this...
Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet...
This paper proposes and tests a methodology for selecting features and test cases with the goal of improving medium term bankruptcy prediction accuracy in large uncontrolled datasets of financial records. We propose a Genetic Programming and Neural Network based objective feature selection methodology to identify key inputs, and then use those inputs to combine multi-level Self-Organising Maps with...
Neural networks have been applied for short-term traffic flow forecasting with reasonable accuracy. Past traffic flow data, which has been captured by on-road sensors, is used as the inputs of neural networks. The size of this data significantly affects the performance of short-term traffic flow forecasting, as too many inputs result in over-specification of neural networks and too few inputs result...
Mangosteen export generates large revenue; however, translucent mangosteens, which contain undesirable internal condition, result in the shipment rejection and decrease the reliability of the export. This research investigates a novel non-destructive classification approach based on acoustic frequency response to detect mangosteens containing translucent fleshes. The set of uniform-distributed multi-frequency...
Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future...
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