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Virtual flow metering (VFM) is an attractive and cost-effective solution to meet the rising multiphase flow monitoring demands in the petroleum industry. It can also augment and backup physical multiphase flow metering. In this study, a heterogeneous ensemble of neural networks and regression trees is proposed to develop a VFM model utilizing bootstrapping and parameter perturbation to generate diversity...
Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC-called a Tensor Processing Unit (TPU)-deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second...
Machine learning algorithm and web-based application systems have played a major role in improving the healthcare organisation in terms of continuous tele-monitoring therapy and maintaining telemedicine management systems. Currently, no intelligent system has been used in terms of managing sickle cell disease. However, this paper presents a system that facilitates a shift from manual methods to automated...
A variety of problems are related to streaming data e.g. infinite length, concept-drift, non-linearly separable classes, and the possible emergence of “novel classes”. We propose a semi-supervised learning method using an incremental neural network to cope with all these problems. Tracking the concept drift is maintained by using incremental learning. Additionally, the extreme value theory is used...
Gas Turbine (GT) is a vital component to a power plant. This system contains many signals that are used to control and protect the GT from damage or accidents caused by vibration, speed, and temperature. Moreover, the vibrations of GT at dangerous levels might lead to damages to the system. In this paper, a concerted effort is made to identify the number of the bearing and vibration levels during...
Signatures are imperative biometric attributes of humans that have long been used for authorization purposes. Most organizations primarily focus on the visual appearance of the signature for verification purposes. Many documents, such as forms, contracts, bank cheques, and credit card transactions require the signing of a signature. Therefore, it is of upmost importance to be able to recognize signatures...
The air conditioning system usually controlled by a simple, proportional integral and derivative (PID) controller with single loop, or by using on-off controller, these controller are widely used because it's simple, but the performance of such a system using these controlling techniques are not accurate, with high power consumption and short compressor life. This paper aims to control air conditioning...
Backpropagation algorithm is widely used to solve many real-world problems, using the concept of Multilayer Perceptron. However, main disadvantages of Backpropagation are the convergence rate of it being relatively slow, and it is often trapped in the local minima. To solve this problem, it is found in the literatures, an evolutionary algorithm such as Particle Swarm Optimization algorithm is applied...
Security system is the immune system for computers which is similar to the immune system in the human body. This includes all operations required to protect computer and systems from intruders. The aim of this work is to develop an anomaly-based intrusion detection system (IDS) that can promptly detect and classify various attacks. Anomaly-based IDSs need to be able to learn the dynamically changing...
The ultimate bond strength between Fiber Reinforced Polymers (FRP) and concrete is one of the most important elements in the performance of the strengthened beam and its failure mode and failure mechanism. In this investigation an Artificial Neural Network (ANN) model has been developed to predict the ultimate bond strength (Pu) between FRP and concrete based on several factors that influence it....
Maintaining the performance of reliable transport protocols, such as TCP, over wireless mesh networks is a challenging problem due to the unique characteristics of wireless mesh networks such as the lossy nature of the communication medium, absence of a base station, similarity in traffic pattern experienced by neighboring mesh nodes, etc. One of the reasons for the poor performance of conventional...
Steam Boilers are important equipment in power plants and the boiler trips may lead to the entire plant shutdown. To maintain performance in normal and safe operation conditions, detecting of the possible boiler trips in critical time is crucial. As a potential solution to these problems, an artificial intelligent monitoring system specialized in boiler high temperature superheater trip has been developed...
This paper presents a Transfer Module for an English-to-Arabic Machine Translation System (MTS) using an English-to-Arabic Bilingual Corpus. We propose an approach to build a transfer module by building a new transfer-based system for machine translation using Artificial Neural Networks (ANN). The idea is to allow the ANN-based transfer module to automatically learn correspondences between source...
In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness...
Neural networks have been widely used in nonlinear time series prediction. They have generated lot of interest due to their comprehensive adaptive and learning abilities. Neural networks have been used in Medical forecasting, Exchange rate forecasting, stock index prediction, and other areas, which show a practical value of neural networks. This paper presents a novel application of the Self-organised...
Dimensionality reduction is an essential task for many large-scale information processing problems such as classifying document sets, searching over Web data sets, etc. It can be used to improve both the efficiency and the effectiveness of classifiers. In this paper, a comparative study is conducted of five Dimension Reduction Techniques in the context of the Arabic text classification problem using...
Cognitive Radio (CR) can access the spectrum temporarily to solve the problem of the near spectrum crunch. The previous transmissions' events are one of the main motivations for the CR actions and its learning procedures. Therefore, self aware CR devices may cause a considerable interference when they transmit for the first time with no practical knowledge. This paper proposes a solution for cognitive...
This paper presents an investigation into the performance of system identification using Backpropagation Multi-layer Perceptron Neural Networks algorithm for identification of a flexible plate system. Details of the implementation and the experimental studies are given and analyzed in the paper. The input-output data of the system were first acquired through the experimental studies using National...
In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area...
In this paper, we present an intelligent approach to analysing prostrate ultrasound images in order to diagnose prostate cancer. Algorithms based on fuzzy image processing are applied first to enhance the contrast of the original image, to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterising the underlying texture of the regions...
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