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The goal of this study is to develop an accurate artificial neural network (ANN)-based model to predict significant quality of refined palm oil which is Free Fatty Acid (FFA) content. The variables; FFA content, Iodine Value (IV), moisture content, bleaching earth and citric acid dosage as well as the pressure and temperature of the deodorizer is used to build the ANN prediction model. A feed forward...
Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. For this purpose farmers need manual monitoring of fruits from harvest till its progress period. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient...
When implementing Artificial Neural Networks with imperative programing languages, the resulting programs are usually highly coupled. This problem usually hampers distribution over multiple processors, especially when the ANN executes on general-purpose processors. An emerging technique called Notification Oriented Paradigm (NOP) facilitates the development of distributed and decoupled systems, and...
State estimation is a vital apparatus in observing the power electric grids. As the measure of the electric power grid keeps on growing, a state estimator must be all the more computationally effective and robust. This paper presents a real time state estimation using a new methodology of multilayer neural networks exhibited in composite topologies, hybrid Cascade and hybrid Parallel topologies in...
A study of Artificial Neural Networks (ANNs) in the elder falls detection problem is proposed. There are many efforts trying to provide an independent life for the elderly people. Fall event is one of the main problems that affect people in this age group. In order to provide a comfortable solution of this problem for elderly people, this paper presents an implementation of falls detection in mobile...
In this study, modeling of Konya wastewater treatment plant was studied by using multilinear regression and artificial neural network with different architectures in SPSS and MATLAB software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account the input values of pH, temperature,...
Data selection is an important component of cross-corpus training and semi-supervised/active learning. However, its effect on acoustic emotion recognition is still not well understood. In this work, we perform an in-depth exploration of various data selection strategies for emotion classification from speech using classifier agreement as the selection metric. Our methods span both the traditional...
Depending on the operational environment of wireless sensor networks, data packet loss happens when parameters changing of net. This paper presents a method to predict the percentage of data packet loss (DPL) by artificial neural networks. The percentage of DPL is presented as the path selecting probability of transmission path to achieving reliable multipath transmission. By comparing with the FEC...
The problems of predicting the Protein-Protein Interactions (PPIs) are characterized by probabilistic constraints using the artificial neural network techniques. In the literature, no specific rules are proposed for determining whether two proteins interact, but various approaches have been proposed to collect the information about the interaction between the proteins. The need and importance of PPIs,...
Mobility management and resource utilization are the two most important research issues in wireless mobile multimedia networks. In order to provide the guarantee of handoff probability; we propose a mobility based call admission control using artificial neural network to predict the accurate future position of mobile user based on the mobility history of the user. For mobility prediction we have used...
This paper presents a novel emotion transformation scheme of speech signal which is text independent and speaker independent. Speech signals as many other signals are inherently multi-scale in nature, owing to contributions from events occurring with different localizations in time and frequency. Therefore, emotion dependent spectral parameters those characterized by single scale features, approximate...
In this paper, artificial neural network (ANN) and improved binary gravitational search algorithm (IBGSA) are utilized to detect objects in images. Watershed algorithm is used to segment images and extract the objects. Color, texture and geometric features are extracted from each object. IBGSA is used as a feature selection method to find the best subset of features for classifying the desired objects...
Wind speed prediction is a basic requirement of wind energy generation with large generation capacity for large-scale wind power penetration. The intermittency and stochastic quality of wind speed leads to a big challenge for high penetration of wind power in electricity systems due to error-prone wind speed prediction methods. There are many artificial neural network (ANN) approaches proposed in...
In the exploding growth of radio mobile and wireless communication applications, microstrip antennas with its advantages of low cost and flexible fabrications, emerge as the most suitable candidate. The direct antenna synthesis could, however do not result in the optimal antenna configuration, and therefore a possible alternative is considering the problem of optimizing the antenna as a system of...
This paper aims at building a portable robotic hand for physically disabled people to perform basic hand movements. Surface Electromyography(EMG) signal is collected from muscles of human forearm to extract the subject's intentions of action, where six kinds of gestures are selected for discussion. An Artificial Neural Network(ANN) is trained and utilized to distinguish the desired movement according...
The purpose of this paper was to evaluate the performance of pedotransfer functions generated by Radial Base Function (RBF) Artificial Neural Network (ANN) to estimate soil water retention at field capacity (FC, suction at −30 kPa) and Permanent Wilting Point (PWP, −1500 kPa) for soils at PROJIR area -RJ/BR. The raw data used was type of soil horizon, texture, bulk density, soil organic carbon content...
The paper contains selected results of research related to neural modeling of electrical power system development (EPS or EP System). Numerical data presented in paper [1, 8] were used to teach the artificial neural network (ANN) a model of EP system development. The ANN was designed in the MATLAB and Simulink environment using the Neural Network Toolbox. The generated model was used to perform simulation...
It is proposed a wearable sensing system based on Inertial Measurement Units (IMUs) for the long-time detection of specific human motion disorders. The system uses a single sensor positioned on the head, close to the ear. The system recognizes noticeable gait features as irregular steps and the gait block (freezing of gait). Respect to other positions on the body, the headset has the maximum sensitivity...
Accurately predicting a user's rating to a service is a challenging task in the presence of malicious users that manipulate the ratings to services. Many existing service rating systems lack the ability that counter the manipulation of rating systems. This paper proposed an artificial neural network (ANN) based service rating scheme that counters the manipulation of service ratings. The scheme takes...
Testing and diagnosis of analog circuits are very important tasks at the quality assurance of integrated circuits and electronic devices. Faults detection and identification are realized using fault dictionary. The architecture of fault dictionary has an essential influence on time and efficiency of diagnosis at whole. An approach to the construction of fault dictionary as the neuromorphic classifier...
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