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In order to reconstruct large-scale gene regulatory networks (GRNs) with high accuracy, a robust evolutionary algorithm, a dynamic multiagent genetic algorithm (dMAGA), is proposed to reconstruct GRNs from time-series expression profiles based on fuzzy cognitive maps (FCMs) in this paper. The algorithm is labeled as dMAGAFCM-GRN. In dMAGAFCM-GRN, agents and their behaviors are designed with the intrinsic...
machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
In this paper, a Neural Network Deployment (NND) algorithm is presented to realize and synthesize Multi-Valued Logic (MVL) functions. The algorithm is combined with back-propagation learning capability and MVL operators. The operators are used to synthesize the functions. Consequently the synthesized expressions are applied by the MVL neural operators. The advantages of NND-MVL algorithm are demonstrated...
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...
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...
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,...
The design of Artificial Neural Network (ANN) is a typical task as it is depends on human experience. There are few techniques like the Back-Propagation algorithm and nature inspired meta-heuristic are one of the most widely used and popular technique for optimizing feed forward neural network training. Artificial Bee Colony (ABC) algorithm is nature inspired meta-heuristic approach based on behavior...
Wireless sensor network (WSN) is a typical application of Ad hoc network in autonomous system (AS). It has attracted considerable attention in the past. Recent years have witnessed a growing interest in the study of localization algorithm for WSN. Self-localization of nodes is one of the key technologies for application of WSN. The localization accuracy is a significant criterion to evaluate the practical...
In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model. But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set. Back propagation based learning algorithms was used for training the network...
Recently a commonly used method for Recognition of Handwritten Digit Application based on Back Propagation Neural Network (BPNN) has been widely applied. However, the original algorithm and its modifications contains a number of free parameters which affect particular networks differently and the slight error rate on the selection of these parameters can cause problems. Thus, this paper presents the...
The gap between abstraction levels in analog design is a major obstacle for advancing analog and mixed-signal design automation. Intelligent surrogate models for low-level analog building blocks are needed to bridge behavioral and transistorlevel simulations. With this objective, artificial neural network (ANN) metamodels are incorporated in Verilog-AMS to capture the highly nonlinear response of...
Fingerprinting localization techniques have been intensively studied in indoor WLAN environment. Artificial neural networks (ANN) based fingerprinting technique could potentially provide high accuracy and robust performance. However, it has the limitations of slow convergence, high complexity and large memory storage requirement, which are the bottlenecks of its wide application, especially in the...
Boarding or holding in the Emergency Department (ED) reduces capacity of the ED and delays patients from receiving specialized care. Estimating accurately the number of admissions from the ED can help determine appropriate level of staffing to reduce holding. We propose a randomized non-linear regression algorithm, RT-KGERS, to estimate the number of admissions a week in advance. We also devise features...
In this paper we present a Dynamic Sampling Framework for use with multi-class imbalanced data containing any number of classes. The framework makes use of existing sampling techniques such as RUS, ROS, and SMOTE and ties the classification algorithm into the sampling process in a wrapper like manner. In doing so the framework is able to search for a desirably sampled training set, thus eliminating...
Artificial neural networks (ANN) are able to simplify classification tasks and have been steadily improving both in accuracy and efficiency. However, there are several issues that need to be addressed when constructing an ANN for handling different scales of data, especially those with a low accuracy score. Parallelism is considered as a practical solution to solve a large workload. However, a comprehensive...
Localization in WSN used to determine the position of node with the help of known positions of anchor nodes. In many of the applications, where coarse accuracy is sufficient, range free localization mechanism are being pursued alternative to range based localization mechanism. Because the range free localization scheme is low cost and consumes low energy. In this paper, we present range free weighted...
A significant number of elders live with memory impairment issues, as a result of the normal aging process. Therefore various kinds of supporting systems have been developed to help the elders, who have mild memory problems. In this paper we propose a Smart Reminder System for reminding forgotten complex activities, in home environment. Subjected complex activities are the activities, which should...
In this paper we are proposing a new Attribute Selection Measure Function (heuristic) on existing C4.5 algorithm. The advantage of heuristic is that the split information never approaches zero, hence produces stable Rule set and Decision Tree. In ideal situation, in admission process in engineering colleges in India, a student takes admission based on AIEEE rank and family pressure. If the student...
Training artificial neural networks (ANNs) is a complex task of great importance in problems of supervised learning. Evolutionary algorithms (EAs) are widely used as global searching techniques for optimization in scientific and engineering problems, and these approaches have been introduced to ANNs to perform various tasks, such as connection weight training and architecture design. Recently, a novel...
Based on BP neural network, the paper set up an island microgrid system to forecast electricity load. In order to improve forecast accuracy and convergence speed, author updates BP neural network in two ways. on one hand, putting forward several limitations of the basic algorithm for BP, the paper firstly gives several commonly used improved BP algorithm to be compared and then select the LM algorithm...
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