Journal of Computer and Robotics
Volume:8 Issue: 1, Winter and Spring 2015

Wireless sensor networks attracting a great deal of research interest. Accurate localization of sensor nodes is a strong requirement in a wide area of applications. In recent years, several techniques have been proposed for localization in wireless sensor networks. In this paper we present a localization scheme with using only one mobile anchor station and received signal strength indicator technique, which reduces average localization errors and execution time. Satisfactory simulation results and also comparison of localization errors and execution time between our scheme and similar previous schemes depicts the efficiency of proposed method against previous schemes.

DNA sequence, containing all genetic traits is not a functional entity. Instead, it transfers to protein sequences by transcription and translation processes. This protein sequence takes on a 3D structure later, which is a functional unit and can manage biological interactions using the information encoded in DNA. Every life process one can figure is undertaken by proteins with specific functional responsibilities. Consequently protein function prediction is a momentous task in bioinformatics. Protein function can be elucidated from its structure. Protein secondary structure prediction has attracted great attention since it’s the input feature of many bioinformatics problems. The variety of proposed computational methods for protein secondary structure prediction is very extensive. Nevertheless they couldn’t achieve much due to the existing obstacles such as abstruse protein data patterns, noise, class imbalance and high dimensionality of encoding schemes of amino acid sequences. With the advent of machine learning and later ensemble approaches, a considerable elevation was made. In order to reach a meaningful conclusion about the strength, bottlenecks and limitations of what have been done in this research area, a review of the literature will be of great benefit. Such review is advantageous not only to wrap what has been accomplished by far but also to cast light for the future decisions about the potential and unseen solutions to this area. Consequently in this paper it’s aimed to review different computational approaches for protein secondary structure prediction with the focus on machine learning methods, addressing different parts of the problem’s area.

in this study, a new approach for control (treatment) of AIDS based on patient entry drug. One of the main problems related to AIDS, is lack of control, lack of identification and early treatment of this disease. Today physicians more than anything, control the disease relying on their experience and knowledge and time-consuming and complex experiments, nevertheless, human errors are inevitable. Now this study investigated dynamic and mathematical model of AIDS. Then 2 inputs (RTI1 and PI2) simplified based on a 1 input. Then, using the linearization on operating point or equilibrium, dynamic linearized by 1 input. Finally, by applying control signal to the disease by using direct adaptive control (STR 3), the disease can be controlled adaptively as the patient's condition is not worse and controlled. The proposed system by combining these methods is able to reach small amounts and a substantial sum of squared errors SSE4, mean absolute error and mean square error MAE5 MSE6. Relying on the dynamic characteristics, in terms of composition and interaction to high matching accuracy. The present methods, despite on high precision, are time-consuming and expensive. By comparing this method and those methods, we will discover accuracy and efficiency of these methods.

Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem and sparse data conditions, this paper makes some contributions. Firstly, we provide an exposition of all-distance sketch (ADS) node labelling which is an efficient algorithm for estimating distance distributions; also we show how the ADS node labels can support the approximation of shortest path (SP) distance. Secondly, we extract items’ features and accordingly we describe an item proximity measurement using ochiai coefficient. Third, we define an estimation of closeness similarity, a natural measure that compares two items based on the similarity of their features and their rating correlations to all other items, then we describe our user similarity model. Finally, we show the effectiveness of collaborative filtering recommendation based on the proposed similarity measure on two datasets of MovieLens and FilmTrust, compared to state-of-the-art methods.

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of different stocks prices. Several factors, such as input variables, preparing data sets, network architectures and training procedures, have huge impact on the accuracy of the neural network prediction. The purpose of this paper is to predict multi-step-ahead prices of the stock market and derive the method, based on Recurrent Neural Networks (RNN), Real-Time Recurrent Learning (RTRL) networks and Nonlinear Autoregressive model process with exogenous input (NARX). This model is trained and tested by Tehran Securities Exchange data.

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of inefficiency of PSO algorithm in high-dimensional search space, some algorithms such as Cooperative PSO offered. Accordingly, in the present article, we intend, in order to develop and improve PSO algorithm take advantage of some optimization methods such as Cooperatives PSO, Comprehensive Learning PSO and fuzzy logic, while enjoying the benefits of some functions and procedures such aslocal search function and Coloning procedure, propose the Enhanced Comprehensive Learning Cooperative Particle Swarm Optimization with Fuzzy Inertia Weight (ECLCFPSO-IW) algorithm. By proposing this algorithm we try to improve mentioned deficiencies of PSO and get better performance in high dimensions.