The paper focuses on Example Based Machine Translation (EBMT) system that translates sentences from English to Hindi. It uses the parallel corpus for translating sentences. Development of a machine translation (MT) system typically demands a large volume of computational resources. Requirement of computational resources (for example, rules) is much less in respect of EBMT. This makes development of EBMT systems for English to Hindi translation feasible, where availability of large-scale computational resources is still scarce. Example based machine translation relies on the database for its translation. The frequency of word occurrence is important for translation in EBMT in the following research.

Smart phones and its applications gain a lot of popularity nowadays. Many people depend on them to finish their tasks banking, social networking, fun and a lot other things. Games with a purpose (GWAP) and microtask crowdsourcing are considered two techniques of the human-computation. GWAPs depend on humans to accomplish their tasks. Porting GWAPs to smart phones will be great in increasing the number of humans in it. One of the systems of human-computation is ESP Game. ESP Game is a type of games with a purpose. ESP game will be good candidate to be ported to smart phones. This paper presents a new mobile game called MemoryLabel. It is a single player mobile game. It helps in labeling images and gives description for them. In addition, the game gives description for objects in the image not the whole image. We deploy our algorithm at the University of Menoufia for evaluation. In addition, the game is published on Google play market for android applications. In this trial, we first focused on measuring the total number of labels generated by our game and also the number of objects that have been labeled. The results reveal that the proposed game has promising results in describing images and objects.

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifying process problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chart patterns (CCPs). The proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. In the feature extraction module, a proper set of the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module adaptive neuro-fuzzy inference system (ANFIS) is investigated. In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module, cuckoo optimization algorithm (COA) is proposed for finding of optimum vector of radius. Simulation results show that the proposed system has high recognition accuracy.

Different approaches have been proposed over the last few years for improving holistic methods for face recognition. Some of them include color processing, different face representations and image processing techniques to increase robustness against illumination changes. There has been also some research about the combination of different recognition methods, both at the feature and score levels. Embedded hidden Markov model (E-HHM) has been widely used in pattern recognition. The performance of Face recognition by E-HMM heavily depends on the choice of model parameters. In this paper, we propose a discriminating set of multi E-HMMs based face recognition algorithm. Experimental results illustrate that compared with the conventional HMM based face recognition algorithm the proposed method obtain better recognition accuracies and higher generalization ability.

This paper presents a boon and amend technique for eradicating the artifacts from the Electroencephalogram (EEG) signals. The abolition of artifacts from scalp EEGs is of considerable implication for both the computerized and visual investigation of fundamental brainwave activities. These noise sources increase the difficulty in analyzing the EEG and procurement clinical information related to pathology. Hence it is critical to design a procedure for diminution of such artifacts in EEG archives. This paper uses a blind extraction algorithm, appropriate for the generality of complex-valued sources and both complex noncircular and circular, is introduced. This is achieved based on higher order statistics of dormant sources, and using the deflation approach Spatially-Constrained Independent Component Analysis (SCICA) to separate the Independent Components (ICs) from the initial EEG signal. As the next phase, level-4 daubechies wavelet db-4 is applied to extract the brain activity from purged artifacts, and lastly the artifacts are projected back and detracted from EEG signals to get clean EEG data. Here, thresholding plays an imperative role in delineating the artifacts and hence an improved thresholding technique called Otsu’s thresholding is applied. Experimental consequences show that the proposed technique results in better removal of artifacts.

One of the most important phenomena of some systems is chaos which is caused by nonlinear dynamics. In this paper, the new 3 dimensional chaotic system is first investigated and then utilized an intelligent controller based on brain emotional learning (BELBIC), this new chaotic system is synchronized. The BELBIC consists of reward signal which accepts positive values. Improper selection of the parameters causes an improper behavior which may cause serious problems such as instability of the system. It is needed to optimize these parameters. Genetic Algorithm (GA), Cuckoo Optimization Algorithm (COA), Particle Swarm Optimization Algorithm (PSO) and Imperialist Competitive Algorithm (ICA) are used to compute the optimal parameters for the reward signal of BELBIC. These algorithms can select appropriate and optimal values for the parameters. These minimize the Cost Function, so the optimal values for the parameters will be founded. Selected cost function is defined to minimizing the least square errors. Cost function enforces the system errors to decay to zero rapidly. Numerical simulation will show that this method much better, faster and more effective than previous methods can be new 3D chaotic system mode to bring synchronized.

Wireless sensor networks (WSN) are emerging in various fields. A large number of sensors in these applications are unattended and work autonomously. Lifetime is an important parameter which is critical for different algorithms for data transfer. Moreover it is responsible for the throughput and the failure of the network. Heterogeneous wireless sensor network, on top of clustering technique, has evolved as the major parameter to increase the lifetime of the Sensor network, data transfer, energy consumption and the scalability of the sensor network. This paper surveys the different clustering algorithm and dependencies for heterogeneous wireless sensor network. This paper is for scholars to gain sufficient knowledge of wireless sensor network (WSN), its important characteristics, and performance metrics with factors responsible for a WSN system. It can help a scholar to start a quick research by understanding all the respective parameters and energy oriented strategies in WSN.

An Quantum-Inspired Evolutionary Algorithm (QEA) is presented for solving the unit commitment problem. The proposed method has been used to achieve the schedule of system units by considering optimal economic dispatch. The QEA method based on the quantum concepts such as Q-bit, present a better population diversity compared with previous evolutionary approaches, and uses quantum gates to achieve better solutions. The proposed method has been tested on a system with 10 generating units, and the results shows the effectiveness of algorithm compared with Other previous references. Furthermore, it can be used to solve the large-scale generating unit commitment problem.