Abstract: Most practical systems have multiple inputs and multiple outputs, and the applicability of neural networks as practical adaptive identifiers and controllers will eventually be judged by their success in multivariable problems. In this paper, we design a model following adaptive controller for a class of a discrete time multivariable nonlinear systems. Radial Basis Function (RBF) neural network with Minimal Resource Allocation Network (MRAN) training algorithm is used for off-line stable identification. It implements a stable model following adaptive controller by utilizing the identification results. Simulation results demonstrate the proposed controller can drive unknown MIMO nonlinear systems to follow the desired trajectory very well.

Abstract: The discourse segmentation problem in Arabic language has not been fully addressed. A technique to segment Arabic discourse into complete sentences is presented. The technique is derived from Arabic Rhetorical system by exploiting the main crucial connector "و ", as defined by Arabic linguists almost one thousand years ago. This approach categorizes the six known rhetorical types of "و " into two classes: segment and unsegment, known as, "Fasl" and "Wasl". Segmentation places are decided according to the type of connector "و ".  A set of twenty two syntactic and semantic features devised from "Fasl and Wasl" rhetorical methods, are chosen to categorize each type of "و ". The system undergoes the learning and testing stages, using SVM machine learning technique to identify the types of the connector "و ". An Arabic discourse corpus is particularly developed for this experiment. We achieved results with an accuracy of 97.95% of discourse segmentation.

Abstract: One of the most frequent operation performed on database is searching. To perform this operation we have different kinds of searching algorithms, some of which are Binary Search, Index Sequential Access Method (ISAM), but these and all other searching algorithms work only on data, which are previously sorted. An efficient algorithm is required in order to make the searching algorithm fast and efficient. This research paper presents a new sorting algorithm named as “Optimized Selection Sort Algorithm, OSSA”.OSSA is designed to perform sorting quickly and more effectively as compared to the existing version of selection sort. The introduction of OSSA version of selection sort algorithm for sorting the data stored in database instead of existing selection sort algorithm will provide an opportunity to the users to save almost 50% of their operation time with almost 100% accuracy.

Abstract: Bootlegging and copyright intrusion are major problems in academic and commercial sectors. Automatic information processing and retrieval are therefore become an urgent need. In this paper a novel approach to authorship affirmation is proposed i.e. initial character n-gram approach dealing with real-world text (or unrestricted text). However, with a small experiment, we attempt to affirm the author more accurately than the previous research has shown. The results obtained show that the technique of the initial n-gram is very effective as it reaches the 100% accuracy level in the field of author identification.

Abstract: The dielectric properties of two sample paddy seeds D219 and D222 were measured using microwave perturbation technique in a frequency range 9-12.2GHz of 33 frequency at interval of 100KHz, at a constant temperature (20 o C), same bulk density and moisture content (nearly same, 10.8% and 11% dry basis). The dielectric constant of D219 sample is higher than D222 before 9.8 GHz then decreases progressively when frequency increases. The loss factor of D219 also decreases sharply if compared with D222. The deviated values have been ignored as noise. The deviated values were presented due to the shape of the samples, unequal perturbation which attribute to full height insertion in cavity and shape perturbation of the cavity. This paper relates to a characterization system for various categories of paddy seeds to store for post harvesting.

Abstract: A Hidden Markov Model (HMM) has been developed to identify of exons in DNA of genes Plasmodium falciparum. Its genome consists of fourteen chromosomes and the most (A+T)-rich genome sequence. The new model using the structure of HMM system based on structure exons region in coding sequence (CDS). This model separates GT bases into G and T state in starts of intron and also AG bases in ends of intron region. This HMM was improved by increasing the state from 20 up to 100 states on the model only on first exon and intron. The HMM framework used Viterbi algorithm for the training and both algorithm Viterbi and Baum-Welch for the test. This model has the maximum accuracy around 78% for identification of exons in DNA of genes Plasmodium falciparum by using 100 states.

Abstract: In this paper, traffic control of the communication systems is examined over the ATM (Asynchronous transfer mode) networks. Fuzzy mechanism and adaptive neuro fuzzy mechanism are designed to controlling flow rate on an ATM switch using a specific method of the ABR (Available Bit Rate) service of the ATM technology. Two models are compared in various cases to evaluate performance in traffic control over the network. Results are discussed within the framework of today’s technology requirements and problems.

Abstract: This paper proposes an approach to recognize the Arabic Alphabet letters spoken by any speaker using artificial neural networks. This represents a fundamental step to recognize Arabic speech (continuous words). This will be useful in converting the spoken words into written text and using of microphone instead of key board, also this can help disable people (handicapped) with limited movement to write any text by voice instead of their hands. A suggested recognition system is implemented to recognize Arabic Alphabet letters of independent speakers. This system is based on analyzing phonetic isolated Arabic alphabet letters. The main features of the voice signal are extracted using Principal Component Analysis (PCA) technique. PCA coefficients corresponding to each alphabet are used to train multilayer perceptron & feed-forward back propagation neural networks to produce recognized binary codes corresponding to each letter. These binary codes (corresponding to alphabet letters) can be decoded to be used as displayed letters on monitors, or printed on paper or used as commands for moving a controlled mechanism. About 96% detection rate has been achieved over large dataset.

Abstract: An mpi cluster is a group of computers which are loosely connected together to provide fast and reliable services. Clusters use in many scientific computing, such as the matrix multiplication. Our experiment is based on the master – slave model in homogenous computers to compute the performance of experiment. We compute the execution time for many examples to compute the speed up. The developed performance model has been checked and it has been shown that the parallel model is faster than the serial model and the computation time was reduced.