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Feature extract ion is a proficient method for reducing dimensions in the analysis and prediction of cancer classification. Microarray procedure has shown great importance in fetching informat ive genes th at needs enhancement in diagnosis. Microarray data is a challenging task due to high dimensional-low sample dataset with a lot of noisy or irrelevant genes and missing data. In this paper, a comparative study to demonstrate the effectiveness of feature ext raction as a dimensionality reduction process is proposed, and concludes by investigating the most efficient approach that can be used to enhance classification of microarray. Principal Co mponent Analysis (PCA) as an unsupervised technique and Partial Least Square (PLS) as a supervised technique are considered, Support Vector Machine (SVM ) classifier were applied on the dataset. The overall result shows that PLS algorithm provides an improved performance of about 95.2% accu racy compared to PCA algorith ms .

Abstract-In this paper, a combination of dimensionality reduction technique, to address the problems of highly correlated data and selection of significant variables out of set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes is proposed. One-Way-ANOVA is employed for feature selection to obtain an optimal number of genes, Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) are employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classification method. Combining feature selection and feature extraction into a generalized model, a robust and efficient dimensional space is obtained. In this approach, redundant and irrelevant features are removed at each step; classification presents an efficient performance of accuracy of about 98% over the state of art.

We investigate Residue Number System (RNS) to binary conversion, which is an important issue concerning the utilization of RNS numbers in Digital Signal Processing (DSP) applications. We propose two new reverse converters for the moduli set . First, we simplify the Chinese Remainder Theorem (CRT) to obtain a reverse converter that uses mod- operations instead of mod- operations required by other state-of-the-art equivalent converters. Next, we further reduce the hardware complexity by making the resulting reverse converter architecture adder based. Two hybrid Cost-Efficient (CE) and Speed-Efficient (SE) reverse converters are proposed. These two hybrid converters are obtained by combining the best state-of-the-art converter with the newly introduced area-delay efficient scheme. The proposed hybrid CE converter outperforms the best state-of-the-art CE converter in terms of delay with similar area cost. Additionally, the proposed hybrid SE converter requires less area cost with smaller delay when compared to the best state-of-the-art equivalent SE converter.

An in-depth study of Stable Election Protocol (SEP) revealed that, distance was not considered in selecting the cluster heads in the network. This allows a distant node that is selected as the head to dissipate huge energy in transmitting data to the Base station (BS). It was further observed that, whenever the Base station is relocated outside the field, the energy consumption of the network is high and hence shortening the lifetime of the network. In this paper, a Gateway-SEP protocol is proposed. The G-SEP modified the election probability of electing cluster heads by considering the distance, average distance and residual energy of the advanced nodes. The scheme also introduced a gateway node at the centre of the network and then installed the BS outside the field. Simulation results using MatLab R2017a showed that, the G-SEP performs better than Zonal-Stable Election protocol (ZSEP) in terms of coverage, stability period, throughput and network lifetime.

Information security is a critical issue in data communication networks. This is more important in wireless communications due to the fact that the transmitted signal could go beyond the communicating participants. Any person with the right equipment could intercept the transmitted information with ease. It is therefore paramount to encrypt information before transmission to prevent intruders from making meaning to intercepted signals. In this paper, an improved Rivest Shamir Adleman (RSA) cryptosystem based on Residue Number System (RNS) is implemented. There are two stages of encryption. The first stage is the traditional RSA and the second stage is to further encrypt the cypher text obtained from RSA using smaller moduli. The first stage of the decryption process is to obtain a partial result through Mixed Radix Conversion (MRC). The final stage of decryption is the RSA decryption process. This is to allow a message m, for which m e < n to be able to be encrypted. The private key length is also enhanced by adding the moduli set to the RSA private key component. It is observed that the proposed system outperforms the existing algorithm in terms of security.

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In this paper, a novel scheme for detecting overflow in Residue Number System (RNS) is presented. A generalized scheme for RNS overflow detection is introduced, followed by a simplified Operands Examination Method for overflow detection for the moduli set . The proposed method detects overflow in RNS addition of two numbers without pre-computing their sum .Moreover, when compared with the best known similar state of the art designs, the proposed scheme requires lesser hardware, eruder htr izre heit epr and is faster.