(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 8, 2017 75 | Page www.ijacsa.thesai.org PCA based Optimization using Conjugate Gradient Descent Algorithm Subhas A. Meti Department of Electronics and Communication Engineering, Research Scholar, VTU Regional Research Center, Belgaum, India V.G. Sangam Department of Electronics and Instrumentation, Dayanand Sagar College of Engineering, Bangalore, India Abstract—The energy dissipation in Wireless Body Area Network (WBAN) systems is the biggest concern as it proportionally affects the system longevity. This energy dissipation in the WBAN system mainly takes place due to the signal interference from other networks causing reduction on the dimensionality. The data prediction in WBAN is also a considerable concern corresponding to misinterpretations and faults in the signals. In this paper a novel combination of Principle Component Analysis (PCA) pre-processing along with optimization using the conjugate gradient descent algorithm is proposed. Experimental observations show an improvement in the mean square error and the regression based correlation coefficient when compared to other standard techniques. Keywords—Associative neural network (AANN); conjugate gradient descent; Non-Linear Principle Component Analysis (NLPCA); Principle Component Analysis (PCA); Wireless Body Area Network (WBAN) I. INTRODUCTION The effective improvement in the wireless communication area corresponding to the wireless sensor network (WSN) providing the wide range applications in different areas like military, medical, etc. A kind of WSN is named as Wireless Body Area Network (WBAN) helps to connect the different medical sensor within and outside the body. These WBANs offers the significant mobility for the patients by portable monitoring gadgets. The monitoring ability of the WBAN is area independent and can access the data network to transfer the patient’s data. Thus, WBAN framework likewise could get to the information systems (e.g. 3G, 4G) to transfer the patients data. The prime concern of WBAN framework is the productivity regarding energy which demonstrates the system lifetime. The energy in the WBAN could be influenced by numerous variables in light of the area of the observing gadgets which produces clamor/obstruction in the sign. The conventional rarities created from other similar gadgets could be because of variables, for example, state of the checking gadgets, interference from other medicinal sensors, and so on. The conventional methods delivered from inside restorative gadgets incorporate impedance of different signs created because of inadvertent physiological criteria. Subsequently, the restorative gadgets of WBAN system produces signal where antiquities may exist. This kind of interference misjudges the sign that injects the errors and along these lines prompting inappropriate signal forecast. The use of neural networks for WBAN systems helps to enhance the system efficiency, signal prediction and artifact reduction. In order to describe the problem of energy dissipation in WBAN, An enhancement was done on the WBAN framework in view of the arrangement of the facilitator utilizing neural system strategies [1]. A learning algorithm was used with Kohonen neural system (KNN) to analyze and classify the biomedical signals in the WBAN framework [2]. By utilizing learning based techniques as a part of the neural systems the general energy utilization was lessened to 90%. One of the issues tended to in the WBAN systems is the planning of numerous WBAN in a specific region. The work of [3] considered the same issue of non- linearity to achieve the system high throughput. If a WBAN system exists in a system of multiple WBAN then dimensionality issue may take place, due to which the communication performance may vary because of channel interface among the WBAN systems [4]. Thus, there is a need of a method which can reduce the dimensions in multiple WBAN systems. The analysis and fault detection of biomedical sensors can be done through the modular neural network consisting of associative neural network (AANN). The significant feature of AANN is that it interprets the obtained outcomes and it can be analyzed through data spaces correlation in space modes, which adversely helps in improvement of prediction capability in WBAN system. This paper is planned as per the sections, where Section II represents the existing research work highlighting the advantage of learning based AANN algorithm. The Section III explains the problem of interest while the next Section IV explains the general modules used in the proposed system. The Section V is subjected to describe the research methodology of the proposed model. The Section VI illustrates the analysis of the outcomes of the system. Finally the Section VII briefs about the conclusion of the proposed system. II. RELATED WORK In past various learning based mechanisms were introduced for different application needs. The AANN is a method which falls under the associated neural network (ASNN). This section discussed few existing researches pertaining to ASNN. The work of Guo-Jian et al. [5] expressed a self-restoration mechanism for the intelligent sensors that implements the AANN to monitor the online insulator contamination status by performing learning. The outcomes of the study suggested that