Abstract-In recent years Mobile Adhoc Network is developing as an important technology in wireless networks. It showing quick progress and has many applications. These types of networks provide strong and fast data delivery to users in wireless network. In WMN routers use advanced antennas to transmit data to each other in multi-hop manner. In MANET, it is easily to attacks because of dynamical changing of network topology, more centralized monitoring, management point and co-operative algorithm. The important fact in mobile adhoc network is security which being accepted by many people. The major problem of MANET is selfish node. Selfish nodes in MANET do not participate in forwarding packets. Due to Some misbehavior reasons a node can be identified as selfishness or malicious. Node selfishness may be a reason to which causes minimum delivery ratio of packet and data loss in the network. In MANET network, node failure is a reason for high end-to-end delay. In this paper we propose chord algorithm to overcome these issues, chord is structured peer-to-peer protocol. To provide P2P Nodes Service in Mobile Adhoc Network Chord is applied in MANET. The major advantage of Chord algorithm is greedy forwarding, aggressive update, passive bootstrapping and overlay broadcasting. Chord in MANET might be efficient, because it is not only providing direct routing but also provides indirect and key based of overlay routing. Our proposed system suggests that Chord technique can outperform in random routing in conventional way. Key words-MANET, Chord Algorithm, Greedy forwarding.I. INTRODUCTION Ad Hoc networks nodes forms network without any fixed infrastructure. Each node in adhoc networks is a self-configuring nodes and it acts as router and system [1]. In this type of environment, if one node wants to forward a data packet to destination node that node will be going to enlist other nodes in that network because of limited range of transmission [2]. Each node in adhoc network will not only acts a host but it also acts as router to transmit packets to other nodes in the adhoc network that may be within a range of direct transmission or not. In adhoc network, routing protocol allows nodes to find multi-hop paths to other node through that network. This type of idea gives fewer infrastructures in MANET networking, because each mobile nodes form their own routing in network themselves on fly. In adhoc network many routing protocols are designed based on only the assumption so that every node transmits every packet, but practically some of them acts as selfish, it means that nodes use network and service but it do not have interest to forward data packets to other nodes to save its resources and energy [3]. Malicious nodes attack is nodes misbehaviour has to bear some energy costs in order to perform the threat. In adhoc network selfish nodes do not have any interest to damage any other packet directly, but it is not interested to spend CPU cycles, battery life, or bandwidth of available network to transmit packets in direct transm...

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Parkinson's disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson's disease. This type of minimal infrastructure equipment helps in analyzing the Parkinson's gait properties without affecting the common behavioral patterns during the clinical practices. Therefore, the Accelerometer Sensor-based Parkinson's Disease Identification System (ASPDIS) is introduced with a kernel-based support vector machine classifier model to make an early prediction of the disease. consequently, the proposed classifier can easily predict various severity levels of Parkinson's disease from the sensor data. The performance of the proposed classifier is compared against the existing models such as random forest, decision tree, and k-nearest neighbor classifiers respectively. As per the experimental observation, the proposed classifier has more capability to differentiate Parkinson's from non-Parkinson patients depending upon the severity levels. Also, it is found that the model has outperformed the existing classifiers concerning prediction time and accuracy respectively.