Introduction: Nowadays, the most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Case description: Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements. Every algorithm has its way of learning patterns and then predicting. Artificial Neural Network (ANN) is a popular method which also incorporate technical analysis for making predictions in financial markets. Discussion and evaluation: Most common techniques used in the forecasting of financial time series are Support Vector Machine (SVM), Support Vector Regression (SVR) and Back Propagation Neural Network (BPNN). In this article, we use neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared. Conclusion: All three algorithms provide an accuracy of 99.9% using tick data. The accuracy over 15-min dataset drops to 96.2%, 97.0% and 98.9% for LM, SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.
We consider a continuous-time Ehrenfest model defined over the integers from −N to N , and subject to catastrophes occurring at constant rate. The effect of each catastrophe instantaneously resets the process to state 0. We investigate both the transient and steady-state probabilities of the above model. Further, the first passage time through state 0 is discussed. We perform a jump-diffusion approximation of the above model, which leads to the Ornstein-Uhlenbeck process with catastrophes. The underlying jump-diffusion process is finally studied, with special attention to the symmetric case arising when the Ehrenfest model has equal upward and downward transition rates.
The lack of centralised infrastructure in ad hoc network makes it vulnerable to various attacks. MANET routing disrupts if participating node do not perform its intended function and start performing malicious activity. A specific attack called Wormmhole attack enables an attacker to record packets at one location in the network, tunnels them to another location, and retransmits them into the network. In this paper, we present a protocol for detecting wormhole attacks without use of any special harware such as directional antenna and precise synchronised clock and the protocol is also independent of physical medium of wireless network. After the route discovery, source node initiates wormhole detection process in the established path which counts hop difference between the neighbours of the one hop away nodes in the route. The destination node detects the wormhole if the hop difference between neighbours of the nodes exceeds the acceptable level. Our simulation results shows that the WHOP is quite excellent in detecting wormhole of large tunnel lengths I. INTRODUCTIONAn ad-hoc network is inherently a self-organized network system without any infrastructure. Typically, the nodes act as both host and router at the same time i.e each node participates in routing by forwarding data for other nodes and deciding which nodes forward data next based on the network connectivity Most previous ad hoc networks research has focused on problems such as routing and communication, assuming a trusted environment. However, many applications run in untrusted environments and require secure communication and routing such as military or police networks, emergency response operations like a flood, tornado, hurricane or earthquake. However, the open nature of the wireless communication channels, the lack of infrastructure, the fast deployment, and the environment where they may be deployed, make them vulnerable to a wide range of security attacks.A particularly severe security attack, called the wormhole attack, has been introduced in the context of ad hoc networks. During this attack, a malicious node captures packets from one location in the network and "tunnels" them to another malicious node at a distant point which replays them locally. The tunnel can be established in many ways e.g. in-band and out-of-band channel. This makes the tunneled packet arrive either sooner or with a lesser number of hops compared to the packets transmitted over normal multi hop routes. This creates the illusion that the two end points of the tunnel are very close to each other. However, it is used by malicious nodes to
SUMMARYIn this paper, we present a performance study to evaluate the mean delay and the average system throughput of IEEE 802.11-based wireless local area networks (WLANs). We consider the distributed coordination function (DCF) mode of medium access control (MAC). Stochastic reward nets (SRNs) are used as a modelling formalism as it readily captures the synchronization between events in the DCF mode of access. We present a SRN-based analytical model to evaluate the mean delay and the average system throughput of the IEEE 802.11 DCF by considering an on-off traffic model and taking into account the freezing of the back-off counter due to channel capture by other stations. We also compute the mean delay suffered by a packet in the system using the SRN formulation and by modelling each station as an M/G/1 queue. We validate our analytical model by comparison with simulations.
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