In data mining, discovery of discrimination results in finding out discriminatory decisions which are hidden in large dataset. The basic problem is to identify the degree of discrimination suffered by a given group in a given context with respect to the classification decision in the analysis of discrimination. Discrimination can be direct and indirect. Discrimination can be handled by simply removing discriminatory attributes from the dataset. Earlier Apriori algorithm was used with three possible approaches: preprocessing, In-processing and postprocessing approach for direct and indirect discrimination. Although these approaches would not solve the discrimination problem, during this process there was much useful information loss. Therefore to handle discrimination and to prevent the discrimination problem without much information loss, some new algorithm came into existence. One of them is CPAR algorithm which is discussed in this paper. The Classification based on predictive association rules (CPAR) is a kind of association classification methods which combines the advantages of both associative classification and traditional rule-based classification. This paper discusses how CPAR algorithm improves the data accuracy and quality of data in comparison with Apriori algorithm.
Leaf spots can be indicative of crop diseases, where leaf batches (spots) are usually examined and subjected to expert opinion. In our proposed system, we are going to develop an integrated image processing system to help automated inspection of these leaf batches and helps identify the disease type. Conventional Expert systems mainly those which used to diagnose the disease in agriculture domain depends only on textual input. Usually abnormalities for a given crop are manifested as symptoms on various plant parts. To enable an expert system to produce correct results, end user must be capable of mapping what they see in a form of abnormal symptoms to answer to questions asked by that expert system. This mapping may be inconsistent if a full understanding of the abnormalities does not exist. The proposed system consists of four stages; the first is the enhancement, which includes HIS transformation, histogram analysis, and intensity adjustment. The second stage is segmentation, which includes adaptation of fuzzy c-means algorithm. Feature extraction is the third stage, which deals with three features, namely color size and shape of spot. The fourth stage is classification, which comprises back propagation based neural networks.
This article provides a comprehensive survey of cognitive radio (CR) technology, focusing on a cross-layer resource allocation scheme, location of primary user and dynamic spectrum access. We first overview the state of the art in CR technology and identify its key functions such as spectrum sensing, Spectrum mobility, Spectrum sharing, resource allocation etc. CR networks impose challenges due to the fluctuating nature of the available spectrum, as well as the diverse QoS requirements of various applications. Spectrum management functions can address these challenges for the realization of this new network paradigm. Resource allocation in CR Network faces unique challenges due to the unpredictability of primary activity and the need for protecting primary communications.
In this article, geometrical nonlinear bending behavior of laminated flat panel is investigated. A nonlinear mathematical model of laminated composite plate has been developed in the framework of higher order shear deformation theory by taking the nonlinearity through Green-Lagrange type nonlinear kinematics. The model has been discretised using a nine nodedisoparametric Lagrangian element with nine degrees of freedom per node and the nonlinear governing equations are obtained through variational method. The desired nonlinear responses are obtained by solving the equations using a direct iterative method. The convergence and comparison test of the model has been done with those available published literature. Some new numerical experimentation has been done and discussed.
In this work, a static relaying protocol, called Decode or Quantize and Forward (DoQF), is introduced for half duplex single-relay networks, and its performance is studied in the context of communications over slow fading wireless channels. The proposed protocol is inspired by the so-called Compress-and-Forward (CF) but only needs statistical Channel State Information at the Transmitter (CSIT). First, we analyse the behaviour of the outage probability Poof the proposed protocol as the SNR ρ tends to infinity. In this case, we prove that ρ2 Po converges to a constant ξ. We refer to this constant as the outage probability gain and we derive its closed-form expression for a general class of wireless channels that includes Rayleigh and Rice. We furthermore prove that the DoQF protocol has the best achievable outage gain in the wide class of half-duplex static relaying protocols and we minimize ξ w.r.t the power allocation to the source and the relay and the durations of the slots. Next, we focus on Rayleigh channels to derive the Diversity-Multiplexing Trade-off (DMT) of the DoQF. Our results show that the DoQF achieves the 2 by 1 MISO DMT upper-bound for multiplexing gain<0.25
In this paper we are implemented Sent bit algorithm using prallel programing approach. The parallel sent bit algorithm can be implemented with help of programming language like Open Computing Language. The aim of proposed system is improving performance of existing system by using parallel programming approach. The proposed system must provide modular approach using parallel programming and this modules execute on parallel processors and store the result on shared memory. Modern GPU’s highly parallel structure make them more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel. GPU computing is the use of a GPU (graphics processing unit) together with a CPU to accelerate general-purpose scientific and engineering applications. To process data in parallel CPU contains few number of cores where as GPU contains 100s of cores. We can compare the speed up in the system by measuring time for execution on same data set for both sequential and parallel implementation. It would be interesting to develop a version of SentBit that is optimized to use a GPU processor
P. A. Bailke , Sonal Raghvendra Kulkarni, S. T. Patil
Classification is an important task in data mining, aims to predict the classes of test data objects. This paper deals with the concern of curse of dimensionality in the Text Classification problem using Text Summarization. Classification and association rule mining can produce well-organized as well as precise classifiers than established techniques. However, an associative classification technique still suffers from the vast set of mined rules. Text Summarization on training dataset is proposed modification in existing algorithm. These techniques use the Reuters-21578 dataset to mine rules and then rank the discovered rules to help the user in identifying useful ones. Finally, the obtained outputs have ensured that the performance of the approach has been effectively improved with regards to classification accuracy, number of derived rules and training time.
The Automatic Identification System (AIS) is an automatic tracking system used on ships and by Vessel traffic services (VTS) for identifying and locating vessels by electronically exchanging data with other nearby ships and AIS Base stations. AIS information supplements marine radar, which continues to be the primary method of collision avoidance for water transport. Information provided by AIS equipment consist GPS as unique identification, position, course, and speed, can be displayed on a screen or an Electronic Chart Display Identification System (ECDIS). AIS are a universal ship borne Automatic Identification System recently introduced for traffic monitoring and safety at sea. Ships exchange navigational information to identify and localize vessels in a short range to avoid collisions. AIS signals use OFDM (Orthogonal Frequency Division Multiplexing) for transmission. There is a growing need to develop a system, which provides global maritime surveillance in order to better handle hazardous cargo transports, to improve safety, and to counteract illegal operations and terrorism. Automatic Identification System (AIS) is a UHF digital mobile communication system based on Self-Organized TDMA (SOTDMA) protocol. Both radar and AIS (automatic identification system) are the most important navigational equipment for a ship.
This paper deals with static stress analysis of the Quench tank. The Quenching process is utilized to enhance the hardness and strength of some Automobile parts. The main objective of this paper is to study the theory behind stress analysis of a quench tank due to storage of oil which is the media through which quenching is done. This article uses finite-element analysis to know the stress distribution of a quench tank especially which is designed in rectangular shape. The numerical simulation needs to be carried out to know the required thickness of the plate due to its internal pressure. The stresses developed in this quench tank are analyzed by using ANSYS, a versatile Finite Element Package. The theoretical values and ANSYS values are compared for quench tank analysis. The results can also significantly help in the process of reducing the cost, and improve product reliability decisively.