An Analysis on Different Methods of Data Mining Techniques with Its Purposes and Issues

It is not wrong to say that the advancement in the technology has a great impact on the human society. It has been altered the way of doing business, providing and receiving the services, managing the organizations etc. The most direct effect is the completed change of 3 information collection, conveying, and exchange. Data mining is an emerging domain that is specifically applied to extract the meaningful data from the large amount of available contents. Various authors have been developed the various prominent methods to for data mining. This study generates a review to the data mining and along with this a brief introduction to the knowledge discovery data base is also given in this work. Difference data extraction processes are cover under this by author

A Modified transmission Algorithm for Resolving Vehicle Routing Problem by Intelligent Water drop Algorithm

A comparison between two technologies ‘Swarm Intelligence’ and ‘Intelligent Water drops’ inorder to overcome the disadvantages of various technologies is an integral concern of this paper . It is nature inspired. IWD algorithm is used to calculate the solutions of the n-queen puzzle with a simple local heuristic. Water of the ocean river easily finds best way from the number of various ways available to reach from its starting to end point. The water drops that flows in rivers has optimal paths that have been obtained by the actions and reactions. With the help of modified IWD algorithm the traveling of salesman problem has also solved. So it is considered as NP-hard Polynomial.NP-hard (Non-deterministic Polynomial-time hard) is a class of problems that are informally,” at least as hard as the hardest problems in NP”. IWD is a fastest algorithm. It provides the minimum distance among the all options. Due to the collaboration of SI and IWD, this algorithm is more efficient. It includes the properties of both SI and IWD. This paper proposes IWD techniques to solve VRP

An Efficient Threshold Based Mixed Noise Removal Technique

Removing or reducing noises from image is very important task in image processing. This paper presents an efficient noise removal technique to restore original digital images corrupted by mixed noise. The proposed filtering technique consists of three steps: noisy pixel detection using fuzzy flag, mixed noise filtering step and calculating threshold value remove the pixel value with replacement conditions. Noises in this methodology are the combination of gaussian noise and salt and pepper noise. This methodology reduces the mixed noise without lossing edges sharpness and information. This methodology gives better results existing many fuzzy algorithms. The proposed technique shows better peak signal noise ratio result with thresholding replacement conditions. Hence, this mixed noise removal technique finds application in numerous segments of image process like digital tv, medical image process, camera, police work systems etc. Wiener filter is used for image enhancement

Prediction of Crop Yield Using LS-SVM

Agriculture sector is the backbone of Indian economy. Yield prediction of crop is very important problem in agriculture field. Predicting the yield is very popular among farmer nowadays, which especially contributes to the correct choice of crop for growing. Earlier, crop production was implemented by considering the farmer's experience on an appropriate field as well as crop. Information technology has an important area to predict the crop production because it includes the latest advancement. In this paper, our aim is to develop the system to achieve high accuracy and resolve the problem of yielding in agriculture. We use the LS-SVM and deep neural network learning technique to predict the yield of crop based physical and chemical feature of plant and soil. This system helps the farmer to take the right decision about sowing particular crop in available land

Spectrum Monitoring Using Fuzzy Logic for OFDM- Based Cognitive Radio Network

Spectrum sensing is a key function of cognitive radio network. This paper presents a spectrum monitoring algorithm according to fuzzy rules for Orthogonal Frequency Division Multiplexing (OFDM) based cognitive radios so that the primary user activity can be detected during the secondary user transmission. A Fuzzy based technique for primary user detection has also been proposed. In comparison with transmitter detection techniques Fuzzy based detection provides good results under low SNR values. This technique improve the jitter and throughput and also probability of false alarm and missed detection is improved

A Modified Cross Correlation Double Talk Detector using Variable Threshold for Acoustic Echo Cancellation

The presence of echo in the communication systems reduces the speech quality and can be overcome by using Acoustic Echo Cancellers (AEC). Acoustic Echo Canceller is a special device which estimates the echo and subtracts it from the microphone signal. Adaptive filters are used for this purpose. Adaptive filters diverge when in addition to far end signal, near end signal is also present. This situation is known as double talk which is handled by double talk detectors (DTD). Cross correlation based DTD is an attractive approach in which cross correlation between two signals give decision parameter. This parameter is compared to constant threshold to give decision. The constant threshold based double talk detector increases the probability of missed detection and increases the residual error of echo canceller. Thus degrades the performance of Acoustic echo canceller. In this paper, a new variable threshold based double talk detector is proposed in which a threshold value is evaluated with respect to the power of near end signal and far end signal. The proposed method shows the reduced probability of missed detections and mean square error, hence improving AEC performance. For updating of the filter coefficients, Variable Step Size Least Mean Square algorithm is used

EVALUATION OF THE ANTILEISHMANIAL EFFICACY OF MEDICINAL PLANT CHENOPODIUM ALBUM LINN. AGAINST EXPERIMENTAL VISCERAL LEISHMANIASIS.

Objective: Visceral leishmaniasis is a neglected tropical disease resulting in death if not properly treated. The ongoing search for better leishmanicidal compounds has brought herbal drugs in to the limelight as safe and effective substitutes to conventional therapies which have various drawbacks. The current study was designed to evaluate the antileishmanial efficacy of medicinal plant Chenopodium album Linn. against L donovani in inbred BALB/c mice.Methods: Inbred BALB/c mice were infected intravenously with 1x107 Leishmania donovani promastigotes and Kept for 30 days. These animals were then treated with two doses (500mg/kg body wt. and 1000mg/kg body wt.) of methanolic extracts of C. album  orally for 7 days.Results: The animals treated with methanolic extracts of C. album, revealed a significant reduction in parasite load. These animals also showed a heightened DTH response and increased IgG2a as an indicator of protective Th1 type of immune response. Moreover, the liver and kidney function tests were found to be in the normal range.Conclusion: Hence the drug proved to be a good antileishmanial but further studies recommended before it is to be tested in higher animal models.Â