Raza Akhter (10 results)

Condition: New. pp. 156.

Condition: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | As temporal data are of growing importance hence require to support new trends for filtering and smoothing. The main goal of this work is to explore new smoothing techniques like wavelet filters in place of traditional moving averages and regression methods, etc for temporal data for further decision making processes like forecasting, similarities search and clustering. Temporal applications are increasingly popular methods for studying a wide range of databases including sensor, weather, stock exchange, seismic and atomic, etc. Time series, a common form of temporal data, are mostly contaminated with noise and outliers. The direct operation and analysis on these types of raw data, may easily lead to wrong decisions. The selection of appropriate smoothing techniques is the pivotal point to reach to the correct conclusions. In this work, the main task has been to select the best possible smoothing technique for selection of features of time series to perform various tasks.…

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi s High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance. 156 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi s High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance. …

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Saleemi AfzalDr. M. Afzal Saleemi completed his Ph.D. in Computer Science in 2007 from University of Karachi. He is working as head of Computer Science Department National University of Sciences and Technology, NUST (PMA). His areas .…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Raza S. AkhterDr. Syed Akhter Raza received his Ph.D. from University of Karachi in the year 2011 and having about 20 years teaching experience at various institutions. His research interests include time series modeling, soft comput.…

Condition: New. Print on Demand pp. 156 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 156.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi's High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch. …