The data preprocessing step is an important step in web usage mining because of the nature of log data, which are heterogeneous, unstructured, and noisy. Given the scalability and efficiency of algorithms in pattern discovery, a preprocessing step must be applied. In this study, the sequential methodologies utilized in the preprocessing of data from web server logs, with an emphasis on sub-phases, such as session identification, user identification, and data cleansing, are comprehensively evaluated and meticulously examined.
In Computer-based applications, there is a need for simple, low-cost devices for user authentication. Biometric authentication methods namely keystroke dynamics are being increasingly used to strengthen the commonly knowledge based method (example a password) effectively and cheaply for many types of applications. Due to the semi-independent nature of the typing behavior it is difficult to masquerade, making it useful as a biometric. In this paper, C4.5 approach is used to classify user as authenticated user or impostor by combining unigraph features (namely Dwell time (DT) and flight time (FT)) and digraph features (namely Up-Up Time (UUT) and Down-Down Time (DDT)). The results show that DT enhances the performance of digraph features by i
... Show MoreThe work in this research presents an experimental and a theoretical study to obtain the effect of using a low permeability geosynthetic material on the longitudinal and lateral coefficients of dispersion. This would have its effect on the contaminants migration through an isotropic, homogenous and saturated soil. The first stage of this research involves the study of the geosynthetic material and in calculating the longitudinal and lateral coefficients of dispersion for an Iraqi sandy soil by using an experimental set-up to simulate the processes. To investigate the effect of using a geosynethtic material on the dispersion coefficients, the test was conducted for each velocity that was used in the experimental work and as follows: with
... Show MoreExperimental activity coefficients at infinite dilution are particularly useful for calculating the parameters needed in an expression for the excess Gibbs energy. If reliable values of γ∞1 and γ∞2 are available, either from direct experiment or from a correlation, it is possible to predict the composition of the azeotrope and vapor-liquid equilibrium over the entire range of composition. These can be used to evaluate two adjustable constants in any desired expression for G E. In this study MOSCED model and SPACE model are two different methods were used to calculate γ∞1 and γ∞2
Meta stable phase of SnO as stoichiometric compound is deposited utilizing thermal evaporation technique under high vacuum onto glass and p-type silicon. These films are subjected to thermal treatment under oxygen for different temperatures (150,350 and 550 °C ). The Sn metal transformed to SnO at 350 oC, which was clearly seen via XRD measurements, SnO was transformed to a nonstoichiometric phase at 550 oC. AFM was used to obtain topography of the deposited films. The grains are combined compactly to form ridges and clusters along the surface of the SnO and Sn3O3 films. Films were transparent in the visible area and the values of the optical band gap for (150,350 and 550 °C ) 3.1,
The study presents the performance of flexural strengthening of concrete members exposed to partially unbonded prestressing with a particular emphasis on the amount (0, 14.2, and 28.5%) of cut strands-symmetrical and asymmetrical damage. In addition to examining the influence of cut strands on the remaining capacity of post-tensioned unbonded members and the effectiveness of carbon fiber reinforced polymer laminates restoration, The investigated results on rectangular members subjected to a four-point static bending load based on the composition of the laminate affected the stress of the CFRP, the failure mode, and flexural strength and deflection are covered in this study. The experimental results revealed that the usage of CFRP la
... Show MoreChannel estimation (CE) is essential for wireless links but becomes progressively onerous as Fifth Generation (5G) Multi-Input Multi-Output (MIMO) systems and extensive fading expand the search space and increase latency. This study redefines CE support as the process of learning to deduce channel type and signal-tonoise ratio (SNR) directly from per-tone Orthogonal Frequency-Division Multiplexing (OFDM) observations,with blind channel state information (CSI). We trained a dual deep model that combined Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks (BRNNs). We used a lookup table (LUT) label for channel type (class indices instead of per-tap values) and ordinal supervision for SNR (0–20 dB,5-dB steps). T
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