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.

The aim of the research is to identify the trends of Saudi society towards visiting art galleries and museums in light of economic and social transformations after the implementation of the 2030 vision plans. The importance of the research is to track the reasons behind the reluctance of members of society to visit such cultural institutions, and to plan proposals and recommendations for how to attract members of society and motivate them. This research followed the descriptive approach, and the study sample reached 103 members of the Saudi community. several research tools were used in this research such as questionnaire, personal interviews in addition to the researcher notes. The results concluded that the art galleries attract the ap

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the proposed LAD-Atan estimator

         The issue of penalized regression model has received considerable critical attention to variable selection. It plays an essential role in dealing with high dimensional data. Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently. However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution. While the least absolute deviation is a good method to get robustness in regression estimation. The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once. Simulation experiments and real data applications show that the p

2-benzamide benzothiazole complexes of Pd(II) , Pt(IV) and Au(III) ions were prepared by microwave assisted radiation. The ligand and the complexes were isolated and characterized in solid state by using FT-IR, UV-Vis spectroscopy, flame atomic absorption, elemental analysis CHNS , magnetic susceptibility measurements , melting points and conductivity measurements. The nature of complexes in liquid state was studied by following the molar ratio method which gave results approximately identical to those obtained from isolated solid state; also, stability constant of the prepared complexes were studied and found that they were stable in molar ratio 1:1.The complexes have a sequar planner geometry except Pt(IV) complex has octahedral .

There are many tools and S/W systems to generate finite state automata, FSA, due to its importance in modeling and simulation and its wide variety of applications. However, no appropriate tool that can generate finite state automata, FSA, for DNA motif template due to the huge size of the motif template. In addition to the optional paths in the motif structure which are represented by the gap. These reasons lead to the unavailability of the specifications of the automata to be generated. This absence of specifications makes the generating process very difficult. This paper presents a novel algorithm to construct FSAs for DNA motif templates. This research is the first research presents the problem of generating FSAs for DNA motif temp

The transmitting and receiving of data consume the most resources in Wireless Sensor Networks (WSNs). The energy supplied by the battery is the most important resource impacting WSN's lifespan in the sensor node. Therefore, because sensor nodes run from their limited battery, energy-saving is necessary. Data aggregation can be defined as a procedure applied for the elimination of redundant transmissions, and it provides fused information to the base stations, which in turn improves the energy effectiveness and increases the lifespan of energy-constrained WSNs. In this paper, a Perceptually Important Points Based Data Aggregation (PIP-DA) method for Wireless Sensor Networks is suggested to reduce redundant data before sending them to the