The study focuses on preprocessing techniques of web mining. Considering this scope, the study has proposed and implemented an efficient data cleaning and unique user identification algorithms. Previously proposed data cleaning algorithm is a generalized approach and lacked transparency. An appropriate model has to be used to implement the new data cleaning algorithm. Over analysis of various related studies and suggestions made by eminent experts, the study finalized decision tree classification model, and appropriate model to implement the new data cleaning algorithm. Simplicity, ease in framing rules and ability to fragment complex decisions to solve a problem motivated to choose decision tree classification model to implement new data cleaning algorithm. Apart from this the study has also modified the previously proposed hash function, used to locate existing web users in web log server. A new error factor is introduced to remove memory address discrepancy. The modified hashing function along with binary search techniques is used to design the new unique user identification algorithm. Various experiments analysis is done using web log servers of eminent universities and colleges from United Arab Emirates and India. Results obtained prove the improved and better performances of the new rule based data cleaning and modified unique user identification algorithms.
As mobile devices become more efficient, we will be using them to accomplish our daily tasks and even to handle emergency situations. One application that falls into this category is this Application developed for the Android Phones. The prime objective of this application is to create a full-fledged android application which could locate a list of nearest hospitals and police stations based and also can map the path [8] from the location to the respective nearest hospitals from the current location and helps the user to notify the hospitals regarding the emergency and police station regarding the crimes as well as the accidents. The user not only finds all the hospitals and police stations in the city, but also can collect evidence in case of nuisance.
Recently machine learning algorithms are utilized for identifying network threats. Threats otherwise called as intrusions, will harm the network in a stern manner, thus it must be dealt cautiously. In the proposed research work, a deep learning model has been applied to recognize and categorize unanticipated and unpredictable cyber-attacks. The UNSW NB-15 dataset has a vital number of features which will be learned by the hidden layers present in the suggested model and classified by the output layer. The suitable quantity of layers, neurons in each layer and the optimizer utilized in the proposed work are obtained through a sequence of trial and error experiments. The concluding model acquired can be utilized for estimating future malicious attacks. There are several data preprocessing techniques available at our disposal. We used two types of techniques in our experiment: 1) Log transformation, MinMaxScaling and factorize technique; and 2) Z-score encoding and dummy encoding technique. In general, the selection of data preprocessing techniques has a direct impact on the output performed by any machine learning process and our research, attempts to prove this concept.
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