Data mining is a critical phase in the information discovery process in databases, and it is a prominent subfield in knowledge management. In the future decades, data mining research will continue to flourish in business and learning organizations. The uses This review research investigates a variety of data mining approaches that have been created to aid in the knowledge management process. The findings are divided into four categories: I knowledge resource; II knowledge categories and/or datasets; III data mining tasks; IV data mining approaches and applications in knowledge management The definition of data mining and data mining functionality are briefly described in the first section of the article. The reasons for knowledge management and the primary knowledge management instruments used in the knowledge management cycle consists of then discussed. Finally, the use of data mining tools between knowledge management pask is described and addressed.
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The design and simulation done on the rectangular micro-strip patch antenna for the ISM band of frequencies is studied in this paper. Micro-strip line feed is used in this paper. The resonance frequency is found between 2.4-2.5 GHz. This band of frequencies is ISM band of wireless applications. The design and simulation are done in Computer Simulation Technology (CST) software with FR4 substrate. By using the length and width expressions, the size of the antenna is calculated. Then the antenna is simulated for the radiation parameters obtained through optimizing and matching to meet the requirements. The parameters of antenna such as Reflection coefficient, Gain, VSWR and Band width are measured.
Srijal, Akanksha Singh,Deepti Shikha Ojha,Surbhi Tripathi Ms. Shweta Mayor Sabharwal
Aditya Narayan Singh, Rahul Kumar Sharma, Aman Agarwal, Amarendra Mishra1 Mukul Pundhir, Anant Vijay
As network technology and entertainment production have evolved, the types of movies available have become increasingly diverse, leaving viewers bewildered as to how to choose among them. The recommendation system was intended to make the selection process more efficient. Users are given product or service recommendations by these systems. These types of solutions have also improved the quality and efficiency of decision-making. A variety of machine learning algorithms may be used to create the recommendation system. Among these methods, we're using Content Based Filtering, Collaborative Based Filtering, k-mean clustering, and the Naive Bayes classifier. The experimental investigation with a cold start revealed the potential of all of these strategies by demonstrating a large improvement in accuracy.