Online learning is one of the educational solutions for students during the COVID-19 pandemic. Worldwide, most universities have shifted much of their learning frameworks to an online learning model to limit physical interaction between people and slow the spread of COVID-19. The effectiveness of online learning depends on many factors, including student and instructor self-efficacy, attitudes, and confidence in using the technology involved; the educational strategies employed; the ability to monitor and evaluate educational outcomes; and student motivation, among many others. In this study, we analyzed how these factors were associated and impacted each other. We developed a comprehensive model after an extensive review of the relevant literature. The model was validated by applying partial least square regression to the data obtained by surveying 469 students who were enrolled in online education. The test results indicated that all the variables had a positive effect on the effectiveness of online learning. The effectiveness of online learning had a significant impact on the benefits of online learning. This showed that the more effective online learning was, the more benefits and positive outcomes the student experienced. The result of this research showed that learning objectives could enable universities to increase the effectiveness of students’ online learning by motivating students to join online classes and developing appropriate learning strategies for their individual needs.

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The term “Business intelligence” is described as a plan or a strategy where the operations like reporting, data analysis, data mining, event processing are performed to improve the production and growth of a business enterprise or a business entity. And on the other hand, the “Knowledge management” is explained as well-organized management of resources and information within a commercial organization it can be a business too. Almost all business will have limitations and challenges which can be also known as the business problems. One of the main business problem is demand, the business plans must work according to the demand of the consumers. Analyzing the demand would provide the solutions for queries like what is the business trend? What is the need of the users? What should be the improvement make in the production? Where is the current position of the enterprise? And who all will be the competitors? For the predictive analysis a dataset of bitcoin is taken. The major aim of the study is to implement the strategies to overcome the business problems mainly the demand prediction. And the objective is to find out the relevant issues and the remedies by using knowledge management and business intelligence to the common business problems. The dataset has columns called lowest price, highest price, open price, close price, trading volume and market capital. The research methodology used is predictive analysis using PCA and K-means clustering algorithm. By this dataset predictive plots are developed as achieved results for easy analysis by using research methodology. PCA and K-means are the algorithm used for accurate prediction. The importance of study is to predict the future sale, as it is very essential for a business enterprise to find future demand so that the organization can improve production.

Purpose: This study aims to enhance the performance of Knowledge management (KM). Additionally, the advantages and the applications of this export system and the smart systems are analyzed.   Theoretical framework: Selecting an algorithm isn’t an easy process. With a deep exploration of techniques and algorithms, the appropriate algorithm should be chosen and implemented to ascertain the solution for the problems like analyzing the trend of the business, identifying the age group, and finding the most desired articles and publications.   Design/methodology/approach: Contented and Expressive review approaches are implemented to conduct the research. The investigators significantly studied the materials associated with robots and expert systems in the reference to knowledge management in libraries. The results are obtained using the data visualization tool tableau. the Genetic algorithm is also used to analyze the results.   Findings: Smart systems are not easy to implement in knowledge management because knowledge management contains a large number of datasets. It has to be categorized first, then needs to be analyzed and the decisions must be taken accordingly.   Research, Practical & Social implications: The expert systems and the robots are to be implemented in the KM so the knowledge management will have enhanced performance with the help of the implementation of smart systems.   Originality/value:  In the study, the Genetic algorithm is used to find the analysis results. This algorithm was chosen because it works well in a noisy environment and is also easy to understand along with this GA is compared with the neural network algorithm.

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