Analyzing the Student's Risk in using Electronic Gadgets using Hybrid Machine Learning Model as Case Study

An exhaustive deformation in the field of education made a mark in holding the electronic gadgets in the hands of the students for learning today due to the pandemic situation of Covid'19. Since education is the elemental process of every human, the educational institutions and the government put forward the learning procedure through virtual mode of learning. This made the chance of operating the electronic gadgets by the student community in a numerous way than the adults. Data analytics is the process of analyzing the data to infer certain predictions and patterns to make decisions in a better way in future. For data analytics the machine learning algorithms are the essential methods of handling data in predictive analytics. The machine learning algorithms are combined to form a hybrid approach for data analytics to improve the accuracy of the model. This paper reveals the hybrid approach of machine learning model to analyse the student data to analyse the risk of the students using the electronic gadgets during online learning. © 2022 IEEE.

A Hybrid Machine Learning Approach for Customer Segmentation Using RFM Analysis

Due to COVID-19 situation, online retailing (electronic retailing) for purchasing goods has recently increased which leads to the need of customer segmentation. Customer segmentation is done based on customers’ past purchase behavior and then divide them into different categories, i.e., loyal customer, potential customer, new customer, customer needs attention, customers require activation. This paper uses recency, frequency, monetary value (RFM) analysis and K-means clustering technique for grouping the customers. Further to enhance the efficiency of segmentation, a decision tree is used to create nested splitting (based on Gini index) inside the each cluster. The implementation of proposed hybrid approach is showing promising results for customer segmentation to take better management decisions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Evolutionary Optimization of COVID-19 Vaccine Distribution with Evolutionary Demands

Vaccination uptake has become the key factor that will determine our success in containing the COVID-19 pandemic. Efficient distribution of vaccines to inoculation spots is crucial to curtailing the spread of the novel coronavirus pneumonia (COVID-19) pandemic. Normally, in a big city, a huge number of vaccines need to be transported from central depot(s) through a set of satellites to widely-scattered inoculation spots by special-purpose vehicles every day. Such a large two-echelon vehicle routing problem is computationally difficult. Moreover, the demands for vaccines evolve with the epidemic spread over time, and the actual demands are hard to determine early and exactly, which not only increases the problem difficulty but also prolongs the distribution time. Based on our practical experience of COVID-19 vaccine distribution in China, we present a hybrid machine learning and evolutionary computation method, which first uses a fuzzy deep learning model to forecast the demands for vaccines for each next day, such that we can pre-distribute the forecasted number of vaccines to the satellites in advance;after obtaining the actual demands, it uses an evolutionary algorithm (EA) to route vehicles to distribute vaccines from the satellites/depots to the inoculation spots on each day. The EA saves historical problem instances and their high-quality solutions in a knowledge base, so as to capture inherent relationship between evolving problem inputs to solutions;when solving a new problem instance on each day, the EA utilizes historical solutions that perform well on the similar instances to improve initial solution quality and hence accelerate convergence. Computational results on real-world instances of vaccine distribution demonstrate that the proposed method can produce solutions with significantly shorter distribution time compared to state-of-the-arts, and hence contribute to accelerating the achievement of herd immunity. IEEE

A hybrid machine learning approach of fuzzy-rough-k-nearest neighbor, latent semantic analysis, and ranker search for efficient disease diagnosis

Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12% of the primary tumor, 96.45% of breast cancer Wisconsin, 94.44% of cryotherapy, 93.81% of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively.