Data mining involves the computational process to find patterns from large data sets. Classification, one of the main domains of data mining, involves known structure generalizing to apply to a new dataset and predict its class. There are various classification algorithms being used to classify various data sets. They are based on different methods such as probability, decision tree, neural network, nearest neighbor, boolean and fuzzy logic, kernel-based etc. In this paper, we apply three diverse classification algorithms on ten datasets. The datasets have been selected based on their size and/or number and nature of attributes. Results have been discussed using some performance evaluation measures like precision, accuracy, F-measure, Kappa statistics, mean absolute error, relative absolute error, ROC Area etc. Comparative analysis has been carried out using the performance evaluation measures of accuracy, precision, and F-measure. We specify features and limitations of the classification algorithms for the diverse nature datasets.
In today’s economic world, the advancement in technology has opened up new forms of economic activities, particularly business. Whilst entrepreneurship is a major factor in business, e-entrepreneurship has become a buzzword facilitated by the rapid advancement of internet and developments in Information and Communication Technologies (ICTs). E-entrepreneurship, in the name of transforming business from the local marketplace to the global one, has revolutionized the entire business processes. This set of new business mechanism has created new opportunities for the startups, which in this regard is termed as e-startups. The purpose of this paper, therefore, is to develop a comprehensive understanding of the concept of eentrepreneurship by addressing related potentials and challenges. Extant literature has been reviewed to this end. The analysis indicated that flexibility of and accessibility to technology and products, less capital and risk in comparison to physical businesses are the major advantages that an e-entrepreneur might enjoy while commencing an e-startup. On the other hand, lack of institutional support, digital security threat, tough competition with established brands, less innovation and lack of academic and practical exposure in terms of business and marketing are some barriers that challenge the operation of e-startups. The conclusion of the paper draws on some recommendations accordingly.
In today’s multifaceted academic context, selecting, adopting, and adapting appropriate teaching methods (TMs) have been a pivotal concern for teachers. No study, to the researchers’ knowledge, has been conducted on compiling the maximum number of TMs in higher education. This study aims to list, describe, and provide a platform of the potential and the most practicing TMs in four major educational disciplines. This article, taking a cross-disciplinary lens, conducts an in-depth review of 90 articles and enumerates 110 TMs of higher education. It also identifies several TMs that are commonly used in each discipline. The article concludes that knowledge generated from this study fills up the existing literature gap. It calls attention to the current TM practices and provides teachers with an outline to employ available TMs in their respective disciplines.
Rock-fall is a natural threat resulting in many annual economic costs and human casualties. Constructive measures including detection or prediction of rock-fall and warning road users at the appropriate time are required to prevent or reduce the risk. This article presents a hybrid early warning system (HEWS) to reduce the rock-fall risks. In this system, the computer vision model is used to detect and track falling rocks, and the logistic regression model is used to predict the rock-fall occurrence. In addition, the hybrid risk reduction model is used to classify the hazard levels and delivers early warning action. In order to determine the system’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and reliability. The results show that the overall system accuracy was 97.9%, and the reliability was 0.98. In addition, a system can reduce the risk probability from (6.39 × 10−3) to (1.13 × 10−8). The result indicates that this system is accurate, reliable, and robust; this confirms the purpose of the HEWS to reduce rock-fall risk.
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