Crop cultivation is one of the oldest activities of civilization. For a long time, crop production was carried out based on knowledge passed from generation to generation. However, due to the rapid growth in the human population of the world, human knowledge-based cultivation is not enough to meet the demanding need. To address this issue, the usage of machine learning-based tools has been studied in this paper. An experiment has been carried out over 0.3 million data. This dataset identifies 46 (...) prominent parameters for cultivation, which is collected from the Department of Agriculture Extension, Bangladesh. Comparison between neural networks and numbers of machine learning algorithms has been carried out in this research. It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively. (shrink)

According to the World Health Organisation, three to five million individuals are infected by influenza, and around 250,000 to 500,000 people die of this infectious disease worldwide. Influenza epidemics pose a serious public health threat. Moreover, graver dangers are encountered with influenza subtypes against which there is little or no preexisting human immunity. Such subtypes of influenza have the potential to cause devastating epidemics. Thus, enhancing surveillance systems for the purpose of detecting influenza epidemics in an early stage can quicken (...) response times and save millions of lives. This paper presents three adapting intelligence models: support vector machine regression, artificial neural network using particle swarm optimisation, and our intelligent time series to predict influenza epidemics. The novelty of the current study is that it proposes a new intelligent model to predict influenza outbreaks. The INTS model combines clustering with a time series model to enhance the prediction of influenza outbreaks. The innovation of our proposed model integrates the results obtained from the existing weighted exponential smoothing model with centroids obtained from clustering. We developed a surveillance system for influenza epidemics using Google search queries. The current research is based on a weighted version of the Center for Disease Control and Prevention influenza-like illness activity level obtained from the Center for Disease Control and Prevention data, as well as query data obtained from the Goggle search engine in the USA. The influenza-like illness data was collected from January 4, 2009, to December 27, 2015, stretching across a total time span of 312 weeks. Google Correlate was used to select search queries related to influenza epidemics. In total, 100 search queries were obtained from Google Correlate, 10 of which were better and more relevant search queries selected in this study. The model was evaluated using online Google search queries collected from Google Correlate. Standard measure performance MSE, RMSE, and MAE were employed to estimate the results of the proposed model. The empirical results of the INTS model showed MSE = 0.003, RMSE = 0.036, and MAE = 0.0185, indicating that the errors of the proposed model are very limited. A comparative model of predicting results between the INTS model, alternative Google Flu Trend, and autoregression with Google search data is also presented. The proposed model outperformed the existing models. (shrink)

This paper argues that open-mindedness is a corrective virtue. It serves as a corrective to the epistemic vice of confirmation bias. Specifically, open-mindedness is the epistemically virtuous disposition to resist the negative effects of confirmation bias on our ability to reason well and to evaluate evidence and arguments. As part of the defense and presentation of our account, we explore four discussions of open-mindedness in the recent literature. All four approaches have strengths and shed light on aspects of the virtue (...) of open-mindedness. Each mentions various symptoms of confirmation bias and some explore aspects of the corrective role of open-mindedness. However, ours is the first to explicitly identify open-mindedness as a corrective virtue to the specific epistemic vice of confirmation bias. We show how the corrective account also permits a response to the concern that open-mindedness might not actually count as a virtue. (shrink)

Business Ethics, the Environment & Responsibility, Volume 31, Issue 2, Page 363-385, April 2022.

Respected modern scholars regard the pre-Buddhist philosopher Sañjaya Belaṭṭhiputta—a significant figure in the Buddhist canon—as the originator of the important classical argument- forms known as the catuṣkoṭi and catuṣkoṭi vinirmukta. We argue that the early Buddhist texts do not in fact support this view of the origin of these argument-forms; the question of their origin is open. While it is certainly true that the Pāli Sāmaññaphala Sutta and some of its parallels portray Sañjaya as deploying the catuṣkoṭi, there is nothing (...) in these passages to suggest that he was its originator. The situation concerning the catuṣkoṭi vinirmukta is perhaps even more surprising: There is nothing in the early Pāli texts and their parallels—nor in Buddhagosa’s famous Pāli commentary on the early texts—to show that Sañjaya even deployed the catuṣkoṭi vinirmukta let alone originated it. Further, our investigations also call into question the standard portrayal of Sañjaya as an obfuscator and prevaricator. It appears he may have been a more interesting and able philosopher than the Sāmaññaphala Sutta—and modern accounts based on it—maintain. (shrink)