A comparison between Asian and Australasia backpackers using cultural consensus analysis

This study tests the differences in the shared understanding of the backpacker cultural domain between two groups: backpackers from Australasia and backpackers from Asian countries. A total of 256 backpackers responded to a questionnaire administered in Kuala Lumpur, Bangkok and Krabi Province (Thailand). Cultural consensus analysis (CCA) guided the data analysis, to identify the shared values and the differences in the backpacker culture of the two groups. The findings revealed that while the two groups share some of the backpacker cultural values, some other values are distinctively different from one another. The study provides the first empirical evidence of the differences in backpacking culture between the two groups using CCA. Based on the study findings, we propose some marketing and managerial implications

Machine Learning Based Automatic Leaf Diseases Detection

The method for applying machine learning to automatically detect leaf diseases is presented in this paper. A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was compiled and pre-processed. Accuracy, precision, and recall measures were used to assess the machine learning algorithm after it had been trained on the labeled dataset. According to the findings, the algorithm was very precise and recallable in its ability to detect leaf illnesses, making it a potential method for practical use. This strategy may help with early leaf disease identification and prevention, increasing crop productivity and lowering the demand for toxic pesticides. Here we are identifying the Bacterial spot, Early blight

A comparison between Asian and Australasia backpackers using cultural consensus analysis

This study tests the differences in the shared understanding of the backpacker cultural domain between two groups: backpackers from Australasia and backpackers from Asian countries. A total of 256 backpackers responded to a questionnaire administered in Kuala Lumpur, Bangkok and Krabi Province (Thailand). Cultural consensus analysis (CCA) guided the data analysis, to identify the shared values and the differences in the backpacker culture of the two groups. The findings revealed that while the two groups share some of the backpacker cultural values, some other values are distinctively different from one another. The study provides the first empirical evidence of the differences in backpacking culture between the two groups using CCA. Based on the study findings, we propose some marketing and managerial implications

Machine Learning Based Automatic Leaf Diseases Detection

The method for applying machine learning to automatically detect leaf diseases is presented in this paper. A convolutional neural network was used to extract pertinent features from leaf image datasets that included healthy and diseased leaves. The dataset was compiled and pre-processed. Accuracy, precision, and recall measures were used to assess the machine learning algorithm after it had been trained on the labeled dataset. According to the findings, the algorithm was very precise and recallable in its ability to detect leaf illnesses, making it a potential method for practical use. This strategy may help with early leaf disease identification and prevention, increasing crop productivity and lowering the demand for toxic pesticides. Here we are identifying the Bacterial spot, Early blight