The paper describes the concept of plaintext encryption by using the Unicode characters. In the case of elliptic curve cryptography, there is not specified rule or algorithm to specify the letters of Tifinagh as well ...The paper describes the concept of plaintext encryption by using the Unicode characters. In the case of elliptic curve cryptography, there is not specified rule or algorithm to specify the letters of Tifinagh as well as special characters. So, the paper gives the transformation of characters Tifinagh into points on elliptic curve by using the corresponding characters Latin. The obtained correspondence has been applied in Menezes-Vanstone cryptosystem based on elliptic curve. Therefore, the paper explains in detail its implementation in Maple 12.展开更多
Date palm production is critical to oasis agriculture,owing to its economic importance and nutritional advantages.Numerous diseases endanger this precious tree,putting a strain on the economy and environment.White sca...Date palm production is critical to oasis agriculture,owing to its economic importance and nutritional advantages.Numerous diseases endanger this precious tree,putting a strain on the economy and environment.White scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates.When an infestation reaches a specific degree,it might result in the tree’s death.To counter this threat,precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary.This decision is crucial for farmers who wish to minimize yield losses while preserving production quality.For this purpose,we propose a feature extraction and machine learning(ML)technique based framework for classifying the stages of infestation by white scale disease(WSD)in date palm trees by investigating their leaflets images.80 gray level co-occurrence matrix(GLCM)texture features and 9 hue,saturation,and value(HSV)color moments features are extracted from both grayscale and color images of the used dataset.To classify the WSD into its four classes(healthy,low infestation degree,medium infestation degree,and high infestation degree),two types of ML algorithms were tested;classical machine learning methods,namely,support vector machine(SVM)and k-nearest neighbors(KNN),and ensemble learning methods such as random forest(RF)and light gradient boosting machine(LightGBM).The ML models were trained and evaluated using two datasets:the first is composed of the extracted GLCM features only,and the second combines GLCM and HSV descriptors.The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%.The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease(DPWSD)and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.展开更多
文摘The paper describes the concept of plaintext encryption by using the Unicode characters. In the case of elliptic curve cryptography, there is not specified rule or algorithm to specify the letters of Tifinagh as well as special characters. So, the paper gives the transformation of characters Tifinagh into points on elliptic curve by using the corresponding characters Latin. The obtained correspondence has been applied in Menezes-Vanstone cryptosystem based on elliptic curve. Therefore, the paper explains in detail its implementation in Maple 12.
文摘Date palm production is critical to oasis agriculture,owing to its economic importance and nutritional advantages.Numerous diseases endanger this precious tree,putting a strain on the economy and environment.White scale Parlatoria blanchardi is a damaging bug that degrades the quality of dates.When an infestation reaches a specific degree,it might result in the tree’s death.To counter this threat,precise detection of infected leaves and its infestation degree is important to decide if chemical treatment is necessary.This decision is crucial for farmers who wish to minimize yield losses while preserving production quality.For this purpose,we propose a feature extraction and machine learning(ML)technique based framework for classifying the stages of infestation by white scale disease(WSD)in date palm trees by investigating their leaflets images.80 gray level co-occurrence matrix(GLCM)texture features and 9 hue,saturation,and value(HSV)color moments features are extracted from both grayscale and color images of the used dataset.To classify the WSD into its four classes(healthy,low infestation degree,medium infestation degree,and high infestation degree),two types of ML algorithms were tested;classical machine learning methods,namely,support vector machine(SVM)and k-nearest neighbors(KNN),and ensemble learning methods such as random forest(RF)and light gradient boosting machine(LightGBM).The ML models were trained and evaluated using two datasets:the first is composed of the extracted GLCM features only,and the second combines GLCM and HSV descriptors.The results indicate that SVM classifier outperformed on combined GLCM and HSV features with an accuracy of 98.29%.The proposed framework could be beneficial to the oasis agricultural community in terms of early detection of date palm white scale disease(DPWSD)and assisting in the adoption of preventive measures to protect both date palm trees and crop yield.
