Creditcard fraud detection mechanism is the most needed one in the present world. This is due to the development of e-commerce and online transaction. The main aim of this paper is to predict fraudulent transactions in a credit card transaction data set using statistical methods. Statistical methods based on Logistic Regression and Random Forest are developed and applied to credit card fraud detection problems. This work mainly focuses on machine learning algorithms as a classification model. The classification model is used for categorizing the dataset into fraud and non-fraudulent transactions. This project comprises methods for analyzing the fraud data to extract meaningful statistics and other characteristics of the data. The use of a model is to predict fraud with different statistical methods. This paper is one of the first to compare the performance of Logistic Regression and Random Forest methods in credit card fraud detection with a transaction data set using R. The results of the two algorithms are based on accuracy, precision, recall, and F1-score. The ROC curve is plotted based on the confusion matrix. The Random Forest and the Logistic Regression algorithms are compared and the algorithm that has the greatest accuracy, precision, recall, and F1 score is considered the best algorithm that is used to detect fraud.
The primary resource of a country is agriculture and crop production. The economic development of the country also resides on the agricultural products which ultimately determines the growth of the citizen. The major crisis in food production is the influence of diseases in plants. This ultimately abolish the economy of the country, as major portion of progress of the nation is dependent on agriculture and its products. The challenges faced by the farmers are the unawareness of the various diseases that affects different parts of the plants. They should be able to identify the early infection caused in plants by different pathogens like bacteria, fungi, virus etc., Main disease-causing agent is found to be the fungus which was the vital factor that produce serious loss in the agriculture. Again, the pesticides and fertilizers used by the agriculturist changes to be hazardous for human beings and wild life species. This problem should be considered as a chief calamity and an alternate measure must be found to support the cultivators. An innovative step adopted by the researchers are prompt detection of the diseases using machine learning and deep learning algorithms. These algorithms use different image processing techniques and computer vision process to classify the disease in plant parts at an earlier stage. This paper provides a detailed review on the fungal infection caused in plant leaves and its identification using deep learning methodology.
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