A credit card is still a highly common form of installment that allows for cashless purchases and is acceptable both offline and online to conduct payments and other trades. The prevalence of cloned credit cards is rising along with innovation. Budgetary fraud always seems to get worse when international communication gets better. These infringements have generated billions of dollars. These actions are executed with such skill that they vaguely align actual commercial transactions. Simple plan requirements and other less composite techniques won't function as a consequence. In order to lessen confusion and re-establish order, all banks currently need a well-organized approach for identifying credit card fraud. It has taken into consideration a number of common machine learning and deep learning methods for the classification of default accounts, which basically entails analyzing credit card fraud. It consist of synthetic neural networks, decision trees, random forests, and logistic regression (ANN). For every approach, the model has been trained and its accuracy has been assessed. The classification computations are merged to create a sample, and then it is compared to different deep learning and machine learning algorithms.
Sentiment analysis aids in determining if a person's feelings are neutral, negative, or positive. Many machine learning and deep learning algorithms exist for assessing people's attitudes on various social media networks. Many researchers focused on students' emotional identification. The purpose of this paper is to analyze the sentiments of academic students regarding the online class experience conducted during the COVID-19 pandemic situation. For this work, the Term Frequency-Inverse Document Frequency (TF-IDF) model is used for the feature extraction and comparison of eight machine learning models were tested for the classification, such as Support Vector Classifier, Multinomial Naïve Bayes, Decision Tree, K-Nearest-Neighbors (KNN), Random Forest, AdaBoost Classifier, Bagging Classifier, Extreme Gradient Boosting Classifier (XGB) and F-Score, accuracy, precision, and Recall are the performance criteria examined. With a test accuracy of 0.97 and precision of 1.0, Multinomial Naive Bayes achieves the highest accurate model.
Researchers are exploring various methods for effectively predicting prices in the stock market. Useful forecasting systems allow traders to better understand data such as future trends. In addition, investors have a great advantage as the analysis provides future market conditions. One such method is the machine learning algorithms for prediction. The aim of this work is to improve the quality of stock market output as predicted using the value of shares.
Blockchain is a rapidly developing modern technology being used in various fields, including IoT-based healthcare, which is widely used nowadays because of its ability to increase the security, robustness, and reliability of distributed systems. Key features such as data immutability, decentralization, transparency, privacy, and distributed ledger are highly necessary to make blockchain an attractive technology. This paper gives a comprehensive survey of the application of IoT based blockchain technology. IoT and blockchain are required to enable real-time information processing and transaction implementation in an orderly manner. Digital data has become important for health services due to the hectic routine of daily life for patients and doctors. This study sheds light on the use of blockchain for IoT-based healthcare.
This paper focuses on the various types of e-Assets. Today, the openness of e-resources in an academic library is wellknown. It furthermore analyzes the inspiration driving the use of e-Resources, the benefits, subject consideration status and overall client achievements that are looked at by clients while getting to e-resources, and the impact of e-resources on clients.
