Air Quality prediction using Multinomial Logistic Regression

Nowadays, Artificial Intelligence (AI) plays a primary role in different applications like medicine, science, health, and finance. In the past five decades, the development and progress of technology have allowed artificial intelligence to take an essential role in human life. Air quality classification is an excellent example of this role. The use of AI in this domain allows humans to predict whether the air is polluted or not. In effect, monitoring air quality and providing periodic and direct statistics are essential requirements to ensure good air quality for individuals in the community. For this reason, a decision-making system is built to decide whether the air is clean or not. Based on this system's decision, necessary practices and measures are taken to improve air quality and ensure air sustainability. In this paper, the multinomial logistic regression technique is used to detect the air pollution level. The proposed method is applied to a real dataset that consists of 145  responses recorded from an air quality multi-sensor device containing chemical sensors. The used device was placed in New York City, USA, from 1/1/2021 to 7/1/2021 (one week) and is freely available for air quality sensors deployed in the field. The result shows the efficacity of this method in air pollution prediction

Credit Card Fraud Detector Based on Machine Learning Techniques

The massive development of technology has affected commerce and given rise to e-commerce and online shopping. Nowadays, consumers prioritize e-shopping over the brick and motor stores due to numerous benefits, including time and transport convenience. However, this progressive upsurge in online payment increases the number of credit card frauds. Therefore, defending against fraudsters’ activity is obligatory and can be achieved by securing credit card transactions. The objective of this paper is to build a model for credit card fraud detection using Machine learning techniques. An innovative approach to credit card fraud detection grounded on machine learning is proposed in this study. Machine learning (ML) is an artificial intelligence subfield comprising learning techniques from experience and completing tasks without being explicitly programmed. Three ML techniques have been used: Support vector machine, logistic regression, Random Forest, and Artificial Neural network. First, the most significant features that affect the type of transaction (fraud or not fraud) have been selected. After that, the ML model was applied. The performance of the proposed approach is tested using a confusion matrix, recall, precision, f-measure, and accuracy. The proposed method is tested using accurate data that consists of 284807 transactions. The result shows the efficiency of the proposed approach