Python
A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection
Aim:
People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of credit cards, the capacity of credit card misuse has also enhanced. Credit card frauds cause significant financial losses for both credit card holders and financial companies. The main aim is to detect fraudulent transactions using credit cards with the help of ML algorithms and deep learning algorithms.
Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy
Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening
Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms
Design and Development of Integrated Human Resource Management System with Face Recognition Attendance
Aim:
Our study aims to design and develop a smart human resource and attendance management system using facial recognition technology to automate employee identification and attendance tracking. The system leverages AI-based face detection to ensure accuracy, eliminate proxy attendance, and streamline HR processes, thereby improving workforce efficiency, data security, and transparency within the organization.
Design of face detection and recognition system monitor students during online examinations using Machine Learning algorithms
Detecting Spam Email with Machine Learning Optimized with Harris Hawks’s optimizer (HHO) Algorithm
Plant Disease Detection Using Machine Learning Techniques
Predicting Heart Diseases Using Machine Learning and Different Data Classification Techniques
The Effect of Fake Reviews on e-Commerce during and After Covid-19 Pandemic: SKL-Based Fake Reviews Detection
