IEEE

A Novel AI Framework for Breast Cancer Molecular Biomarker Response Score Detection on Cells Level Using Marker-Based Watershed Segmentation and Machine Learning Classifiers

Abstract: Breast cancer is a highly complex disease that requires precise molecular subtyping to guide tailored treatment strategies. In this study, we employed a marker-based watershed segmentation ...
IEEE

Imbalanced Data Problem in Machine Learning: A Review

Abstract: One of the prominent challenges encountered in real-world data is an imbalance, characterized by unequal distribution of observations across different target classes, which complicates ...
IEEE

Evaluating the Performance of Clinical data using Machine learning Approach– An Ensemble Model

Abstract: In the rapidly evolving field of healthcare, accurate clinical predictions are paramount for effective disease management and treatment planning. This paper introduces a novel ensemble ...
IEEE

A Comparative Study of Machine Learning Algorithms for Predicting Customer Churn: Analyzing Sequential, Random Forest, and Decision Tree Classifier Models

Abstract: Predicting customer churn is vital for businesses aiming to retain their customer base and enhance profitability. This study investigates the effectiveness of three prominent machine ...
IEEE

Enhancing Customer Retention: A Federated Machine Learning Framework for Banking Churn Prediction

Abstract: Customer attrition has become the significant challenge for the bank, making large volume of customers to migrate to other banks, as the banks keeps providing multiple benefits to the ...
IEEE

Forest Fire Forecasting Leveraging Modis Satellite Fire Data Using Machine Learning For Jharkhand State, India

Abstract: Forest fires pose significant environmental and economic threats, necessitating robust predictive models for better management and prevention. This study focuses on the temporal and spatial ...
IEEE

Analyzing Customer Feedback on Amazon to Reveal Sentiment Trends using Machine Learning

Abstract: Online reviews on platforms like Amazon provide valuable insights into customer experiences, but manually processing this data is time-consuming and impractical. This paper “Analyzing ...
IEEE

Harnessing Machine Learning for Effective Customer Segmentation

Abstract: The paper’s objective is to utilize machine learning techniques for customer segmentation. We aim to identify significant patterns and segments within a customer base to enhance marketing ...
IEEE

Optimizing Customer Satisfaction Through Sentiment Analysis: A BERT-Based Machine Learning Approach to Extract Insights

Abstract: In the era of digital transformation, customer feedback has become crucial for improving service quality. This study aims to enhance customer satisfaction through sentiment analysis ...
IEEE

Land Cover Segmentation for Agricultural Monitoring Using Synthetic Aperture Radar (SAR) Imagery with Convolutional Neural Networks and Attention-Based Transformer Models

Abstract: Automated classification of land cover types is one of the most crucial aspects within many remote sensing applications, including the monitoring of agricultural land and territories. This ...
IEEE

Customer Segmentation using Mini Batch K-Means Clustering Algorithm

Abstract: Today, especially in this era of data, everything is becoming more mechanized. Data is the new electricity of the globe. This knowledge may help a lot of businesses in their marketing ...
IEEE

Machine Learning Insights for Predicting Customer Behavior in Online Retail

Abstract: The research work reports a comprehensive analysis of predictive analytics of customer behavior on e-commerce based on high-end data science and artificial intelligence techniques. The ...