IEEE

Solar Panels Segmentation in Remote Sensing Data Using Segment Anything Models 2 and 2.1

Abstract: This letter provides insights on the effectiveness of the zero-shot, prompt-based Segment Anything Model (SAM) and its updated versions, SAM 2 and SAM 2.1, along with the nonpromptable ...
IEEE

Medical Image Segmentation for Anomaly Detection Using Deep Learning Techniques

Abstract: Medical images are the standard approach for the analysis and diagnosis of critical issues of diseases. To minimize the time-consuming inspection and evaluation process of the medical images ...
IEEE

On-the-Fly Improving Segment Anything for Medical Image Segmentation Using Auxiliary Online Learning

Abstract: The current variants of the Segment Anything Model (SAM), which include the original SAM and Medical SAM, still lack the capability to produce sufficiently accurate segmentation for medical ...
IEEE

Data-Driven Solutions for Human Trafficking: Detection and Impact Analysis Using Machine Learning

Abstract: Human trafficking is a serious problem that requires new ideas and data-driven approaches to identify and solve. In this study, we analyze large datasets to identify patterns and anomalies ...
IEEE

Few-Shot Weakly Supervised Segmentation for Retinal Fundus Images using Meta-Learning

Abstract: Automated segmentation of the optic disc (OD) and the optic cup (OC) in retinal fundus images plays a pivotal role in early glaucoma diagnosis. Many studies have employed deep learning ...
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

A Deep Learning-Based Image Segmentation Model for Extracting Migratory Birds and Insects Echoes Using Polarimetric Weather Radar Data

Abstract: Weather radars are essential for monitoring airborne animals, including birds and insects. Despite the potential of dual-polarization radar moments for echo classification, extracting ...
IEEE

Cluster Boost: Stacked Machine Learning Model for Customer Behaviour Analysis in E-commerce

Abstract: This study focuses on the development of a stacked model, named Cluster Boost, which integrates K-means clustering and Gradient Boosting to analyse customer behaviour in e-commerce. Cluster ...
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

Deep Learning-Based Flood Detection System Using Semantic Segmentation

Abstract: Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to ...
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

Enhancing Nucleus and Cytoplasm Segmentation in Cervical Cytology Using Deep Learning

Abstract: Cervical cancer, with over 660,000 cases in 2022, ranks as the fourth most frequent malignancy in women globally, with low- and middle-income nations accounting for approximately 94% of the ...