A comprehensive review of diabetic retinopathy detection and grading based on deep learning and metaheuristic optimization techniques
Diabetic retinopathy (DR) is a microvascular disorder that causes retinal damage and
irreversible blindness. It is a condition instigated by prolonged Diabetes Mellitus. According …
irreversible blindness. It is a condition instigated by prolonged Diabetes Mellitus. According …
Diabetic retinopathy detection using supervised and unsupervised deep learning: a review study
The severe progression of Diabetes Mellitus (DM) stands out as one of the most significant
concerns for healthcare officials worldwide. Diabetic Retinopathy (DR) is a common …
concerns for healthcare officials worldwide. Diabetic Retinopathy (DR) is a common …
Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam
Understanding the negative effects of climate change and changes to land use/land cover
on natural hazards is an important feature of sustainable development worldwide, as these …
on natural hazards is an important feature of sustainable development worldwide, as these …
Predictive Maintenance in Smart Grids with Long Short-Term Memory Networks (LSTM)
This research explores the application of Long Short-Term Memory Systems (LSTMs) and
conventional machine learning calculations for prescient support in shrewd grids …
conventional machine learning calculations for prescient support in shrewd grids …
Edge Computing Enabled Anomaly Detection in IoT Environments Using Federated Learning
This research explores the integration of edge computing and unified learning procedures
for peculiarity locations in Internet of Things (IoT) situations. Four inconsistency discovery …
for peculiarity locations in Internet of Things (IoT) situations. Four inconsistency discovery …
Real-Time Anomaly Detection in Large-Scale Sensor Networks using Isolation Forests
This research delves into real-time anomaly detection in enormous-scope sensor networks,
employing Isolation Forest, One-Class SVM, Local Outlier Factor, and Recursive Partitioning …
employing Isolation Forest, One-Class SVM, Local Outlier Factor, and Recursive Partitioning …
Federated Learning for Privacy-Preserving Medical Data Analytics in Big Data
This examination investigates the application of Federated Learning (FedAvg, FedSGD,
FedProx, HEFL) for security safeguarding medical data analytics about big data. Utilizing a …
FedProx, HEFL) for security safeguarding medical data analytics about big data. Utilizing a …
Behavioral Biometrics Authentication Systems: Leveraging Machine Learning for Enhanced Cybersecurity
This study focuses on behavioral biometrics and machine learning procedure that is
employed vigorous in the verification frameworks to improve cybersecurity. Elaborating …
employed vigorous in the verification frameworks to improve cybersecurity. Elaborating …
Scalable Image Processing with Apache Spark and Convolutional Neural Networks
This research presents a new framework called Spark-CNN that incorporates Apache
Sparks distributed computing capabilities with Convolutional Neural Networks (CNNs) for …
Sparks distributed computing capabilities with Convolutional Neural Networks (CNNs) for …
Time Series Analysis for Power Grid Anomaly Detection using LSTM Networks
This research addresses the basic concern of inconsistency location in control frameworks
through an in-depth investigation of time arrangement examination procedures, with a …
through an in-depth investigation of time arrangement examination procedures, with a …