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Found 15 papers, 0 papers with code
Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning
This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data.
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis
With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field.
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD).
Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research
First, for the massive data related to user evaluation provided by operators, key features are extracted by data preprocessing and feature engineering methods, and a multi-dimensional feature set with statistical significance is constructed; then, linear regression, decision tree, LightGBM, and other machine learning algorithms build multiple basic models to find the best basic model; finally, integrates Averaging, Voting, Blending, Stacking and other integrated algorithms to refine multiple fusion models, and finally establish the most suitable fusion model for operator user evaluation.
Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
Overall, the paragraph effectively communicates the importance of machine learning technology in addressing resource allocation and virtual machine migration challenges in cloud computing.
Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble
In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization.
LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models
In addressing the computational and memory demands of fine-tuning Large Language Models(LLMs), we propose LoRA-SP(Streamlined Partial Parameter Adaptation), a novel approach utilizing randomized half-selective parameter freezing within the Low-Rank Adaptation(LoRA)framework.
Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management
Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.
Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization
With the continuous expansion of the scale of cloud computing applications, artificial intelligence technologies such as Deep Learning and Reinforcement Learning have gradually become the key tools to solve the automated task scheduling of large-scale cloud computing systems.
Machine Learning-Based Vehicle Intention Trajectory Recognition and Prediction for Autonomous Driving
This paper introduces a deep learning-based prediction method for autonomous driving lane change behavior, aiming to facilitate safe lane changes and thereby improve road safety.
Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendations
With the rapid development of artificial intelligence technology, Transformer structural pre-training model has become an important tool for large language model (LLM) tasks.
Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research
In recent years, advancements in natural language processing (NLP) have been fueled by deep learning techniques, particularly through the utilization of powerful computing resources like GPUs and TPUs.
Utilizing GANs for Fraud Detection: Model Training with Synthetic Transaction Data
Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions.
Utilizing Deep Learning for Enhancing Network Resilience in Finance
In the age of the Internet, people's lives are increasingly dependent on today's network technology.
DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans
Gastrointestinal (GI) tract cancers pose a global health challenge, demanding precise radiotherapy planning for optimal treatment outcomes.
