This paper focuses on employing various machine learning algorithms- K Nearest Neighbors and Extra Trees. By analyzing patterns in behavioral, physiological, and contextual data, the model aims to identify early indic...
详细信息
Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routi...
详细信息
Power load forecasting is one of the important tasks in controlling system costs, and accurate and effective load forecasting can reasonably arrange the operating status of power grid generators. Machine learning, as ...
详细信息
Cognitive Radio (CR) provides an intelligent solution to address spectrum scarcity in wireless communication systems. This research work deals with the designing, simulation, and fabrication of a frequency reconfigura...
详细信息
Focusing on the analysis and prediction of momentum in tennis matches, this study explores the feasibility of using reinforcement learning (RL) and machine learning methods to solve the problem of dynamic changes in t...
详细信息
As more information about people39;s health is gathered and analyzed, privacy concerns have become increasingly essential. This paper summarizes how machine learning (ML) methods are presently used to safeguard priv...
详细信息
ISBN:
(纸本)9798350372977;9798350372984
As more information about people's health is gathered and analyzed, privacy concerns have become increasingly essential. This paper summarizes how machine learning (ML) methods are presently used to safeguard privacy in e-health apps. Furthermore, we discuss how privacy-preserving approaches such as differential privacy, secure multi-party computing, and homomorphic cryptography may be utilized to protect privacy in ML. The study also conducts an overview of the literature on how these methods can be used in various e-health scenarios, such as predictive modeling, disease detection, and clinical decision support. Based on the privacy-preserving technique, we classified current privacy-preserving ML work into four categories (i.e., data encryption, data anonymization, model encryption, and model obfuscation). Finally, the article discusses the challenges of applying privacy-preserving machine learning (PPML) in the e-health domain and what types of research could be conducted as open future problems in this field.
The number of hidden nodes have strong influence on the accuracy of ELM (Extreme learning Machine). More hidden nodes are needed as the increase of the size of training data set. Either ELM or Multi-hidden layer neura...
详细信息
The integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understan...
详细信息
ISBN:
(纸本)9783031777301;9783031777318
The integration of Artificial Intelligence (AI), particularly deep learning models like VGG16 and ResNet50, in the analysis of functional magnetic resonance imaging (fMRI) data has significantly advanced our understanding of brain functionality and the diagnosis of neurological disorders. This paper explores the application of Convolutional Neural Networks (CNNs) to enhance the accuracy and efficiency of fMRI data analysis, addressing challenges such as high dimensionality, noise, and the need for complex data preprocessing. Our study evaluates the performance of VGG16 and ResNet50 models and one 3D CNN on 2D and 3D fMRI datasets, highlighting the limitations of VGG16 in handling 2D data and demonstrating the superior performance of ResNet50 on balanced and unbalanced datasets. Additionally, we investigate the impact of using 3D data from the Human Connectome Project (HCP), achieving up to 98% accuracy on the validation set. The results indicate that CNNs can effectively replace traditional Independent Component Analysis (ICA) methods by leveraging their capability for automatic feature extraction and end-to-end learning.
The accurate classification of Land Use and Land Cover (LULC) is imperative for spatial applications such as resource management, environmental monitoring, and satellite image analysis. Methods such as Convolutional N...
详细信息
In recent years, the fusion of sensor technology with embedded systems has proven its worth in the domain of personalized healthcare. The purpose of the In-home healthcare system lies in its continuous monitoring of t...
详细信息
暂无评论