This research provides analysis of biomedical voice measurements based dataset and final performance results of Support Vector machine and Random Forest Classifier. The performance of the two algorithms is assessed us...
详细信息
In the dynamic era of online education, the pursuit of a personalized and effective learning experience is paramount. A transformative approach in online education by integrating Multimodal data Mining and data Synthe...
详细信息
The aim of the project is to compare the performance of four different machinelearning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of...
详细信息
Blockchain and machinelearning (BML) are two of the most rapidly advancing technologies that are revolutionizing various industries worldwide. Blockchain is a widely known decentralized, immutable technology that pro...
详细信息
As the need for early detection and mitigation of potential threats from near-Earth objects continues to grow, this study presents a comprehensive approach to predicting hazardous asteroids through the application of ...
详细信息
This research project is a pivotal exploration at the crossroads of the use of machinelearning for facial gesture detection, which involves interpreting facial emotions. Key steps in the process include data gatherin...
详细信息
The affective computing field usually concerns data that is difficult, expensive or time-consuming to label. One way to overcome this limitation is the application of Semi-Supervised machinelearning, that typically w...
详细信息
ISBN:
(纸本)9798350304367;9798350304374
The affective computing field usually concerns data that is difficult, expensive or time-consuming to label. One way to overcome this limitation is the application of Semi-Supervised machinelearning, that typically works with a small set of labeled data and a larger one of unlabeled data. This paper assesses the suitability of these techniques on the prediction of affective state, by analyzing the physiological and emotional response data of 30 different subjects while watching several emotion-eliciting videos. Three Semi-Supervised learning algorithms are compared with their Supervised base classifiers in both a subject-independent and subject-dependent analyses, across a widely extended dataset. In view of the results, it can be concluded that Semi-Supervised learning did not outperform their respective Supervised base classifiers for this specific dataset as it was expected. Subject-dependent classification resulted in accuracy rates between 68% and 85%, whereas the accuracy rates were between 38% and 72% for subject-independent classification.
Federated learning has emerged as a promising technique in machinelearning, enabling collaborative training across distributed datasets. Particularly in fields like healthcare, where data privacy is paramount, federa...
详细信息
In recent years, machinelearning has been widely used to process large-scale data and train complex models. However, there are certain security risks in the construction process of machinelearning models. data train...
详细信息
ISBN:
(纸本)9798350391961;9798350391954
In recent years, machinelearning has been widely used to process large-scale data and train complex models. However, there are certain security risks in the construction process of machinelearning models. data trainers may infringe on the privacy of data owners and use personal data to train machinelearning models without authorization. To truly prevent unauthorized training of personal data, this paper proposes a privacy-preserving personal data sharing system, called PPDS. And this paper adopts the combination of CP-ABE with outsourced decryption and secure multi-party computation to ensure the secure authorization and training of personal privacy data. In PPDS, data users can only obtain the trained models instead of the authorized data, effectively preventing the privacy leakage of personal data. Moreover, the experimental results show that PPDS is a safe and effective scheme to achieve personal data sharing.
The increasing growth of the aviation industry has been accompanied by increasing problems of flight delays. This research aims to predict flight delays by employing machinelearning models to enhance the effectivenes...
详细信息
暂无评论