The identification of Prakriti types for the human body is a long-lost
medical practice in finding the harmony between the nature of human beings and
their behaviour. There are 3 fundamental Prakriti types of individuals. A
person can belong to any Dosha. In the existing models, researchers have made
use of SVM, KNN, PCA, Decision Tree, and various other algorithms. The output
of these algorithms was quite decent, but it can be enhanced with the help of
Multinomial Naive Bayes and K-modes clustering. Most of the researchers have
confined themselves to 3 basic classes. This might not be accurate in the
real-world scenario, where overlapping might occur. Considering these, we have
classified the Doshas into 7 categories, which includes overlapping of Doshas.
These are namely, VATT-Dosha, PITT-Dosha, KAPH-Dosha, VATT-PITT-Dosha,
PITT-KAPH-Dosha, KAPH-VATT-Dosha, and VATT-PITT-KAPH-Dosha. The data used
contains a balanced set of all individual entries on which preprocessing steps
of machine learning have been performed. Chi-Square test for handling
categorical data is being used for feature selection. For model fitting, the
method used in this approach is K-modes clustering. The empirical results
demonstrate a better result while using the MNB classifier. All key findings of
this work have achieved 0.90 accuracy, 0.81 precision, 0.91 F-score, and 0.90
recall. The discussion suggests a provident analysis of the seven clusters and
predicts their occurrence. The results have been consolidated to improve the
Ayurvedic advancements with machine learning.

通过使用多项式朴素贝叶斯和 K-modes 聚类算法，对人体的 Prakriti 类型进行分类和预测，并展示了该方法在提高阿育吠陀医学与机器学习的进展方面的效果。

利用多项式朴素贝叶斯和 K-modes 聚类优化阿育吠陀诊断：对 Prakriti 类型和 Dosha 重叠性的研究

Enhancing Ayurvedic Diagnosis using Multinomial Naive Bayes and K-modes  Clustering: An Investigation into Prakriti Types and Dosha Overlapping

The digitalization of news media become a good indicator of progress and
signal to more threats. Media disinformation or fake news is one of these
threats, and it is necessary to take any action in fighting disinformation.
This paper utilizes ground truth-based annotations and TF-IDF as feature
extraction for the news articles which is then used as a training data set for
Multinomial Naive Bayes. The model has an accuracy of 99.46% in training and
88.98% in predicting unseen data. Tagging fake news as real news is a
concerning point on the prediction that is indicated in the F1 score of 89.68%.
This could lead to a negative impact. To prevent this to happen it is suggested
to further improve the corpus collection, and use an ensemble machine learning
to reinforce the prediction

利用基于地面真值注释和 TF-IDF 作为特征提取的新闻文章作为训练数据集的研究表明，使用 Multinomial Naive Bayes 的模型在训练中的准确率为 99.46％，在预测未见数据时为 88.98％；但其在将假新闻标记为真实新闻方面的问题需要进一步研究，提高语料库收集，并使用集成机器学习来加强预测的建议。

使用多项式朴素贝叶斯算法和词项频率 - 逆文档频率（TF-IDF 向量化）检查菲律宾新闻推文的可信度

Utilization of Multinomial Naive Bayes Algorithm and Term Frequency  Inverse Document Frequency (TF-IDF Vectorizer) in Checking the Credibility of  News Tweet in the Philippines

A lot of news sources picked up on Typhoon Rai (also known locally as Typhoon
Odette), along with fake news outlets. The study honed in on the issue, to
create a model that can identify between legitimate and illegitimate news
articles. With this in mind, we chose the following machine learning algorithms
in our development: Logistic Regression, Random Forest and Multinomial Naive
Bayes. Bag of Words, TF-IDF and Lemmatization were implemented in the Model.
Gathering 160 datasets from legitimate and illegitimate sources, the machine
learning was trained and tested. By combining all the machine learning
techniques, the Combined BOW model was able to reach an accuracy of 91.07%,
precision of 88.33%, recall of 94.64%, and F1 score of 91.38% and Combined
TF-IDF model was able to reach an accuracy of 91.18%, precision of 86.89%,
recall of 94.64%, and F1 score of 90.60%.

研究使用机器学习算法 (逻辑回归，随机森林和多项式朴素贝叶斯) 和文本挖掘技术 (词袋，TF-IDF 和词形还原) 发展了一个可用于区分真假新闻的模型，并使用 160 个数据集进行了训练和测试，在使用这些技术的情况下，识别出具有 91.07% 的准确度。