In addition to this, this study compares the performance of machine learning algorithms. Random Forest and XGBoost algorithms are trained using the sounds of names and the typical gender of the referents as the
We applied probabilistic machine learning algorithms like Naïve Bayes and Maximum Entropy to classify candidate names. The objective is to estimate the probability of a label (whether a name is scientific or not) given a candidate string along with its contextual information. Naïve Bayes and Ma...
researchers have relied on machine learning techniques. Machine learning methods are based on extracting features from a set of training dataset and performing statistical analysis to be able to predict and classify the URLs into benign and malicious. There are two types of features that can be ext...
1 using the dataset of Table 6. The k-fold cross-validation test option was chosen to train and evaluate the selected machine learning algorithms in order to protect against overfitting and provide an accurate model performance estimation (Baumer et al., 2017; Drakos, 2019). The default value...
“I’m XYZ’s AI Bot. I use LLMs and advanced queries to parse your text and algorithms to provide the best answers.” Many of your visitors would be left scratching their heads because they have no idea what an LLM is or what you mean by advanced queries. Instead, you can have a...
Among the seven, chosen out of 180 applicants, are Israel-based Atidot Ltd. and Endor Software Ltd. Founded in 2016, Atidoth uses artificial intelligence and machine learning algorithms and predictive analytics to assist life insurance companies in their decision making; Endor, founded in 2014, ...
Domain generation algorithms detection with feature extraction and Domain Center construction 2023, PLoS ONEYuwei Zeng received the B.S. degree in computer science from the Zhengzhou University, China, in 2016. He is currently a Ph.D. candidate at the Institute of Information Engineering, Chinese ...
Other approaches There is a growing number of algorithms and tools in machine learning and natural language processing that aim to recognize parts of texts. They include statistical parsing [17], context-free grammars [18], fuzzy context- free grammars [19], and named entity recognition [20]....
On top of that, a solid understanding of machine learning algorithms, deep neural networks and other supervised methods will allow the novice data scientist to make sense of seemingly unstructured data. No dataset will be too large to be analyzed anymore. Proficiency in Python is a prerequisite ...
Other use-cases of SUIT MAY define their own MTI algorithms.¶ @@ -1391,9 +1392,9 @@ Authentication Algorithms¶ Key Exchange Algorithms¶ Key Exchange Algorithms (OPTIONAL)