Titlebook: Data Science and Network Engineering; Proceedings of ICDSN Suyel Namasudra,Munesh Chandra Trivedi,Pascal Lore Conference proceedings 2024 T

Exploit

Computational Analysis and Methodology,per tested several machine learning methods using supervised classification techniques and got the highest accuracy among other low-resource languages in most of the models tested and among which Multinomial Naive Bayes classification gives an accuracy of 96% and is the highest when compared to the other models.

inventory

HEED

Teenager Friendly News Classification Using Machine Learning Modelwell-known statistical measures and machine learning (ML) models. In this proposed system, we compare Linear Support Vector Classifier (LSVC), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Random Forest Classifier (RFC), and Decision Tree Classifier (DTC) algorithms in which LR outperforms the other algorithms.

厚颜

chemoprevention

小故事

A Framework for Extractive Text Summarization of Single Text Document in Tamil Language Using Frequearity and so on. In this paper, a framework for extractive text summarization using features extracted from a Tamil document has been proposed. The summarizer is based on Fuzzy logic inference engine. The framework describes the modules involved in the generation of Extractive Text Summary for a single document.

拱形大桥

An Approach to Mizo Language News Classification Using Machine Learningper tested several machine learning methods using supervised classification techniques and got the highest accuracy among other low-resource languages in most of the models tested and among which Multinomial Naive Bayes classification gives an accuracy of 96% and is the highest when compared to the other models.

Brocas-Area

柔软

Evaluation of Hand-Crafted Features for the Classification of Spam SMS in Dravidian Languagesrauds share. This immediate reaction to the fraud messages makes people lose their balance in bank accounts or fall into some other horrible events. These types of fake or spam messages have to be identified earlier before they come to users’ Inbox. This paper proposes a Spam message filtering model

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Training Algorithms for Mixtures of Normalizing Flowst do not necessarily provide an actual implementation of the method. These four algorithms are gradient ascent maximizing the log-likelihood of the data, (soft) expectation–maximization, hard expectation–maximization, and gradient ascent maximizing the evidence lower bound. Our contribution or the n