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Research ArticleOpen accessEvery word in the titleParameter-Expanded Data Augmentation for Analyzing Nominal Data With Missing Values Using Multinomial Probit ModelsSuwash Silwal, Xiao ZhangNominal data occur in many scientific fields, such as health-related studies, transportation, and econometrics. Due to the ubiquitousness of missing data, it is inevitable to analyze nominal data with missing values. As is well known, statistical methods for analyzing nominal variables are quite limited. The Multinomial Probit (MNP) model has been an essential tool for analyzing nominal categorical data, but … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 23 May 2026 · 1,814 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleArabic Fake News Detection Across Generational Text Representations: From Traditional Models to Transformer-Based MethodologiesNoor M. AlkudahThe rapid proliferation of fake news on Arabic social media has amplified societal and political risks, yet research on automatic detection in Arabic remains limited due to scarce datasets, morphological complexity, and underexplored preprocessing strategies. This study presents a comprehensive benchmark for Arabic fake news detection, unifying seven Machine Learning (ML) algorithms, three Deep Learning (DL) models, and a transformer-based … …Journal of Computer Science, Vol. 22, Issue 4 · 17 Apr 2026 · 1,877 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative Analysis of Neural Network Models for Predicting EUR/USD Direction: An Empirical StudyEl Badaoui Mohamed, Raouyane Brahim, El Moumen Samira, Bellafkih MostafaThis paper presents a rigorous comparative analysis of six feedforward neural network models for predicting the directional movement of the EUR/USD currency pair. The evaluated models include the Learning Vector Quantization, Cascade Neural Network, Feedforward Neural Network, Single Layer Perceptron, Multi-Layer Perceptron, and Radial Basis Function network. Utilizing daily historical data from April 2009 to May 2024, each model was … …Journal of Computer Science, Vol. 22, Issue 1 · 3 Feb 2026 · 2,340 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleBoosting Arabic Named Entity Recognition with K-Fold Cross Validation on LSTM and Bi-LSTM ModelsHamid Sadeq Mahdi Alsultani, Ahmed H. AliwyNamed-Entity-Recognition(NER) is one of the most important Information-Extraction (IE) use cases, whichis used to improve the performance of Natural Languages Processing (NLP) tasks,such as Relation-Extraction (RE), Question-Answering (QA). Recently, Arabic NER is tackled in differentways by researchers. In this study, we assess the performance of two widelyused models, namely, LSTM and Bi-LSTM on the NER task in the Arabic languageand … …Journal of Computer Science, Vol. 18, Issue 9 · 6 Sep 2022 · 6,321 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time SeriesSaugat Aryal, Dheynoshan Nadarajah, Prabath Lakmal Rupasinghe et al.Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has … …Journal of Computer Science, Vol. 16, Issue 10 · 27 Oct 2020 · 6,978 accessesView PDFCite
Review ArticleOpen accessEvery word in the titleA Survey on Event Detection Models for Text Data StreamsWafa Zubair AL-Dyani, Farzana Kabir Ahmad, Siti Sakira KamaruddinEvent Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: … …Journal of Computer Science, Vol. 16, Issue 7 · 14 Jul 2020 · 7,044 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleComparison of Probit and Logistic Regression Models in the Analysis of Dichotomous OutcomesAmrutha Jose, Mariyamma Philip, Lavanya Tumkur Prasanna, M. ManjulaProbit and logistic regression models are members of the family of generalized linear models, used for estimating the functional relationship between the dichotomous dependent and independent variables. The current study is designed to find the performance of logistic and probit regression models in different conditions under multivariate normality. The objective of the study is to compare the performance of probit … …Current Research in Biostatistics, Vol. 10, Issue 1 · 26 Mar 2020 · 17,046 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleExploratory Data Analysis of Applications of Encryption for Netflix by ML ModelsAnanth Maria Pushpa, Subramanian ShanmugamThis study examines the performance of various machine learning algorithms in analyzing Netflix's dataset, with a focus on encryption methods used for data protection. We evaluated several models including Ada BoostM1, IBk, Random Forest, and Decision Stump using multiple performance metrics. This work analysis revealed that the Ada BoostM1 model achieved the highest accuracy at 97.14%, outperforming other algorithms. It … …Journal of Computer Science, Vol. 21, Issue 9 · 24 Nov 2025 · 1,930 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleHybrid Deep Learning Models for Text Classification: Performance Evaluation of TriDistilBERT and BiGRU ArchitecturesAmira Samy TalaatText can be a valuable source of information, but its unstructured nature makes analysis challenging and time-consuming. Machine Learning (ML) algorithms can efficiently analyze and structure text, enabling organizations to automate processes and uncover insights that support better decision-making. This study focuses on applying ML to a classification problem using two datasets. Four deep learning models are introduced, combining Bi … …Journal of Computer Science, Vol. 21, Issue 9 · 10 Oct 2025 · 2,799 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleOptimizing Lentil Breeding Models for South-East Kazakhstan: Yield, Adaptability, and Protein EnhancementSaikenova Alma Zhumabaevna, Kudaibergenov Mukhtar Sarsenbekovich, Kanatkyzy Makpal et al.This study presents the development of a model for optimizing lentil breeding in the agroclimatic conditions of southeastern Kazakhstan. By evaluating 31 lentil varieties and hybrid populations (F1, F2, and F3), key economically valuable traits such as yield, protein content, drought resistance, and adaptability to mechanized harvesting were identified. A systematic selection process enabled the creation of lentil variety models … …OnLine Journal of Biological Sciences, Vol. 25, Issue 3 · 24 Sep 2025 · 3,207 accessesView PDFCite