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273 results for “error performance”
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Research ArticleOpen accessIn the abstractComparison of Multiple Linear Regression and Neural Network Models in Bank Performance Prediction in BotswanaHassan Kablay, Victor GumboBank performance is critical to the banking sector and the economy as a whole. In this study, Multiple Linear Regression (MLR) technique and feed forward Neural Network (NN) are used to predict the performance of 11 banks in Botswana. Return on Assets (RoA) is used as the dependent variable, while management quality, credit risk, liquidity, financial leverage and capital adequacy … …Journal of Mathematics and Statistics, Vol. 17, Issue 1 · 10 Jan 2022 · 6,465 accessesView PDFCite
Research ArticleOpen accessIn the abstractDeep Learning Analysis of Drug Reviews: Identifying Patient Conditions, Predicting Drug Ratings, and Sentiment AnalysisRafat Hammad, Mohammad Ashraf Ottom, Ayah Bani BakerUnderstanding patients' experiences with medications is essential for improving drug effectiveness, detecting potential side effects, and enhancing overall patient satisfaction. This research investigates applying machine learning to patient drug reviews across three main tasks: Identifying patient conditions, predicting drug ratings, and performing sentiment analysis. We implemented and evaluated Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Recurrent Neural Networks … …Journal of Computer Science, Vol. 22, Issue 9 · 7 Sep 2026 · 736 accessesView PDFCite
Research ArticleOpen accessIn the abstractHybrid Data Security Approach Based on GAN Teganographyand AES-RSA Encryption in a Distributed SystemDerrick Méthode Bagaza, Abraham Walake, Mamoudou Kourouma, Blaise Omer YenkeData security in distributed environments has become a major challenge due to the dispersion of resources, the expansion of the attack surface, and the increasing sophistication of cyber threats. Although traditional mechanisms such as data encryption, multi-factor authentication, and access control systems help to strengthen information protection, these approaches still have certain limitations when faced with advanced attacks and human … …Journal of Computer Science, Vol. 22, Issue 8 · 28 Jul 2026 · 1,062 accessesView PDFCite
Research ArticleOpen accessIn the abstractAdaptive Cross-Validation Under Concept Drift for Time Series ForecastingKunjira Kingphai, Prapakorn Kanjina, Kamol Sanittham, Wacharong WongsanurakTime-series forecasting often involves non-stationary data, making i.i.d. validation unreliable and fixed-window protocols vulnerable to leakage and biased error estimates. We propose Adaptive Time-Series Cross-Validation (ATSCV), a drift-aware evaluation framework that uses statistical change-point detection to partition each series into contiguous, approximately stationary regimes, followed by forward-chaining folds that respect those boundaries. By aligning train–validation splits with distributional changes (with … …Journal of Computer Science, Vol. 22, Issue 7 · 7 Jul 2026 · 1,488 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Comparative Study of Resampling, Cost-Sensitive, and Ensemble Techniques for Handling Class Imbalance in Indonesian Financial DataGunawan Kurnia, Ditdit Nugeraha UtamaHandling class imbalance is a critical challenge in machine learning applications, particularly in financial domains where minority instances often represent significant anomalies such as fraud or audit risks. Various oversampling and undersampling methods were tested, alongside cost-sensitive adjustments and ensemble models including Random Forest, AdaBoost, Gradient Boosting, and XGBoost. The evaluation, based on 10-fold stratified cross-validation and performance metrics such … …Journal of Computer Science, Vol. 22, Issue 2 · 27 Feb 2026 · 1,874 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Suggested Nonparametric Bivariate Logistic Density Estimator with Application on the Productivity of Egyptian Wheat during 2019/2020Samah M. Abo-El-HadidIn this study, the nonparametric standard logistic density estimator, introduced by Abo-El-Hadid (2018), is extended to the bivariate case. The multiplicative standard logistic distribution is used as a kernel function to derive the bivariate kernel estimator. The statistical properties of the resulting estimator are studied, which are: The asymptotic bias, variance, Mean Squared Error (MSE) and Integrated Mean Squared Error … …Journal of Mathematics and Statistics, Vol. 17, Issue 1 · 27 May 2021 · 6,711 accessesView PDFCite
Research ArticleOpen accessIn the abstractAutomatic Re-Formulation of user’s Irrational Behavior in Speech Recognition using Acoustic Nudging ModelLydia Kehinde Ajayi, Ambrose Azeta, Isaac Odun-Ayo et al.In automatic speech recognition for development of automatic speech recognition applications, there has been numerous claims on the presence of speech recognition errors known as classified into lexical and acoustic errors. These errors distort speech signals thereby depreciating the accuracy and performance rate of speech recognition applications. Even though lexical speech recognition error problem has been partially combated, acoustic speech … …Journal of Computer Science, Vol. 16, Issue 12 · 22 Dec 2020 · 6,509 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparative 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 · 6,967 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Time Series Forecasting for the Cumulative Confirmed and Critical Cases of the Covid-19 Pandemic in Saudi Arabia using Autoregressive Integrated Moving Average (ARIMA) ModelSamer H. Atawneh, Osamah A.M. Ghaleb, Ahmad MohdAziz Hussein et al.Reviews at present, different machine learning techniques and algorithms have been applied for predicting significant factors of the Coronavirus Disease-2019 (COVID-19) such as the outbreak and diagnosis. In this study, the most accurate time series forecasting model, namely, the Autoregressive Integrated Moving Average (ARIMA) model is used to forecast the expected cumulative number of confirmed and critical cases in Saudi … …Journal of Computer Science, Vol. 16, Issue 9 · 30 Sep 2020 · 7,596 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Method to Predict Processor Performance by Modeling Different Architecture ParametersJoseph IssaPredicting processor throughput and performance is one of the essential aspects of computer architecture. It is crucial to model processor performance behavior for future architectures based on the existing data set. Modeling processor performance for a given workload enables architects to enhance processor features to meet specific performance targets for a given benchmark. Developing an estimation method to predict performance … …Journal of Computer Science, Vol. 16, Issue 4 · 22 Apr 2020 · 7,317 accessesView PDFCite