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38 articles for “Error Reduction”
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Research ArticleOpen accessIn the abstractEnhancing Kidney Stone Detection Accuracy Using Image Processing and a Hybrid Convolutional Deep Belief NetworkGeetha M. N, Barkathulla A, S. Nandhini Devi et al.Automated CKD is a rapidly expanding non-communicable ailment that significantly contributes to global mortality and morbidity. CKD impacts more than 10% of the worldwide population, leading to millions of deaths annually. This paper proposes an enhanced deep learning model for renal disease detection that integrates convolutional and involution layers to enhance classification accuracy. CKD is a worldwide health issue characterised … …Journal of Computer Science, Vol. 22, Issue 10 · 9 Oct 2026 · 774 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Dam Safety and Management: Long Short-Term Memory Based Predictive Models for Accurate Alert ForecastingNisha C. M, N. ThangarasuDam management and early alert systems are critical for effective water resource management. Accurate prediction of dam alert signals facilitates proactive decision-making, thereby aiding in the effective management and reduction of potential risks linked to dam operations. Within this research, Long Short-Term Memory (LSTM) networks are utilized to forecast dam alert signals issued from the dam by leveraging daily parameters, … …Journal of Computer Science, Vol. 22, Issue 1 · 6 Feb 2026 · 1,632 accessesView PDFCite
Research ArticleOpen accessIn the abstractUsing Linear Chouquet Integral Algorithm to Design Optimal Time-Cost Model in Large Construction ProjectsHamid Jafari Nia, Kaveh Ostad-Ali-Askari, Hamidreza BabaaliNowadays, the problem of scheduling and executing projects with the least cost and time has become one of the main concerns of project managers. In this regard, researchers have used different methods and patterns. One of these methods is to use concurrent engineering in projects. Simultaneous engineering means the creation of a project schedule for projects based on existing units … …American Journal of Engineering and Applied Sciences, Vol. 13, Issue 4 · 18 Nov 2020 · 6,689 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting of Multistep Multivariate Financial Data Through GSO Algorithm Infused Vanilla LSTM ModelNikhitha Pai, Ilango Velchamy, Nithya B RameshPredicting time series in the financial domain requires using models that can do sequence processing tasks. One such model is the LSTM model which belongs to the family of deep learning networks. The performance measure of LSTM in a regression task is to bring down the error in the predicted values. In this study, a novel fused LSTM model integrating … …Journal of Computer Science, Vol. 19, Issue 7 · 10 Jul 2023 · 6,077 accessesView PDFCite
Research ArticleOpen accessIn the abstractANALYSIS OF BAYESIAN CLASSIFIER ACCURACYFelipe Schneider Costa, Maria Marlene De Souza Pires, Silvia Modesto NassarThe naïve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naïve) assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables), the … …Journal of Computer Science, Vol. 9, Issue 11 · 26 Sep 2013 · 8,379 accessesView PDFCite
Research ArticleOpen accessIn the abstractAN EFFICIENT POWER REDUCTION TECHNIQUE IN ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING SYSTEM USING TONE RESERVATIONV. Nandalal, S. SophiaIn a basic communication system, the data’s are modulated onto a single carrier frequency. The available bandwidth is then totally occupied by each symbol. This kind of system can lead to Inter-Symbol-Interference (ISI) in case of frequency selective fading. The basic idea of Orthogonal Frequency Division Multiplexing (OFDM) is to divide the available spectrum into several orthogonal sub channels so … …Journal of Computer Science, Vol. 9, Issue 9 · 6 Aug 2013 · 6,475 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Reduction Algorithm for Analysis of Linear NeuronMohammad Dweib, Yousef AbuzirProblem statement: The aim of feature selection is to select a feature set that is relevant for a given application. Feature selection is complex and remains an important issue in many domains. In the field of neural networks, feature selection has been used in many applications and their methods have been employed. In this study we present neural network approaches … …Journal of Computer Science, Vol. 8, Issue 4 · 2 Feb 2012 · 6,845 accessesView PDFCite
Research ArticleOpen accessIn the abstractA New High Order Algorithm with Low Computational Complexity for Electric Field SimulationMohammad Khatim Hasan, Jumat Sulaiman, Samsul Arifin Abdul KarimProblem statement: This research reported on new approach to improve speed of simulation time for free space electric wave propagation from an antenna. The existing method, Finite-Difference Time-Domain (FDTD) have been proven to solve the problem accurately, however, one of the drawbacks of the method was it needs a long processing time to simulate problem. Research efforts to increase the … …Journal of Computer Science, Vol. 6, Issue 7 · 31 Jul 2010 · 6,400 accessesView PDFCite
Research ArticleOpen accessIn the abstractVulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction MethodAhmed M.A. Haidar, Azah Mohamed, Aini HussainVulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of … …American Journal of Applied Sciences, Vol. 5, Issue 6 · 30 Jun 2008 · 7,155 accessesView PDFCite
Research ArticleOpen accessIn the abstractRotational Linear Discriminant Analysis Using Bayes Rule for Dimensionality ReductionAlok Sharma, Kuldip K. PaliwalLinear discriminant analysis (LDA) finds an orientation that projects high dimensional feature vectors to reduced dimensional feature space in such a way that the overlapping between the classes in this feature space is minimum. This overlapping is usually finite and produces finite classification error which is further minimized by rotational LDA technique. This rotational LDA technique rotates the classes individually … …Journal of Computer Science, Vol. 2, Issue 9 · 30 Sep 2006 · 6,589 accessesView PDFCite