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About 10,000 papers for “computational complexity”

Computational complexity.

Shangping Wang (3368915), Tingting Gao (1734508), Yaling Zhang (1687540) · Figshare · 2018

Cited by 0Open access

Computational complexity

Kronsjo, L, Birmingham Univ. (United Kingdom). School of Computer Science · OpenGrey (Institut de l'Information Scientifique et Technique) · 1990

Cited by 0

Computational Complexity

Arto Salomaa · Cambridge University Press eBooks · 1985

The classification of mathematical problems into decidable and undecidable ones has been discussed quite extensively in previous chapters. Indeed, from the mathematical point of view, this classification is the most fundamental one. It is also of definite practical significance in discouraging attempts to design too-general systems, such as systems for deciding the equivalence of programs or the halting of a program. There are, in fact, instances of such attempts in the past!

Cited by 27

Computational Complexity

Fuchun Guo, Willy Susilo, Khou Nguyen et al. · Undergraduate topics in computer science · 2025

Cited by 6

Computational complexity.

Zisong Wang (8815727), Zhiliang Cheng (1306029), Xiujian Ding (20391283) et al. · Figshare · 2024

<div><p>The process of extracting oil and gas via borehole drilling is largely dependent on subsurface structures, and thus, well log analysis is a major concern for economic feasibility. Well logs are essential for understanding the geology below the earth’s surface, which allows for the estimation of the available hydrocarbon resources. The incompleteness of these logs, on the other hand, is a major hindrance to downstream analysis success. This study, however, addresses the above challenges and presents a deep Long-Short Term Memory (LSTM) model specialized using a new hyperparameter tuning algorithm. There is an evidence gap that we try to fill: well log prediction using LSTM has not been extensively documented, particularly on reconstruction of missing data. In order to remedy this, we develop a new algorithm entitled Elite Preservation Strategy Chimp Optimization Algorithm (EPSCHOA), which will improve the tuning of LSTM hyperparameters. EPSCHOA enhances prediction performance by preserving the diversity of the strongest candidates and transforming the most effective predictor resources into less effective ones. A comparative analysis of the LSTM-EPSCHOA model was carried out with both LSTM and E-LSTM models, including their various extensions, LSTM-CHOA, LSTM-HGSA, LSTM-IMPA, LSTM-SEB-CHOA, and LSTM-GOLCHOA, even as common forecasting models using Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient Boosting (GB), and AutoRegressive Integrated Moving Average (ARIMA). The results of the performance tests demonstrate that the LSTM-EPSCHOA model outperforms in all aspects, as evidenced by its R<sup>2</sup> values of.98, RMSE of 0.022, and MAPE of 0.701% during training, and R<sup>2</sup> values of 0.96, RMSE of 0.025, and MAPE of 0.698% during testing. These are considerably superior to other measures used compared to what was achieved using explicit modeling using LSTM, which stood at R<sup>2</sup> of 0.59, RMSE of 0.101, and MAPE of 2.588%. The LSTM-EPSCHOA proved to give models faster rates of convergence and lower error measurements than usual models, which clearly demonstrated its efficiency in solving the problem of inadequate well-log data. The new approach is regarded as having many useful potentials to boost well-log interpretations in the oil sector.</p></div>

Cited by 0Open access