An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

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Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study by Jorge Marques Silva, Susana M. Vieira, Duarte Valério, João C. C. Henriques, Paul D. Sclavounos

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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

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Skip-based combined prediction method for distributed photovoltaic power generation by WU Minglang, PANG Zhenjiang, HONG Haimin, ZHAN Zhaowu, JIN Fei, TANG Yuanyang, YE Xuan

“…In the feature extraction, we use statistical analysis, features cross-correlation, periodicity information, approximate entropy, and the temperature of PV panels to achieve deep feature extraction of time, weather, and power generation data, enriching the model inputs. …”
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IMPROVING THE ACCURACY OF DETERMINING DEVIANT GROUPS IN THE SELECTION AND MONITORING OF THE CRITICAL INFORMATION INFRASTRUCTURE ENTERPRISES STAFF by Vladimir L. Evseev, Anton S. Burakov, Anatoly V. Marchenko

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To the analysis of methods and mechanisms of predictive modeling of onboard equipment reliability when solving problems of aircraft maintenance workload planning by B. I. Ogunvoul, V. D. Budaev, D. O. Sizikov, N. V. Gorbakon, A. V. Vlasova

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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…The empirical results show that the GGBERO-optimized BIGRU model produced a Mean Squared Error (MSE) of 1.0 × 10⁻⁵, the best tested approach. …”
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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

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A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities by Xu Pengru, Zhou Junhui, Kausar Nasreen, Lin Chunlei, Lu Qianqian, Ghaderpour Ebrahim, Pamucar Dragan, Zadeh Ardashir M.

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Improving stock price forecasting with M-A-BiLSTM: a novel approach by Zihan Liu

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Shaping Inflation Expectations in the Czech Economy: a Case of Financial Analysts and Corporate Managers by Martin Mandel, Jan Vejmělek

“…In particular, their yearly inflation expectations exhibit systematic errors. Surprisingly, the time series of financial analysts’ inflation expectations contain a seasonal component.…”
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Reliability analysis in curriculum development for social science education driven by machine learning by Rui Mao

“…Performance evaluation was conducted on the linear regression, random forest and artificial neural networks (ANN) through statistical metrics such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). …”
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Forecasting day-ahead electric power prices with functional data analysis by Faheem Jan, Hasnain Iftikhar, Mehwish Tahir, Mehak Khan

“…The model’s prediction performance was evaluated using data on electricity prices from the British electricity market, considering forecast error indicators and the same forecast statistical test. …”
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Quantitative analysis of patent translation by Yvonne Tsai

“…A simple statistical analysis was used to explain the general features found in this translation quality assessment, namely range, median, and mean. …”
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A hybrid long short-term memory with generalized additive model and post-hoc explainable artificial intelligence with causal inference for air pollutants prediction in Kimberley, S... by Israel Edem Agbehadji, Ibidun Christiana Obagbuwa

“…LSTM model captures both nonlinear relationships and temporal long-term dependencies in time-series data, and GAM provides insight into the statistical relationship between selected features and the target pollutant. …”
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Diverse Counterfactual Explanations (DiCE) Role in Improving Sales and e-Commerce Strategies by Simona-Vasilica Oprea, Adela Bâra

“…Furthermore, we identify other features that could lead to the same price goal. The linear regression model achieved an <i>R</i>² score of 96.15% on the test set, along with a mean absolute error (<i>MAE</i>) of 108.31 and mean absolute percentage error (<i>MAPE</i>) of 5.43%, indicating strong predictive performance. …”
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Enhanced Signal-to-Noise Ratio Estimation in Optical Fiber Communications: A Pilot-Based Approach by Mohamed Al-Nahhal, Ibrahim Al-Nahhal, Sunish Kumar Orappanpara Soman, Octavia A. Dobre

“…The architectures of these proposed estimators employ feedforward NNs (FFNNs) for the SNR estimation, with PAF-CR utilizing a two-hidden layer FFNN and PAF-AE employing a single-hidden layer FFNN. Novel features are extracted from pilot signals to utilize the pilot overhead in transmitted signals, such as mean absolute error and mean signed deviation, which statistically measure the error between transmitted and received pilot signals. …”
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LiMMCov: An interactive research tool for efficiently selecting covariance structures in linear mixed models using insights from time series analysis. by Perseverence Savieri, Lara Stas, Kurt Barbé

“…Incorrect covariance structure specification can lead to inflated type I error rates, reduced statistical power, and inefficient estimation, ultimately compromising the reliability of statistical inferences. …”
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Leveraging graph neural networks and gate recurrent units for accurate and transparent prediction of baseball pitching speed by Chen Yang, Pengfei Jin, Yan Chen

“…The prediction accuracy of the models was evaluated using mean absolute error (MAE), mean squared error (MSE), and R-squared (R2). …”
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Assessment of Bone Aging—A Comparison of Different Methods for Evaluating Bone Tissue by Paweł Kamiński, Aleksander Gali, Rafał Obuchowicz, Michał Strzelecki, Adam Piórkowski, Marcin Kociołek, Elżbieta Pociask, Joanna Kwiecień, Karolina Nurzyńska

“…Machine learning models demonstrated that when using uncorrelated features, the optimal mean absolute error (MAE) for age estimation is 5.20, whereas when employing convolutional networks on the texture feature maps yields the best result of 9.56. …”
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