EFFECTS OF MACRO-ECONOMIC VARIABLES ON FISHERIES SUPPLY IN NIGERIA (1980-2019) by Ahmed Olugbenga BUSARI, Abolupe Oluyemi AYANBOYE

“…Thus, it is recommended that there is a need for significant improvement in aquaculture production and reduction in fish importation to cushion the effect of exchange, inflation, and interest rate on the economy.…”
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Adaptive fractal dynamics: a time-varying Hurst approach to volatility modeling in equity markets by Abe Webb, Siddharth Mahajan, Mateo Sandhu, Rohan Agarwal, Arjun Velan

“…Additionally, the model achieves an average absolute option pricing error of 1.8 %, markedly lower than that of traditional specifications. …”
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Extreme heat prediction through deep learning and explainable AI. by Fatima Shafiq, Amna Zafar, Muhammad Usman Ghani Khan, Sajid Iqbal, Abdulmohsen Saud Albesher, Muhammad Nabeel Asghar

“…Our findings demonstrate that the LSTM model outperforms others with a lead time of 1-3 days and minimal error metrics, achieving an accuracy of 96.2%. Through the utilization of SHAP and LIME XAI methods, we elucidate the significance of humidity and maximum temperature in accurately predicting extreme heat events. …”
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Transforming higher education with robotic process automation: enhancing efficiency, innovation, and student-centered learning by Vivek Bhardwaj, Mukesh Kumar

“…Case studies reveal common success factors, including significant time savings, error reduction, and improved satisfaction for both students and staff. …”
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A Data-Driven Approach for Energy Consumption Modeling and Optimization of Welding Robot Systems by Minling Pan, Bingqi Jia, Lei Zhang, Haihong Pan, Lin Chen

“…A predictive model was developed using an extreme gradient boosting (XGBoost) regression technique focused on joint torque data, which achieved a mean absolute percentage error (MAPE) of 1.86%. Furthermore, trajectory optimization was achieved by adjusting the spatial position of the workpiece, effectively reducing EC. …”
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Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification by Aulia Afifah, Endah Ratna Arumi, Maimunah Maimunah, Setiya Nugroho

“…Overall, MobileNetV1 benefited more significantly from preprocessing variations than MobileNetV2, particularly in terms of accuracy improvement and reduction in prediction error. These findings underscore the importance of effective preprocessing in enhancing model performance for waste image classification…”
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Big data-driven corporate financial forecasting and decision support: a study of CNN-LSTM machine learning models by Aixiang Yang

“…Experimental results demonstrate the superior performance of the proposed model, achieving a Mean Squared Error (MSE) of 0.020 and an R2 score of 0.411, significantly outperforming benchmark models (ARIMA, Random Forest, XGBoost, and standalone LSTM). …”
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Earthwork Network Architecture (ENA): Research for Earthwork Quantity Estimation Method Improvement with Large Language Model by Taewook Kang, Kyubyung Kang

“…Traditional methods for quantity takeoff are often time-consuming and susceptible to human error, particularly when working with unstructured datasets such as CAD drawings. …”
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Study on the effect of humidity on coal mine dust concentration monitoring and its compensation strategy by ZHANG Anran

“…Results showed that the monitoring error of the humidity-compensated multi-angle light-scattering unit was ≤11.2%, a 2.9% reduction compared to the uncompensated unit, confirming the effectiveness of the compensation method.…”
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High-normal blood glucose levels may be associated with decreased spatial perception in young healthy adults. by Rima Abdul Razzak, Abdulla Faisal Alshaiji, Abdulrahman Ahmed Qareeballa, Mohamed Wael Mohamed, Jeff Bagust, Sharon Docherty

“…Linear regression analysis showed that blood glucose level at testing correlated positively with all alignment spatial error parameters, indicating a probable reduction of spatial perception ability with higher blood glucose levels. …”
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A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data by Wenbo Xuan, Pengchao Chen, Rui Li, Fuxiang Wang, Kuan Fu, Zhitao Wen

“…Through practical experimental analysis, the reconstruction error such as MAE is reduced by about 27.7% and the compression ratio is improved by about 14% compared with traditional methods such as PCA. …”
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Zero-Offset VSP Monitoring of CO2 Storage: Impedance Inversion and Wedge Modelling at the Ketzin Pilot Site by Julia Götz, Stefan Lüth, Charlotte M. Krawczyk, Calin Cosma

“…With band limited impedance inversion a velocity reduction at the top of the reservoir of 30%, influenced by casing reverberations as well as CO2 injection, is found. …”
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Influence of Rice Husk Ash on Crystalline Phases and Mechanical Characteristics of Sustainable Bricks by F. Booth, M.B. Carranza, G. Novak, N. Okulik, A. Mocciaro, N. Rendtorff

“…Several models were analyzed to predict the elastic modulus, and the DEM model provided the best predictions, with a relative error of 1.43% for 15% RHA. The analysis of mechanical properties of porosity was conducted using various theoretical models, which allowed for a better understanding of the relationship between the porous structure of the bricks and their mechanical behavior. …”
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Intelligent Noise Removal from EMG Signal Using Focused Time-Lagged Recurrent Neural Network by S. N. Kale, S. V. Dudul

“…Results show that the proposed optimal FTLRNN model has an MSE (Mean Square Error) as low as 0.000067 and 0.000048, correlation coefficient as high as 0.99950 and 0.99939 for noise signal and EMG signal, respectively, when validated on the test dataset. …”
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Time-Series Interval Forecasting with Dual-Output Monte Carlo Dropout: A Case Study on Durian Exports by Unyamanee Kummaraka, Patchanok Srisuradetchai

“…The dual-output architecture employs a custom loss function, combining mean squared error with Softplus-derived predictive variance, ensuring non-negative variance values. …”
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Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank by Jukyung Lee, Hyosung Joo, Jihwan Woo

“…The proposed approach was validated with an open dataset, achieving a 35.5% (i.e., resulting in a mean absolute error of 2.5 beats per minute) higher accuracy than those of conventional methods. …”
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Towards an Explainable Artificial Intelligence approach for smart grid systems by Mahmoud Alfayan, Hani Hagras

“…The transition from a Type-1 to a Type-2 fuzzy logic system resulted in enhanced prediction performance, as evidenced by a reduction in the root mean square error (RMSE) from 8.7734 to 5.9422, resulting in 32.2% enhancement in RMSE. …”
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Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models by Yafeng Feng, Yong Sun, Chengfang Hang

“…Experimental results show that our model achieves a significant improvement in classification accuracy, with a 12% increase in emotional state recognition compared to traditional models, and a 15% reduction in prediction error for physiological state estimation. …”
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Hybrid Spectrum Sensing Using Neural Network–Based MF and ED for Enhanced Detection in Rayleigh Channel by Arun Kumar, Nishant Gaur, Aziz Nanthaamornphong

“…Furthermore, the capability of the NN to mitigate noise and interference results in enhanced bit error rate (BER) performance, ensuring reliable communication. …”
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Highly concentrated iron ore slurry flow through pipeline with and without chemical additive; part I: Experimental investigations and proposed model for the prediction of pressure... by Mishra Stuti, Kaushal Deo Raj

“…Based on comparison with experimental data, it is observed that the proposed modified Slatter’s method can predict the pressure drop with an error of ±15%.…”
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