Time series analysis of construction accidents in Saudi Arabia with consideration of COVID19 lockdown effects by Badr T. Alsulami

“…By integrating historical pattern analysis with predictive modeling, the study provides a data-driven basis for proactive safety planning and accident prevention in the Saudi construction industry.…”
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A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies by Yingchun He, Yi-haw Jan, Fan Yang, Yunru Ma, Chun Pei

“…Spearman’s rank correlation analysis, intraclass correlation coefficient (ICC), error analysis, and t-test were conducted to compare the consistency of data obtained from the two movement capture systems, including the peak frequency of acceleration, transition time point between movement statuses, and the complexity index average (CIA) of the movement status based on multiscale entropy analysis.The KR algorithm showed excellent consistency (ICC1,3=0.988) between the two methods when estimating the peak acceleration frequency. …”
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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

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Improving Investment Forecasting: A Comparative Analysis of Machine Learning Models as Key GDP Indicators by Cheng-Hong Yang, Tshimologo Molefyane, Borcy Lee, Ting-Jen Hsueh, Yu-da Lin

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Efficiency Comparison of Prediction Methods and Analysis of Factors Affecting Savings of People in the Central Region of Thailand by Achara Phaeobang, Saichon Sinsomboonthong

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Age estimation using dental radiography in the Brazilian population: a systematic review and meta-analysis by Allan Abuabara, Livia Azeredo Alves Antunes, Leonardo Santos Antunes, Kaliane Rodrigues da Cruz, Thais Vilalba Paniagua Machado do Nascimento, Paulo Henrique Condeixa França, Erika Calvano Küchler, Cristiano Miranda de Araujo, Flares Baratto-Filho

“…For males, the mean error ranged from 0.2 to 1.75 years. For females, the mean error ranged from 0.01 to 1.27 years. …”
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Vibration modeling and simulation analysis of wind turbine flexible tower based on BP neural network by Xiao Ju Yin, Qi Zheng Mu, Bo Li, Zhi Liang Du, Guo Ce Shao, Li Zhou

“…For the instability problem of the wind turbine flexible tower caused by mechanical fatigue and vibration, Back Propagation (BP) neural network is used to establish the tower vibration prediction model to solve the wind turbine tower vibration prediction analysis. To find correlation association factors impacting the flexible tower vibration, the multi-source heterogeneous data from various operating situations of wind turbines are optimized using correlation analysis. …”
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An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics by Shuo Zhang, Mengya Pan

“…Using the prediction model, which was constructed with the AutoGluon framework, the experimental results indicate that models under the AutoGluon framework (e.g., KNeighborsDist classifier) significantly outperform traditional machine learning models in terms of accuracy, precision, recall, and F1-Score, with the highest accuracy rate reaching 0.960. Model error analysis shows that models under the AutoGluon framework perform better in error control. …”
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Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis by Xiaotian Bai, Xiao Hou, Dazhi Lv, Dazhi Lv, Jialin Wei, Jialin Wei, Yiling Song, Zhengyan Tang, Hongfeng Huo, Hongfeng Huo, Jingmin Liu

“…A backpropagation neural network, optimized by integrating particle swarm optimization and genetic algorithm, was constructed to predict foot soft tissue stiffness using plantar pressure data collected during walking. Mean impact value analysis was conducted in parallel to investigate the relative importance of different plantar pressure features.ResultsThe predicted values for the training set are slightly higher than the actual values (MBE = 0.77N/m, RMSE = 11.89 N/m), with a maximum relative error of 7.82% and an average relative error of 1.98%, and the predicted values for the test set are slightly lower than the actual values (MBE = −4.43N/m, RMSE = 14.73 N/m), with a maximum relative error of 7.35% and an average relative error of 2.55%. …”
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Analysis of Universal Decoding Techniques for 6G Ultra-Reliable and Low-Latency Communication Scenario by Abhilasha Gautam, Prabhat Thakur, Ghanshyam Singh

“…Simulations are carried out in MATLAB (R2024a) for the error rate performance of decoders, and plots are provided for the comparative analysis from the results of inferred data. …”
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Indoor Air Quality Prediction in Sick Building Using Machine and Deep Learning: Comparative Analysis by Hayder Qasim Flayyih, Jumana Waleed, Amer M. Ibrahim

“…This necessity was fulfilled through an intensive review of the already existing DL and ML models. This analysis aims to implement the specified models and assess the efficiency of their prediction by computing several performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Median Absolute Error (Median AE), and Coefficient of determination (R2). …”
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Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals Related to E-Cigarette Use by James J. Yang, Anne Buu

“…Real data analysis on 6-day heart rate data from a young adult e-cigarette user reveals a distinct clustering of vaping events in the scatter plot of the first and third eigen-sequences, indicating the potential of developing “digital biomarkers” for vaping behavior based on extracted eigen-sequences in future studies. …”
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STAR-RIS enabled covert integration of sensing communication and over-the-air computing with analysis and optimization by Miao Zhang, Chao Wang, Tianyu Ren, Jing Li

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Advancing wind energy potential estimation through multidistribution wind speed analysis in coastal Pakistan by Ghulam Abbas, Arshad Ali, Zohaib Mushtaq, Ateeq Ur Rehman, Seada Hussen, Habib Hamam

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Comparative analysis prediction of prostate and testicular cancer mortality using machine learning: accuracy study by Aurélio Gomes de Albuquerque Neto, David Medeiros Nery, João Paulo Araújo Braz, Carla Ferreira do Nascimento, Tiago Almeida de Oliveira, Brígida Gabriele Albuquerque Barra, Leonardo Thiago Duarte Barreto Nobre, Diego Bonfada, Janine Karla França da Silva Braz

“…DESIGN AND SETTING: A comparative analysis of the pyMannKendall and Prophet machine-learning algorithms was conducted to develop predictive models using data from DATASUS (TabNet) to Caicó (Brazil) and Rio Grande do Norte (Brazil). …”
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An Auto-Adjusting Algorithm to Enhance Indoor Localization Accuracy: A Real-Time Experimental Analysis by Othman A. Alfahad, Husham Saied, Emad Malaekah, Ahmed Al Rashdi, Mohammed Emam, Mohsen Bakouri

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Tactical Analysis of Table Tennis Video Skills Based on Image Fuzzy Edge Recognition Algorithm by Weiqiang Li

“…The experimental data show that the recognition accuracy of the proposed fuzzy edge recognition algorithm is 98.1%, and the error and rejection rates are less than 1.6%. …”
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Elastic-plastic fracture analysis of pressure pipelines with axial cracks based on the interaction integral method. by Qi Song, Huifen Peng, Junyu Yao, Min Luo

“…Importantly, the empirical verification data substantially aligns with the previously mentioned theoretical analysis results in a noteworthy concurrence.…”
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Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns by Zhe Wang, Xiang Que, Meifang Li, Zhuoming Liu, Xun Shi, Xiaogang Ma, Chao Fan, Yan Lin

“…Compared with Geographically Weighted Regression (GWR), the STWR model proved more potent and reliable with higher R2 and lower estimated standard errors (SE). The STWR model is highly flexible in terms of spatiotemporal variations in data. …”
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Machine learning-based analysis on pharmaceutical compounds interaction with polymer to estimate drug solubility in formulations by Ahmad J. Obaidullah, Wael A. Mahdi

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