A New Hybrid Class of Distributions: Model Characteristics and Stress–Strength Reliability Studies by Mustapha Muhammad, Jinsen Xiao, Badamasi Abba, Isyaku Muhammad, Refka Ghodhbani

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MODELING HYPERTENSION DISEASE RISK IN INDONESIA USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND BINARY LOGISTIC REGRESSION APPROACHES by Nur Chamidah, Ardana Tegar Hendrawan, Figo Surya Ardiyanto, Martha Sayyida Hammami, Nurul Izzah, Salsabila Niken Hariadi

“…The analysis shows the Apparent Error Rate (APPER) for MARS is 84.706%, while for binary logistic regression it is 80%, indicating MARS is better at classifying hypertension data in Indonesia. …”
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Reliability, validity, and diagnostic accuracy of the apathy evaluation scale in chronic stroke survivors by Akram Jamali, Tourandokht Baluchnejadmojarad, Seyede Zohreh Jazaeri, Shiva Abedi, Hajar Mehdizadeh, Parvaneh Taghavi Azar Sharabiani, Ghorban Taghizadeh

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RESULTS OF SNOW COVER FORECAST IN THE CAUCASUS REGION USING THE METHOD OF SINGULAR-SPECTRAL ANALYSIS by Boris Azretaliyevich Ashabokov, Alla Amarbiyevna Tashilova, Lara Asirovna Kesheva

“…Materials and methods of research: The forecast of changes in snow cover characteristics is no less important than the forecast of climate changes (temperature and liquid precipitation). …”
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Evaluation of The Determinants of Domestic Bonds in Ghana by Zahra Moro, Oscar Agyemang Opoku

“…A sample of 6 Government of Ghana bonds which have been on the secondary market for at least two years with a tenor of at least three years was used by the researcher. Based on the stationary and the existence on cointegration vectors, the study investigated long and short run effects using ARDL (1,0,1,1,1,1) and ARDL (1,2,2,2,2,2) Error Correction Models respectively. …”
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Improved effective Łojasiewicz inequality and applications by Saugata Basu, Ali Mohammad-Nezhad

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Precipitable Water Vapor Retrieval Based on GNSS Data and Its Application in Extreme Rainfall by Tian Xian, Ke Su, Jushuo Zhang, Huaquan Hu, Haipeng Wang

“…Hence, based on the GNSS observation data from July 2023, this study retrieves PWV using the Global Pressure and Temperature 3 (GPT3) model and evaluates its application performance in the “7·31” extremely torrential rain event in Beijing in 2023. Research has found the following: (1) Tropospheric parameters, including the PWV, zenith tropospheric delay (ZTD), and zenith wet delay (ZWD), exhibit high consistency and are significantly affected by weather conditions, particularly exhibiting an increasing-then-decreasing trend during rainfall events. (2) Through comparisons with the PWV values through the integration based on fifth-generation European Centre for Medium-Range Weather Forecasts (ERA-5) reanalysis data, it was found that results obtained using the GPT3 model exhibit high accuracy, with GNSS PWV achieving a standard deviation (STD) of 0.795 mm and a root mean square error (RMSE) of 3.886 mm. (3) During the rainfall period, GNSS PWV remains at a high level (>50 mm), and a strong correlation exists between GNSS PWV and peak hourly precipitation. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…The significance of this research lies in its ability to automate and accelerate complex structural analysis using Adaptive Neuro-Fuzzy Inference Systems (ANFISs), achieving an average error of less than 2% in multi-variable prediction scenarios. …”
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Optimal Scheduling Strategy for Wind-Solar-Hydro Alliance Considering Compensation of Regulation by Hydropower by Xianshan LI, Shengbiao DING, Fei LI, Xin LI

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An enhanced method of CNNs by incorporating the clustering-guided block for concrete crack recognition by Hui Li, Chenyu Liu, Ning Zhang, Wei Shi

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MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba by Jianqiang Zhang, Jing Hou, Qiusheng He, Zhengwei Yuan, Hao Xue

“…We assigned higher weights to error-prone keypoints to strengthen the model’s attention to these points. …”
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InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities. by Yasir Khan Jadoon, Muhammad Attique Khan, Yasir Noman Khalid, Jamel Baili, Nebojsa Bacanin, MinKyung Hong, Yunyoung Nam

“…The ablation study demonstrated that a margin of error value of 70.1338 ± 3.053 (±4.35%) and 82.7813 ± 2.852 (±3.45%) for the confidence level 95%,1.960σx̄ is obtained for HMDB51 and UCF datasets respectively. …”
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POVERTY MACRO SYSTEM DYNAMICS MODELING BASED ON SIMULTANEOUS EQUATIONS MODELS by Bagus Sumargo, Irman Firmansyah, Asep Anwar Nugraha, Mulyono Mulyono, Dania Siregar, Felia Aidah Nuriza

“…The mean absolute percentage error (MAPE) is 2.34%, meaning that the macro poverty model is valid. …”
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Estimating chlorophyll content in tea leaves using spectral reflectance and deep learning methods by Yuta Tsuchiya, Yuhei Hirono, Rei Sonobe

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Spatial correlation guided cross scale feature fusion for age and gender estimation by Shiyi Jiang, Qing Ji, Hukui Shi, Che Chen, Yang Xu

“…Comprehensive experiments demonstrate that SCGNet achieves state-of-the-art performance with minimum Mean Absolute Error (MAE) 4.01% for age estimation on IMDB-Clean (2.9% improvement over VOLO-D1) and highest gender classification accuracy on IMDB-Clean, UTKFace, and Lagenda datasets, showing improvements in cross-scene adaptability compared to VOLO and MiVOLO models respectively. …”
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EVALUATION OF THE ACCURACY OF COMPUTER MEASUREMENT OF A PHOTOGRAMMETRIC MODEL OF THE JAW by Dmytro M. Korol, Dmytro D. Kindiy, Kateryna D. Toncheva, Ruslan V. Kozak, Vitalii V. Yarkovyi, Sergiy G. Zubchenko, Mykhailo O. Ramus

“…The difference between the two measurement methods was comparable to the results of other researchers. The analysis of the results proved that computer measurement on a 3D model is 27% better by the standard deviation criterion, 28% better by the mean error criterion, and 26% better by the coefficient of variation. …”
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NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models by I. Aydin, U. G. Sefercik

“…The performance of the NDVI maps was analyzed using R², Root Mean Square Error (RMSE), Normalized Median Absolute Deviation (NMAD) and Standard Deviation (STD) metrics. …”
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Applying a Four-Way Factorial Experimental Model to Diagnose Optimum kNN Parameters for Precise Aboveground Biomass Mapping by Chinsu Lin, Nova D. Doyog

“…Results indicated that these sampling schemes produced AGB estimations with average error rates ranging from 13% to 241%. …”
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Assessment of groundwater quality for agricultural purposes in Qazvin Province, northwestern Iran: A fuzzy inference and indicator Kriging approach by Mohammad Javad Masoudi, Afshin Ashrafzadeh, Mohammadreza Khaledian, Somaye Janatrostami

“…In ordinary Kriging, the average Root Mean Square Error (RMSE) values for EC, SAR, RSC, TH, Na⁺, and Cl⁻ were 0.94 dS/m, 1.54, 1.42 meq/L, 3.13 mg/L, 3.03 mg/L, and 2.62 mg/L, respectively, indicating reliable assessments of groundwater quality parameters. …”
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Evaluation and Analysis of Next-Generation FY-4A LPW Products over Various Climatic Regions in China by Wenyuan Zhang, Xinyu Xiao, Jinsong Peng, Shubi Zhang, Endrit Shehaj, Gregor Moeller

“…The results show that all four FY-4A LPWs are consistent with ERA5 LPWs, with an overall root mean square error (RMSE) of 2.58, 0.90, 1.30, and 1.01 mm, respectively. …”
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