A Resource-Efficient Time-Domain-Based Algorithm to Estimate Respiration Rate From Single-Lead ECG Signal by G. B. Krishnapriya, R. N. Ponnalagu, Sanket Goel

“…On the benchmark dataset, MPD achieved a mean absolute error (MAE) of 3.66 bpm and mean absolute percentage error (MAPE) of 23.69%, outperforming the Count-Orig method (MAE = 5.09 bpm, MAPE = 32.76%). …”
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Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach by Lili Zhan, Yongxin Xu, Jinshan Zhu, Zhangshuo Liu

“…Results show that the developed SVD-DNN model has the best retrieval accuracy [mean R = 0.90, root mean square error (RMSE) = 1.21, mean absolute error (MAE) = 1.05], outperforming the SVD-RF and SVD-SVM models. …”
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Multi-Level Decomposition and Interpretability-Enhanced Air Conditioning Load Forecasting Study by Xinting Yang, Ling Zhang, Hong Zhao, Wenhua Zhang, Chuan Long, Gang Wu, Junhao Zhao, Xiaodong Shen

“…To validate model performance, this paper uses air-conditioning load data from Nanchong City and Sichuan Province, for experimental analysis. …”
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Fusion of DS-InSAR and THPF-LSTM for monitoring and predicting surface deformation in closed mines by Jianyang ZHANG, Hongdong FAN, Xiangyang ZHU, Minghu SUN

“…The method of this paper realizes the integrated analysis of monitoring and prediction of surface deformation in closed mines.…”
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Cognitive considerations for adults with sickle cell disease completing the brief pain inventory by Julia A. O'Brien, Jermon A. Drake, Donald J. Bearden, Kim E. Ono, Soumitri Sil, Lindsey L. Cohen, Alana Karras, Enrico M. Novelli, Charles R. Jonassaint

“…Methods:. A secondary analysis was completed using data from a study examining brain aging and cognitive impairment in SCD. …”
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Comprehensive Comparison of Machine Learning and Kalman Filter Battery State of Charge Estimators by Romulo Vieira, Phil Kollmeyer, Lea Pitault, Ali Emadi

“…Ten different measurement offset error values are added to the data, capturing sensor error that may be present in the vehicle, and incorrect initial SoC estimates are also considered. …”
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Estimation of foveal avascular zone area from a B-scan OCT image using machine learning algorithms. by Taku Toyama, Ichiro Maruko, Han Peng Zhou, Miki Ikeda, Taiji Hasegawa, Tomohiro Iida, Makoto Aihara, Takashi Ueta

“…The models' performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R2). …”
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Heat Treatment-Driven Structural and Morphological Transformation Under Non-Parametric Tests on Metal–Ceramic-Sputtered Coatings by Federico Gamboa-Soto, Roberto Bautista-García, Diana I. Llanes-Gil López, Juan E. Bermea, René Tinoco Mendiola, Sion F. Olive-Méndez, Andrés González-Hernández

“…The average crystallite size before and after the thermal treatment was analyzed by the K-W correlation, revealing significant changes considering a reliability level of 95% and a significance error of 5%. The analysis revealed a strong correlation between experimental data and statistical treatment results.…”
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Research on digital twin modeling method for combustion process based on model reduction by Yue Zhang, Jiale Li

“…Comparative analyses are conducted on the modal data obtained from different methods. Furthermore, based on modal data analysis, a Support Vector Machine (SVM) regression model is selected to reconstruct the temperature field. …”
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Multiwavelength Study of Five Distant Transient Gamma-Ray AGNs with z > 1 by Hai-Qin Wang, Fang-Kun Peng, Rui Xue, Lu-Ming Sun

“…We collect the optical data and historical data to perform a spectral energy distribution (SED) analysis. …”
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Sequential testing of complementary hypotheses about population density by Diego F. Rincon, Izzy McCabe, David W. Crowder

“…Abstract Making inferences about population density is paramount in ecology and pest management for decision‐makers who often seek to determine how a population compares to a pre‐established static or dynamic threshold through sampling or monitoring. Sequential data analysis is appealing for monitoring and decision making as it is more cost‐efficient than fixed‐sample‐size approaches. …”
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Analyzing and Modeling the Dynamic Electrical Characteristics of Nanocomposite Large-Range Strain Gauges by Alex M. Wonnacott, Anton E. Bowden, Ulrike H. Mitchell, David T. Fullwood

“…The inverse model was calibrated for a particular sensor from a small amount of cyclic data during a single test. The inverse model accurately predicted key strain characteristics with a percent error as low as 0.5%. …”
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A New Multistage Encryption Scheme Using Linear Feedback Register and Chaos-Based Quantum Map by Adel R. Alharbi, Jawad Ahmad, null Arshad, Sajjad Shaukat Jamal, Fawad Masood, Yazeed Yasin Ghadi, Nikolaos Pitropakis, William J Buchanan

“…With the increasing volume of data transmission through insecure communication channels, big data security has become one of the important concerns in the cybersecurity domain. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…The suggested deep learning algorithms' performance is evaluated using criteria such as precision, specificity, sensitivity, accuracy, and error rate. This assessment demonstrates the potential of the recommended method to significantly contribute to the healthcare system by offering consistent and reliable predictions across various types of illnesses, as shown in a comparative analysis against existing models.…”
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Assessment of Baseflow Separation Methods Used in the Estimations of Design-Related Storm Hydrographs Across Various Return Periods by Oscar E. Coronado-Hernández, Rafael D. Méndez-Anillo, Manuel Saba

“…A frequency analysis of hydrological data was conducted using Pearson Type III and Generalized Extreme Value distributions to identify the most suitable statistical models for estimating extreme events regarding peak flow and maximum storm hydrograph volume. …”
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How Useful Is a Distributed Ledger for Tracking and Tracing in Supply Chains? A Systems Thinking Approach by Christian Straubert, Eric Sucky

“…<i>Results</i>: Based on our analysis, DLT is only necessary in special cases, e.g., when the owners of the data have an interest in deleting the data, but the data stakeholders do not. …”
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Benchmarking and improving algorithms for attributing satellite-observed contrails to flights by A. Sarna, V. Meijer, R. Chevallier, A. Duncan, K. McConnaughay, S. Geraedts, K. McCloskey

“…The problem of contrail-to-flight attribution is complicated by several factors, such as the time required for a contrail to become visible in satellite imagery, high air traffic densities, and errors in wind data. Recent work has introduced automated algorithms for solving the attribution problem, but it lacks an evaluation against ground-truth data. …”
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A Study to Assess the Perceptions, Attitudes, and Practices of Edentulous Patients Regarding the Maintenance of Complete Denture Prostheses by Misbah Khalid, Omaima Arshad, Raza Ullah Khan, Aamir Ghafoor, Romeysah Adnan, Hira Riaz, Muhammad Amer Khan

“…The sample size of 200 edentulous patients was calculated to achieve a 90% confidence interval with a 5% margin of error. The data collection procedure followed a structured process to ensure that relevant and accurate information was gathered from the study participants. …”
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Enhancing Stock Price Forecasting with CNN-BiGRU-Attention: A Case Study on INDY by Madilyn Louisa, Gumgum Darmawan, Bertho Tantular

“…It also enriches the academic literature on the application of deep learning techniques in financial data analysis and stock market forecasting within a complex and dynamic environment.…”
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Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…The GBM model also showed excellent results in generalization performance, and it effectively learned the data patterns without overfitting in residual analysis and mean absolute error (MAE) evaluation. …”
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