RMIS-Net: a fast medical image segmentation network based on multilayer perceptron by Binbin Zhang, Guoliang Xu, Yiying Xing, Nanjie Li, Deguang Li

“…The network incorporates layer normalization and dropout regularization to ensure training stability, complemented by Gaussian error linear unit (GELU) activation functions for improved non-linear modeling. …”
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Biologically inspired hybrid model for Alzheimer’s disease classification using structural MRI in the ADNI dataset by Houmem Slimi, Imen Cherif, Sabeur Abid, Mounir Sayadi

“…Trained on a three-class task [AD, mild cognitive impairment (MCI), and cognitively normal (CN) subjects], the hybrid network optimizes mean squared error (MSE) loss with L2 regularization and Adam, incorporating early stopping to enhance generalization. …”
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Towards monitoring biodiversity in Amazonian forests: how regular samples capture meso-scale altitudinal variation in 25 km2 plots. by Darren Norris, Marie-Josée Fortin, William E Magnusson

“…Kolmogorov-Smirnov-tests, correlation and root-mean-square-error were used to measure sample representativeness, similarity and accuracy respectively. …”
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Moving toward the digitalization of neuropsychological tests: An exploratory study on usability and operator perception by Maria Grazia Maggio, Fabio Mauro Giambò, Martina Barbera, Paolo De Pasquale, Francesca Bruno, Andrea Calderone, Angelo Quartarone, Amelia Rizzo, Rocco Salvatore Calabrò

“…Key qualitative feedback indicated that participants appreciated the speed, efficiency, and reduced error rates of digital tools, with many noting improvements in data organization and reporting. …”
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Multi-scale attention patching encoder network: a deployable model for continuous estimation of hand kinematics from surface electromyographic signals by Chuang Lin, Qiong Xiao, Penghui Zhao

“…The results show that the average Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), coefficient of determination (R2), and smoothness (SMOOTH) of the sMAPEN model are 0.9082, 0.0646°, 0.8163, and − 0.0017, respectively, which significantly outperforms that of the state-of-the-art methods in all metrics (p < 0.01). …”
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Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management by Alireza Etaati, Mostafa Bastam, Ehsan Ataie

“…The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.…”
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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. …”
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Insights into Phase Transformations during Selective Laser Melting of Ti6Al4V: A Numerical Approach by Gasser Abdelal, Daniel Higgins, Chi-Wai Chan, Brian Falzon

“…The developed model is a robust tool for guiding the design and optimisation of SLM processes, reducing the reliance on trial-and-error methods, and enhancing the efficiency and quality of additive manufacturing for critical applications in aerospace, biomedical, and automotive industries.…”
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Hydrological modeling of ungauged wadis in arid environments using GIS: a case study of Wadi Madoneh in Jordan by Nezar Hammouri, Ali El-Naqa

“…The flow comparison graph indicates that the calibrated model fits well with the observed runoff data, with a peak-weighted root mean square error (RMS) of less than 2%. This calibration was performed by applying different curve numbers in the simulated model. …”
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Comparison of three one-dimensional time-domain electromagnetic forward algorithms by Frederik Alexander Falk, Anders Vest Christiansen, Thomas Mejer Hansen

“…First, we analyze the relative modeling error of each algorithm’s forward calculation for conductive half-space models, compared to an analytic solution. …”
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An Automated Framework for Lane Closure Detection on Highway Using Connected Vehicle Data and Machine Learning Models by Ashutosh Dumka, Raghupathi Kandiboina, Aparna Joshi, Skylar Knickerbocker, Neal Hawkins, Anuj Sharma

“…The traditional methods, which use primarily manual reporting or sensor-based methods, can be error-prone, inefficient, and costly. This study introduces an innovative real-time lane closure detection approach using connected vehicle (CV) data and machine learning techniques. …”
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Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors by Ana González-Castro, José Alberto Benítez-Andrades, Rubén González-González, Camino Prada-García, Raquel Leirós-Rodríguez

“…Performance was evaluated based on mean squared error (MSE) and coefficient of determination ( R 2 ), to assess how combining multiple data types influences prediction accuracy. …”
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Psychometric evaluation of the Persian version of the adult chronic kidney disease self-management instrument in the Iranian population by Simin Alasvand, Mehrnaz Ahmadi, Hamid Sharif-Nia, Marziyeh Asadizaker

“…The absolute reliability, as determined by Standard Error of Measurement results, was 3.16. Additionally, the Minimum Detectable Change was estimated to be 8.75. …”
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Dual-band reconfigurable circularly polarized antenna with novel phase shifter and beamforming network by Majid Allahverdizadeh, Reza Masoumi, Mohammad Bemani

“…The output phases are 0°, 90°, 180°, and 270° in the first state, and 0°, 270°, 180°, and 90° in the second state, with a maximum phase error of 5°. When coupled with the antenna subarray, the system shows an axial ratio below 1.07 dB in the lower band and below 1.05 dB in the higher band. …”
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Evaluation of Network Design and Solutions of Fisheye Camera Calibration for 3D Reconstruction by Sina Rezaei, Hossein Arefi

“…In comparison to 3D calibration, self-calibration, and a pre-calibration strategy, the two-step calibration process has demonstrated an average improvement of 2826 points in the 3D sparse point cloud and a 0.22 m decrease in the re-projection error value derived from the front lens images of two individual spherical cameras. …”
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Optimizing Security of Radio Frequency Identification Systems in Assistive Devices: A Novel Unidirectional Systolic Design for Dickson-Based Field Multiplier by Atef Ibrahim, Fayez Gebali

“…The method of implementing field multiplication operation significantly influences the system’s resilience against side-channel attacks; for instance, implementation using unidirectional systolic array structures can provide enhanced error detection capabilities, improving resistance to side-channel attacks compared to traditional bidirectional multipliers. …”
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A Multi-Scale Fusion Convolutional Network for Time-Series Silicon Prediction in Blast Furnaces by Qiancheng Hao, Wenjing Liu, Wenze Gao, Xianpeng Wang

“…Compared with LSTM and the GRU, MSF-CNN reduces the Root Mean Square Error (RMSE) by approximately 22% and 21%, respectively, and improves the Hit Rate (HR) by over 3.5% and 4%, highlighting its superiority in capturing complex temporal dependencies. …”
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LHS-GA Based H-Infinity Control for Robust Airfoil Flutter Suppression by Malek Rekik, Omar Khaled, Karolos Grigoriadis, Matthew A. Franchek

“…Unlike frequency-domain methods that rely on trial-and-error or conservative assumptions, LHS-GA automatically synthesizes a robust static controller by directly addressing time-domain constraints and performance metrics. …”
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Automated machine learning for image-based detection of dental plaque on permanent teeth by Teerachate Nantakeeratipat, Natchapon Apisaksirikul, Boonyaon Boonrojsaree, Sirapob Boonkijkullatat, Arida Simaphichet

“…However, they are time-consuming and prone to human error. This study aims to investigate the feasibility of using Google Cloud's Vertex artificial intelligence (AI) automated machine learning (AutoML) to develop a model for detecting dental plaque levels on permanent teeth using undyed photographic images.MethodsPhotographic images of both undyed and corresponding erythrosine solution-dyed upper anterior permanent teeth from 100 dental students were captured using a smartphone camera. …”
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Enhancing phase change thermal energy storage material properties prediction with digital technologies by Minghao Yu, Jing Liu, Cheng Chen, Mingyue Li

“…Traditional experimental approaches, while effective, are resource-intensive and time-consuming, often requiring extensive trial-and-error methods. To address these limitations, the integration of digital technologies, such as computational modeling and machine learning (ML), has become increasingly important.MethodsThis paper proposes a hybrid multiscale modeling framework that integrates molecular dynamics (MD) simulations, finite element methods (FEM) from continuum mechanics, and supervised ML algorithms—including deep neural networks and gradient boosting regressors—to enable accurate and efficient prediction of material properties across scales. …”
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