AcuSim: A Synthetic Dataset for Cervicocranial Acupuncture Points Localisation by Qilei Sun, Jiatao Ma, Paul Craig, Linjun Dai, Eng Gee Lim

“…The study validates a convolutional neural network (CNN) on the proposed dataset with an accuracy of 99.73% and shows that 92.86% of predictions in validation set align within a 5mm threshold of margin error when compared to expert-annotated data. This dataset addresses the limitations of prior datasets and can be applied to applications of acupoint detection and visualization, further advancing automation in Traditional Chinese Medicine (TCM).…”
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Cancelable biometric authentication leveraging empirical mode decomposition and quaternion representations for IoT security by Mahmoud Nasr, Krzysztof Brzostowski, Adam Piórkowski, Fathi E. Abd El-Samie

“…These findings highlight the method capacity to tackle significant security issues, while ensuring optimal performance in practical applications.…”
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Optimized deep neural network architectures for energy consumption and PV production forecasting by Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei, Razgar Ebrahimy

“…The results demonstrate that ODNN outperforms the average performance of other methods, achieving a 27% improvement in forecasting accuracy and a 22% reduction in error across various metrics. These findings demonstrate the significant potential of GA-PSO as an effective tool to optimize DNN models in energy forecasting applications.…”
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Bionic Compass Method Based on Atmospheric Polarization Optimization in Non-Ideal Clear Condition by Yuyang Li, Xia Wang, Min Zhang, Ruiqiang Li, Qiyang Sun

“…Existing polarization navigation models are primarily based on Rayleigh scattering theory, which is applicable to high-altitude, dry, and clear weather conditions. …”
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Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization by Sofia El Amoury, Youssef Smili, Youssef Fakhri

“…This performance-driven method avoids the inefficiencies of manual design and iterative trial and error. Experimental results indicate that the PSO-optimized CNN achieves a classification accuracy of 99.19%, demonstrating significant potential for improving diagnostic precision in complex medical imaging applications and underscoring the value of automated architecture search in advancing critical healthcare technology.…”
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Analysis of Reaction between α-Lipoic Acid and 2-Chloro-1-methylquinolinium Tetrafluoroborate Used as a Precolumn Derivatization Technique in Chromatographic Determination of α-Lip... by Magdalena Godlewska, Angelika Odachowska, Monika Turkowicz, Joanna Karpinska

“…The error of the determination did not exceed 0.5% in relation to the declared value.…”
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Deep Learning Method for Breakdown Voltage and Forward <i>I-V</i> Characteristic Prediction of Silicon Carbide Schottky Barrier Diodes by Hao Zhou, Xiang Wang, Shulong Wang, Chenyu Liu, Dongliang Chen, Jiarui Li, Lan Ma, Guohao Zhang

“…After training for 1000 epochs, the deep learning-based model could predict not only point values like <i>V</i>_br but also curves, such as forward <i>I-V</i> characteristics, with a mean squared error (MSE) of less than 10⁻³. Our research shows the applicability and high efficiency of introducing deep learning into device characteristic prediction.…”
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A Gradient-Based Optimization Algorithm for Optimal Control Problems With General Conformable Fractional Derivatives by Essia Ben Alaia, Slim Dhahri, Omar Naifar

“…Comprehensive comparisons including Caputo derivatives demonstrate 17.8% cost reduction and 40.1% lower terminal state error. Parameter sensitivity analyses further validate kernel selection strategies. …”
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Performance Evaluation of Activation Functions in Deep Residual Networks for Short-Term Load Forecasting by Junchen Liu, Faisul Arif Ahmad, Khairulmizam Samsudin, Fazirulhisyam Hashim, Mohd Zainal Abidin Ab Kadir

“…This study provides valuable insights for advancing STLF research and guiding practical applications in load forecasting.…”
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A Framework for Optimal Worker Selection in Spatial Crowdsourcing Using Bayesian Network by Nor Aniza Abdullah, Mohammad Mustaneer Rahman, Md. Mujibur Rahman, Khairil Imran Ghauth

“…Spatial Crowdsourcing (SC) is a new paradigm of crowdsourcing applications. Unlike traditional crowdsourcing, SC outsources tasks to distributed potential workers, and those who accept the task are required to travel to a predefined location to complete it. …”
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Evaluation of Clipping Based Iterative PAPR Reduction Techniques for FBMC Systems by Zsolt Kollár, Lajos Varga, Bálint Horváth, Péter Bakki, János Bitó

“…The low ACLR of the transmitted FBMC signal makes it especially favorable in cognitive radio applications, where strict requirements are posed on out-of-band radiation. …”
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A statistical study on factors influencing piezoelectric properties of upside-down composites towards machine learning-driven development for recycling by Sivagnana Sundaram Anandakrishnan, Suhas Yadav, Mohadeseh Tabeshfar, Mikko Nelo, Jani Peräntie, Yang Bai

“…Using upside-down composites to recycle retired/discarded piezoceramics and then give them a second life in sensor applications paves the way towards sustainable production of piezoelectric materials. …”
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Hole-Free Symmetric Complementary Sparse Array Design for High-Precision DOA Estimation by He Ma, Libao Liu, Zhihong Gan, Yang Gao, Xingpeng Mao

“…This paper proposes a new hole-free sparse array configuration for remote sensing applications to achieve improved DOA estimation performance using DCA. …”
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RAT-CC: A Recurrent Autoencoder for Time-Series Compression and Classification by Giacomo Chiarot, Sebastiano Vascon, Claudio Silvestri, Idoia Ochoa

“…Besides this, most applications need to classify these data efficiently, and having to reconstruct the original data from the compressed representation to then classify them is not optimal. …”
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Online Visual Detection System for Head Warping and Lower Buckling of Hot-Rolled Rough Slab by Shitao Ge, Yan Peng, Jianliang Sun, Licheng Han

“…The detection system in industrial applications has demonstrated high accuracy: detection error ≤ ±5 mm, type recognition rate ≥ 99%. …”
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Active learning framework to optimize process parameters for additive-manufactured Ti-6Al-4V with high strength and ductility by Jeong Ah Lee, Jaejung Park, Man Jae Sagong, Soung Yeoul Ahn, Jung-Wook Cho, Seungchul Lee, Hyoung Seop Kim

“…Abstract Optimizing process and heat-treatment parameters of laser powder bed fusion for producing Ti-6Al-4V alloys with high strength and ductility is crucial to meet performance demands in various applications. Nevertheless, inherent trade-offs between strength and ductility render traditional trial-and-error methods inefficient. …”
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Estimating small area population from health intervention campaign surveys and partially observed settlement data by Chibuzor Christopher Nnanatu, Amy Bonnie, Josiah Joseph, Ortis Yankey, Duygu Cihan, Assane Gadiaga, Hal Voepel, Thomas Abbott, Heather R. Chamberlain, Mercedita Tia, Marielle Sander, Justin Davis, Attila N. Lazar, Andrew J. Tatem

“…Reductions in relative error rates were 32–73% in a simulation study, and ~32% when applied to malaria survey data in Papua New Guinea. …”
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An integrated approach to image quality: comparative analysis of bilinear and nearest neighbor interpolation by Wusat Ullah, Seher Ilyas, Hamza Naveed, Saalam Ali

“…By providing a comparative framework, this work contributes to the field by enhancing methods for assessing and optimizing image quality in digital imaging applications.…”
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Analytical simulation of the nonlinear Caputo fractional equations by Ali Ahadi, Seyed Mostafa Mousavi, Amir Mohammad Alinia, Hossein Khademi

“…The present study underscores the applicability of these approaches in several scientific and technological domains, hence promoting more advancements in the numerical examination of fractional systems.…”
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Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design by Yingzhao Shao, Junyi Wang, Xiaodong Han, Yunsong Li, Yaolin Li, Zhanpeng Tao

“…Compared to the prototype network, this fault-tolerant design method increases tolerance to SEU error accumulation by five times while increasing resource consumption by less than 15%, making it more suitable for spaceborne on-orbit applications than traditional fault-tolerant design approaches.…”
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