Composition and Notation of Parameters in Electronic Music: Approximate Reductionist Graphical Notation by Dino Rešidbegović

“…It attempts to define and explain a different, specific approach, which helps in understanding the technology, its applications and features to meet the problems of present compositions by using specific notation for electronic instruments or computers. …”
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Walking-Age Estimator Based on Gait Parameters Using Kernel Regression by Tomohito Kuroda, Shogo Okamoto, Yasuhiro Akiyama

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Fuzzy deep learning architecture for cucumber plant disease detection and classification by Anas Bilal, Junaid Ali Khan, Abdulkareem Alzahrani, Khalid Almohammadi, Maha Alamri, Xiaowen Liu

“…The architecture achieves 98% classification accuracy, outperforming leading models like VGG-19, DarkNet-19, and ResNet-50. Moreover, the computational time per run (40–90 s) is significantly lower than these models, which use higher learnable parameters (5.7 million). …”
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Monitoring water quality parameters using multi-source data-driven machine learning models by Yubo Zhao, Mo Chen, Jinyu He, Yanping Ma

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Research on Clustering Based PWR pin-by-pin Homogenization Parameters Compression Method by LI Tianya1, 2, LUO Qi3, YAO Dong2, HE Caiyun1, CAI Yun2, WANG Dan1, DUAN Yongqiang1, LIAO Hongkuan1, XIAO Peng2

“…In the high-fidelity numerical calculation method of reactor physics, the pin-by-pin two-step method can achieve a balance between computational accuracy and computational cost, and is currently a feasible and realistic high fidelity numerical calculation method. …”

Spatial and Channel Attention Integration with Separable Squeeze-and-Excitation Networks for Image Classifications by Nazmul Shahadat, Shleshma Regmi, Anup Rijal

“…Furthermore, we introduce a novel separable strategy in the SE block, enabling effective feature re-calibration across space with reduced computational cost. …”
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A Parameter-Free Topological Disassembly-Guided Method for Hyperspectral Target Detection by Xiaotong Sun, Lina Zhuang, Lianru Gao, Hongmin Gao, Xu Sun, Bing Zhang

“…Traditional model-driven methods often underperform due to the mismatches between model assumptions and real data, while data-driven methods face challenges such as complex training processes, parameter tuning, and high computational costs. To address these challenges, this article adopts a model-free and parameter-free design philosophy. …”
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Lightweight Sheep Face Recognition Model Combining Grouped Convolution and Parameter Fusion by Gaochao Liu, Lijun Kang, Yongqiang Dai

“…Finally, parameter fusion optimization work was carried out for the detection head, and the construction of the Parameter Fusion Detection (PFDetect) module was achieved, which significantly reduced the number of model parameters and computational complexity. …”
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A lightweight context-aware framework for toxic mushroom detection in complex ecological environments by Zhanchen Wei, Jiali Wang, Haohai You, Ruiqing Ji, Fude Wang, Lei Shi, Helong Yu

“…PM-YOLO achieves multi-scale feature alignment through hierarchical context fusion, performs adaptive attention weighting for morphological variations, and maintains a low computational cost while significantly improving accuracy. …”
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Prediction of Ki-67 expression in gastric gastrointestinal stromal tumors using histogram analysis of monochromatic and iodine images derived from spectral CT by Xianwang Liu, Tao Han, Yuzhu Wang, Hong Liu, Juan Deng, Caiqiang Xue, Shenglin Li, Junlin Zhou

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Towards real-time interest point detection and description for mobile and robotic devices by Patrick Rowsome, Muhammad Adil Raja, R. Muhammad Atif Azad

“…As a result, this paper presents a lightweight variant of the R2D2 network that significantly reduces parameters and computational complexity while crucially maintaining an acceptable level of accuracy. …”
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A Hybrid Model for Early Melanoma Detection: Integrating YOLOv9 and Faster R-CNN for Enhanced Diagnostic Accuracy by Mohamed I. Marie, Mohamed S. Elredeny, Ahmad Essayed Yakoub

“…The architecture delivers an average inference speed of 31.3 frames per second (FPS), surpassing clinical real-time thresholds. Additionally, computational profiling confirms its practical feasibility with 78.3 million parameters, 134.8 GFLOPs, and a 324 MB memory footprint. …”
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Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine by Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa

“…Recent advances in the application of spectral bands from satellite observations and machine learning algorithms (MLA) in the Google Earth Engine (GEE) cloud-computing platform have been demonstrated to enhance the accuracy of mapping forest resources. …”
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Metering Automation System 3.0 Base Version Based on Machine Learning by Sheng Li, Leping Zhang, Hang Dai, Lukun Zeng, Yuan Ai, Shuang Qi, Yuanzhai Cui

“…However, traditional machine learning methods and standalone deep learning architectures struggle to balance spatiotemporal feature extraction, computational efficiency, and deployment constraints for high-frequency multivariate metering data. …”
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The Comparison of Distributed P2P Trust Models Based on Quantitative Parameters in the File Downloading Scenarios by Jingpei Wang, Jie Liu

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On the minimum number of radiation field parameters to specify gas cooling and heating functions by David Robinson, Camille Avestruz, Nickolay Y. Gnedin

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Parameters Identification for Photovoltaic Module Based on an Improved Artificial Fish Swarm Algorithm by Wei Han, Hong-Hua Wang, Ling Chen

“…Different from the traditional linear model, the model of PV module has the features of nonlinearity and multiparameters. Since conventional methods are incapable of identifying the parameters of PV module, an excellent optimization algorithm is required. …”
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C-parameter version of robust bounded one-class support vector classification by Junyou Ye, Zhixia Yang, Yongxing Hu, Zheng Zhang

“…This paper presents a novel C-parameter version of bounded one-class support vector classification (C-BOCSVC) to determine a unique decision boundary. …”
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Structural Parameter Identification Using Multi-Objective Modified Directional Bat Algorithm by LIU Li-jun, LIN Ying-hai, SU Yong-hui, LEI Ying

“…To simulate real-world conditions， noise was added to modal parameters. Dynamic features， such as natural frequencies and mode shapes， were used for damage detection. …”
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EEG–fNIRS signal integration for motor imagery classification using deep learning and evidence theory by Mohammed E. Seno, Niladri Maiti, Maulik Patel, Mihirkumar M. Patel, Kalpesh B. Chaudhary, Ashish Pasaya, Babacar Toure

“…To address the limitations of traditional unimodal brain-computer interface BCI) technologies based on electroencephalography (EEG) such as low spatial resolution and high susceptibility to noise an increasing number of neuroscience-driven studies have begun to focus on BCI systems that fuse EEG signals with functional near-infrared spectroscopy (fNIRS) signals. …”
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