An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis by Haien Wang, Xingxing Jiang, Wenjun Guo, Juanjuan Shi, Zhongkui Zhu

“…Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. …”
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Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8 by Jiang Liu, Jingxin Yu, Changfu Zhang, Huankang Cui, Jinpeng Zhao, Wengang Zheng, Fan Xu, Xiaoming Wei

“…Three key innovations address YOLOv8’s limitations: (1) an SE attention module boosts feature representation in cluttered environments, (2) GhostConv replaces standard convolution to reduce computational load by 19% while preserving feature discrimination, and (3) a scale-adaptive WIoU_v2 loss function optimizes gradient allocation for variable-quality data. …”
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Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism by Xingyu Tong, Zhihong Liang, Fangrong Liu

“…The model parameters and computational complexity are significantly reduced by streamlining the Inception modules from nine to seven and introducing depth-separable convolution. …”
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High-Resolution Direction of Arrival Estimation of Underwater Multitargets Using Swarming Intelligence of Flower Pollination Heuristics by Nauman Ahmed, Huigang Wang, Shanshan Tu, Norah A.M. Alsaif, Muhammad Asif Zahoor Raja, Muhammad Kashif, Ammar Armghan, Yasser S. Abdalla, Wasiq Ali, Farman Ali

“…Moreover, acoustic waves impinging from the far-field multitarget are evaluated using the different number of hydrophones of uniform linear array (ULA). The measuring parameters like robustness against noise and element quantity, estimation accuracy, computation complexity, various numbers of hydrophones, variability analysis, frequency distribution and cumulative distribution function of root mean square error (RMSE), and resolution ability are applied for analyzing the performance of the proposed model with additive white Gaussian noise (AWGN). …”
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RDCU-Net: A Multi-Scale Residual Dilated Convolution U-Net with Spatial Pyramid Pooling for Brain Tumor Segmentation by Mohammad Soltani-Gol, Akbar Asgharzadeh-Bonab, Hamid Soltanian-Zadeh, Jalil Mazlum

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Analytic Continual Learning-Based Non-Intrusive Load Monitoring Adaptive to Diverse New Appliances by Chaofan Lan, Qingquan Luo, Tao Yu, Minhang Liang, Wenlong Guo, Zhenning Pan

“…Meanwhile, a supervised contrastive learning strategy is applied to enhance the distinctiveness among appliance types in the feature extraction module. When the novelty detection branch determines that new data need to be learned, the parameters of the dual branches are updated by recursively calculating the analytical solution using only the current data. …”
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String processing model for knowledge-driven systems by V. P. Ivashenko

“…The paper gives definitions of concepts necessary for the calculation of metric features calculated over strings. As a result of the experiments, theoretical estimates of the computational complexity of the implemented operations and the validity of the choice of parameters of the used data structures were confirmed, which ensures near-optimal throughput and operation time indicators of operations. …”
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Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation by Tareq Mahmod AlZubi, Hamza Mukhtar

“…Moreover, our model demonstrates superior efficiency with a computational cost of 371.3 GFLOPs, 26.53 million tuned parameters, and an inference time of 0.058 seconds per iteration. …”
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Design of an integrated model using deep reinforcement learning and Variational Autoencoders for enhanced quantum security by Harshala Shingne, Diptee Chikmurge, Priya Parkhi, Poorva Agrawal

“…This multi-agent system strengthens both the security and computational efficiency by reducing attack vulnerabilities by 15–18 % and lowering the computational complexity by 20–25 %. …”
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Meat analogues: The relationship between mechanical anisotropy, macrostructure, and microstructure by Miek Schlangen, Iris van der Doef, Atze Jan van der Goot, Mathias P. Clausen, Thomas E. Kodger

“…Mechanical properties are assessed using tensile testing, microstructure is studied using X-ray tomography and confocal laser scanning microscopy, and macrostructure is quantified using a computer vision algorithm based on segmentation and shape features. …”
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A Lightweight Algorithm for Detection and Grading of Olive Ripeness Based on Improved YOLOv11n by Fengwu Zhu, Suyu Wang, Min Liu, Weijie Wang, Weizhi Feng

“…Concurrently, existing deep learning-based detection models face issues such as insufficient feature extraction for small targets and difficulties in deployment due to their need for large numbers of parameters. …”
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A Multi-Encoder BHTP Autoencoder for Robust Lithium Battery SOH Prediction Under Small-Sample Scenarios by Chang Liu, Shunli Wang, Zhiqiang Ma, Siyuan Guo, Yixiong Qin

“…By utilizing a pre-training and fine-tuning strategy, the proposed method effectively reduces computational complexity and the number of model parameters while maintaining high prediction accuracy. …”
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LGR-Net: A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates by Ruizhen Gao, Meng Chen, Yue Pan, Jiaxin Zhang, Haipeng Zhang, Ziyue Zhao

“…The experimental results show that LGR-Net outperforms other YOLO-series models in terms of overall performance, achieving optimal results in terms of precision (<i>p</i> = 98.7%), recall (R = 98.9%), mAP (99.4%), and parameter count (2,412,118). LGR-Net achieves low computational complexity and high detection accuracy, providing an efficient and effective solution for defect detection in elevator guide rail pressure plates.…”
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Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…The findings demonstrate that Optuna consistently outperforms its counterparts, achieving the highest predictive accuracy and the lowest computational training time. The findings also highlight SHAP&#x2019;s superiority in offering detailed, consistent, and actionable insights, making it the preferred method for both global feature importance and individual feature analysis in high-stakes engineering applications.…”
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(IoT) Network intrusion detection system using optimization algorithms by Luo Shan

“…Compared with traditional models like the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) and Support Vector Machine (SVM), the proposed framework significantly improves the sensitivity and generalization ability for detecting various types of attacks through dynamic feature selection and parameter optimization. Experimental results demonstrate that the hybrid algorithm exhibits superior real-time responsiveness in binary classification tasks, thanks to its lightweight design that reduces dependency on computational resources. …”
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The entropy-transformed Gompertz distribution: Distributional insights and cross-disciplinary utilizations

“…Some of its core characteristics, such as its statistical and computational features, are clearly presented. A thorough simulation analysis has been done to examine the final behavior of maximum likelihood estimators while estimating model parameters. …”
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Efficient tuna detection and counting with improved YOLOv8 and ByteTrack in pelagic fisheries by Yuanchen Cheng, Zichen Zhang, Yuqing Liu, Jie Li, Zhou Fu

“…Experimental results show that the improved YOLOv8n-DMTNet model achieves a 9.2% increase in mAP@0.5 and a 6.4% increase in mAP@0.5:0.95 compared to YOLOv8n in the tuna detection task, while reducing the number of parameters by 42.3% and computational complexity by 33.3%. …”
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Design of an Intelligent Pear Bagging End-Effector Based on Yolov8 and SGBM Algorithm by Jing Ruijun, Liu JingKai, LiXin, ZhiguoZhao

“…We describe an intelligent bagging end-effector for pears, which employs the Yolov8 algorithm for fruitlets detection and the Semi-Global Block Matching (SGBM) algorithm to acquire three-dimensional spatial information of the targets. To address the computational limitations of embedded devices in agricultural intelligent equipment, we improve the YOLOv8 model by replacing its neck component with the Asymptotic Feature Pyramid Network (AFPN) and incorporating Context Guided (CG) blocks into the C2f module. …”
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RSDCNet: An efficient and lightweight deep learning model for benign and malignant pathology detection in breast cancer by Yuan Liu, Haipeng Li, Zhu Zhu, Chen Chen, Xiaojing Zhang, Gongsheng Jin, Hongtao Li

“…Compared to traditional methods and other leading models, RSDCNet not only reduces computational resource consumption but also offers improved feature extraction and clinical interpretability. …”
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Rethinking spatial-temporal contrastive learning for Urban traffic flow forecasting: multi-level augmentation framework by Lin Pan, Qianqian Ren, Zilong Li, Xingfeng Lv

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