Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures by Yudong Shi, Yinggan Zhang, Jiansen Wen, Zhou Cui, Jianhui Chen, Xiaochun Huang, Cuilian Wen, Baisheng Sa, Zhimei Sun

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EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection by Haojie Chen, Lumei Su, Riben Shu, Tianyou Li, Fan Yin

“…This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. …”
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YOLOv8n-SMMP: A Lightweight YOLO Forest Fire Detection Model by Nianzu Zhou, Demin Gao, Zhengli Zhu

“…Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. …”
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FPFS-YOLO: An Insulator Defect Detection Model Integrating FasterNet and an Attention Mechanism by Yujiao Chai, Xiaomin Yao, Manlong Chen, Sirui Shan

“…In this study, to mitigate parameter redundancy in the backbone of the YOLO11n model, the FasterNet lightweight network was introduced, and some convolution was embedded into the shallow network to enhance its feature extraction ability. …”
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YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization by Likitha Reddy Yeddula, Archana Pallakonda, Rayappa David Amar Raj, Rama Muni Reddy Yanamala, K. Krishna Prakasha, Mallempati Sunil Kumar

“…The proposed approach combines a BiFPN based neck, Ghost Bottlenecks, and Efficient Channel Attention (ECA) to improve multi scale representation, decrease redundant computation, and increase feature extraction. The model performs better in terms of detection accuracy and efficiency, as shown by experimental findings on three benchmark datasets: PVEL-AD, PV-Multi-Defect, Solar Panel Anomalies. …”
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EmotionNet-X: An Optimized CNN Architecture for Robust Facial Emotion Analysis by Syed Muhammad Aqleem Abbas, Qaisar Abbas, Syed Muhammad Naqi

“…We propose EmotionNet-X, a lightweight CNN architecture with 19.9M parameters and 18 ms/image inference time. Key innovations include a streamlined design (four convolutional layers, seven dropout layers) and batch normalization for robust feature learning. …”
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GRID SEARCH AND RANDOM SEARCH HYPERPARAMETER TUNING OPTIMIZATION IN XGBOOST ALGORITHM FOR PARKINSON’S DISEASE CLASSIFICATION by Shafa Fitria Aqilah Khansa, Nurissaidah Ulinnuha, Wika Dianita Utami

“…The dataset from Kaggle consists of 2105 records from 2024 and includes 32 clinical and demographic features such as age, gender, BMI, medical history, and Parkinson's symptoms. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…The algorithm is then compared with other classical algorithms using video streams from intersections with varying traffic volumes, all executed on a mobile edge computer (MEC) with limited computing power.Results and DiscussionsThrough ablation test, original YOLOv5 achieved mAP@90 at 0.894 and parameter quantity at 21.2M. …”
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Advancing e-waste classification with customizable YOLO based deep learning models by P. Akhil Rajeev, Vivek Dharewa, D. Lakshmi, G. Vishnuvarthanan, Jayant Giri, T. Sathish, Mubarak Alrashoud

“…Notably, this model showcased a significant reduction in training time while leveraging the computational power of the Tesla T4 GPU on Google Colab. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. …”
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TSDCA-BA: An Ultra-Lightweight Speech Enhancement Model for Real-Time Hearing Aids with Multi-Scale STFT Fusion by Zujie Fan, Zikun Guo, Yanxing Lai, Jaesoo Kim

“…Experimental results demonstrate that among lightweight models with fewer than 200K parameters, the proposed approach outperforms most existing methods in both denoising performance and computational efficiency, significantly reducing processing overhead. …”
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Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO by Di Zhao, Yulin Cheng, Sizhe Mao

“…Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. …”
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Komparasi Metode Klasifikasi untuk Deteksi Ekspresi Wajah Dengan Fitur Facial Landmark by Fitra A. Bachtiar, Muhammad Wafi

“…Facial Landmark is used as a facial component features. The classification model used in this study is ELM, SVM, and k-NN. …”
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Visual Detection on Aircraft Wing Icing Process Using a Lightweight Deep Learning Model by Yang Yan, Chao Tang, Jirong Huang, Zhixiong Cen, Zonghong Xie

“…WID-DeeplabV3+ adopts the lightweight MobileNetV3 as its backbone network to enhance the extraction of edge features in icing areas. Ghost Convolution and Atrous Spatial Pyramid Pooling modules are incorporated to reduce model parameters and computational complexity. …”
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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports. by Guomei Cui, Chuanjun Wang

“…Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. …”
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Investigation of Inlet Guide Vane Efficiency in Centrifugal Compressors for the Turbo Refrigeration Machines by Danilishin A.M., Kozhukhov Y. V., Fateeva E.S., Aksenov A.A.

“…The article investigates the influence of geometric parameters of inlet guide vanes (IGVs), including blade profiling and design features, on their operational efficiency. …”
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I-YOLOv11n: A Lightweight and Efficient Small Target Detection Framework for UAV Aerial Images by Yukai Ma, Caiping Xi, Ting Ma, Han Sun, Huiyang Lu, Xiang Xu, Chen Xu

“…However, existing detection algorithms still have weak small target representation ability, extensive computational resource overhead, and poor deployment adaptability. …”
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Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System by Monsurat Omolara Balogun, Latifat Adeola Odeniyi, Elijah Olusola Omidiora, Stephen Olatunde Olabiyisi, Adeleye Samuel Falohun

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Experimental Study on Compressive Capacity Behavior of Helical Anchors in Aeolian Sand and Optimization of Design Methods by Qingsheng Chen, Wei Liu, Linhe Li, Yijin Wu, Yi Zhang, Songzhao Qu, Yue Zhang, Fei Liu, Yonghua Guo

“…The research findings demonstrate the following: (1) Compressive capacity exhibits significant enhancement with increasing helix diameter yet displays limited sensitivity to helix number. (2) Load–displacement curves progress through three distinct phases—initial quasi-linear, intermediate non-linear, and terminal quasi-linear stages—under escalating pressure. (3) At embedment depths of <i>H</i> < 5<i>D</i>, tensile capacity diminishes by approximately 80% relative to compressive capacity, manifesting as characteristic shallow anchor failure patterns. (4) When <i>H</i> ≥ 5<i>D</i>, stress redistribution transitions from bowl-shaped to elliptical contours, with ≤10% divergence between uplift/compressive capacities, establishing 5<i>D</i> as the critical threshold defining shallow versus deep anchor behavior. (5) The helix spacing ratio (<i>S</i>/<i>D</i>) governs the failure mode transition, where cylindrical shear (CS) dominates at <i>S</i>/<i>D</i> ≤ 4, while individual bearing (IB) prevails at <i>S</i>/<i>D</i> > 4. (6) XGBoost feature importance analysis confirms internal friction angle, helix diameter, and embedment depth as the three parameters exerting the most pronounced influence on capacity. (7) The proposed computational models for <i>N</i>_q and <i>K</i>_u demonstrate exceptional concordance with numerical simulations (mean deviation = 1.03, variance = 0.012). …”
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SC-CoSF: Self-Correcting Collaborative and Co-Training for Image Fusion and Semantic Segmentation by Dongrui Yang, Lihong Qiao, Yucheng Shu

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