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. …”
Get full text

A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…The method builds upon YOLOv10 and integrates Gated Convolution (gConv) into the C2f module, forming a novel C2f-gConv structure that effectively reduces model parameters and computational complexity. In addition, a Global Attention Mechanism (GAM) is inserted between the backbone and the feature fusion layers to enrich semantic representations and improve the detection of occluded fruits. …”
Get full text

SC-CoSF: Self-Correcting Collaborative and Co-Training for Image Fusion and Semantic Segmentation by Dongrui Yang, Lihong Qiao, Yucheng Shu

Get full text

StrawberryNet: Fast and Precise Recognition of Strawberry Disease Based on Channel and Spatial Information Reconstruction by Xiang Li, Lin Jiao, Kang Liu, Qihuang Liu, Ziyan Wang

Get full text

Local Auxiliary Spatial–Spectral Decoupling Transformer Network for Cross-Scene Hyperspectral Image Classification by Qiusheng Chen, Zhuoqun Fang, Zhaokui Li, Qian Du, Shizhuo Deng, Tong Jia, Dongyue Chen

Get full text

LiSA-MobileNetV2: an extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification by Yongqi Xu, Dongcheng Li, Changcheng Li, Zheming Yuan, Zhijun Dai

“…Although lightweight convolutional neural networks (CNNs) are widely adopted for plant disease recognition due to their computational efficiency, they often suffer from limited feature representation and classification accuracy. …”
Get full text

DWE: Research on Water Quality Inversion and Scalability in Small Watersheds Using UAV Remote Sensing Data by Hao Li, Zhijun Xie, Xing Jin, Changchun Peng, Cheng Ren, Yunfei Mao, Kefan Zhou, Yingying Li

Get full text

BESW-YOLO: A Lightweight SAR Image Detection Model Based on YOLOv8n for Complex Scenarios by Xiao Tang, Kun Cao, Yunzhi Xia, Enkun Cui, Weining Zhao, Qiong Chen

Get full text

A lightweight detection algorithm of PCB surface defects based on YOLO. by Shiwei Yu, Feng Pan, Xiaoqiang Zhang, Linhua Zhou, Liang Zhang, Jikui Wang

“…Finally, the PANet network structure is replaced with the bidirectional feature pyramid network (BIFPN) structure to enhance the fusion of multi-scale features in the network. …”
Get full text

YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model by Shuang Liu, Haobin Xu, Ying Deng, Yixin Cai, Yongjie Wu, Xiaohao Zhong, Jingyuan Zheng, Zhiqiang Lin, Miaohong Ruan, Jianqing Chen, Fengxiang Zhang, Huiying Li, Fenglin Zhong

“…The model incorporates the inverted bottleneck structure of LeYOLO-small to design the backbone network, utilizing depthwise separable convolutions and cross-stage feature reuse modules to achieve lightweight design, reducing the number of parameters while enhancing multi-scale feature extraction capabilities. …”
Get full text

Investigating lightweight and interpretable machine learning models for efficient and explainable stress detection by Debasish Ghose, Ayan Chatterjee, Indika A. M. Balapuwaduge, Yuan Lin, Soumya P. Dash

“…We have developed ML models incorporating efficient feature selection techniques and hyper-parameter tuning. …”
Get full text

Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach by Illia Mushta, Sulev Koks, Anton Popov, Oleksandr Lysenko

“…To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. …”
Get full text

Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network by Kai Bai, Siyi Jin, Zhaoshuo Zhang, Shengsheng Dai

Get full text

A Lightweight Kernel Density Estimation and Adaptive Synthetic Sampling Method for Fault Diagnosis of Rotating Machinery with Imbalanced Data by Wenhao Lu, Wei Wang, Xuefei Qin, Zhiqiang Cai

“…Comparative experiments further demonstrate that KAMS not only delivers exceptional diagnostic performance but also significantly reduces network parameters and computational resource requirements.…”
Get full text

The Inverse Scattering of Three-Dimensional Inhomogeneous Steady-State Sound Field Models by Zhaoxi Sun, Wenbin Zhang, Meiling Zhao

“…Through an innovative sliced data processing strategy, the 3D reconstruction problem is decomposed into a combination of 2D problems, thereby significantly reducing the computational cost. The designed multi-channel U-Net fully utilizes the strengths of both the encoder and decoder, exhibiting strong feature extraction and spatial detail recovery capabilities. …”
Get full text

Inertial-relaxed splitting for composite monotone inclusions by Oré, Ernesto, Mahey, Philippe, Ocaña, Eladio

“…A unified setting is formalized and applied to different average maps whose corresponding fixed points are related to the solutions of the inclusion problem associated with our extended model. An interesting feature of the resulting algorithms we have designed is that they present two distinct versions with a Gauss–Seidel or a Jacobi flavor, extending in that sense former proximal ADMM methods, both including inertial and relaxation parameters. …”
Get full text

Smart intrusion detection model to identify unknown attacks for improved road safety and management by Faisal Alshammari, Abdullah Alsaleh

“…These results significantly outperform baseline models, including support vector machines (SVM) and random forests (RF), as well as recent methods such as transformer-based and hybrid RNN-CNN approaches. Key parameters used for benchmarking include accuracy, detection rate, false alarm rate, precision, F1-Score and AUC-ROC, demonstrating the model’s balanced performance and computational efficiency. …”
Get full text

MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation by Akram Syed, Baifan Chen, Adeel Ahmed Abbasi, Sharjeel Abid Butt, Xiaoqing Fang

“…To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. …”
Get full text

Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning by Zhenwu Lei, Yue Zhang, Jing Wang, Meng Zhou

“…Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. …”
Get full text

Design of Jitter Compensation Algorithm for Robot Vision Based on Optical Flow and Kalman Filter by B. R. Wang, Y. L. Jin, D. L. Shao, Y. Xu

“…In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. …”
Get full text