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

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

Active accessibility: A review of operational measures of walking and cycling accessibility by David S. Vale, Miguel Saraiva, Mauro Pereira

“…While active travel has been shown to be associated with features of the built environment such as density and land-use mix, it is also associated with walking and cycling accessibility—which we designate as active accessibility. …”
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

Uncertainty analysis in the design of Type-IV composite pressure vessels for hydrogen storage by Yao Koutsawa, Lyazid Bouhala

“…Latin Hypercube Sampling (LHS) efficiently explored the uncertainty space, while Polynomial Chaos Expansion (PCE) modeled BP responses, with Sparse PCE reducing computational costs by selecting influential polynomial terms. …”
Get full text

Influence of Microfin Tube on Heat Transfer during Flow Boiling of R134a Refrigerant by Neeraj Kumar Vidhyarthi, Sandipan Deb, Sameer Sheshrao Gajghate, Elaine Maria Cardoso, Mantu Das, Sagnik Pal, Ajoy Kumar Das

“…Furthermore, the experimental data are rigorously validated through computation analysis with authentic flow boiling heat transfer models, demonstrating strong agreement.…”
Get full text

A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis by Zhigang Zhang, Chunrong Xue, Xiaobo Li, Yinjun Wang, Liming Wang

“…Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. …”
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

Probing Triple Higgs Self-Coupling and Effect of Beam Polarization in Lepton Colliders by Ijaz Ahmed, Ujala Nawaz, Taimoor Khurshid, Shamona Fawad Qazi

“…By elongating the Standard Model’s scalar sector, using incipient Higgs doublet along with a quadratic (Higgs) potential can reveal many incipient features of the model and the possibility of the emergence of additional Higgs self-couplings. …”
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

“…Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. …”
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

Multi-Subject Remote Heart Sound Monitoring Using mmWave MIMO RADAR by Isabella Lenz, Yu Rong, Adarsh A. Venkataramani, Daniel W. Bliss

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

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

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

Get full text

CMCD: A Consistency Model-Based Change Detection Method for Remote Sensing Images by Xiongjie Li, Weiying Xie, Jiaqing Zhang, Yunsong Li

“…Furthermore, these methods utilize diffusion networks to extract key features from dual-temporal remote images and generate change maps, yet they often overlook the model's parameter size and the time cost associated with iterative sampling. …”
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

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