LD-Det: Lightweight Ship Target Detection Method in SAR Images via Dual Domain Feature Fusion by Hang Yu, Bingzong Liu, Lei Wang, Teng Li

“…Therefore, to achieve fast data transmission and little computation complexity, the design of lightweight computing models becomes a research hot point. …”
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LCD-Net: A Lightweight Remote Sensing Change Detection Network Combining Feature Fusion and Gating Mechanism by Wenyu Liu, Jindong Li, Haoji Wang, Run Tan, Yali Fu, Qichuan Tian

“…While traditional CNN-based methods have improved detection accuracy, they often suffer from high computational complexity and large parameter counts, limiting their use in resource-constrained environments. …”
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A Lightweight Multi-Frequency Feature Fusion Network with Efficient Attention for Breast Tumor Classification in Pathology Images by Hailong Chen, Qingqing Song, Guantong Chen

“…At the same time, the incorporation of a linear attention (LA) mechanism lowers the model’s computational complexity and further enhances its global feature extraction capability. …”
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Multiclass Crop Interpretation via a Lightweight Attentive Feature Fusion Network Using Vehicle-View Images by Wenyue Li, Bingfang Wu, Runyu Fan, Fuyou Tian, Miao Zhang, Zhaoying Zhou, Jun Hu, Ruyi Feng, Fangming Wu

“…The experimental results show that CropNet has better semantic segmentation results with fewer model parameters and lower computational costs.…”
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Brain image registration optimization method via SAM-Med3D multi-scale feature migration by Mo Nan

“…A cross-attention mechanism was designed to achieve hierarchical fusion of anatomical features and local details. Innovative introduction of lightweight channel attention adapter, complete feature space mapping with few parameters, reduce the computational overhead. …”
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Advanced bearing fault detection at varying rotational speeds using PSO-optimized SVM and CDET feature selection by Hongxu Chai, Xiaoshi Ma, Feng Zhu, Yandong Hu

“…Furthermore, the parameters of both CDET and support vector machine (SVM) classifiers are jointly optimized by PSO, resulting in enhanced classification accuracy and computational efficiency. …”
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HFF-Net: A hybrid convolutional neural network for diabetic retinopathy screening and grading by Muhammad Hassaan Ashraf, Hamed Alghamdi

“…This approach can lead to information loss in the initial stages due to limited feature utilization across adjacent layers. To address this limitation, we propose a Hierarchical Features Fusion Convolutional Neural Network (HFF-Net) within a Diabetic Retinopathy Screening and Grading (DRSG) framework. …”
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EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion by Qian Tang, Zhen Wang, Xuqi Wang, Shan-Wen Zhang

“…To address these issues, we propose EMHANet, a lightweight network that integrates edge texture detail extraction, multi-scale feature fusion, and hybrid attention mechanism. EMHANet consists of MobileNetV3 for feature extraction, an Edge Feature Integration Module (EFIM) for low-level edge details, a Multi-scale Contextual Information Enhancement Module (MCIEM) for high-level feature refinement, and a lightweight decoder for saliency prediction. …”
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Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integral Equations of the Volterra Type: Mathematical Methodology and Illustrative Applica... by Dan Gabriel Cacuci

“…Using a single large-scale (adjoint) computation, the 1st-FASAM-NIE-V enables the most efficient computation of the exact expressions of all first-order sensitivities of the decoder response to the feature functions and also with respect to the optimal values of the NIE-net’s parameters/weights after the respective NIE-Volterra-net was optimized to represent the underlying physical system. …”
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The features of the kinematics for the joint functioning of the main mechanisms and working equipment of an open-pit excavator during excavation of rocks by Komissarov A.P., Lukashuk O.A., Nabiullin R.Sh., Letnev K.Yu.

“…Methods of mechanism analysis, mathematical modeling and computational experiment were used. On the basis of a simulation model of the excavation process, a computational experiment was performed to calculate the operating parameters of the main mechanisms for the EKG-20A excavator manufactured by PJSC Uralmashplant when working out an excavator face. …”
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Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques by Rabiu Aminu, Samantha M. Cook, David Ljungberg, Oliver Hensel, Abozar Nasirahmadi

“…The concept of explainable artificial intelligence was adopted by incorporating permutation feature importance ranking and Shapley Additive explanations values to identify the feature set that optimized a model's performance while reducing computational complexity. …”
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A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for Speech Emotion Recognition by Sunil Kumar Prabhakar, Dong-Ok Won

“…Speech emotion recognition (SER) tasks are conducted to extract emotional features from speech signals. The characteristic parameters are analyzed, and the speech emotional states are judged. …”
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Enhancing Emotion Recognition in Speech Based on Self-Supervised Learning: Cross-Attention Fusion of Acoustic and Semantic Features by Bashar M. Deeb, Andrey V. Savchenko, Ilya Makarov

“…However, state-of-the-art models for speech emotion recognition use many parameters, which leads to computational complexity. …”
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Automatic Recognition of Tunnel Water Leakage Based on Adaptive Information Extraction Network and Multiscale Feature Enhancement Module by Dandan Wang, Gongyu Hou, Qinhuang Chen, Weiyi Li, Haoxiang Li, Yaohua Shao, Xunan Yu

“…An adaptive information extraction network, integrating spatial and channel squeeze-and-excitation mechanisms, is adopted in the encoder to enhance critical feature representation and accelerate inference. Additionally, a multiscale and lightweight feature enhancement module is introduced to capture global contextual information while reducing the number of parameters. …”
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Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach by Gurman Gill, Matthew Toews, Reinhard R. Beichel

“…Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. …”
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Classification of Prominent Cacao Pod Diseases Using Multi-Feature Visual Analysis and k-Nearest Neighbors Algorithm by Earl Clarence San Diego, Seph Gerald Rodrin, Edwin Arboleda

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Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM by Nilesh Bhaskarrao Bahadure, Arun Kumar Ray, Har Pal Thethi

“…Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. …”
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GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification by Junding Sun, Haoxiang Dong, Yanlong Gao, Xiaosheng Wu, Jianlong Wang, Yudong Zhang

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Definition of the Main Features of Material Assemblies for Thermal Protective Clothing During External High-temperature Effect Modelling by Nataliia Ostapenko, Marina Kolosnichenko, Larysa Tretiakova, Tatyana Lutsker, Kalina Pashkevich, Alla Rubanka, Halyna Tokar

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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…Experimental results highlight the superior performance of the LGBM classifier, achieving an impressive 99% accuracy, recall, f1, and precision score of 98% with a computational runtime of just 0.084 seconds. K-fold cross-validation ensured the robustness of the models, and optimization techniques were applied to fine-tune parameters for maximum accuracy. …”
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