FTA-Net: Frequency-Temporal-Aware Network for Remote Sensing Change Detection by Taojun Zhu, Zikai Zhao, Min Xia, Junqing Huang, Liguo Weng, Kai Hu, Haifeng Lin, Wenyu Zhao

“…FTA-Net outperforms state-of-the-art methods on three challenging CD datasets, and it have fewer parameters (4.93M) and lower computational cost (6.71 G).…”
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Investigating the Effects of Spectroscopic Method in Estimating Soluble Solid Content Values and Firmness of Cherries from an Environmental Point of View: Prediction of Environment... by Shirzad Naim, Shahgholi Gholamhossein, Ardabili Sina, Szymanek Mariusz

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CriSALAD: Robust Visual Place Recognition Using Cross-Image Information and Optimal Transport Aggregation by Jinyi Xu, Yuhang Ming, Minyang Xu, Yaqi Fan, Yuan Zhang, Wanzeng Kong

“…Specifically, we adapt pre-trained VFMs for VPR by incorporating a parameter-efficient adapter inspired by Xception, ensuring effective task adaptation while preserving computational efficiency. …”
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Ranking Assisted Unsupervised Morphological Disambiguation of Turkish by Hayri Volkan Agun, Ozkan Aslan

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An enhanced alpha evolution moss growth optimizer for prognostic prediction in spontaneous intracerebral hemorrhage by Lingxian Hou, Yongsheng Wang, Xiuqi Lin, Chengye Li, Huangrong Guo, Congcong Jin, Yi Chen, Huiling Chen, Jing Ji, Wenzong Zhu

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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…MEST-GCN improved upon the spatial-temporal graph convolutional network (ST-GCN) by removing redundant layers to simplify the model structure and reduce the number of parameters. It also introduced a multi-dimensional feature fusion attention module (M2FA). …”
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A Method of Word Sense Disambiguation with Recurrent Netural Networks by ZHANG Chunxiang, ZHOU Xuesong, GAO Xueyao

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Understanding user experience for mobile applications: a systematic literature review by Guoying Lu, Siyuan Qu, Yining Chen

“…We developed the Scenarios, Themes, Features, and Methodologies framework to examine both the theoretical and practical applications across multiple dimensions. …”
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Development and calibration of roundabout safety performance functions using machine learning: a case study from Amman, Jordan by Diana Al-Nabulsi, Aya Hassouneh

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DAMI-YOLOv8l: A multi-scale detection framework for light-trapping insect pest monitoring by Xiao Chen, Xinting Yang, Huan Hu, Tianjun Li, Zijie Zhou, Wenyong Li

“…The DMC module improves multi-scale feature extraction to enable the effective capture and merging of features across different detection scales while reducing network parameters. …”
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EnsembleXAI-Motor: A Lightweight Framework for Fault Classification in Electric Vehicle Drive Motors Using Feature Selection, Ensemble Learning, and Explainable AI by Md. Ehsanul Haque, Mahe Zabin, Jia Uddin

“…A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. …”
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Implementation of Chernobyl disaster optimizer based feature selection approach to predict software defects [version 2; peer review: 2 approved, 1 not approved] by Himansu Das, Ajay Kumar Jena, Kunal Anand

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Research on scenario recognition for THz channels based on mRMR-GA by HAO Xinyu, LIAO Xi, WANG Yang, LIN Feng, LUO Jiao, ZHANG Jie

“…To address the challenges of excessive feature parameter redundancy and insufficient scene correlation in terahertz (THz) channel scenario recognition, a recognition algorithm integrating the minimal redundancy maximal relevance (mRMR) criterion with genetic algorithm (GA) optimization was constructed based on feature selection theory and evolutionary computation principles. …”
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SDNet: a lightweight ship detection network in remote sensing images by super-resolution enhancement and detail completion by Yu Tong, Jun Liu, Guixing Cao, Leyang Li, Yufei Wang

“…The main detector branch uses the adaptive cross-stage partial convolution (ACPC) module to form an efficient backbone. The feature pyramid network (FPN) combines with the cross-level wavelet transform multi-head attention (CWTMA) module for ship feature extraction. …”
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Semantic segmentation of underwater images based on the improved SegFormer by Bowei Chen, Bowei Chen, Wei Zhao, Wei Zhao, Qiusheng Zhang, Mingliang Li, Mingyang Qi, You Tang, You Tang, You Tang

“…Compared to the standard SegFormer, it demonstrates improvements of 3.73% in MIoU, 1.98% in mRecall, 3.38% in mPrecision, and 2.44% in mF1score, with an increase of 9.89M parameters. The results demonstrate that the proposed method achieves superior segmentation accuracy with minimal additional computation, showcasing high performance in underwater image segmentation.…”
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The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules by Zuhua Song, Qian Liu, Jie Huang, Dan Zhang, Jiayi Yu, Bi Zhou, Jiang Ma, Ya Zou, Yuwei Chen, Zhuoyue Tang

“…Four typical radiological features and 19 DLCT quantitative parameters in the arterial phase and venous phase were measured. …”
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A Lightweight Semantic Segmentation Model for Underwater Images Based on DeepLabv3+ by Chongjing Xiao, Zhiyu Zhou, Yanjun Hu

“…The framework employs MobileOne-S0 as the lightweight backbone for feature extraction, integrates Simple, Parameter-Free Attention Module (SimAM) into deep feature layers, replaces global average pooling in the Atrous Spatial Pyramid Pooling (ASPP) module with strip pooling, and adopts a content-guided attention (CGA)-based mixup fusion scheme to effectively combine high-level and low-level features while minimizing parameter redundancy. …”
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A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process by Thadeu Pezzin Melo, Jefferson Andrade, Karin Satie Komati

“…In step (iii), twelve features were identified as the most relevant based on the Random Forest importance index. …”
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LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing by Tingting Yong, Xiaofang Liu

“…The core of LSANet is the large separable kernel attention mechanism, which efficiently expands the receptive field while retaining low computational overhead. By integrating this mechanism into an enhanced residual feature distillation module, the network captures long-range dependencies more effectively than traditional shallow residual blocks. …”
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Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources by Hongwei Guo, Chao Zhang, Hongyuan Fang, Timon Rabczuk, Xiaoying Zhuang

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