Advanced deep transfer learning techniques for efficient detection of cotton plant diseases by Prashant Johri, SeongKi Kim, Kumud Dixit, Prakhar Sharma, Barkha Kakkar, Yogesh Kumar, Jana Shafi, Muhammad Fazal Ijaz

“…Hence, the significance of this work lies in its potential to mitigate the impact of these diseases, which cause significant damage to the cotton and decrease fibre quality and promote sustainable agricultural practices.MethodsThis paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection. …”
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ESL-YOLO: Small Object Detection with Effective Feature Enhancement and Spatial-Context-Guided Fusion Network for Remote Sensing by Xiangyue Zheng, Yijuan Qiu, Gang Zhang, Tao Lei, Ping Jiang

“…This model includes: (1) an innovative plug-and-play feature enhancement module that incorporates multi-scale local contextual information to bolster detection performance for small objects; (2) a spatial-context-guided multi-scale feature fusion framework that enables effective integration of shallow features, thereby minimizing spatial information loss; and (3) a local attention pyramid module aimed at mitigating background noise while highlighting small object characteristics. …”
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An Optimization Method for PCB Surface Defect Detection Model Based on Measurement of Defect Characteristics and Backbone Network Feature Information by Huixiang Liu, Xin Zhao, Qiong Liu, Wenbai Chen

“…Additionally, SLFFM employs the bi-level routing attention (BRA) mechanism as a feature aggregation module, mitigating defect-background similarity issues. …”
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MDEU-Net: Medical Image Segmentation Network Based on Multi-Head Multi-Scale Cross-Axis by Shengxian Yan, Yuyang Lei, Jing Zhang, Xiao Gao, Xiang Li, Penghui Wang, Hui Cao

“…The proposed architecture enables the model to focus on both local and global information while capturing features at various spatial scales. Additionally, a gated attention mechanism facilitates efficient feature fusion by selectively emphasizing key features rather than relying on simple concatenation and improves the model’s ability to capture critical details at multiple scales. …”
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Sonar-based object detection for autonomous underwater vehicles in marine environments by Zhen Wang, Zhen Wang, Jianxin Guo, Shanwen Zhang, Yucheng Zhang

“…To address these challenges in forward-looking sonar (FLS) images, we propose a novel multi-level feature aggregation network (MLFANet). Specifically, to mitigate the impact of seabed reverberation noise, we designed a low-level feature aggregation module (LFAM), which enhances key low-level image features, such as texture, edges, and contours in the object regions. …”
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Building consistency in explanations: Harmonizing CNN attributions for satellite-based land cover classification by Timo T. Stomberg, Lennart A. Reißner, Martin G. Schultz, Ribana Roscher

“…We demonstrate that Grad-CAM attributions are inherently well-aligned with the features, whereas other gradient-based attribution methods exhibit significant noise, mitigated through harmonization. …”
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Purple Yam (Dioscorea alata) Extract Increasing Dopamine Levels and Improving the Brain's Microscopic Features in Parkinson's Model Mice by Sapto Yuliani, Dwi Utami, Laela Hayu Nurani, Muhammad Marwan Ramadhan, Nadia Putri Ainiyah, Mochammad Saiful Bachri, Wahyu Widyaningsih, Danang Prasetyaning Amukti

“…These findings suggest that D. alata extract, particularly at a dose of 400 mg/kgBW, exhibits potential antiparkinsonian activity by elevating dopamine levels and mitigating dopaminergic neuronal damage in a haloperidol-induced PD mouse model. …”
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DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images by Xiaojia Yan, Shiyan Sun, Huimin Zhu, Qingping Hu, Wenjian Ying, Yinglei Li

“…Second, we construct a Multi-scale Feature Aggregation Module (MFAM) that integrates dual-branch spatial attention mechanisms to achieve efficient cross-layer feature fusion, mitigating information conflicts between shallow details and deep semantics. …”
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Purple Yam (Dioscorea alata) Extract Increasing Dopamine Levels and Improving the Brain's Microscopic Features in Parkinson's Model Mice by Sapto Yuliani, Dwi Utami, Laela Hayu Nurani, Muhammad Marwan Ramadhan, Nadia Putri Ainiyah, Mochammad Saiful Bachri, Wahyu Widyaningsih, Danang Prasetyaning Amukti

“…These findings suggest that D. alata extract, particularly at a dose of 400 mg/kgBW, exhibits potential antiparkinsonian activity by elevating dopamine levels and mitigating dopaminergic neuronal damage in a haloperidol-induced PD mouse model. …”
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Prediction of BTEX concentrations in the air of Southern East Azerbaijan province, Iran using ensemble machine learning and feature analysis by Mansour Baziar, Negar Jafari, Ali Oghazyan, Amir Mohammadi, Ali Abdolahnejad, Ali Behnami

“…This research underscores the potential of advanced machine learning techniques to monitor air quality and guide policy decisions aimed at mitigating health risks associated with VOCs exposure.…”
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Epidemiological features of travel-related cases Mpox (Monkeypox) cases detected at airports in Pakistan; Insights from Screening Surveillance by Dr Nadia Noreen

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Non-Destructive Detection of Chilled Mutton Freshness Using a Dual-Branch Hierarchical Spectral Feature-Aware Network by Jixiang E, Chengjun Zhai, Xinhua Jiang, Ziyang Xu, Muqiu Wudan, Danyang Li

“…By leveraging multi-head attention and cross-scale fusion, the model more effectively captures both the overall spectral variation trends and fine-grained feature details. Third, at the classification output stage, dynamic loss weighting is set according to training epochs and relative losses to balance classification performance, effectively mitigating the impact of insufficiently discriminative intermediate features. …”
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Extraction of Garlic in the North China Plain Using Multi-Feature Combinations from Active and Passive Time Series Data by Chuang Peng, Binglong Gao, Wei Wang, Wenji Zhu, Yongqi Chen, Chao Dong

“…In this study, historical data were utilized to restore Sentinel-2 remote sensing images, aimed at mitigating cloud and rain interference. Feature combinations were devised, incorporating two vegetation indices into a comprehensive time series, along with Sentinel-1 synthetic aperture radar (SAR) time series and other temporal datasets. …”
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Enhanced Feature Selectivity in MobileNetV2 for Skin Cancer Detection through Scaled Dot-Product Attention by Omar Gasmann, Nazmul Shahadat

“…Addressing the limitations of current MobileNet V2 and V3 architectures, we integrate a Scaled Dot-Product Attention mechanism to improve feature selectivity while maintaining computational efficiency. …”
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Improving machine learning algorithm for risk of early pressure injury prediction in admission patients using probability feature aggregation by Shu-Chen Chang, Shu-Mei Lai, Mei-Wen Wu, Shou-Chuan Sun, Mei-Chu Chen, Chiao-Min Chen

“…Objective Pressure injuries (PIs) pose a significant concern in hospital care, necessitating early and accurate prediction to mitigate adverse outcomes. Methods The proposed approach receives multiple patients records, selects key features of discrete numerical based on their relevance to PIs, and trains a random forest (RF) machine learning (ML) algorithm to build a predictive model. …”
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An object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery by Tsung-Han Wen, Tee-Ann Teo

“…This study presents an object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery. …”
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Hybrid vegetation-seawall coastal systems for wave hazard reduction: analytics for cost-effective design from optimized features by Erfan Amini, Reza Marsooli, Somayeh Moazeni, Bilal M. Ayyub

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Enhanced framework for credit card fraud detection using robust feature selection and a stacking ensemble model approach by Rahul Kumar Gupta, Asmaul Hassan, Samir Kumar Majhi, Nikhat Parveen, Abu Taha Zamani, Raju Anitha, Binayak Ojha, Abhinav Kumar Singh, Debendra Muduli

“…This study introduces an innovative machine learning-based fraud detection framework that incorporates sophisticated preprocessing methods like SMOTE-ENN for class imbalance mitigation, autoencoder for dimensionality reduction, and TOPSIS for optimal feature selection. …”
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PRICOS: A Robust Paddy Rice Index Combining Optical and Synthetic Aperture Radar Features for Improved Mapping Efficiency by Yifeng Lou, Gang Yang, Weiwei Sun, Ke Huang, Jingfeng Huang, Lihua Wang, Weiwei Liu

“…By integrating multi-sensor data with minimal sample dependency, PRICOS provides a robust, adaptable solution for large-scale paddy rice mapping, advancing precision agriculture and climate change mitigation efforts.…”
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Mapping herbaceous wetlands using combined phenological and hydrological features from time-series Sentinel-1/2 imagery by Zhaolong Yang, Xiaodong Na

“…The results showed the following. (1) The proposed method was stable and scalable and resulted in OAs of 92.69%, 89.18%, and 88.61% and kappa coefficients of 0.91, 0.87, and 0.86 in 2019, 2020, and 2021, respectively. (2) The crucial phenological periods to distinguish between herbaceous marshes and meadows were June, July, and August, and the optimal CVHIs corresponded to the phenological stages of the wetlands vegetation. (3) The optimal feature variables and its derivation time were selected from the CHVIs based on the TempCNN algorithm, which mitigated the impacts of seasonal variability of vegetation and hydrological conditions on the classification accuracies.…”
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