Chemometric analysis of ethoxylated polymer products using extracted MALDI-TOF-MS peak distribution features. by Elizabeth Dickinson, Richard N Cawthorne, Peter Dodds, Graham M Atkinson, Pablo F García Thomas, Lucy Jones, Molly Crosbie, Janet Cook, Julie Wilson

“…We demonstrate that using ratio-scaled extracted features gives superior results to the full spectrum, both in terms of identifying subtle compositional differences that would otherwise be missed, and in interpretability. Importantly, the integrated auto-assignment of peak distributions to possible compounds allows the results to be reported in terms of the most abundant oligomers of the raw material constituents. …”
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The application of deep learning technology in smart agriculture: Lightweight apple leaf disease detection model by Luo Man

“…Also a novel C2f-SC module integrates SCCONV convolution into the C2f module, creating a lightweight architecture while reducing computational complexity. …”
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Cross-modality fusion with EEG and text for enhanced emotion detection in English writing by Jing Wang, Ci Zhang

“…Traditional approaches to emotion detection primarily leverage textual features, using natural language processing techniques such as sentiment analysis, which, while effective, may miss subtle nuances of emotions. These methods often fall short in recognizing the complex, multimodal nature of human emotions, as they ignore physiological cues that could provide richer emotional insights.MethodsTo address these limitations, this paper proposes Emotion Fusion-Transformer, a cross-modality fusion model that integrates EEG signals and textual data to enhance emotion detection in English writing. …”
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Artificial intelligence in educational leadership: a comprehensive taxonomy and future directions by Martin Sposato

“…This systemic gap has led to fragmented adoption practices, missed opportunities for innovation, and potential risks in deployment of AI solutions. …”
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EFL teachers’ practices and perspectives on self- and collaborative contextualized reflective practice by Yenni Rozimela, Sitti Fatimah, Nora Fudhla

“…This study reports the implementation of a contextualized RP model that integrates the Lesson Study (LS) principles by Indonesian English as a Foreign Language (EFL) teachers. …”
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Sugarcane Feed Volume Detection in Stacked Scenarios Based on Improved YOLO-ASM by Xiao Lai, Guanglong Fu

“…At the target detection level, we integrate a Convolutional Block Attention Module (CBAM) into the YOLOv5s backbone network. …”
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DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…Simulation results confirm that the DTHC method achieves a spectrum hole detection accuracy of 99%, with a miss-detection probability below 0.9%, demonstrating its effectiveness in enhancing CRN performance while maintaining low complexity.…”
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YOLO-DAFS: A Composite-Enhanced Underwater Object Detection Algorithm by Shengfu Luo, Chao Dong, Guixin Dong, Rongmin Chen, Bing Zheng, Ming Xiang, Peng Zhang, Zhanwei Li

“…The detection head adopts DyHead-GDC, integrating ghost depthwise separable convolution with DyHead for greater efficiency. …”
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Developing an Explainable Artificial Intelligence System for the Mobile-Based Diagnosis of Febrile Diseases Using Random Forest, LIME, and GPT by Kingsley F. Attai, Constance Amannah, Moses Ekpenyong, Daniel E. Asuquo, Oryina K. Akputu, Okure U. Obot, Peterben C. Ajuga, Jeremiah C. Obi, Omosivie Maduka, Christie Akwaowo, Faith-Michael Uzoka

“…These results indicate missed true-positive cases, necessitating further model fine-tuning. …”
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Quality assurance of hyperspectral imaging systems for neural network supported plant phenotyping by Justus Detring, Abel Barreto, Anne-Katrin Mahlein, Stefan Paulus

“…The use of the integrated LED illumination for HSI can cause a distortion of the spectral response at 677nm and 752nm. …”
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Research on Foreign Object Intrusion Detection for Railway Tracks Utilizing Risk Assessment and YOLO Detection by Shanping Ning, Rui Guo, Pengfei Guo, Lu Xiong, Bangbang Chen

“…The results demonstrate that the proposed detection model can effectively identify foreign object intrusions on tracks, mitigating issues such as missed and false alarms. It achieves a 3.7% improvement in mean average precision (mAP) compared to the baseline model. …”
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Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 by Fangyu Hu, Mairheba Abula, Di Wang, Xuan Li, Ning Yan, Qu Xie, Xuedong Zhang

“…This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11. By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. …”
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New Perspectives on Lung Cancer Screening and Artificial Intelligence by Leonardo Duranti, Luca Tavecchio, Luigi Rolli, Piergiorgio Solli

“…Both AI and liquid biopsy show the potential to improve sensitivity and specificity, enabling the detection of early-stage cancers that traditional methods, like low-dose CT (LDCT) scans, might miss. Integrating AI and biomarker-driven methods offers significant promise for transforming lung cancer screening. …”
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Maximising contributions of midwives in Africa towards achieving MNH targets: Lessons learned by Doreen K. Kaura, Jemima A. Dennis-Antwi, Frances D. Ganges, Sarah N. Ngoma

“…They are a critical human resource in mitigating near misses, morbidities and mortality in Maternal and Neonatal Health (MNH). …”
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A Small-Sample Target Detection Method for Transmission Line Hill Fires Based on Meta-Learning YOLOv11 by Yaoran Huo, Yang Zhang, Jian Xu, Xu Dai, Luocheng Shen, Conghong Liu, Xia Fang

“…The experimental results show that the accuracy of MA-YOLO is improved by 10.8% in few-shot scenarios. MA-YOLO misses fewer hill fire targets in different scenarios and is less likely to be affected by complex backgrounds.…”
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YOLO-SRSA: An Improved YOLOv7 Network for the Abnormal Detection of Power Equipment by Wan Zou, Yiping Jiang, Wenlong Liao, Songhai Fan, Yueping Yang, Jin Hou, Hao Tang

“…Existing models have high false and missed detection rates in complex weather and multi-scale equipment scenarios. …”
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Modeling land cover changes using an enhanced Markov-future land use simulation model with spatial distribution considerations: a case study in the Yellow River Basin by Jianchen Zhang, Heying Li, Hanwen Zhang, Jiayao Wang, Guangxia Wang, JianWei Xu, Haohua Zheng, HuiLing Ma

“…This study introduces an innovative approach by designing shape control parameters and developing an enhanced Markov-FLUS model. The model integrates artificial neural networks to capture the relationships between the land use type occurrence probability and driving factors. …”
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Improved YOLOv8-Based Segmentation Method for Strawberry Leaf and Powdery Mildew Lesions in Natural Backgrounds by Mingzhou Chen, Wei Zou, Xiangjie Niu, Pengfei Fan, Haowei Liu, Cuiling Li, Changyuan Zhai

“…To improve performance, SCDown and PSA modules were integrated into the backbone to reduce redundancy, decrease computational load, and enhance detection of small objects and complex backgrounds. …”
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OptiASAR: Optimized Aspect-Based Sentiment Analysis of Reviews With BiLSTM-GRU and NER-BERT in Healthcare Decision-Making by Divanshu Singh, Sunita Shivprakash Barve, Amit Krishna Dwivedi

“…Traditional sentiment analysis methods may not capture the diverse aspects and often miss these subtleties. This paper proposes an OptiASAR (Optimized Aspect-based Sentiment Analysis of Reviews) model integrating a Bidirectional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM-GRU) for enhanced sequence modeling, improving the capture of long-term dependencies in healthcare reviews. …”
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A Robust YOLOv5 Model with SE Attention and BIFPN for Jishan Jujube Detection in Complex Agricultural Environments by Hao Chen, Lijun Su, Yiren Tian, Yixin Chai, Gang Hu, Weiyi Mu

“…These results highlight the model’s effectiveness in addressing missed detections of small and occluded fruits while maintaining higher confidence in predictions.…”
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