Association of the tumor microenvironment collagen score and immunoscore with colon cancer lymph node metastasis by Chenyan Long, Jiaxin Cheng, Mingyuan Feng, Botao Yan, Yiran Li, Wei Jiang, Dexin Chen, Jun Yan

“…The new model performed significantly better than the traditional clinicopathological model [AUROC: 0.715 (95% CI: 0.649–0.780), p < 0.001]. …”
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Integrated Analysis to Reveal Heterogeneity of Tumor‐Associated Neutrophils in Glioma by Wen Wang, Junsheng Li, Qiheng He, Chenglong Liu, Siyu Wang, Zhiyao Zheng, Bojian Zhang, Siqi Mou, Wei Sun, Jizong Zhao

“…We developed a risk score model. Expression of selected genes was confirmed using immunohistochemistry (IHC). …”
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Coseismic Landslide Mapping Based on Trans-UNet and Transfer Learning by Tianhe Ren, Wenping Gong, Jun Chen, Liang Gao, Jiahao Wu, Xuyang Xiang

“…During model training, a transfer learning strategy is employed to enhance the performance of the model on low-resolution images by utilizing rich feature information obtained from high-resolution images. …”
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Privacy-Oriented Successive Approximation Image Position Follower Processing by Ying Miao, Danyang Shao, Zhimin Yan

“…To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. …”
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Automated Detection of Poor-Quality Scintigraphic Images Using Machine Learning by Anil K. Pandey, Akshima Sharma, Param D. Sharma, Chandra S. Bal, Rakesh Kumar

“…Hyperparameter tuning of the model was done in which five-fold cross-validation was performed. …”
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Mamba-STFM: A Mamba-Based Spatiotemporal Fusion Method for Remote Sensing Images by Qiyuan Zhang, Xiaodan Zhang, Chen Quan, Tong Zhao, Wei Huo, Yuanchen Huang

“…Extensive experiments on public datasets demonstrate that Mamba-STFM outperforms existing methods in fusion quality; ablation studies validate the effectiveness of each core module; and efficiency analyses and application comparisons further confirm the model’s superior performance.…”
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Enhancing IoT device security: CNN-SVM hybrid approach for real-time detection of DoS and DDoS attacks by Al-Na’amneh Qais, Aljaidi Mohammad, Nasayreh Ahmad, Gharaibeh Hasan, Al Mamlook Rabia Emhamed, Jaradat Ameera S., Alsarhan Ayoub, Samara Ghassan

“…This research offers a hybrid model that combines CNN for feature extraction with SVM as a classifier. …”
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MXT-YOLOv7t: An Efficient Real-Time Object Detection for Autonomous Driving in Mixed Traffic Environments by Afdhal Afdhal, Khairun Saddami, Mirshal Arief, Sugiarto Sugiarto, Zahrul Fuadi, Nasaruddin Nasaruddin

“…Then, we evaluate the performance by training and testing the proposed model alongside several other previous YOLO variant models using the same MXT-Dataset. …”
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GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network With High Renewables by Dinesh Kumar Mahto, Mahipal Bukya, Rajesh Kumar, Akhilesh Mathur, Vikash Kumar Saini

“…The proposed GAT model showcases its effectiveness and promises results for addressing the OPF problem in the distribution network, as evidenced by performance evaluation metrics.…”
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Analysis of the Impact of Effectiveness of Media (Seda-va-Sima) Advertisement on the Extent of Consumers’ Urge to Consume National Commodities by Abolfazl Danaei

“…Two perspectives on consumer attitudes towards domestic versus foreign products exist: 1) Rational Processing Model: Consumers evaluate and select products based on rational processes. 2) Emotional Processing Model: This perspective suggests that product choice is influenced by emotional factors. …”
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A Novel Methodology to Develop Mining Stope Stability Graphs on Imbalanced Datasets Using Probabilistic Approaches by Lucas de Almeida Gama Paixao, William Pratt Rogers, Erisvaldo Bitencourt de Jesus

“…The approach includes rebalancing of the dataset using the Synthetic Minority Over-Sampling Technique (SMOTE) and feature selection using permutation importance to identify key features that impact instability, using those to construct a bi-dimensional stability graph that provides both improved performance and interpretability. …”
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Deep Learning-Based Medical Object Detection: A Survey by Mohammadreza Saraei, Mehrshad Lalinia, Eung-Joo Lee

“…Notable improvements include integrating You Only Look Once (YOLO)-based architectures, Vision Transformers (ViT), and hybrid attention mechanisms, significantly enhancing detection accuracy and efficiency. Standout models, such as YOLOv8m, Hybrid YOLO-NAS, and YOLOv4+ViT, have demonstrated exceptional performance, achieving mean average precision (mAP) scores between 98.6% and 99.5%. …”
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Optical fiber eavesdropping detection method based on machine learning by Xiaolian CHEN, Yi QIN, Jie ZHANG, Yajie LI, Haokun SONG, Huibin ZHANG

“…Optical fiber eavesdropping is one of the major hidden dangers of power grid information security,but detection is difficult due to its high concealment.Aiming at the eavesdropping problems faced by communication networks,an optical fiber eavesdropping detection method based on machine learning was proposed.Firstly,seven-dimensions feature vector extraction method was designed based on the influence of eavesdropping on the physical layer of transmission.Then eavesdropping was simulated and experimental feature vectors were collected.Finally,two machine learning algorithms were used for classification detection and model optimization.Experiments show that the performance of the neural network classification is better than the K-nearest neighbor classification,and it can achieve 98.1% eavesdropping recognition rate in 10% splitting ratio eavesdropping.…”
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Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy by Bin Jia, Ziying Mai, Chaoqun Xiang, Qiwen Chen, Min Cheng, Longkai Zhang, Xue Xiao

“…To further improve the model’s performance and applicability, it is necessary to continuously expand the sample set with different varieties and locations for wide variation.…”
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Trustworthy-constraint Deep Graph Learning for Enterprise Financial Risk Prediction by Wenting Ma

“…Deep learning-Based methods achieve encouraging the financial risk prediction performance due to the power ability of the feature learning. …”
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Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology by Petr Kuritcyn, Rosalie Kletzander, Sophia Eisenberg, Thomas Wittenberg, Volker Bruns, Katja Evert, Felix Keil, Paul K. Ziegler, Katrin Bankov, Peter Wild, Markus Eckstein, Arndt Hartmann, Carol I. Geppert, Michaela Benz

“…The models achieved classification performance of around 90% on a multiscanner and multicenter database, which is on par with the accuracy achieved on the primary single-center single-scanner data. …”
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Hydrothermal behaviour of hybrid nanofluid flow in two types of shell and helical coil tube heat exchangers with a new design. Numerical approach by Dheyaa J. Jasim, Seyed Hossein Hashemi Karouei, Thamer J. Mohammed, Ahmed Salah Al-Shati

“…Following this, the impact of various hybrid nanofluids on thermal performance is assessed, comparing fluids with volume concentrations of φ1 = φ2 = 0.3 to pure water (φ1 = φ2 = 0).The findings reveal that Case [A], featuring a unique geometry with Water/Ag_HEG, achieves the highest thermal performance across all examined Reynolds numbers. …”
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Design of a Wave-Shaped Towing Cable for Stable, Low-Drag Acoustic Monitoring in Wave Glider Systems by Tian Yingyuan, Chen Yaxun, Qi Yang, Lv Yunfei, Li Weijia

“…A novel acoustic towing system featuring a wave-shaped cable, with strategically distributed float-sinker pairs, is presented here. …”
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Using the excitation/inhibition ratio to optimize the classification of autism and schizophrenia by Lavinia Carmen Uscătescu, Christopher J. Hyatt, Jack Dunn, Martin Kronbichler, Vince Calhoun, Silvia Corbera, Kevin Pelphrey, Brian Pittman, Godfrey Pearlson, Michal Assaf

“…In the replication dataset, the highest classification performance was obtained when combining H with PANSS, ADOS, BVAQ, EQ and IQ (i.e., model e; AUC = 83%). …”
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Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks by Gleb Melshin, Anthony DiMaggio, Nadia Zeramdini, Michael MacKinley, Lena Palaniyappan, Alban Voppel

“…The classifier trained on negative symptom burden performed with somewhat less accuracy (80.5%; AUC = 0.73) but the model detecting blunted affect above a predefined clinical threshold achieved 87.8% accuracy (AUC = 0.79). …”
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