Research on a Surface Roughness Measurement Under ResNet-Based Roughness Classification and Light-Section With Seam-Driven Image Stitching (RCLS) by Huashen Guan, Qiushen Cai, Xiaobin Li, Guofu Sun

“…First, the images were classified with ResNet neural network, then stitched and enhanced by scale invariant feature transform (SIFT) and optimized random sample consensus (RANSAC) algorithm for the best visual effect. …”
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Continuous prediction of human knee joint angle using a sparrow search algorithm optimized random forest model based on sEMG signals by Liuyi Ling, Zhu Lin, Bin Feng, Liyu Wei, Li Jin, Yiwen Wang

“…A knee angle prediction model based on SSA-RF algorithm was trained and evaluated using feature sample data. In the four motion mode experiments, the SSA-RF model achieved a minimum root-mean-square error of 1.569° for predicting knee joint angle, the average absolute error was only 1.05 °, and the coefficient of determination was as high as 0.99. …”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

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Landslide Susceptibility Assessment in Ya’an Based on Coupling of GWR and TabNet by Jiatian Li, Ruirui Wang, Wei Shi, Le Yang, Jiahao Wei, Fei Liu, Kaiwei Xiong

“…The performance of TabNet is compared against Random Forest, Light Gradient Boosting Machine, deep neural networks, and Residual Networks. The experimental results indicate that (1) the GWR-based sampling strategy substantially improves model performance across all tested models; (2) TabNet trained using the GWR-based negative samples achieves superior performance over all other evaluated models, with an average AUC of 0.9828, exhibiting both high accuracy and interpretability; and (3) elevation, land cover, and annual Normalized Difference Vegetation Index are identified as dominant predictors through TabNet’s feature importance analysis. …”
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Exploring novel molecular mechanisms underlying recurrent pregnancy loss in decidual tissues by Hui Ding, Yajie Gao, Yuan Gao, Yulu Chen, Ruimin Liu, Caili Wang, Yuqing Gao

“…Three RPL decidual tissue transcriptomic datasets (GSE113790, GSE161969, and GSE178535) were integrated for differential expression, weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses. …”
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JAUS to EtherCAT Bridge: Toward Real-Time and Deterministic Joint Architecture for Unmanned Systems by Jie Sheng, Sam Chung, Leo Hansel, Don McLane, Joel Morrah, Seung-Ho Baeg, Sangdeok Park

“…In this research, we first develop a basic C++ implementation of JAUS and evaluate its performance with quantitative data and compare the results with published performance data of Controller Area Network (CAN) to determine the feasibility of the JAUS standard. …”
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Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning by Jean Max T. Habib, A. A. Poguda

“…We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. …”
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Functional Connectivity Assessment in Alzheimer's Disease: A Comparative Study of Linear and Non-linear fMRI Analysis Approaches by Hessam Ahmadi, Emad Fatemizadeh, Ali Motie Nasrabadi

“… Purpose: Brain connectivity studies unveil the intricate interactions within neural networks. Various approaches exist to explore brain connectivity, yet the debate between the efficacy of linear versus non-linear methods remains unresolved due to the advantages and limitations of each. …”
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Forecasting Temperature Time Series Data Using Combined Statistical and Deep Learning Methods: A Case Study of Nairobi County Daily Temperature by John Kamwele Mutinda, Amos Kipkorir Langat, Samuel Musili Mwalili

“…The experimental results for temperature prediction show that the hybrid models combining VMD with statistical and deep learning networks achieved improved forecasting accuracy compared with baseline models. …”
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Ensemble semi-supervised learning in facial expression recognition by Purnawansyah Purnawansyah, Adam Adnan, Herdianti Darwis, Aji Prasetya Wibawa, Triyanna Widyaningtyas, Haviluddin Haviluddin

“…Evaluations were conducted across three dataset scenarios: original, feature-extracted, and augmented, using various label-to-unlabeled ratios. …”
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Advances in Deep Learning for Semantic Segmentation of Low-Contrast Images: A Systematic Review of Methods, Challenges, and Future Directions by Claudio Urrea, Maximiliano Vélez

“…Key advancements include attention mechanisms, multi-scale feature extraction, and hybrid architectures combining Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs), which expand the Effective Receptive Field (ERF), improve feature representation, and optimize information flow. …”
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AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness by Anantaya Timtong, Atthaphon Ariyarit, Wanwanut Boongsood, Prasert Aengchuan, Attasit Wiangkham

“…This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/III fracture toughness in polymethyl methacrylate . …”
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Federated Knowledge Distillation With 3D Transformer Adaptation for Weakly Labeled Multi-Organ Medical Image Segmentation by Tareq Mahmod AlZubi, Hamza Mukhtar

“…To address these challenges, we propose the Federated 3D Knowledge Distillation Network (Fed3D-KDNet), a hybrid federated learning (FL) framework that integrates both global and local knowledge distillation mechanisms. …”
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Identification of Biomarkers Associated with Heart Failure Caused by Idiopathic Dilated Cardiomyopathy Using WGCNA and Machine Learning Algorithms by Mengyi Sun, Linping Li

“…Weighted gene co-expression network analysis (WGCNA) was utilized to search for key module genes. …”
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Solar radiation prediction: A multi-model machine learning and deep learning approach by C Vanlalchhuanawmi, Subhasish Deb, Md. Minarul Islam, Taha Selim Ustun

“…Among them, gradient boosting regressor (GBR) and recurrent neural network (RNN) emerged as top performers in ML and deep learning, respectively. …”
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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…The experimental outcome shows that the proposed classifier achieved an accuracy of 99.98% which is comparably better than existing convolutional neural network models with transfer learning and IBSA-NET.…”
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Plastic to apparel: an analysis of sustainable purchasing intention using a machine learning ensemble by Carmella Andrea L. Cabrera, Ardvin Kester S. Ong, John Francis T. Diaz, Maela Madel L. Cahigas, Ma. Janice J. Gumasing

“…To analyze the data, the study utilized machine learning methods, such as Random Forest Classifier (RFC) and Artificial Neural Network (ANN). Data preprocessing using feature selection and correlation analysis was conducted to validate the available data, performed data cleaning process, and data aggregation. …”
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Unravelling the molecular landscape of polycystic ovary syndrome (PCOS) and role of inflammation through transcriptomics analysis of human ovarian granulosa cells by Kanika Mahra, Vineet Singh, Jae-Ho Shin

“…Method The bulk RNA-seq data of granulosa cells of human ovaries of PCOS-affected and healthy women were analyzed to evaluate the inflammatory regulation in PCOS. After quality trimming, the raw RNA-seq data were aligned to the human genome, and gene expression was quantified using featureCounts with Ensembl annotation. …”
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NMD-FusionNet: a multimodal fusion-based medical imaging-assisted diagnostic model for liver cancer by Qing Ye, Minghao Luo, Jing Zhou, Chunlei Cheng, Lin Peng, Jia Wu

“…Traditional diagnostic approaches often lack generalizability and effective feature extraction, increasing the risk of misdiagnosis. …”
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High-throughput phenotyping tools for blueberry count, weight, and size estimation based on modified YOLOv5s by Xingjian Li, Sushan Ru, Zixuan He, James D. Spiers, Lirong Xiang

“…The second pipeline deployed YOLOv5 models with additional modifications using the Ghost module and bi-Feature Pyramid Network (biFPN). A total of 198 images of blueberries, together with manually measured berry count and average berry weight, were used to train and test the model performance. …”
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