A novel adenosine-to-inosine RNA editing-based nomogram for predicting prognosis of hepatocellular carcinoma by Shiqiong Huang, Ji Sun

“…The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy of the signature. …”
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Ranking Assisted Unsupervised Morphological Disambiguation of Turkish by Hayri Volkan Agun, Ozkan Aslan

“…The training process of PageRank is notably straightforward and efficient, requiring <inline-formula> <tex-math notation="LaTeX">$O(n^{2})$ </tex-math></inline-formula> parameter adjustments, which is considerably fewer than those required by the backpropagation method used in neural network training. …”
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A THREE-TERM CONJUGATE GRADIENT METHOD FOR LARGE-SCALE MINIMIZATION IN ARTIFICIAL NEURAL NETWORKS by Umar A Omesa, Muhammad Y. Waziri, Issam A. R. Moghrabi, Sulaiman M. Ibrahim, Gudu E B, Fakai S L, Rabiu Bashir Yunus, Elissa Nadia Madi

“…To evaluate the performance of the new method we considered some standard test problems for unconstrained optimization and applied the proposed method to train different ANNs on some benchmark data sets contained in the NN toolbox. …”
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Tri-band vehicle and vessel dataset for artificial intelligence research by Yingjian Liu, Gangnian Zhao, Shuzhen Fan, Cheng Fei, Junliang Liu, Zhishuo Zhang, Liqian Wang, Yongfu Li, Xian Zhao, Zhaojun Liu

“…About 60% of the dataset has been manually labeled with object instances to train and evaluate well-established object detection algorithms. …”
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Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study by Jiexin Chen, Qiongbing Zheng, Youmian Lan, Meijing Li, Ling Lin

“…The nomogram was evaluated by measuring the AUC, calibration curve, and DCA curve of training and validation sets. …”
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Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. by Zhuoran Wang, Anoop D Shah, A Rosemary Tate, Spiros Denaxas, John Shawe-Taylor, Harry Hemingway

“…For training the algorithm, we used texts classified as positive and negative according to their associated Read diagnostic codes, rather than by manual annotation. …”
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Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas by Ao-Yu Li, Jie Bu, Hui-Ni Xiao, Zi-Yue Zhao, Jia-Lin Zhang, Bin Yu, Hui Li, Jin-Ping Li, Tao Xiao

“…We utilized transcriptomic, clinical, and mutation data of sarcoma patients (training cohort) obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) server. …”
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Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The selected data set is pre-processed and feature selection for the optimization for the improvement in accuracy, and automation decision making the framework of the convolution neural network along with the ensemble boosted tree classifier developed is optimized using the jellyfish optimization and Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) model for the recognition of patterns and numerical vectors in the real-world data after processing of output then it is sent back as the input for the recurrent network to make the decision in the shipbuilding process. By evaluating the performance results and confusion matrix through the training and testing output all the metrics for training and testing are classified in the confusion matrix. …”
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Prediction of persistent type II endoleak after endovascular aortic repair using machine learning based on preoperative clinical data and radiomic by Jinqing Mo, Qi Liu, Kangjie Wang, Lin Huang, Chen Yao

“…Feature selection was performed before machine learning model training. Six common machine learning algorithms were used to predict persistent T2ELs based on preoperative features. …”
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A nomogram combining clinical features, O-RADS US, and radiomics based on ultrasound imaging for diagnosing ovarian cancer by Wenting Xie, Yaoqin Wang, Zhongshi Du, Yijie Chen, Xiaohui Ke, Tingfan Wu, Zhilan Wang, Lina Tang

“…A total of 981 patients with ovarian masses from two centers were randomly divided into the training cohort (n = 686) and the validation cohort (n = 295). …”
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A Seq-to-Seq Temporal Convolutional Network for Volleyball Jump Monitoring Using a Waist-Mounted IMU by Meng Shang, Camilla de Bleecker, Jos Vanrenterghem, Roel de Ridder, Sabine Verschueren, Carolina Varon, Walter de Raedt, Bart Vanrumste

“…A Multi-Layer Temporal Convolutional Network (MS-TCN) was applied for sequence-to-sequence (seq-to-seq) classification without using the sliding window technique. The model was evaluated on volleyball players during a lab session with a fixed protocol of jumping and landing tasks, and during four volleyball training sessions, respectively. …”
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Ultrasound-based radiomic nomogram for predicting the invasive status of breast cancer: a multicenter study by Dan Yan, Jingwen Xie, Wanling Cheng, Wen Xue, Yaohong Den, JianXing Zhang

“…A total of 1125 radiomic features were extracted from the training set of CUS images, and Radiomics Scores (Rad-scores) were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. …”
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Enhanced Magnetic Resonance Imaging-Based Brain Tumor Classification with a Hybrid Swin Transformer and ResNet50V2 Model by Abeer Fayez Al Bataineh, Khalid M. O. Nahar, Hayel Khafajeh, Ghassan Samara, Raed Alazaidah, Ahmad Nasayreh, Ayah Bashkami, Hasan Gharaibeh, Waed Dawaghreh

“…Employing data augmentation and transfer learning techniques enhances model performance, leading to more dependable and cost-effective training. The suggested model achieves an impressive accuracy of 99.9% on the binary-labeled dataset and 96.8% on the four-labeled dataset, outperforming the VGG16, MobileNetV2, Resnet50V2, EfficientNetV2B3, ConvNeXtTiny, and convolutional neural network (CNN) algorithms used for comparison. …”
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Addressing Data Scarcity in Crack Detection via CrackModel: A Novel Dataset Synthesis Approach by Jian Ma, Yuan Meng, Weidong Yan, Guoqi Liu, Xueyan Guo

“…This model is capable of extracting and storing crack information from hundreds of images of wooden structures with cracks and synthesizing the data with images of intact structures to generate high-fidelity data for training detection algorithms. To evaluate the effectiveness of synthetic data, systematic experiments were conducted using YOLO-based detection models on both synthetic images and real data. …”
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Torque Prediction In Deep Hole Drilling: Artificial Neural Networks Versus Nonlinear Regression Model by Ngoc Hung- Chu, Hoai Nam- Nguyen, Van Du- Nguyen, Dang Binh- Nguyen

“…In this paper, we have developed a two-layer artificial neural network (ANN) model for training using the Levenberg-Marquardt algorithm to predict torque during deep drilling. …”
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Context Aware Task Orchestration With Deep Reinforcement Learning in Real Time Fog Computing Simulation Environment by Alp Gokhan Hossucu, Suat Ozdemir

“…The system was evaluated in a simulation developed under different fog computing environmental conditions in terms of edge device and task density. …”
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Site-specific prediction of O-GlcNAc modification in proteins using evolutionary scale model. by Ayesha Khalid, Afshan Kaleem, Wajahat Qazi, Roheena Abdullah, Mehwish Iqtedar, Shagufta Naz

“…Computational approaches, including protein language models and machine learning algorithms, have emerged as valuable tools for predicting O-GlcNAc sites, reducing experimental costs, and enhancing efficiency. …”
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Influence of Clinician-Related Factors on Adherence to the American Heart Association Guidelines for Acute Coronary Syndrome among Clinicians at Kenya Ports Authority clinics in Mo... by Mary Kavinya Mailu, Nilufa Jivraj Shariff, Ruth Mbugua

“…Significant associations were found between adherence and clinician gender, work experience, job cadre, and training on ACS guidelines. The study recommends enhancing resource allocation for ACS management, ensuring availability of essential medications and equipment, and developing simplified clinical algorithms to support guideline adherence. …”
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Interpretable multiparametric MRI radiomics-based machine learning model for preoperative differentiation between benign and malignant prostate masses: a diagnostic, multicenter st... by Wenjun Zhou, Wenjun Zhou, Zhangcheng Liu, Zhangcheng Liu, Jindong Zhang, Shuai Su, Yu Luo, Lincen Jiang, Kun Han, Guohua Huang, Jue Wang, Jianhua Lan, Delin Wang

“…Model performance was evaluated with internal and external validation, using area under the curve (AUC), calibration curves, and decision curve analysis to select the optimal model. …”
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Using Machine Learning to Predict Outcomes Following Thoracic and Complex Endovascular Aortic Aneurysm Repair by Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Badr Aljabri, Leen Al‐Omran, Duminda N. Wijeysundera, Ori D. Rotstein, Thomas F. Lindsay, Charles de Mestral, Muhammad Mamdani, Graham Roche‐Nagle, Mohammed Al‐Omran

“…We developed machine learning algorithms to predict outcomes following TEVAR and complex EVAR. …”
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