A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction by Zhonghua Wu, Wenzhong Yang, Meng Zhang, Fuyuan Wei, Xinfang Liu

“…To bridge the gap between the optimization objective and task performance metrics, we employ the REINFORCE algorithm to optimize our generative model, designing a novel reward function to simultaneously capture the accuracy of temporal prediction and the quality of generation. …”
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SAHVAI‐3D and 4D: Automated AI Volumetric Measurement of Subarachnoid Hemorrhage on Noncontrast Head CT by Melina Wirtz, Saif Salman, Yujia Wei, Vishal N. Patel, Rohan Sharma, Vikash Gupta, Qiangqiang Gu, Benoit Dherin, Sanjana Reddy, Rabih G. Tawk, Bradley J. Erickson, W. David Freeman

“…The SAHVAI deep learning algorithm generated automated SAHV values in cubic centimeters. …”
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Physics-informed modeling and process optimization of friction stir welding of AA7075-T6 with a zinc interlayer by Dejene Alemayehu Ifa, Dame Alemayehu Efa, Naol Dessalegn Dejene, Sololo Kebede Nemomsa

“…The models that are included in the hybrid framework of ANN, SVR, and RFR have all demonstrated noteworthy prediction strengths. The optimum FSW parameters were shown to be: a tool speed of 600 rpm, a pin radius of 5.71 mm, a shoulder radius of 20 mm, and a plunge force of 6369.48 N, with a predicted peak temperature value of 675.71 K. …”
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Multiple Regression-Based Dynamic Amplification Factor Investigation of Monorail Tourism Transit Systems by Hong Zhang, Changxing Wu, Wenlong Liu, Shiqi Wei, Yonggang Wang

“…On this basis, the key regression parameters were selected by using the Pearson correlation coefficient method and the random forest algorithm, and the DAFs prediction model was constructed based on the least absolute shrinkage and selection operator (LASSO) regression theory. …”
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DAFDM: A Discerning Deep Learning Model for Active Fire Detection Based on Landsat-8 Imagery by Xu Gao, Wenzhong Shi, Min Zhang, Lukang Wang

“…The proposed method outperforms other AF detection algorithms, achieving IoU and F1-score of 87.28% and 93.21%, respectively. …”
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ALGORITMOS SUPERVISADOS PARA LA PREDICCIÓN DEL ANCHO DE BANDA DE LAS APLICACIONES EN AMAZON WEB SERVICE DESDE UNA PYME RURAL by Ramiro Osorio Diaz, Martha Yaneth Segura Ruiz, Mauricio Alonso Villalba

“…A comparative study of threeneural network algorithms that model a multilayer neural network is performed, selecting the one that has a minimumerror that approaches zero; the selected algorithm is trained from a data source to predict the network traffic thatconnects to the cloud. …”
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Integrated Bidding and Battery Scheduling in a Microgrid for Sealed-Bid Double Auction Power Trading With Peer Microgrids Under Uncertainty and Its Blockchain-Based Implementation by Zubin J. B., Sunitha R., Gopakumar Pathirikkat

“…The Q-learning relies solely on its historical bidding outcomes without attempting to predict the bids of other participants. In parallel, battery operations are optimized using a hybrid method that combines Genetic Algorithm (GA) and Simulated Annealing (SA), explicitly incorporating the bid buffer capacity to align scheduling with market commitments. …”
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Evaluation of Baby Leaf Products Using Hyperspectral Imaging Techniques by Antonietta Eliana Barrasso, Claudio Perone, Roberto Romaniello

“…Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. …”
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An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN by Mohamed Abdelkader Aboamer, Mohamed Yacin Sikkandar, Sachin Gupta, Luis Vives, Kapil Joshi, Batyrkhan Omarov, Sitesh Kumar Singh

“…This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. …”
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Prognosticating salvage stereotactic radiosurgery outcomes in relapsed primary central nervous system lymphoma: A machine learning-driven decision tree analysis by Huili Zhao, Shenao Zhang, Lang Chen, Xin Liu, Aihong Cao, Peng Du

“…The cohort was randomly divided into training and validation sets (7:3 ratio). The C5.0 algorithm was employed to develop a decision tree model for predicting treatment response. …”
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Application of Categorical Boosting to Modelling the Friction Behaviour of DC05 Steel Sheets in Strip Drawing Test by Marek Szewczyk, Krzysztof Szwajka, Sherwan Mohammed Najm, Salwa O Mohammed

“…A Shapley decision plot exhibits the coefficient of friction prediction models via cumulative SHapley Additive exPlanations (SHAP) data. …”
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(Meta)verse as the next escaper from competition public enforcement by Luca Megale

“…Thus, amplifyingthe ability of predicting consumers’ behaviour and undertakings’ market power (to be added to the already present concerns on data exploitation). …”
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Unravelling NK cell subset dynamics and specific gene signatures post-ibrutinib therapy in chronic lymphocytic leukaemia via single-cell transcriptomics by Chunlan Liu, Tianjian Ding, Rong Zou, Aili Zhang, Zhengzhuo Zhi, Sili Wang

“…A cell subset-specific novel index (CNI) was constructed based on these core genes and was shown to be capable of predicting responses to immunotherapy. Oncopredictive algorithms and molecular docking screenings further identified semaxanib and ulixertinib as potential therapeutic candidates for CLL. …”
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Integrative multi-omics analysis reveals the role of toll-like receptor signaling in pancreatic cancer by Jie Peng, Jiaao Sun, Youfeng Yu, Qihang Yuan, Yong Zhang

“…The model showed strong performance in predicting the survival state of pancreatic cancer samples. …”
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Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…The differences in performance of all seven machine learning algorithms are significant at the 95% level.…”
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Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Machine learning includes models and algorithms that mimic the architecture of biological neural networks. …”
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Identification and experimental validation of BMX as a crucial PANoptosis‑related gene for immune response in Spinal Cord Injury. by Tianbao Feng, Jiating Hu, Mi Xie, Guodong Shi, Qi Wang, Jingyuan Yao, Xiaoqin Liu

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. BMX and CASP5 were consistently identified across all algorithms. …”
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Few-Shot Learning With Prototypical Networks for Improved Memory Forensics by Muhammad Fahad Malik, Ammara Gul, Ayesha Saadia, Faeiz M. Alserhani

“…Our goal isn’t to predict exact class labels, but to assess the similarity between query images and prototypes using a distance metric. …”
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Unbalance Responses of Rotor/Stator Systems with Nonlinear Bearings by the Time Finite Element Method by D. Demailly, F. Thouverez, L. Jézéquel

“…The current trend is to take into account many kinds of non-linearities in order to obtain more realistic predictions. The use of algorithms based on nonlinear methods is therefore needed. …”
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The Relationship Between Stiff Knee Gait Runner’s Dystonia and Musculoskeletal Knee Pathology: A Case Series by Jared A. Stowers, Derek S. Day, Steven Jow, Sarah Heins, Euan Forrest, Yonathan M. Assefa, Paige M. Lind, Afreen Mushtaheed, Frances T. Sheehan, Katharine E. Alter

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