From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The approach assists in predicting key values of the dependent variable associated with defect occurrence, enabling foundries to enhance product quality, reduce waste, and augment overall production process efficiency. …”
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Bias-aware degradation models for reinforced concrete bridges based on XAI by Francesca Marsili, Filippo Landi, Rade Hajdin, Sylvia Kessler

“…The analysis comprises four steps: (1) cluster analysis of damage transition times using the k-means algorithm to identify damage patterns with similar damage evolution rates (fast, normal, slow, corresponding to bridge components with a fragile, normal, and robust deterioration behavior); (2) Random Forest classification to predict the cluster based on bridge inventory data; (3) SHAP analysis to explain the predictions of the Random Forest classifier; (4) application of the gamma process to the grouped damage transition times to assess damage evolution. …”
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Structure, short-range order, and phase stability of the Al x CrFeCoNi high-entropy alloy: insights from a perturbative, DFT-based analysis by Christopher D. Woodgate, George A. Marchant, Livia B. Pártay, Julie B. Staunton

“…When the underlying lattice is fcc, at low concentrations of Al, depending on the value of x, we predict either an L12 or D022 ordering emerging below approximately 1000 K. …”
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Travel route recommendation with a trajectory learning model by Xiangping Wu, Zheng Zhang, Wangjun Wan

“…Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. …”
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Identification of differentially expressed genes associated with ferroptosis in ulcerative colitis. by Fang Zhang, Xin Jiang, Xuyu Chen, Zheng Wang, Jianlei Xia, Bingcheng Wang, Mei Wang, Yanbing Ding

“…<h4>Results</h4>Eleven ferroptosis-related DEGs were identified (nine upregulated and two downregulated genes) in UC, with eight genes chosen from the PPI network. MCC algorithm demonstrated that SLC7A11, PSAT1, SLC7A5, ACSF2, and ACSL4 were hub genes, predicting TFs, miRNAs and drugs. …”
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Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques by Rami Mustafa A. Mohammad, Malak Aljabri, Menna Aboulnour, Samiha Mirza, Ahmad Alshobaiki

“…With the constant increase in the number of severe COVID-19 infections, an essential area of research has been directed towards predicting the mortality rate of these patients, in order to make informed medical decisions about the necessary healthcare priorities. …”
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Constructing a web recommender system using web usage mining and user’s profiles by T. Mombeini, A. Harounabadi, J. Rezaeian Sheshdeh

“…Therefore, recommender servers use the web usage mining technique to predict users’ browsing patterns and recommend those patterns in the form of a suggestion list. …”
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Incorporation of Sub‐Resolution Porosity Into Two‐Phase Flow Models With a Multiscale Pore Network for Complex Microporous Rocks by Sajjad Foroughi, Branko Bijeljic, Ying Gao, Martin J. Blunt

“…We then show that our model can successfully predict steady‐state relative permeability measurements on a water‐wet Estaillades carbonate sample within the uncertainty of the experiments and modeling. …”
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Quantitative structure-activity relationship study of some angiotensin-converting enzyme inhibitor drugs in the treatment of hypertension based on Monte Carlo optimization method by Shahram Lotfi, Shahin Ahmadi, Ali Azimi

“…Materials & Methods: In this study, quantitative structure-activity relationship to predict the inhibitory activity of the data set containing 255 angiotensin-converting enzyme inhibitor compounds based on the algorithm Monte Carlo was studied. …”
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Spatial transcriptomics and scRNA-seq: decoding tumor complexity and constructing prognostic models in colorectal cancer by Wei Song, Yatao Wang, Min Zhou, Fengqin Guo, Yanliang Liu

“…We developed a 13-gene prognostic signature (PS) using machine learning algorithm (StepCox backward), which predicts the prognosis of CRC patients. …”
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Variance exchange process: overcoming the problem of singular information matrices in quadratic three−variable response designs by Okim Ikpan, Felix Nwobi

“…For such matrices, the variances of predicted responses at variance points cannot be evaluated, and the variance exchange process, not possible. …”
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Methodological levels of research the post-industrial development of the city by Olha Suptelo

“…The main vector of these transformations is the transition to the post-industrial stage of development, which was predicted by a number of scientists from many industries at the turn of the nineteenth and twentieth centuries. …”
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Bioinformatics analysis of potential targets influencing the prognosis of OSCC by ZOU Xian, SONG Tao

“…The prognostic model based on SPP1 and ITGA3 genes can effectively predict the prognosis of OSCC patients. [Conclusion:] ITGA3 and SPP1 may be biomarkers for the prognosis of OSCC patients. …”
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The J-PLUS collaboration. Additive versus multiplicative systematics in surveys of the large scale structure of the Universe by Carlos Hernández-Monteagudo, G. Aricò, J. Chaves-Montero, L. R. Abramo, P. Arnalte-Mur, A. Hernán-Caballero, Francisco José Galindo-Guil, C. López-Sanjuan, V. Marra, R. von Marttens, E. Tempel, J. Cenarro, D. Cristóbal-Hornillos, A. Marín-Franch, M. Moles, J. Varela, H. Vázquez Ramió, J. Alcaniz, R. Dupke, A. Ederoclite, L. Sodré Jr., R. E. Angulo

“…Nonetheless, in scenarios of mild/low impact of systematics, we find that our hybrid approach converges towards the standard method that assumes additive contamination, as predicted by our model describing systematics. Our methodology also allows for the estimation of biases induced by systematics residuals on different angular scales and under different observational configurations, although these predictions necessarily restrict to the subset of <em>known/identified</em> potential systematics, and say nothing about ``unknown unknowns" possibly impacting the data.…”
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Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach by Mahadee Al Mobin

“…The study concludes that relative humidity is redundant in predicting dengue infection, while meteorological factors exhibit more significant short-term impacts compared to long-term and immediate impacts. …”
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Intelligent Resource Allocation for Immersive VoD Multimedia in NG-EPON and B5G Converged Access Networks by Razat Kharga, AliAkbar Nikoukar, I-Shyan Hwang

“…The SDN framework manages the entire network, predicts bandwidth requirements, and operates the immersive media dynamic bandwidth allocation (IMS-DBA) algorithm to efficiently allocate bandwidth to IVoD network traffic, ensuring that QoS metrics are met for IM services. …”
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The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi'an, China. by Wang Xiang, Zezhi Wang, Xin Pan, Xiaobing Liu, Xuedong Yan, Li Chen

“…The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. …”
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The impact of quantitative platform on candidacy for bronchoscopic lung volume reduction: a multi-center retrospective cohort study by Max Wayne, Suchitra Pilli, Hee Jae Choi, Nathaniel Moulton, Praveen Chenna, Allen Cole Burks, Alexander Chen

“…Results: A total of 83 patients referred for BLVR across three centers were included; patients were a median 67 years old, had a median post bronchodilator FEV1 of 30% predicted (IQR: 25, 38), a median residual volume of 220% predicted (IQR: 185, 268), and 29 (34.9%) received endobronchial valves. …”
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Microseismic Signals in Heading Face of Tengdong Coal Mine and Their Application for Rock Burst Monitoring by JianJu Ren, Wenlong Zhang, Zheng Wu, Ji Li, Ying Shen

“…Through trial operation, it is found that large energy (three-channel and four-channel triggering) coal vibration events successfully predicted a rock burst. The MS system of 117 track gateway of Tengdong coal mine should be able to remove the interference signals in real time through the algorithm and take the number of large energy coal vibration signal rather than all coal vibration events as the predictor for rock burst risk monitoring.…”
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Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique by Lijie Jiang, Qi Li, Huiqing Liao, Hourong Liu, Bowen Tan

“…Abstract This study develops and evaluates advanced hybrid machine learning models—ADA-ARD (AdaBoost on ARD Regression), ADA-BRR (AdaBoost on Bayesian Ridge Regression), and ADA-GPR (AdaBoost on Gaussian Process Regression)—optimized via the Black Widow Optimization Algorithm (BWOA) to predict the density of supercritical carbon dioxide (SC-CO2) and the solubility of niflumic acid, critical for pharmaceutical processes. …”
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