Evaluation method of fracability of shale oil reservoir considering influence of interlayer by LIU Yexuan,LIU Xiangjun,DING Yi,ZHOU Xin,LIANG Lixi

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Rock blasting evaluation - image recognition method based on deep learning by Haibao YI, Aixiang Wu, Xiliang Zhang

“…And a segmented function R-R block size distribution correction model was proposed, which can better characterize the characteristics of blasting block size distribution and serves as a guide for accurate prediction of block size distribution. Overall, image recognition technology can significantly improve the efficiency and accuracy of blasting evaluation, solve many drawbacks of traditional manual analysis methods, and holds great application prospects in blasting quality evaluation.…”
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Prediction of Sensory Crispness of Potato Chips Using a Reference-Calibration Method by Han Sub Kwak, Sang Sook Kim, Yoon Hyuk Chang, Mohammed Saleh, Youngseung Lee

“…Reference calibration is a useful technique when sensory evaluation is not feasible or practical. This study was conducted to predict the crispness perception of potato chips evaluated by instrumental means through the reference-calibrated method. …”
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Logging Prediction Method of Organic Carbon in Mixed Deposits Based on Machine Learning by CHEN Liangyu, HU Lang, XIN Jintao, LI Yonggui, CHEN Zhi, FU Jianwei

“…The quantitative evaluation and prediction of total organic carbon (TOC) content is an important part of source rock quality evaluation. …”
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Reliable B cell epitope predictions: impacts of method development and improved benchmarking. by Jens Vindahl Kringelum, Claus Lundegaard, Ole Lund, Morten Nielsen

“…Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. …”
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Strength prominence index: a link prediction method in fuzzy social network by Sakshi Dev Pandey, Sovan Samanta, A. S. Ranadive, Leo Mrsic, Antonios Kalampakas, Tofigh Allahviranloo

“…This paper introduces a link prediction method based on the strength and prominence of seed node pairs, referred to as the strength prominence index. …”
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Prognosis method to predict small-sized breast cancer affected by fibrocystic disease by S. A. Velichko, E. M. Slonimskaya, I. G. Frolova, D. G. Bukharin, A. V. Doroshenko

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Water Quality Prediction Method Based on Reinforcement Learning Graph Neural Network by Mingming Yan, Zhe Wang

“…We evaluate this method on a significant dataset we collected, achieving strong experimental results. …”
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A Convolutional Neural Network-Based Stress Prediction Method for Airfoil Structures by Wendi Jia, Quanlong Chen

“…As a vital component of an aircraft, the structural integrity of the wing is closely linked to both flight performance and safety, making it essential to accurately predict the stresses within its structure. However, conventional stress calculation methods often encounter significant computational costs and lengthy analysis times when addressing highly nonlinear and complex geometries. …”
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Performance Prediction of Refractured Wells Based on Embedded Discrete-Fracture Modeling Method by Yawen Du, Xiaolu Cao, Bailu Teng

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Two-step method for predicting Young's modulus of nanocomposites containing nanodiamond particles by Hadi Nematollahi, Mohsen Mohammadi, Muhammad Tajammal Munir, Yasser Zare, Kyong Yop Rhee

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Integrating spatiotemperporal features into fault prediction using a multi-dimensional method by Chun-Yi Lin, Yu-Chuan Tseng, Wu-Sung Yao

“…This study proposes a method to validate multidimensional fault prediction models. …”
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Prediction of Monthly Temperature Over China Based on a Machine Learning Method by Ping Mei, Zixin Yin, Haoyu Wang, Changzheng Liu, Yaoming Liao, Qiang Zhang, Liping Yin

“…These characteristics limit both traditional empirical forecasting and machine learning methods. This paper proposes a novel method called dynamically modeled machine learning to predict monthly temperature anomalies over China. …”
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Access control relationship prediction method based on GNN dual source learning by Dibin SHAN, Xuehui DU, Wenjuan WANG, Aodi LIU, Na WANG

“…With the rapid development and wide application of big data technology, users’ unauthorized access to resources becomes one of the main problems that restrict the secure sharing and controlled access to big data resources.The ReBAC (Relationship-Based Access Control) model uses the relationship between entities to formulate access control rules, which enhances the logical expression of policies and realizes dynamic access control.However, It still faces the problems of missing entity relationship data and complex relationship paths of rules.To overcome these problems, a link prediction model LPMDLG based on GNN dual-source learning was proposed to transform the big data entity-relationship prediction problem into a link prediction problem with directed multiple graphs.A topology learning method based on directed enclosing subgraphs was designed in this modeled.And a directed dual-radius node labeling algorithm was proposed to learn the topological structure features of nodes and subgraphs from entity relationship graphs through three segments, including directed enclosing subgraph extraction, subgraph node labeling calculation and topological structure feature learning.A node embedding feature learning method based on directed neighbor subgraph was proposed, which incorporated elements such as attention coefficients and relationship types, and learned its node embedding features through the sessions of directed neighbor subgraph extraction and node embedding feature learning.A two-source fusion scoring network was designed to jointly calculate the edge scores by topology and node embedding to obtain the link prediction results of entity-relationship graphs.The experiment results of link prediction show that the proposed model obtains better prediction results under the evaluation metrics of AUC-PR, MRR and Hits@N compared with the baseline models such as R-GCN, SEAL, GraIL and TACT.The ablation experiment results illustrate that the model’s dual-source learning scheme outperforms the link prediction effect of a single scheme.The rule matching experiment results verify that the model achieves automatic authorization of some entities and compression of the relational path of rules.The model effectively improves the effect of link prediction and it can meet the demand of big data access control relationship prediction.…”
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Prediction method of coalbed methane enrichment area based on multi parameter fusion by Tao WANG, Shuwei WANG, Xianxu ZHANG, Xiangpeng ZHANG, Rui SHAN

“…In this study, an evaluation method of coalbed methane enrichment unit is proposed, which takes the prediction of geological elements of coalbed methane occurrence as the research object and integrates multiple seismic attribute parameters. …”
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Mine microseismic signal denoising method and application based on Adaboost_LSTM prediction by Xueyi SHANG, Yong CHEN, Jie CHEN, Linlin CHEN, Yuanyuan PU

“…Then, a method for microseismic signal denoising based on the difference between model predictive data and observational data was developed. …”
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An efficient method for predicting the morphology of proppant packs based on a surrogate model by Tao ZHANG, Hangyu ZHOU, Yifan ZHANG, Jianchun GUO, Haoran GOU, Tang TANG

“…Specifically, 60% of the data was used for training the models, 20% was used for validation to fine-tune the models’ parameters, and another 20% was used for testing to evaluate the models’ performance on unseen data. The results showed that the predictions of proppant placement patterns were highly consistent with the numerical simulation results. …”
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Evaluation Model Based on the SGCNiFormer for the Influence of Different Storage Environments on Wheat Quality by Qingchuan Zhang, Zexi Song, Mingwen Bi

“…To address these challenges, this paper proposes a dual model system of “prediction-evaluation”, which integrates a dynamic quality prediction model based on SGCNiFormer with an evaluation framework based on K-Smeans clustering to establish a closed-loop mechanism from quality prediction to storage effect evaluation. …”
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Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants by Fatemeh Hajimohammadali, Emanuele Crisostomi, Mauro Tucci, Nunzia Fontana

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Predicting cancer mortality using machine learning methods: a global vs. Iran analysis by Hossein Sadeghi, Fatemeh Seif

“…This study leverages Machine Learning (ML) to analyze global and Iran-specific cancer data, aiming to improve predictive accuracy for cancer mortality. Methods Using datasets from Global Cancer Observatory (GLOBOCAN) and the Iran National Cancer Registry (INCR), we evaluate the performance of ML models, including XGBoost, Random Forest, and Support Vector Machines, in predicting cancer outcomes. …”
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