Predicting liver metastasis in pancreatic neuroendocrine tumors with an interpretable machine learning algorithm: a SEER-based study by Jinzhe Bi, Yaqun Yu

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An Investigation Towards Resampling Techniques and Classification Algorithms on CM1 NASA PROMISE Dataset for Software Defect Prediction by Agung Fatwanto, Muh Nur Aslam, Rebbecah Ndugi, Muhammad Syafrudin

“…In the context of classification models, ensemble-based algorithms, which extend the decision tree classification mechanism such as Random Forest and eXtreme Gradient Boosting, achieved sufficiently good performance for predicting defective software modules. …”
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An Adaptive Parameter Evolutionary Marine Predators Algorithm for Joint Resource Scheduling of Cooperative Jamming Networked Radar Systems by Dejiang Lu, Siyi Cheng, You Chen, Xi Zhang, Haoyang Li, Tianjian Yang

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Application of the ant colony optimization algorithm for the construction of a short version of the German alcohol decisional balance scale by Anne Moehring, Christian Meyer, Ulrich John, Hans-Juergen Rumpf, Gallus Bischof, Jennis Freyer-Adam, Sophie Baumann, Andreas Staudt

“…The aim of this paper was to demonstrate the use of the ACO algorithm by constructing a short version of the German Alcohol Decisional Balance Scale (ADBS). …”
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Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data by Jiali Wang, Jia Chen

“…Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved Boosting algorithms were proposed, namely Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting Tree (XGBoost), and Gradient Boosting Decision Tree (CatBoost). …”
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Enhanced Performance of PAM7 MISO Underwater VLC System Utilizing Machine Learning Algorithm Based on DBSCAN by Meng Shi, Yiheng Zhao, Weixiang Yu, Yuchong Chen, Nan Chi

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Application of multi-objective neural network algorithm in industrial polymerization reactors for reducing energy cost and maximising productivity by Fakhrony Sholahudin Rohman, Sharifah Rafidah Wan Alwi, Dinie Muhammad, Ashraf Azmi, Zainuddin Abd Manan, Jeng Shiun Lim, Hong An Er, Siti Nor Azreen Ahmad Termizi

“…In this work, we implement Multi-objective optimization neural network algorithm (MONNA) in LDPE tubular reactor for maximising productivity, conversion and minimising energy costs with three scenario of problem optimization, i.e. maximising productivity and reducing energy cost for the first problem (P1); increasing conversion and reducing energy costs for the second problem (P2); and increasing productivity and reducing by-products for the third problem (P3). …”
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Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization by Xia Wang, Wei Zhao, Jia-Ning Tang, Zhong-Bin Dai, Ya-Ning Feng

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Basketball team optimization algorithm (BTOA): a novel sport-inspired meta-heuristic optimizer for engineering applications by Yujie Chen, Guangyu Wang, Baichuan Yin, Chongyun Ma, Zhiqiao Wu, Ming Gao

“…Additional comparisons against several recently proposed algorithms and competition-winning algorithms further highlight BTOA’s consistent advantage. …”
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Utilizing machine learning models for predicting outcomes in acute pancreatitis: development and validation in three retrospective cohorts by Kaier Gu, Yang Liu

“…In the training set, key variables were screened using univariate logistic regression and the LASSO method. Six ML algorithms were employed to construct predictive models. …”
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Exploiting the Regularized Greedy Forest Algorithm Through Active Learning for Predicting Student Grades: A Case Study by Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis

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Discovering sequential patterns and interrelations among multiple diseases in electronic medical records using cSPADE algorithm by He Ma, Qianxin Huang, Hong Zhang, Hui Song, Bo Zhang, Ying Liu, Lin Zhang

“…Conclusion Our findings demonstrate the effectiveness of the constrained SPADE (cSPADE) algorithm in comorbidity research and highlight several clinically significant sequential comorbidity patterns. …”
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Multi-objective Genetic Optimization of Ethane Thermal Cracking Reactor

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Solving Fuzzy Games Problems by Using Ranking Functions by Baghdad Science Journal

“…We use the trapezoidal membership function to transform the data into fuzzy numbers and utilize the three different ranking function algorithms. Then we compare between these three ranking algorithms by using trapezoidal fuzzy numbers for the decision maker to get the best gains…”
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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

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N-SOFT SETS ASSOCIATION RULE AND ITS APPLICATION FOR PROMOTION STRATEGY IN DISTANCE EDUCATION by Fatia Fatimah, Selly Anastassia Amellia Kharis, Fauzan Ihza Fajar

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Decision-making method for residual support force of hydraulic supports during pressurized moving under fragmented roof conditions in ultra-thin coal seams by ZHANG Chuanwei, ZHANG Gangqiang, LU Zhengxiong, LI Linyue, HE Zhengwei, GONG Lingxiao, HUANG Junfeng

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Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity by Jin-Xia Yang, Jin-Xia Yang, Yue Liu, Yue Liu, Rong Huang, Hai-ying Wu, Ya-yun Wang, Su-ying Cao, Guo-ying Wang, Jian-Min Zhang, Zi-Sheng Ai, Hui-min Zhou

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A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

“…The Stacking model (XGBoost+GBDT+SVM) achieved superior performance (AUROC=0.865). Three independent risk factors were identified: fetal growth restriction (aOR=3.50, p = 0.047), serum cystatin C (aOR=11.27, p = 0.018), and C-reactive protein (aOR=1.37, p < 0.001). …”
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Predicting Diabetes Mellitus with Machine Learning Techniques by Heba Ahmed Jassim, Omar R. Kadhim, Zahraa Khduair Taha, Johnny Koh Siaw Paw, Yaw Chong Tak, Tiong Sieh Kiong

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