Engineering project management technology based on visual simulation module and particle swarm optimization by Hua Tian

“…The particle swarm multi-objective optimization algorithm performed well in reducing project cost prediction errors. …”
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Comparative study on inversion of the unsaturated hydraulic parameters using optimization and Bayesian estimation methods by KE Fengqiao, MAN Jun, ZENG Lingzao, WU Laosheng

“…However, this method is sensitive to the initial guess of parameters, and the obtained predictions occasionally deviate from the measurements. 2) The MCMC algorithm can provide state predictions which better fit measurements. …”
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Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection by J. Pérez-Aracil, C. Peláez-Rodríguez, Ronan McAdam, Antonello Squintu, Cosmin M. Marina, Eugenio Lorente-Ramos, Niklas Luther, Verónica Torralba, Enrico Scoccimarro, Leone Cavicchia, Matteo Giuliani, Eduardo Zorita, Felicitas Hansen, David Barriopedro, Ricardo García-Herrera, Pedro A. Gutiérrez, Jürg Luterbacher, Elena Xoplaki, Andrea Castelletti, S. Salcedo-Sanz

“…This research can potentially enhance our understanding of HW drivers and predictability.…”
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Research on lightweight malware classification method based on image domain by SUN Jingzhang, CHENG Yinan, ZOU Binghui, QIAO Tonghua, FU Sizheng, ZHANG Qi, CAO Chunjie

“…To address the high deployment costs and long prediction times associated with traditional malware classification methods, a lightweight malware visualization classification method was proposed. …”
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HR Management Big Data Mining Based on Computational Intelligence and Deep Learning by Genliang Zhao, Zhe Xue

“…To this end, this paper proposes an end-to-end competency-aware job requirement generation framework to automate the job requirement generation, and the prediction based on competency themes can realize the skill prediction in job requirements. …”
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Deep-Learning-Based Computer-Aided Grading of Cervical Spinal Stenosis from MR Images: Accuracy and Clinical Alignment by Zhiling Wang, Xinquan Chen, Bin Liu, Jinjin Hai, Kai Qiao, Zhen Yuan, Lianjun Yang, Bin Yan, Zhihai Su, Hai Lu

“…<b>Objective:</b> This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. …”
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Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM by Huayuan Ma, Xinghua Li, Qiang Liu, Xie Xingbo, Chong Ji, Changxiao Zhao

“…Taking eigenvector composed of CEE (components of energy entropy) as input, multiclassification SVM algorithm was used for training and prediction. Prediction accuracy was more than 80%. …”
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Constructing a tumor immune microenvironment-driven prognostic model in acute myeloid leukemia using bioinformatics and validation data by Amir Abbas Navidinia, Ali Keshavarz, Bentol Hoda Kuhestani Dehaghi, Reza Khayami, Najibe Karami, Vahid Amiri, Mehdi Allahbakhshian Farsani

“…ROC analysis demonstrated predictive accuracy (AUC: 63.38–68.5% for 1–5-year survival). …”
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Atrial fibrillation and chronic kidney disease: main clinical characteristics of patients in selected subjects of the Russian Federation by M. A. Druzhilov, T. Yu. Kuznetsova, O. Yu. Druzhilova, U. D. Arustamova, D. V. Gavrilov, A. V. Gusev

“…The information was taken from the Webiomed predictive analytics platform, including 80775 patients with AF (men, 42,5%, mean age, 70,0±14,3 years) who underwent outpatient and/or inpatient treatment in medical organizations in 6 Russian subjects in 2016-2019 with data on blood creatinine levels. …”
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Deep learning assisted analysis of biomarker changes in refractory neovascular AMD after switch to faricimab by Michael Hafner, Franziska Eckardt, Jakob Siedlecki, Benedikt Schworm, Tina R. Herold, Ben Asani, Siegfried G. Priglinger, Johannes B. Schiefelbein

“…Future research should focus on refining AI models to improve predictive accuracy and assessing long-term outcomes to further optimize disease management. …”
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Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification by Zhan Wang, Zhaokai Zhou, Shuai Yang, Zhengrui Li, Run Shi, Ruizhi Wang, Kui Liu, Xiaojuan Tang, Qi Li

“…Lastly, various machine learning algorithms were applied to identify novel potential targets of BLCA, following which their pro-tumorigenic effects were experimentally verified. …”
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Identification and validation of hub m7G-related genes and infiltrating immune cells in osteoarthritis based on integrated computational and bioinformatics analysis by Zhenhui Huo, Chongyi Fan, Kehan Li, Chenyue Xu, Yingzhen Niu, Fei Wang

“…Functional enrichment, drug target prediction, and target gene-related miRNA prediction were performed for these genes. …”
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Dynamic Workload Management System in the Public Sector: A Comparative Analysis by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou, Spyros Sioutas

“…Using a dataset encompassing public/private sector experience, educational history, and age, we evaluate the effectiveness of seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Bagged Decision Trees, and XGBoost in predicting employee capability and optimizing task allocation. …”
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Optimal Operation Strategy of Cascade Hydro-Wind-Solar-Pumped Storage Complementary System Considering Flexible Regulation Ability by XIA Jinlei, TANG Yijie, WANG Lingling, JIANG Chuanwen, GU Jiu

“…To overcome the limitations of traditional models such as low predictive accuracy and the subjective selection of long short-term memory (LSTM) hyperparameters, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of LSTM and the optimized LSTM model is then used to forecast the output of wind and solar power. …”
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A novel anthropometric method to accurately evaluate tissue deformation by Chongyang Ye, Xiaolu Li, Haiyan Song, Yu Shi, Ruixin Liang, Jun Zhang, Ka Po Lee, Zhaolong Chen, Beibei Zhou, Raymond Kai-Yu Tong, Kit-Lun Yick, Sun-Pui Ng, Joanne Yip

“…However, displacement from movement affects alignment so accurately measuring tissue deformation with different wear conditions becomes challenging.MethodsTo address this issue, an analytical model is constructed to predict tissue deformation by using the Boussinesq solution, which is based on the elastic theory and stress function method. …”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. The Coupled Simulated Annealing (CSA) algorithm was employed to optimize the hyperparameters of these models, enhancing their predictive performance. …”
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Association between serum hypertriglyceridemia and hematological indices: data mining approaches by Somayeh Ghiasi Hafezi, Amin Mansoori, Alireza Kooshki, Marzieh Hosseini, Sahar Ghoflchi, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…RF model showed to have higher accuracy in predicting the TG level in both males and females. Conclusion Our model assessed the association between serum TG with several hematological factors like RLR, RPR, and PHR. …”
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Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

“…In the neural network models, option prices were predicted using Python and its machine learning algorithms. …”
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Modeling of the Power Station Boiler Combustion Efficiency Considering Multiple Work Condition with Feature Selection by TANG Zhenhao, WU Xiaoyan, CAO Shengxian

“…It is difficult for power station boiler efficiency to measure precisely A datadriven modeling method is proposed to establish the boiler combustion efficiency model, according to the machine learning theories A classification and regression trees (CART) algorithm provides correlated variables which have significant relation with the boiler combustion efficiency by data analysis Then, a KNearest Neighbor (KNN) classifies the samples to distinguish the data from different work conditions Based on the classified data, a least square support vector machine (LSSVM) optimized by differential evolution (DE) algorithm is proposed to establish a datadriven model (DDMMF) The parameters of LSSVM are optimized dynamically by DE to improve the model accuracy Finally, the prediction model is corrected dynamically for further improvement of the prediction accuracy The experimental results based on actual production data illustrate that the proposed approach can predict the boiler combustion efficiency accurately, which meets the requirements of boiler control and optimization…”
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A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis by Seok-Woo Jang, Gye-Young Kim

“…The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. …”
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