Efficacy and predictive biomarkers of immunotherapy in Epstein-Barr virus-associated gastric cancer by Lin Shen, Xiaochen Zhao, Zhenghang Wang, Yuezong Bai, Feilong Zhao, Zhi Peng, Jinping Cai, Tong Xie, Shuang Tong, Xiaofan Wei

“…The molecular predictive biomarkers for ICB efficacy were identified to improve the prediction accuracy of ICB efficacy in 22 patients with EBVaGC.Results Compared with orthogonal assay (EBER-ISH) results, the NGS-based algorithm achieved high performance with a sensitivity of 95.7% (22/23) and a specificity of 100% (53/53). …”
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Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures by Min-Ying Li, Kang-Di Lu, Yu-Xing Dai, Guo-Qiang Zeng

“…The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.…”
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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side by Ali Sharida, Abdullah Berkay Bayindir, Sertac Bayhan, Haitham Abu-Rub

“…Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. …”
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Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model by Tianyue Hu, Zhiheng Bao, Baiyang Zhang, Xinnan Gao

“…A case study is conducted based on historical data (1997–2021) from the construction industry, and the research findings indicate that: (1) the GIOWA combination forecasting model effectively integrates the algorithmic strengths of SVR and LSTM, achieving an average prediction accuracy of 98.16%, which signifies a remarkable improvement over both individual models; (2) the carbon emissions in China’s construction industry will maintain a downward trend during the period 2022–2030, although the decline rate is expected to decrease gradually; (3) by 2030, a reduction of nearly 35% in carbon emissions is anticipated relative to the historical peak. …”
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Development and Analysis of a Methodology for Selecting Infrastructure Metrics for Predictive Incident Monitoring by Andrew Egorkin

“…This paper proposes a formalized two-stage metric selection procedure designed to improve the accuracy and efficiency of predictive monitoring: (1) a multicriteria correlation filter using Pearson coefficients (|r| > 0.60), Kendall’s τ (> 0.50), and Maximal Information Coefficient (MICe > 0.35) to eliminate redundant and non-linearly related features; (2) verification of causal relationships using the Granger test (lag = 5, p < 0.01), the PCMCI algorithm (FDR = 10%), and the Directed Information metric (DI > 0.1 bits/step) to identify true drivers of the target metric. …”
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Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs by Khaled Alshaketheep, Hind Al-Ahmed, Ahmad Mansour

“…The results underscore the importance of integrating human-centered design with algorithmic precision to translate predictive insights into actionable business value.…”
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AI based predictive acceptability model for effective vaccine delivery in healthcare systems by Muhammad Shuaib Qureshi, Muhammad Bilal Qureshi, Urooj Iqrar, Ali Raza, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Ayman Qahmash

“…A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. …”
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Predictive analytics in gamified education: A hybrid model for identifying at-risk students by Devanshu Sawarkar, Latika Pinjarkar, Pratham Agrawal, Devansh Motghare

“…This research proposes a hybrid predictive model designed to identify at-risk students within a gamified education environment accurately. …”
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Optimizing public health management with predictive analytics: leveraging the power of random forest by Hongman Wang, Yifan Song, Yifan Song, Hua Bi

“…This study employs a Random Forest Algorithm (RFA) to address this limitation and enhance the predictive modeling of community health outcomes. …”
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Traffic flow prediction based on improved deep extreme learning machine by Xiujuan Tian, Shuaihu Wu, Xue Xing, Huanying Liu, Heyao Gao, Chun Chen

“…Abstract A new hybrid prediction model is proposed for short-term traffic flow, which is based on Deep Extreme Learning Machine improved by Sparrow Search Algorithm (SSA-DELM). …”
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Predictive density profile control with discrete pellets, applied to integrated simulations of ITER by C.A. Orrico, M. van Berkel, T.O.S.J. Bosman, L. Ceelen, W.P.M.H. Heemels, F. Koechl, D. Krishnamoorthy

“…The model predictive control (MPC) scheme we deploy combines the offset-free method to correct prediction model inaccuracies and our novel modified penalty term homotopy algorithm for real-time MPC (PTH-MPC). …”
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Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model by Jonas Turnwald, David Valero, Pedro Forte, Katja Weiss, Elias Villiger, Mabliny Thuany, Volker Scheer, Matthias Wilhelm, Marilia Andrade, Ivan Cuk, Pantelis T. Nikolaidis, Beat Knechtle

“…Utilizing a dataset with ultramarathon races from 1863 to 2022, a machine learning model based on the XGBoost algorithm was developed to predict the race speed based on the aforementioned variables. …”
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Model Reduction and Chattering Mitigation in Multi-Model Predictive Control for Quadrotor UAVs by Ghulam E. Mustafa Abro, Sufyan Ali Memon, Saddaf Rubab, Kaznah Alshammari, Faheem Khan

“…This approach reduces the computational complexity and the number of necessary Linear Model Predictive Controllers (LMPCs). To improve performance, we implement an adaptive gain scheduling algorithm to mitigate the chattering effect frequently seen in multi-model approaches. …”
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Sialyltransferase-related genes as predictive factors for therapeutic response and prognosis in cervical cancer by Jia Shao, Can Zhang, Yaonan Tang, Aiqin He, Xiangyan Cheng

“…“MCPcounter” R package, ESTIMATE algorithm and TIMER online tools were used to depict the tumor immune microenvironment in CC. …”
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Machine-learning synergy in high-entropy alloys: A review by Sally Elkatatny, Walaa Abd-Elaziem, Tamer A. Sebaey, Moustafa A. Darwish, Atef Hamada

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Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction by Yanli Xu, Yalan Shi

“…Therefore, this paper proposes a communication coverage model based on mobile user route prediction and a UAV dynamic deployment algorithm (RUDD). …”
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Rolling Force Prediction of Hot Rolling Based on GA-MELM by Jingyi Liu, Xinxin Liu, Ba Tuan Le

“…In this paper, a rolling force prediction method based on genetic algorithm (GA), particle swarm optimization algorithm (PSO), and multiple hidden layer extreme learning machine (MELM) is proposed, namely, PSO-GA-MELM algorithm, which takes MELM as the basic model for rolling force prediction. …”
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Load prediction based elastic resource scheduling strategy in Flink by Ziyang LI, Jiong YU, Yuefei WANG, Chen BIAN, Yonglin PU, Yitian ZHANG, Yu LIU

“…In order to solve the problem that the load of big data stream computing platform fluctuates drastically while the cluster was suffering from the performance bottleneck due to the shortage of computing resources,the load prediction based elastic resource scheduling strategy in Flink (LPERS-Flink) was proposed.Firstly,the load prediction model was set up as the foundation to propose the load prediction algorithm and predict the variation tendency of the processing load.Secondly,the resource judgment model was set up to identify the performance bottleneck and resource redundancy of the cluster while the resource scheduling algorithm was proposed to draw up the resource rescheduling plan.Finally,the online load migration algorithm was proposed to execute the resource rescheduling plan and migrate processing load among nodes efficiently.The experimental results show that the strategy provides better performance promotion in the application with drastically fluctuating processing load.The scale and resource configuration of the cluster responded to the variation of processing load in time and the communication overhead of the load migration was reduced effectively.…”
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Protein homodimers structure prediction based on deep neural network by A. Y. Hadarovich, A. A. Kalinouski, A. V. Tuzikov

“…Recently Deep Neural Networks allowed to predict structures of individual proteins greatly prevailing in accuracy over other algorithmic approaches. …”
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Road Accident Prediction Model for the Roads of National Significance of Lithuania by Vilma Jasiūnienė, Donatas Čygas

“…Chapter 4 describes the testing and analysis of software intended for the implementation of accident prediction algorithm.…”
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