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Intelligent Systems Conference

作者： Ampofo, Isaac Atta Senior Abdulai, Mobarik Karikari, Maxwell Twumasi, Lydia Ampofo, Isaac Atta Junior Ampofo, Beatrice Univ Liverpool Brownlow Hill Liverpool L69 7ZX Merseyside England Univ Educ Winneba Ghana Kwame Nkrumah Univ Sci & Technol Kumasi Ghana Aketen Appaih Menka Univ Skills Training & Entrep 1277 IPT Rd Kumasi Ghana

ISBN: (纸本)9783031664274;9783031664281

The incidence of cancer is rising dramatically on a global scale. Many people receive a late-stage diagnosis despite though cancer is preventable and well-treated in its early stages. Furthermore, cancer frequently returns following protracted treatment. Therefore, it is essential to anticipate cancer recurrence to proactively pursue specific treatments. The study review literature done on machine learning algorithm for cancer prediction using bibliometric review and Vosviewer. The studies that were taken into consideration for this research were obtained from the Web of Science database. A range of search terms, such as pertinent keywords (machine learning, cancer detection, cancer prediction), were utilized to examine the title, keywords, and abstract of articles inside the database covering the years 2014 through 2023. 1914 documents were used for the study. Articles published in IEEE Access have a greater impact, but Scientific Reports had received more citations than articles in other journals-roughly 1018 of all the citations for the chosen documents come from articles published in this journal. Research on International evaluation of an AI system for screening of breast cancer had the highest citations 1059. India has the highest documents of 430 representing 22.419%. For citations, the USA has the highest of 122,072. Egyptian Knowledge Bank EKB has the highest documents of 61 representing 3.180%. National Natural Science Foundation of China (NSFC) has the highest documents of 131 representing 6.830%. machine learning must be incorporated into clinical practice and trials due to the changing nature of cancer and how it is treated over time.

关键词： Cancer Bibliometric review Prediction VoS viewer machine learning algorithm

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Nonlinear deflection and thermal post-buckling analysis of sector annular poroelastic composite nanodisks using mathematical simulation and machine learning algorithm

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THIN-WALLED STRUCTURES 2025年 207卷

作者： Xu, Zhijun Han, Yang El-Meligy, Mohammed El Hindi, Khalil Safarpour, Hamed Henan Univ Technol Sch Civil Engn Zhengzhou 450001 Peoples R China Jadara Univ Jadara Univ Res Ctr PO Box 733 Irbid Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11543 Saudi Arabia

Poroelastic nanodisks offer mechanical engineers enhanced control over material properties, enabling precise tuning of mechanical responses for advanced applications in sensors, actuators, and nano-mechanical systems. This study presents a comprehensive analysis of thermally-affected multi-directional functionally graded sector annular nanodisks, focusing on their thermal-post buckling and nonlinear deflection behaviors. Utilizing a refined quasi-3D logarithmic theory (RQLT), the study incorporates the effects of Von-Karman nonlinearity to accurately capture the large deflection responses of these advanced nanostructures under thermal loading. The material properties of the nanodisks are graded in multiple directions, enhancing their ability to withstand thermal stresses and maintain structural integrity. To solve the complex governing equations derived from the RQLT, a nonlinear discrete-singular convolution (DSC) solution procedure is employed. This novel numerical technique allows for precise computation of the nonlinear deformation and stability characteristics of the nanodisks, providing insights into their behavior under various thermal conditions. The nonlinear DSC method's ability to handle singularities and discontinuities makes it particularly suitable for this type of advanced analysis. After obtaining the mathematics simulation data, a machine learning algorithm is used to test, train, and validate the results for future analysis of the mentioned problem with low computational cost. The results demonstrate the critical influence of thermal gradients and material gradation on the post-buckling and nonlinear deflection responses of sector annular nanodisks. The interplay between thermal effects and material properties highlights the necessity for incorporating multi-directional functionally graded materials in the design of high-performance nanostructures. This study's findings are pivotal for the development of next-generation nanodisks used in thermal en

关键词： Poroelastic thermal post-buckling TD-MD-FGM RQLT Sector nanodisk machine learning algorithm

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Transient bending analysis of the graphene nanoplatelets reinforced sandwich concrete building structure validated by machine learning algorithm

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2025年第3期32卷 260-281页

作者： Zhou, Xia Chen, Yuyuan Abbas, Mohamed Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou Sichuan Peoples R China Luzhou Fuyu Engn Planning Co Ltd Luzhou Sichuan Peoples R China King Khalid Univ Coll Engn Elect Engn Dept Abha Saudi Arabia

This paper demonstrates a thorough examination of the bending behavior of sandwich concrete building structures that are reinforced with graphene nanoplatelets (GPLs). The analysis is confirmed using a machine learning technique. Sandwich structures have notable benefits in terms of strength, longevity, and thermal insulation, making them well-suited for many building applications. Integrating GPLs into the concrete matrix improves the mechanical characteristics and performance of these structures, especially in terms of bending behavior. This study utilizes a machine learning technique to verify the characterization of the temporary bending behavior of a concrete building structure reinforced with graphene nanoplatelets. The approach utilizes a dataset consisting of simulated bending data to create a prediction model that can reliably estimate the temporary bending response of the reinforced structure under different loading situations. The machine learning algorithm's effectiveness and dependability in optimizing the design and performance of graphene nanoplatelets reinforced sandwich concrete building structures are demonstrated through validation against simulated results. This provides engineers and designers with a powerful tool. This study enhances the comprehension and use of machine learning approaches in analyzing and designing sophisticated structural materials and systems.

关键词： Transient bending GPLs concrete building structure machine learning algorithm quasi-3D higher-order plate theory

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On the measurement of the nonlinear dynamics of sandwich sector plate surrounded by the auxetic concrete foundation: Introducing a machine learning algorithm for nonlinear problems

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MEASUREMENT 2025年 241卷

作者： Li, Zhonghong Yan, Gongxing Fouly, Ahmed Albahkali, Thamer Chongqing Chem Ind Vocat Coll Sch Architectural Engn Chongqing 401228 Peoples R China Luzhou Vocat & Tech Coll Sch Intelligent Construct Luzhou 646000 Sichuan Peoples R China Luzhou Key Lab Intelligent Construct & Low Carbon Luzhou 646000 Sichuan Peoples R China King Saud Univ Coll Engn Dept Mech Engn POB 800 Riyadh 11421 Saudi Arabia

The measurement of the nonlinear dynamics of sandwich rotary sector plates is crucial for measurement engineers as it enables the precise analysis and understanding of the behavior of advanced composite structures under dynamic conditions. These measurements help in identifying and mitigating potential issues related to vibration, stability, and fatigue, which are critical in industries like aerospace, automotive, and civil engineering. Accurate data on nonlinear dynamics aids in the optimization of design, enhancing performance, durability, and safety. Furthermore, it supports the development of predictive maintenance strategies, reducing downtime and operational costs, and contributing to the advancement of engineering materials and techniques. So, in the current work, for the first time, nonlinear dynamics of sandwich rotary sector plate via a mathematical modeling and machine learning algorithm is presented. First, due to a lack of information on the nonlinear dynamics of sandwich rotary sector plates, a dataset for training, testing, and validating the machine learning algorithm is presented. For this purpose, using Hamilton's principle, Von-Karman nonlinearity, and first-order shear deformation theory (FSDT), the nonlinear governing equations are obtained. Also, due to increasing stiffness and stability, an auxetic concrete foundation covers the sandwich structure. After that using the two-dimensional generalized differential quadrature method (GDQM) and Newmark's time integration method (NTIM), the nonlinear equations are solved. This research provides valuable insights for engineers in designing advanced composite structures with improved dynamic properties. The results also contribute to the broader understanding of nonlinear dynamic interactions in complex material systems, paving the way for innovative applications in various engineering fields.

关键词： Nonlinear dynamics Sandwich rotary sector plate Von-Karman nonlinearity Auxetic concrete foundation machine learning algorithm

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A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

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SEPARATION SCIENCE AND TECHNOLOGY 2025年第1期60卷 75-93页

作者： Vijayalakshmi, Muniyandi Kunjiappan, Selvaraj Ravindranath, Akkala Kannan, Suthendran Sundar, Krishnan Pandian, Sureshbabu Ram Kumar Kalasalingam Acad Res & Educ Dept Biotechnol Krishnankoil 626126 Tamil Nadu India Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Krishnankoil India Kalasalingam Acad Res & Educ Dept Informat Technol Krishnankoil India

Ultrasound-assisted solvent extraction (UASE) was appslied to extract phytoconstituents from Semecarpus anacardium L. The effective extraction parameters were optimized using the Response Surface Methodology (RSM) with Central Composite Face Centered design (CCFC), and the optimization parameters were validated through Adaptive Neuro-Fuzzy Inference System (ANFIS) and machine learning (ML) algorithm models. The four independent parameters, i.e. ethanol concentration (X-1: 62-72% v/v), temperature (X-2: 38-48 degrees C), ultrasonic-exposure time (X-3: 12-24 min), and particle size (X-4: 2-5 mm) were optimized. The maximum yield of phytocompounds was achieved at X-1 = 67%, X-2 = 43 degrees C, X-3 = 18 min, and X-4 = 3.5 mm. The optimized yield of total phenolic, flavonoid, and antioxidant activity was considered when selecting process parameters. In this scenario, the optimized yields of total polyphenolic content (y(1) = 619.25 mg gallic acid equivalent (GAE)/g), total flavonoid content (y(2) = 151.24 mg Quercetin equivalents (QE)/g), free radical scavenging abilities (%DPPH*sc (y(3) = 66.41%), and %ABTS*sc (y(4) = 56.94%). The LC-MS peaks identify the presence of semecarpetin, butein, and amentoflavone, whereas GS-MS peaks represent seventeen gaseous components. Furthermore, the bioactive-rich optimized extract was nontoxic and supported the growth of macrophage- and epithelial cells in vitro. Conclusively, optimizing the UASE parameters enhanced the yield of bioactive compounds of S. anacardium L. [GRAPHICS]

关键词： Semecarpus anacardium Linn. optimization response surface methodology ANFIS machine learning algorithm ultrasound-assisted solvent extraction

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Estimating epidemic trajectories of SARS-CoV-2 and influenza A virus based on wastewater monitoring and a novel machine learning algorithm

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 951卷 175830页

作者： Fu, Songzhe Zhang, Yixiang Li, Yinghui Zhang, Ziqiang Du, Chen Wang, Rui Peng, Yuejing Yue, Zhijiao Xu, Zheng Hu, Qinghua Northwest Univ Sch Med Key Lab Resource Biol & Biotechnol Western China Minist Educ Xian 710069 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci SIBS CAS Ctr Excellence Mol Plant Sci Shanghai Peoples R China Shenzhen Ctr Dis Control & Prevent Shenzhen Peoples R China Dalian Ocean Univ Coll Marine Sci & Environm Dalian 116023 Peoples R China Southern Univ Sci & Technol Yantian Hosp Shenzhen 518081 Peoples R China Univ Chinese Acad Sci Shanghai Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Inst Biomed & Biotechnol Shenzhen 518055 Guangdong Peoples R China

The COVID-19 pandemic has altered the circulation of non-SARS-CoV-2 respiratory viruses. In this study, we carried out wastewater surveillance of SARS-CoV-2 and influenza A virus (IAV) in three key port cities in China through real-time quantitative PCR (RT-qPCR). Next, a novel machine learning algorithm (MLA) based on Gaussian model and random forest model was used to predict the epidemic trajectories of SARS-CoV-2 and IAV. The results showed that from February 2023 to January 2024, three port cities experienced two waves of SARSCoV-2 infection, which peaked in late-May and late-August 2023, respectively. Two waves of IAV were observed in the spring and winter of 2023, respectively with considerable variations in terms of onset/offset date and duration. Furthermore, we employed MLA to extract the key features of epidemic trajectories of SARS-CoV-2 and IAV from February 3rd, to October 15th, 2023, and thereby predicted the epidemic trends of SARS-CoV-2 and IAV from October 16th, 2023 to April 22nd, 2024, which showed high consistency with the observed values. These collective findings offer an important understanding of SARS-CoV-2 and IAV epidemics, suggesting that wastewater surveillance together with MLA emerges as a powerful tool for risk assessment of respiratory viral diseases and improving public health preparedness.

关键词： SARS-CoV-2 Influenza A virus machine learning algorithm Epidemic trajectories Prediction

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Co-firing characteristic prediction of solid waste and coal for supercritical CO2 power cycle based on CFD simulation and machine learning algorithm

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WASTE MANAGEMENT 2024年 190卷 74-87页

作者： Cui, Ying Wang, Xinwang Jiang, Shujun Gu, Xiaoyong Yao, Zhongran Wuxi Inst Technol Sch Automot & Transportat Wuxi 214000 Jiangsu Provinc Peoples R China Southeast Univ Sch Energy & Environm Key Lab Energy Convers & Proc Measurement & Contro Minist Educ Xuanwu Dist Nanjing 210096 Jiangsu Peoples R China Monash Univ Dept Chem Engn ARC Res Hub Computat Particle Technol Clayton Vic 3800 Australia Wuxi Inst Technol Sch Integrated Circuits Wuxi 214000 Jiangsu Provinc Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210018 Peoples R China

The co-firing technology of combustible solid waste (CSW) and coal in the supercritical CO2 (S-CO2) circulating fluidized bed (CFB) can effectively deal with domestic waste, promote social and environmental benefits, improve the coal conversion rate, and reduce pollutant emission. This study focuses on the co-firing characteristics of CSW and coal under S-CO2 power cycle, and simulations are conducted by employing Multiphase Particle-in-cell (MP-PIC) method integrated with the comprehensive chemical reaction models in a 300 MW S-CO2 CFB boiler. Effects of operating parameters including fuel mixture proportion and first stage stoichiometry on the gas emission characteristics are further analyzed. Based on training and testing database based on the simulation results, a novel Improved Whale Optimization algorithm and Bi-dictionary Long Short-Term Memory (IWOA-BiLSTM) algorithm model is established to predict CFB temperature, NOx emission concentration, and SO2 emission concentration, respectively. Results show that CO and SO2 decrease with the coal mass ratio of the fuel mixture increasing, while NOx increases. With the increase of first stage stoichiometry, CO increases, NOx declines, and the change of SO2 is not obvious. Compared with two other basic algorithm models, the prediction error of the proposed algorithm model for the three targets is minimal with the average relative error of 0.032 %, 0.231 %, and 0.157 %, respectively, which can meet the prediction requirements with acceptable accuracy.

关键词： Supercirtical CO (2 )power cycle Combustible solid waste MP-PIC simulation machine learning algorithm Co-firing characteristic prediction

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Construction and application of intelligent human resource management system based on machine learning algorithm

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Yang, Ping Li, Jie Sichuan Univ Sci & Engn Sch Econ Zigong 643000 Sichuan Peoples R China Sichuan Univ Sci & Engn Sch Foreign Languages Zigong Peoples R China

Traditional human resource management systems face the problems of low efficiency and insufficient accuracy in data processing and decision support, while intelligent human resource management systems based on machine learning algorithms can significantly improve management efficiency and decision-making accuracy through advanced data analysis and automated decision-making. By analyzing the training duration and response efficiency of various algorithms, it was observed that the machine learning algorithm's training time is approximately 10.3 times longer than that of the non-optimized machine learning algorithm and 3.6 times longer than that of the intelligent management system, indicating strong time performance. Additionally, the response time of the classification model after applying feature selection is slightly improved compared to the original machine learning algorithm, measuring 225% of the non-optimized version and 98% of the response time of the intelligent HR management system. The key technologies involved in system development include neural network models and classification analysis techniques, and the implementation of these algorithms in the intelligent HR management system provides notable benefits.

关键词： machine learning algorithm intelligent management system human resource management response speed

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Far away or close, marine fisheries spatial coagglomeration and fisheries' economic benefits in coastal cities of China-Based on machine learning algorithm and fsQCA method

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OCEAN & COASTAL MANAGEMENT 2025年 267卷

作者： Qin, Man Wang, Jing Ocean Univ China Sch Management Qingdao 266100 Peoples R China Ocean Univ China Inst Marine Dev OUC Qingdao 266100 Peoples R China

As the traditional cornerstone of the ocean's primary industries, the rationality of the spatial layout of marine fisheries holds immense significance for the construction of a modern marine industry system and the advancement of the fisheries' economic benefits. Based on this, the paper focuses on marine fisheries, with marine engineering equipment manufacturing and marine shipbuilding serving as auxiliary sectors. By leveraging an enhanced Sinkhorn algorithm to optimize the Wasserstein distance, it calculates coagglomeration indices through hypothesis testing and Monte Carlo simulations. FsQCA is then employed to assess whether coagglomeration enhances fisheries' economic benefits. Key findings include: (1) Marine fisheries exhibit lower coagglomeration with marine engineering equipment manufacturing and marine shipbuilding than between the latter two due to differing development models and needs. (2) Urban coagglomeration indices vary by region, influenced by policies, resources, and competition, with northern cities having lower indices in 2015 but higher in 2022 compared to the south. (3) Coagglomeration is not pivotal for high fisheries' economic benefits;instead, non-coagglomeration emerged as a key factor in 2022. Therefore, this study puts forward the following suggestions: Optimize the allocation of resources, promote regional coordinated development;Strengthen policy support to alleviate the current situation of industry competition;Optimize the spatial layout and promote highquality coagglomeration of industry. The study aims to provide decision-making support for optimizing marine fisheries layout, promoting high-quality fisheries' economic development, and building a modern marine industry system.

关键词： Coastal cities machine learning algorithm Marine fisheries Industrial coagglomeration FsQCA method Fisheries' economic benefits

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Application of machine learning algorithm and Carrera unified formulation in thermal buckling analysis of a functionally graded graphene origami enabled auxetic metamaterial sandwich plate with an auxetic concrete foundation

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年

作者： Lu, Qiang Yang, Qing Atif, M. El-Meligy, Mohammed Chongqing Vocat Inst Engn Sch Civil Engn Chongqing 402260 Peoples R China King Saud Univ Coll Sci Dept Phys & Astron Riyadh Saudi Arabia Middle East Univ MEU Res Unit Amman Jordan Jadara Univ Res Ctr Irbid Jordan

This study presents a comprehensive thermal buckling analysis of sandwich plates composed of functionally graded graphene origami-enabled auxetic metamaterial (FG-GOEAM) face sheets on an auxetic concrete foundation, using Carrera's unified formulation (CUF) as the theoretical framework. FG-GOEAM materials are emerging as advanced composites, combining exceptional mechanical resilience, tunable auxetic behavior, and high thermal stability, making them suitable for extreme environments. By employing CUF, a powerful and adaptable modeling approach, this work accurately captures the complex mechanical interactions within the FG-GOEAM sandwich structure under thermal loads, incorporating both material gradation and auxetic properties. To further enhance the precision and efficiency of this thermal buckling analysis, a deep neural network (DNN) is developed as a machine learning algorithm to predict critical temperature differences, based on a dataset generated through mathematics simulation. The DNN model demonstrates excellent predictive capability, validated by close alignment between its estimates and CUF results, thus reducing computational costs while maintaining high accuracy. Parametric studies are conducted to assess the effects of material gradation, aspect ratios, and foundation properties on thermal buckling performance. The results highlight the superior thermal stability of FG-GOEAM structures and the potential of DNNs to serve as reliable, computationally efficient tools for advanced structural analysis. This study provides a novel, integrated framework for high-fidelity thermal buckling prediction in complex auxetic composites, paving the way for broader applications in engineering fields requiring lightweight, thermally stable structures.

关键词： Carrera unified formulation thermal buckling FG-GOEAM face sheets auxetic concrete foundation machine learning algorithm

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