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Conference on IT in Business, Industry and Government (CSIBIG)

作者： Singh, Jainendra Maharaja Surajmal Inst Dept Comp Sci New Delhi India

ISBN: (纸本)9781479930630;9781479930647

with great power of data comes great responsibility! A big data initiative should not only focus on the volume, velocity or variety of the data, but also on the best way to protect it. Security is usually an afterthought, but Elemental provides the right technology framework to get you the deep visibility and multilayer security any big data project requires. Multilevel protection of your data processing nodes means implementing security controls at the application, operating system and network level while keeping a bird's eye on the entire system using actionable intelligence to deter any malicious activity, emerging threats and vulnerabilities. Advances in machine learning (ML) provide new challenges and solutions to the security problems encountered in applications, technologies and theories. machine learning (ML) techniques have found widespread applications and implementations in security issues. Many ML techniques, approaches, algorithms, methods and tools are extensively used by security experts and researchers to achieve better results and to design robust systems.

关键词： Big Data Analytic machine learning algorithm Security Challenges Mahout Hadoop

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A novel machine learning-based proposal for early prediction of endometriosis disease

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 271卷

作者： Enamorado-Diaz, Elena Morales-Trujillo, Leticia Garcia-Garcia, Julian-Alberto Marcos, Ana T. Navarro-Pando, Jose Escalona-Cuaresma, Maria-Jose Univ Seville Grp ES3 Engn & Sci Software Syst Grp Ave Reina Mercedes Seville 41012 Spain G7innovat Co Genet Unit Calle Radio Sevilla Seville 41001 Spain Inst Estudio Biol Reprod Humana INEBIR Catedra Reprod & Genet Humana Seville Spain Univ Europea Atlantico UNEATLANTICO Santander Spain Fdn Univ Iberoamericana FUNIBER Seville Spain San Juan de Dios Hosp Seville Spain

Pyrethroids, which are widely utilized in agriculture, household products, and public health for their potent insecticidal properties, elicit significant concerns regarding their potential endocrine-disrupting effects. However, previous studies have yielded inconsistent data, largely due to the absence of a standardized screening system. To address this limitation, the present study introduces an Integrated Approach to Testing and Assessment (IATA) to evaluate the endocrine-disrupting potential of pyrethroids, aligned with the Adverse Outcome Pathway (AOP) framework. Employing this IATA-based methodology, the endocrine-disrupting effects of five pyrethroids, allethrin, phenothrin, deltamethrin, cypermethrin, and lambda-cyhalothrin-were investigated, with a focus on hormone levels of 17(3-estradiol (E2) and testosterone (T). Enzyme-linked immunosorbent assays (ELISA) and receptor transactivation assays were utilized to assess the direct receptor interactions and alternative disruption mechanisms. The results demonstrated that lambda-cyhalothrin and phenothrin significantly elevated E2 levels, while all tested compounds substantially reduced T levels. Notably, transactivation assays indicatedBackground: Endometriosis is one of the causes of female infertility, with some studies estimating its prevalence at around 10 % of reproductive-age women worldwide and between 30 and 50 % in symptomatic women. However, its diagnosis is complex and often delayed, highlighting the need for more accessible and accurate diagnostic methods. The difficulty lies in its diverse etiology and the variability of symptoms among those affected. Methods: This study proposes a predictive model based on supervised machine learning for the early identification of endometriosis, providing support for decision-making by healthcare professionals. For this purpose, an anonymised dataset of 5,143 female patients diagnosed with endometriosis at the private fertility clinic Inebir was used. The model in

关键词： Endometriosis Predictive model machine learning algorithm Clinical Decision Support System

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Identification of biomarkers for risk assessment of arsenicosis based on untargeted metabolomics and machine learning algorithms

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SCIENCE OF THE TOTAL ENVIRONMENT 2023年第1期870卷 161861页

作者： Zhang, Jin Ma, Lu Li, Boyan Chen, Xiong Wang, Dapeng Zhang, Aihua Guizhou Med Univ Sch Publ Hlth Key Lab Environm Pollut Monitoring & Dis Control Minist Educ Guiyang 550025 Peoples R China

Background: Long-term exposure to inorganic arsenic may lead to arsenicosis. There are, however, currently no vali-dated metabolic biomarkers used for the identification of arsenicosis risk. This study aims to identify metabolites as-sociated with arsenicosis and establish prediction models for risk assessment based on untargeted metabolomics and machine learning ***: In total, 105 coal-borne arsenicosis patients, with 35 subjects in each of the mild, moderate, and severe sub-groups according to their symptom severity, and 60 healthy residents were enrolled from Guizhou, China. Ultra-high performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) was utilized to acquire the plasma metabolic profiles of the studied subjects. Statistical analysis was used to identify disease-associated metabolites. Ma-chine learning algorithms and the identified metabolic biomarkers were resorted to assess the arsenicosis risk. Results: A total of 143 metabolic biomarkers, with organic acids being the majority, were identified to be closely asso-ciated with arsenicosis, and the most involved pathway was glycine, serine, and threonine metabolism. Comparative analysis of metabolites in arsenicosis patients with different symptom severity revealed 422 altered molecules, where disrupted metabolism of beta-alanine and arginine demonstrated the most significance. For risk assessment, the model established by a single biomarker (L-carnosine) could undoubtedly discriminate arsenicosis patients from the healthy. For classifying arsenicosis patients with different severity, the model established using 52 metabolites and linear discriminate analysis (LDA) algorithm yielded an accuracy of 0.970-0.979 on calibration set (n = 132) and 0.818-0.848 on validation set (n = 33).Conclusion: Altered metabolites and disrupted pathways are prevalent in arsenicosis patients;The disrupted metabo-lism of one carbon and dysfunction of antioxidant defense system may partially be caus

关键词： Arsenicosis Metabolomics Biomarker Bioinformatics machine learning algorithm

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Parameter prediction optimization of data center's heat dissipation system using machine learning algorithms

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APPLIED THERMAL ENGINEERING 2023年第1期232卷

作者： Chen, Xianzhong Tu, Rang Yang, Xu Univ Sci & Technol Beijing Sch Civil & Resources Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

In this study, an optimization control method based on knowledge and operational data was investigated for a centralized water-cooling heat dissipation system in a data center. First, a numerical model describing the heat transfer and thermodynamic processes of the heat dissipation system was established, and five thermal characteristic parameters were extracted to identify the system. The thermal characteristic parameters, used as inputs to the model, were identified in real time using the mechanism formula from the operational data. Subsequently, the operational data of the heat dissipation system in a real data center were used to validate the numerical model. The model accuracy was within 15 %. The water flow rate of the actual system was optimized, showing that the average PUE of four typical cases under the optimized conditions were 1.12, 1.25, 1.13 and 1.3, respectively, with an improving rate between 4.3 % and 12.4 %. Next, considering the reasonable value ranges of the five thermal characteristic parameters and four direct input parameters, 62,208 groups of working conditions were designed, and the optimized water flow rate and the corresponding COP and PUE for each working condition were obtained using the numerical model. Finally, a prediction model that combined the heat transfer mechanisms was established based on an optimized dataset. machine learning algorithms were applied to establish neural networks between the nine inputs and three optimized operating parameters. Based on the prediction model and thermal characteristic parameter identification module, software was developed to suggest optimized water flow rates and predict the PUE and COP in real time from the operational data.

关键词： Data center Heat dissipation system Numerical model Thermal characteristic parameter machine learning algorithm

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Fault prediction algorithm for offshore wind energy conversion system based on machine learning

Fault prediction algorithm for offshore wind energy conversi...

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International Conference on High Performance Big Data and Intelligent Systems (HPBD and IS)

作者： Yang, Jing Li, Junda Shangdong JiaoTong Univ Weihai Weihai 264209 Shandong Peoples R China Shanxi Agr Univ Jinzhong Jinzhong 030800 Shanxi Peoples R China

ISBN: (纸本)9781665413275

In view of the low accuracy and long time sum di fference of fault prediction methods in offshore wind power con version system, a fault prediction algorithm for offshore wind p ower conversion system is designed based on SVM method in m achine learning. The fault data of offshore wind power conversi on system are preprocessed by principal component analysis, a nd the fault characteristics of offshore wind power conversion s ystem are extracted by leftflush method. Based on the principle of SVM, the fault prediction LSSVM model of offshore wind p ower conversion system is constructed. The test results show th at this method has good fault prediction effect for offshore win d power conversion system, and can effectively improve the acc uracy of fault prediction and shorten the time of fault predictio n.

关键词： machine learning algorithm principal componen t analysis method SVM offshore wind energy conversion system fault prediction

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machine learning-based improved plastic stress distribution method for strength prediction of CFST members

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STRUCTURES 2025年 74卷

作者： Zhou, Zheng Yang, Lifu Zhou, Xuhong Shi, Caijun Hunan Univ Coll Civil Engn Changsha 410082 Peoples R China

The plastic stress distribution (PSD) method is used as the primary method in typical design codes for calculating the strength of concrete-filled steel tubular (CFST) members. However, the PSD method can only be applied to members with limited ranges of material strengths and cross-sectional slenderness ratios, and the calculation methods are lacking or relatively complicated for members beyond the limitations. In this work, an improved PSD method is proposed for the strength prediction of CFST members with different cross-sectional slenderness ratios and material strengths, by using the concrete strength factor fi1 to consider the possible compressive strength increase or reduction of the concrete due to the confinement or local buckling from the steel tube and the steel strength factor fi2 to consider the potential compressive strength reduction of the steel tube due to local buckling. The direct inverse analysis using the improved particle swarm optimization algorithm is then conducted to identify the strength factors fi1 and fi2 based on the extensive experimental results. machine learning algorithms are finally explored to train the rules between the strength factors (fi1 and fi2) and the member properties including the diameter or width and depth, wall thickness, and material strength, and the predicted results are in good agreement with the experimental results. These results further specify the accurate plastic stress distribution, and provide a strong justification for possible changes to the design codes to expand the use of the PSD method to all types of CFST members.

关键词： Concrete-filled steel tubes Plastic stress distribution method Direct inverse analysis Particle swarm optimization algorithm machine learning algorithm

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machine learning-enhanced combustion modeling for predicting laminar burning velocity of ammonia-hydrogen mixtures using improved reaction mechanisms

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ENERGY 2025年 320卷

作者： Rao, Anas Li, Wei Abbasi, Muhammad Salman Shahid, Muhammad Ihsan Farhan, Muhammad Zulfiqar, Sana Chen, Tianhao Ma, Fanhua Li, Xin Tsinghua Univ Sch Vehicle & Mobil Natl Key Lab Intelligent Green Vehicles & Transpor Beijing 100084 Peoples R China Univ Engn & Technol Dept Mech Engn Lahore 54890 Pakistan Xijing Univ Engn Res Ctr Hydrogen Energy Equipment & Safety De Xian 710123 Shaanxi Peoples R China Xijing Univ Sch Mech Engn Xian 710123 Shaanxi Peoples R China Univ Management & Technol C-2 Block Phase 1 Johar Town Lahore 54770 Punjab Pakistan

The ammonia/hydrogen mixture is a promising zero-carbon fuel for internal combustion engines, with optimal efficiency in spark ignition engines at equivalence ratios of 0.6-1 and hydrogen fractions of 0.4-0.6. However, existing reaction mechanisms lose accuracy in this range, limiting combustion modeling. To address this, prediction accuracy is improved using refined reaction kinetics and machine learning algorithms. GRI Mech3.0 is refined by enhancing H/O, N2O, HNO, NH, and NH2 mechanisms, forming Model I for pure ammonia and Model II for lean burn ammonia-hydrogen mixtures. The simulated laminar burning speed of these models is compared with literature and seven other mechanisms. Combustion analysis includes sensitivity analysis of coefficients, nitric oxide emission rates, sub-mechanisms, and reaction sensitivities to assess their impact, with machine learning algorithms to improve accuracy. Model II achieves the highest accuracy under stoichiometric (RMSE = 1.4590 cm/s) and lean burn conditions (RMSE = 1.3701 cm/s). As different mechanisms suit various conditions, machine learning algorithms further enhance prediction accuracy. The support vector machine with particle swarm optimization improves computational accuracy by 2.9745 times over reaction kinetics, demonstrating its effectiveness. This study develops refined reaction mechanisms and machine learning models for practical applications in ammonia-hydrogen fueled SI engines.

关键词： Ammonia/hydrogen fuel Laminar burning speed Reaction kinetics machine learning algorithm PSO-SVM

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Rapid and sensitive detection of pharmaceutical pollutants in aquaculture by aluminum foil substrate based SERS method combined with deep learning algorithm

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ANALYTICA CHIMICA ACTA 2025年 1351卷 343920页

作者： Huang, Zixi Lei, Yongqian Liang, Weixin Cai, Yili Guo, Pengran Sun, Jian Guangdong Univ Technol Sch Environm Sci & Engn Guangzhou Key Lab Environm Catalysis & Pollut Cont Guangdong Key Lab Environm Catalysis & Hlth Risk C Guangzhou 510006 Peoples R China Guangdong Acad Sci Inst Anal China Natl Analyt Ctr Guangzhou Guangdong Prov Key Lab Chem Measurement & Emergenc Guangzhou 510070 Peoples R China Guangdong Acad Sci Inst Anal Guangdong Prov Engn Res Ctr Online Monitoring Wate China Natl Analyt Ctr Guangzhou 510070 Peoples R China

Background: Pharmaceutical residual such as antibiotics and disinfectants in aquaculture wastewater have significant potential risks for environment and human health. Surface enhanced Raman spectroscopy (SERS) has been widely used for the detection of pharmaceuticals due to its high sensitivity, low cost, and rapidity. However, it is remain a challenge for high-sensitivity SERS detection and accurate identification of complex pollutants. Results: Hence, in this work, we developed an aluminum foil (AlF) based SERS detection substrate and established a multilayer perceptron (MLP) deep learning model for the rapid identification of antibiotic components in a mixture. The detection method demonstrated exceptional performance, achieving a high SERS enhancement factor of 4.2 x 10(5) and excellent sensitivity for trace amounts of fleroxacin (2.7 x 10(-8) mol/L), levofloxacin (1.95 x 10(-8) mol/L), and pefloxacin (6.9 x 10(-8) mol/L),sulfadiazine, methylene blue, and malachite green at a concentration of 1 x 10(-8) mol/L can all be detected, the concentrations of the six target compounds and their Raman intensities exhibit a good linear relationship. Moreover, the AlF SERS substrate can be prepared rapidly without adding organic reagents, and it exhibited good reproducibility, with RSD<9.6 %. Additionally, the algorithm model can accurately identify the contaminants mixture of sulfadiazine, methylene blue, and malachite green with a recognition accuracy of 97.8 %, an F1-score of 98.2 %, and a 5-fold cross validation score of 97.4 %, the interpretation analysis using Shapley Additive Explanations (SHAP) reveals that MLP model can specifically concentrate on the distribution of characteristic peaks. Significance: The experimental results indicated that the MLP model demonstrated strong performance and good robustness in complex matrices. This research provides a promising detection and identification method for the antibiotics and disinfectants in actual aquaculture wastewate

关键词： Surface-enhance Raman scattering (SERS) Aquaculture wastewater Pharmaceutical pollutants machine learning algorithm Rapid detection

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Integrated finite element analysis and machine learning approach for exploring the flexural behavior of steel-spontaneous combustion coal gangue aggregate concrete composite beams with studs

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STRUCTURES 2025年 72卷

作者： Zhang, Tirui Wang, Qinghe Zhang, Xinlong Liu, Yuting Ding, Jinan Shenyang Jianzhu Univ Sch Civil Engn Shenyang 110168 Peoples R China China Northeast Architectural Design & Res Inst Co Shenyang 110000 Peoples R China Shenyang Jianzhu Univ Sch Management Shenyang 110168 Peoples R China

This study investigates how varying the replacement ratio of spontaneous-combustion coal gangue aggregate (SCGA) affects the steel-concrete composite beams' flexural behavior with studs. Using ABAQUS software, pushout models of studs and flexural models of composite beams were developed, with their reliability confirmed through comparisons with previously published experimental findings. A parameter analysis was conducted to quantify the impact of the SCGA replacement ratio on the studs' shear resistance and the composite beams' flexural behavior. Additionally, machine learning techniques were employed to set up models for predicting the studs' shear resistance and assessing both the composite beams' flexural capacity and bending stiffness, with their predictive performance evaluated accordingly. The impact of each parameter on the studs' shear resistance and the composite beams' flexural behavior was also quantified. The findings suggest that the studs' shear resistance and the composite beams' flexural behavior decline as the SCGA replacement ratio increases. Specifically, compared with natural concrete specimens, the shear resistance of studs exhibiting replacement ratios of 50 % and 100 % was found to drop by 4.04 %-5.68 % and 7.90 %-11.18 %, respectively. The composite beams' flexural capacity decreased by 1.05 %-2.62 % and 2.24 %-5.44 %, while the bending stiffness decreased by 3.62 %-4.65 % and 8.27 %-10.00 %, respectively. The XGBoost model, optimized with advanced algorithms, effectively predicts the studs' shear resistance and assesses both the composite beams' flexural capacity and bending stiffness, with prediction errors maintained within 15 % and a minimum comprehensive performance index of 0.20. SHapley Additive exPlanations value analysis identified the stud's diameter and the SCGA concrete's compressive strength as the main influence factors of the studs' shear resistance. In terms of composite beams' flexural capacity, the key factor is the SCGA co

关键词： Spontaneous-combustion coal gangue aggregate concrete Steel-concrete composite beam Shear stud Flexural behavior Finite element analysis machine learning algorithm

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Advancing smart transportation: A review of computer vision and photogrammetry in learning-based dimensional road pavement defect detection

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COMPUTER SCIENCE REVIEW 2025年 56卷

作者： Tafida, Adamu Alaloul, Wesam Salah Zawawi, Noor Amila Bt Wan Musarat, Muhammad Ali Sani, Adamu Abubakar Univ Teknol PETRONAS Dept Civil & Environm Engn Bandar Seri Iskandar 32610 Perak Malaysia Univ Teknol PETRONAS Inst Autonomous Syst Offshore Engn Ctr Bandar Seri Iskandar 32610 Perak Malaysia Abubakar Tafawa Balewa Univ Dept Quant Surveying Bauchi Nigeria Abubakar Tafawa Balewa Univ Dept Bldg Bauchi Nigeria

Road infrastructure networks are crucial in facilitating smart mobility, as indicated by the emergence of innovative transportation concepts that offer improved efficiency and environmental sustainability. This study seeks to review the literature regarding road pavement condition assessment performance improvement tools which utilize various computer vision and photogrammetry tools aided by machine learning algorithms towards mitigating challenges encountered and promoting smart transportation trends. A comprehensive search of available literature was conducted, and relevant studies were analyzed to identify computer vision and photogrammetry tools used, learning-based algorithms deployed and contribution to the improvement of road infrastructure to aid smart transportation. The review considered emerging challenges of the techniques, identified research gaps and explored the potentials of the techniques as it relates to aiding wider acceptance of the implementation of autonomous vehicles and smart transportation The study found gaps in knowledge relating to the computer vision (CV) and photogrammetry tools standardization of evaluation parameters, the applicability of the models for real-time assessment and implications regarding the adoption of autonomous vehicles and smart transportation which were not sufficiently considered in the previous cited literature. Future research areas were highlighted and its implication regarding the promotion of smart transportation.

关键词： Smart transportation Road infrastructure Computer vision and photogrammetry machine learning algorithm Road defect detection

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