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14th International Conference on Software Technology and Engineering, ICSTE 2024

作者： Von Golosinda, Kenneth Van Marcellones, Jasper Ampongol, Luke Jellergil P. Magloyuan, Neil P. Cerna, Patrick D. Mapua Malayan Colleges Mindanao Davao City Philippines

ISBN: (纸本)9798350378955

Driver drowsiness contributes significantly to road accidents worldwide, and while drowsiness detection systems have already been implemented on higher-end cars, DriSafePh introduces an embedded system using a Raspberry Pi that can be implemented in any enclosed vehicle. DriSafePh aims to address the prevalent issue of driver drowsiness on the road by developing a real-time driver drowsiness detection system and leveraging a hybrid machine learning algorithm that processes facial landmarks and gestures to accurately detect drowsiness, which has yet to be used in its field and will be compared to other algorithms already in use. Additionally, the researcher aims to provide a warning notification to the driver through a text-to-speech algorithm when the appropriate drowsiness level is detected and optimize the machine learning model's performance. The study conducted a survey to gather drivers' opinions on a drowsiness detection system and their personal experiences with drowsy driving. The results showed that the respondents viewed the working prototype positively. Additionally, the data revealed that more than 70% of the respondents had experienced drowsy driving, highlighting the need for such a device. The recommended approach of the DriSafePh model was evaluated using a performance matrix, and the detection for the face, eye, and mouth provides a high accuracy level of 96-99%. In the meantime, eye closure and yawn detection accuracy were able to match the other existing algorithms on the Raspberry Pi device with an accuracy of 96.89% and 85.93%, respectively. © 2024 IEEE.

关键词： drowsiness detection eye closure hybrid machine learning algorithm Yawn detection

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Enhancing energy capacity of the car's hood door under excitation frequency via functionally graded triply periodic minimal surface material: Application of machine learning and mathematical simulation

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2024年

作者： Long, Feng Jiang, Lijian Yang, Xiaohua El-Meligy, Mohammed A. Saleem, Kashif Lishui Vocat & Tech Coll Intelligent Mfg Coll Lishui Peoples R China Lishui Univ Engn Coll Lishui Peoples R China Jadara Univ Res Ctr Irbid Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh 11362 Saudi Arabia

This study investigates the nonlinear dynamic response and vibration characteristics of a car's hood door, focusing on its energy capacity under various excitation frequencies. The hood door is modeled using a functionally graded triply periodic minimal surface (FG-TPMS) material, which offers superior mechanical properties and lightweight design. The analysis is conducted using a higher-order shear deformation theory (HSDT), which accounts for shear deformations more accurately than classical theories. The incorporation of von Karman nonlinear terms captures the geometric nonlinearity due to large deformations, providing a realistic simulation of the hood door's behavior under dynamic loads. To solve the complex equations of motion derived from the HSDT and von Karman terms, a fourth-order Runge-Kutta method is employed. This numerical method ensures accurate time integration and stability of the solution. The dynamic response is further analyzed to determine the energy absorption capacity of the hood door material, crucial for safety and durability in automotive applications. Validation of the numerical model is performed using previous published articles and a hybrid machine learning algorithm, which combines data-driven approaches with traditional physics-based models. This hybrid validation enhances the accuracy and reliability of the predicted responses, ensuring that the proposed model can be effectively used in the design and optimization of car components. The results demonstrate the potential of FG-TPMS materials in automotive applications, offering improved energy absorption and vibration control, which are essential for enhancing vehicle safety and performance.

关键词： Energy capacity nonlinear dynamics car's hood door FG-TPMS material hybrid machine learning algorithm

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Comparative study of a newly proposed machine learning classification to detect damage occurrence in structures

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期127卷

作者： Ahmadian, Vahid Aval, S. Bahram Beheshti Noori, Mohammad Wang, Tianyu Altabey, Wael A. K N Toosi Univ Technol Dept Civil Engn Tehran Iran Calif Polytech State Univ San Luis Obispo Dept Mech Engn San Luis Obispo CA 93402 USA Univ Leeds Sch Civil Engn Leeds LS2 9JT England Shanghai Inst Technol Sch Urban Construct & Safety Engn Shanghai 201418 Peoples R China Southeast Univ Int Inst Urban Syst Engn IIUSE Nanjing 210096 Jiangsu Peoples R China Alexandria Univ Fac Engn Dept Mech Engn Alexandria 21544 Egypt

Over the past two decades, an increasing number of large-scale structures have been built around the world. Constructing these structures has been a time consuming and highly expensive process. Thus, providing a structural health monitoring system to guarantee their proper functionality is important. In recent years, the advancement of technology and artificial intelligence methods based on signal processing and machine learning has attracted the attention of researchers. The challenges currently exist in the field of structural health moni-toring to identify and classify damages to achieve high accuracy in a health-monitoring program. The presence of noise in measurement, various exciting load types, and varying environmental conditions cause difficulty in the practical identification and classification of damage in structures. Recent studies have employed finite element modeling to test the effectiveness of proposed methods for identifying damages in structures. However, detecting damage in real-world structures as mentioned above, presents unique difficulties, and the effectiveness of the proposed methods for damage detection in real-world structures remains uncertain. In order to improve the performance of damage detection methods and increase the accuracy of these methods as much as possible, the most important action is to identify damage sensitive data in the structure. The next challenge is to choose a high performance algorithm for damage identification and classification. One of the advanced algorithms, which has a very high ability to extract the desired features from the measured data, is the XGBoost algorithm. This algorithm has recently attracted the attention of researchers and has been used in different fields. So far, the ability of this algorithm has not been examined in the field of damage detection in order to extract desirable features. This article deals with the identification, classification, and severity of damages in the SMC benchmark bridg

关键词： Structural health monitoring Damage detection hybrid machine learning algorithm XGBoost Stacking method

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Estimating SVCV waterborne transmission and predicting experimental epidemic development:A modeling study using a machine learning approach

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Water Biology and Security 2024年第1期3卷 60-70页

作者： Jiaji Pan Qijin Zeng Wei Qin Jixiang Chu Haibo Jiang Haiyan Chang Jun Xiao Hao Feng State Key Laboratory of Developmental Biology of Freshwater Fish College of Life ScienceHunan Normal UniversityChangsha410081China College of Engineering and Design Hunan Normal UniversityChangsha410081China

Viral infectious diseases significantly threaten the sustainability of freshwater fish *** lack of studies on epidemic transmission patterns and mechanisms inhibits the development of containment strategies from the viewpoint of veterinary public *** study raises an epidemic mathematical model considering water transmission with the aim of analyzing the transmission process more *** basic reproduction number R0 was derived by the model parameter including the water transmission coefficient and was used for the analysis of the virus *** viremia of carp virus(SVCV)and zebrafish were used as model viruses and animals,respectively,to conduct the transmission *** through water was achieved by connecting two aquarium tanks with a water channel but blocking the fish movement between the *** the collected experimental data,we determined the optimal hybrid machine learning algorithm to analyze the transmission process using an established mathematical *** addition,future transmission was predicted and validated using the epidemic model and an optimal ***,the sensitivity of model parameters and the simulations of R0 variation were performed based on the modified complex epidemic *** study is of significance in providing theoretical guidance for minimizing R0 by manipulating model parameters with containment *** importantly,since the modified model and algorithm demonstrated better performance in handling freshwater fish transmission problems,this study advances the future application of transmissible disease modeling with larger datasets in freshwater fish aquaculture.

关键词： Epidemic mathematical model hybrid machine learning algorithm Reproduction number Sensitivity analysis SVCV transmission

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Enhancing reliability assessment of curved low-stiffness track-viaducts with an adaptive surrogate-based approach emphasizing track dynamic geometric state

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Journal of Central South University 2024年第11期31卷 4262-4275页

作者： CHENG Fang LIU Hui YANG Rui Institute of Artificial Intelligence&Robotics(IAIR) Key Laboratory of Traffic Safety on Track of Ministry of EducationSchool of Traffic and Transportation EngineeringCentral South UniversityChangsha 410075China

Traditional track dynamic geometric state(TDGS)simulation incurs substantial computational burdens,posing challenges for developing reliability assessment approach that accounts for *** overcome these,firstly,a simulation-based TDGS model is established,and a surrogate-based model,grid search algorithm-particle swarm optimization-genetic algorithm-multi-output least squares support vector regression,is *** them,hyperparameter optimization algorithm’s effectiveness is confirmed through test ***,an adaptive surrogate-based probability density evolution method(PDEM)considering random track geometry irregularity(TGI)is ***,taking curved train-steel spring floating slab track-U beam as case study,the surrogate-based model trained on simulation datasets not only shows accuracy in both time and frequency domains,but also surpasses existing ***,the adaptive surrogate-based PDEM shows high accuracy and efficiency,outperforming Monte Carlo simulation and simulation-based *** reliability assessment shows that the TDGS part peak management indexes,left/right vertical dynamic irregularity,right alignment dynamic irregularity,and track twist,have reliability values of 0.9648,0.9918,0.9978,and 0.9901,*** TDGS mean management index,i.e.,track quality index,has reliability value of *** findings show that the proposed framework can accurately and efficiently assess the reliability of curved low-stiffness track-viaducts,providing a theoretical basis for the TGI maintenance.

关键词： reliability assessment track dynamic geometric state hybrid machine learning algorithm adaptive learning strategy probability density evolution method

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Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area

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ECOLOGICAL INDICATORS 2021年 129卷 107869-107869页

作者： Mohajane, Meriame Costache, Romulus Karimi, Firoozeh Quoc Bao Pham Essahlaoui, Ali Hoang Nguyen Laneve, Giovanni Oudija, Fatiha Moulay Ismail Univ Fac Sci Dept Biol Soil & Environm Microbiol Team BP 11201 Zitoune Meknes Morocco Moulay Ismail Univ Fac Sci Dept Geol Geoengn & Environm LabWater Sci & Environm Engn BP 11201 Zitoune Meknes Morocco Transilvania Univ Brasov Dept Civil Engn 5 Turnului Str Brasov 500152 Romania Univ North Carolina Greensboro Dept Geog Environm & Sustainabil Greensboro NC 27402 USA Thu Dau Mot Univ Inst Appl Technol Thu Dau Binh Duong Prov Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min 18 Pho Vien Hanoi 100000 Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res 18 Pho Vien Hanoi 100000 Vietnam Univ Roma La Sapienza Scuola Ingn Aerosp Via Salaria 851 I-00138 Rome Italy

Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FRLR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas.

关键词： Forest fire hybrid machine learning algorithm Remote sensing Mediterranean area

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Phishing Classification Models an Empirical Study of Induction Factors for Effective Classification 2

Phishing Classification Models an Empirical Study of Inducti...

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2nd Al-Sadiq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA)

作者： Zuhair, Hiba Selamat, Ali Al Nahrain Univ Coll Informat Engn Dept Syst Engn Baghdad Iraq UTM Fac Comp Software Engn Dept Ctr Informat & Commun Technol Johor Baharu Malaysia

ISBN: (纸本)9781538642412

recently, researchers have devoted prominent machine learning-based anti-phishing models to survive a supreme cyber-security versus phishing evolution on the cyberspace. Yet, such models remain incompetent to detect new phish in a real-time application. In this concern, this paper advocates an empirical analysis with the recently published works via a chronological validation. Chronological validation achieved by testing the works on three benchmarking data sets to appraise the causality between their detection outcomes and their limitations. Throughout chronological validation, the tested works have fallen short at detecting new phish web pages with an accessible detection accuracy. High to moderate faults and misclassifications are resulted as implications for their limitations and fixed real-time settings. Accordingly, this paper infers that by elevating the tested models in terms of using new and hybrid features, robust subset of features, and actively learned classifiers;an adaptive anti-phishing model with adjustable settings will be resilient against the up-to-date and scalable web flows. With such inferences, this paper highlights what future trends to develop along with depicting a taxonomy of current status and open problems as a guide to the researchers for their future achievements.

关键词： phish web page big data hybrid machine learning algorithm active learning adaptive model

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