• Title/Summary/Keyword: feature models

Search Result 1,135, Processing Time 0.028 seconds

Novel four-unknowns quasi 3D theory for bending, buckling and free vibration of functionally graded carbon nanotubes reinforced composite laminated nanoplates

  • Khadir, Adnan I.;Daikh, Ahmed Amine;Eltaher, Mohamed A.
    • Advances in nano research
    • /
    • v.11 no.6
    • /
    • pp.621-640
    • /
    • 2021
  • Effect of thickness stretching on mechanical behavior of functionally graded (FG) carbon nanotubes reinforced composite (CNTRC) laminated nanoplates resting on elastic foundation is analyzed in this paper using a novel quasi 3D higher-order shear deformation theory. The key feature of this theoretical formulation is that, in addition to considering the thickness stretching effect, the number of unknowns of the displacement field is reduced to four, and which is more than five in the other models. Single-walled carbon nanotubes (SWCNTs) are the reinforced elements and are distributed with four power-law functions which are, uniform distribution, V-distribution, O-distribution and X-distribution. To cover various boundary conditions, an analytical solution is developed based on Galerkin method to solve the governing equilibrium equations by considering the nonlocal strain gradient theory. A modified two-dimensional variable Winkler elastic foundation is proposed in this study for the first time. A parametric study is executed to determine the influence of the reinforcement patterns, power-law index, nonlocal parameter, length scale parameter, thickness and aspect ratios, elastic foundation, thermal environments, and various boundary conditions on stresses, displacements, buckling loads and frequencies of the CNTRC laminated nanoplate.

Single nucleotide polymorphism marker combinations for classifying Yeonsan Ogye chicken using a machine learning approach

  • Eunjin, Cho;Sunghyun, Cho;Minjun, Kim;Thisarani Kalhari, Ediriweera;Dongwon, Seo;Seung-Sook, Lee;Jihye, Cha;Daehyeok, Jin;Young-Kuk, Kim;Jun Heon, Lee
    • Journal of Animal Science and Technology
    • /
    • v.64 no.5
    • /
    • pp.830-841
    • /
    • 2022
  • Genetic analysis has great potential as a tool to differentiate between different species and breeds of livestock. In this study, the optimal combinations of single nucleotide polymorphism (SNP) markers for discriminating the Yeonsan Ogye chicken (Gallus gallus domesticus) breed were identified using high-density 600K SNP array data. In 3,904 individuals from 198 chicken breeds, SNP markers specific to the target population were discovered through a case-control genome-wide association study (GWAS) and filtered out based on the linkage disequilibrium blocks. Significant SNP markers were selected by feature selection applying two machine learning algorithms: Random Forest (RF) and AdaBoost (AB). Using a machine learning approach, the 38 (RF) and 43 (AB) optimal SNP marker combinations for the Yeonsan Ogye chicken population demonstrated 100% accuracy. Hence, the GWAS and machine learning models used in this study can be efficiently utilized to identify the optimal combination of markers for discriminating target populations using multiple SNP markers.

Characteristics, mathematical modeling and conditional simulation of cross-wind layer forces on square section high-rise buildings

  • Ailin, Zhang;Shi, Zhang;Xiaoda, Xu;Yi, Hui;Giuseppe, Piccardo
    • Wind and Structures
    • /
    • v.35 no.6
    • /
    • pp.369-383
    • /
    • 2022
  • Wind tunnel experiment was carried out to study the cross-wind layer forces on a square cross-section building model using a synchronous multi-pressure sensing system. The stationarity of measured wind loadings are firstly examined, revealing the non-stationary feature of cross-wind forces. By converting the measured non-stationary wind forces into an energetically equivalent stationary process, the characteristics of local wind forces are studied, such as power spectrum density and spanwise coherence function. Mathematical models to describe properties of cross-wind forces at different layers are thus established. Then, a conditional simulation method, which is able to ex-tend pressure measurements starting from experimentally measured points, is proposed for the cross-wind loading. The method can reproduce the non-stationary cross-wind force by simulating a stationary process and the corresponding time varying amplitudes independently; in this way the non-stationary wind forces can finally be obtained by combining the two parts together. The feasibility and reliability of the proposed method is highlighted by an ex-ample of across wind loading simulation, based on the experimental results analyzed in the first part of the paper.

Novel quasi 3D theory for mechanical responses of FG-CNTs reinforced composite nanoplates

  • Alazwari, Mashhour A.;Daikh, Ahmed Amine;Eltaher, Mohamed A.
    • Advances in nano research
    • /
    • v.12 no.2
    • /
    • pp.117-137
    • /
    • 2022
  • Effect of thickness stretching on free vibration, bending and buckling behavior of carbon nanotubes reinforced composite (CNTRC) laminated nanoplates rested on new variable elastic foundation is investigated in this paper using a developed four-unknown quasi-3D higher-order shear deformation theory (HSDT). The key feature of this theoretical formulation is that, in addition to considering the thickness stretching effect, the number of unknowns of the displacement field is reduced to four, and which is more than five in the other models. Two new forms of CNTs reinforcement distribution are proposed and analyzed based on cosine functions. By considering the higher-order nonlocal strain gradient theory, microstructure and length scale influences are included. Variational method is developed to derive the governing equation and Galerkin method is employed to derive an analytical solution of governing equilibrium equations. Two-dimensional variable Winkler elastic foundation is suggested in this study for the first time. A parametric study is executed to determine the impact of the reinforcement patterns, nonlocal parameter, length scale parameter, side-t-thickness ratio and aspect ratio, elastic foundation and various boundary conditions on bending, buckling and free vibration responses of the CNTRC plate.

Ensemble Modulation Pattern based Paddy Crop Assist for Atmospheric Data

  • Sampath Kumar, S.;Manjunatha Reddy, B.N.;Nataraju, M.
    • International Journal of Computer Science & Network Security
    • /
    • v.22 no.9
    • /
    • pp.403-413
    • /
    • 2022
  • Classification and analysis are improved factors for the realtime automation system. In the field of agriculture, the cultivation of different paddy crop depends on the atmosphere and the soil nature. We need to analyze the moisture level in the area to predict the type of paddy that can be cultivated. For this process, Ensemble Modulation Pattern system and Block Probability Neural Network based classification models are used to analyze the moisture and temperature of land area. The dataset consists of the collections of moisture and temperature at various data samples for a land. The Ensemble Modulation Pattern based feature analysis method, the extract of the moisture and temperature in various day patterns are analyzed and framed as the pattern for given dataset. Then from that, an improved neural network architecture based on the block probability analysis are used to classify the data pattern to predict the class of paddy crop according to the features of dataset. From that classification result, the measurement of data represents the type of paddy according to the weather condition and other features. This type of classification model assists where to plant the crop and also prevents the damage to crop due to the excess of water or excess of temperature. The result analysis presents the comparison result of proposed work with the other state-of-art methods of data classification.

Comparing automated and non-automated machine learning for autism spectrum disorders classification using facial images

  • Elshoky, Basma Ramdan Gamal;Younis, Eman M.G.;Ali, Abdelmgeid Amin;Ibrahim, Osman Ali Sadek
    • ETRI Journal
    • /
    • v.44 no.4
    • /
    • pp.613-623
    • /
    • 2022
  • Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

Gaussian process regression model to predict factor of safety of slope stability

  • Arsalan, Mahmoodzadeh;Hamid Reza, Nejati;Nafiseh, Rezaie;Adil Hussein, Mohammed;Hawkar Hashim, Ibrahim;Mokhtar, Mohammadi;Shima, Rashidi
    • Geomechanics and Engineering
    • /
    • v.31 no.5
    • /
    • pp.453-460
    • /
    • 2022
  • It is essential for geotechnical engineers to conduct studies and make predictions about the stability of slopes, since collapse of a slope may result in catastrophic events. The Gaussian process regression (GPR) approach was carried out for the purpose of predicting the factor of safety (FOS) of the slopes in the study that was presented here. The model makes use of a total of 327 slope cases from Iran, each of which has a unique combination of geometric and shear strength parameters that were analyzed by PLAXIS software in order to determine their FOS. The K-fold (K = 5) technique of cross-validation (CV) was used in order to conduct an analysis of the accuracy of the models' predictions. In conclusion, the GPR model showed excellent ability in the prediction of FOS of slope stability, with an R2 value of 0.8355, RMSE value of 0.1372, and MAPE value of 6.6389%, respectively. According to the results of the sensitivity analysis, the characteristics (friction angle) and (unit weight) are, in descending order, the most effective, the next most effective, and the least effective parameters for determining slope stability.

Ensemble Deep Learning Model using Random Forest for Patient Shock Detection

  • Minsu Jeong;Namhwa Lee;Byuk Sung Ko;Inwhee Joe
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.17 no.4
    • /
    • pp.1080-1099
    • /
    • 2023
  • Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.

Subsurface anomaly detection utilizing synthetic GPR images and deep learning model

  • Ahmad Abdelmawla;Shihan Ma;Jidong J. Yang;S. Sonny Kim
    • Geomechanics and Engineering
    • /
    • v.33 no.2
    • /
    • pp.203-209
    • /
    • 2023
  • One major advantage of ground penetrating radar (GPR) over other field test methods is its ability to obtain subsurface images of roads in an efficient and non-intrusive manner. Not only can the strata of pavement structure be retrieved from the GPR scan images, but also various irregularities, such as cracks and internal cavities. This article introduces a deep learning-based approach, focusing on detecting subsurface cracks by recognizing their distinctive hyperbolic signatures in the GPR scan images. Given the limited road sections that contain target features, two data augmentation methods, i.e., feature insertion and generation, are implemented, resulting in 9,174 GPR scan images. One of the most popular real-time object detection models, You Only Learn One Representation (YOLOR), is trained for detecting the target features for two types of subsurface cracks: bottom cracks and full cracks from the GPR scan images. The former represents partial cracks initiated from the bottom of the asphalt layer or base layers, while the latter includes extended cracks that penetrate these layers. Our experiments show the test average precisions of 0.769, 0.803 and 0.735 for all cracks, bottom cracks, and full cracks, respectively. This demonstrates the practicality of deep learning-based methods in detecting subsurface cracks from GPR scan images.

Revisiting the Nexus of Foreign Direct Investment, Financial Development, and Economic Growth: The Case of Emerging Economies

  • KUMAR, Jai;SOOMRO, Ahmed Nawaz;KUMARI, Joti
    • The Journal of Asian Finance, Economics and Business
    • /
    • v.9 no.1
    • /
    • pp.203-211
    • /
    • 2022
  • Foreign direct investment (FDI) has increased at an exponential rate during the last two decades. It is now a feature of emerging market economies as well. Foreign direct investment and financial development are important factors in an economy's growth. Various studies have examined the impact of foreign direct investment and financial development on economic growth in different countries and areas. However, the findings are currently inconclusive. Using updated data from 1970 to 2020, this study will examine the relationships between FDI, financial development, and economic growth in 30 rising economies.GDP is the dependent variable, while FDI, financial development, trade openness, infrastructure, exchange rate, and GDP growth are the independent factors. To estimate the panel data, we used the most recent econometric models. The study's major findings suggest that FDI and financial development are critical determinants in emerging economies' economic progress. Furthermore, multiple robustness checks supported the study's empirical findings. The results of this study include various practical recommendations for investors, governments, and policymakers, given the increased interest in global economic integration and member states' reliance on FDI as a critical aspect of sustaining prosperity.