A Parametric Investigation on the Neo-Hookean Material Constant

This paper assesses the Neo-Hookean material parameters pertaining to deformation behaviour of hyperelastic material by means of numerical analysis. A mathematical model relating stress and stretch is derived based on Neo-Hookeans strain energy function to evaluate the contribution of the material constant, C1, in the constitutive equation by varying its value. A systematic parametric study was constructed and for that purpose, a Matlab programme was developed for execution. The results show that the parameter (C1) is significant in describing material properties behaviour. The results and findings of the current study further enhances the understanding of Neo-Hookean model and hyperelastic materials behaviour. The ultimate future aim of this study is to come up with an alternative constitutive equation that may describe skin behaviour accurately. This study is novel as no similar parametric study on Neo-Hookean model has been reported before

The Identification of High Potential Archers Based on Fitness and Motor Ability Variables: A Support Vector Machine Approach

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme

The application of support vector machine in classifying potential archers using bio-mechanical indicators

This study classifies potential archers from a set of bio-mechanical indicators trained via different Support Vector Machine (SVM) models. 50 youth archers drawn from a number of archery programmes completed a one end archery shooting score test. Bio-mechanical evaluation of postural sway, bow movement, muscles activation of flexor and extensor as well as static balance were recorded. k-means clustering technique was used to cluster the archers based on the indicators tested. Fine, medium and coarse radial basis function kernel-based SVM models were trained based on the measured indicators. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the employment of SVM is able to assist coaches in identifying potential athletes in the sport of archery

Classification of skin cancer by means of transfer learning models

Skin cancer is a disease of human skin affected with abberrant or damaged cell and that lead to the formation of tumours. Skin cancer can be mainly classified into melanoma and non-melanoma, where melanoma is more deadly if misdiagnosis at the early stage. Traditional way of skin cancer classification required dermatologist to classify the cancer based on CT-scan, MRI or X-ray, which may promote risks of misdiagnosis. Hence deep learning is introduced to carry out the image feature extraction for the classification tasks by using the ISIC dataset. With the aids of InceptionV3 on different machine learning model, the skin cancer classification can be carry out by Artificial Intelligence. As a result of this study, Logistic Regression achieved overall classification accuracy of 78.3%, proven it has the ability to classify skin cancer based on skin lesion image

The normal vehicle forces effects of a two in-wheel electric vehicle towards the human brain on different road profile maneuver

Noise, harshness and vibrations are a non-trivial aspect of ride or human comfort, and car manufacturers often sought to improve the aforesaid comfort level. In previous studies, human biodynamic model and vehicle model are often modelled separately. Human model is used to study human alertness level and health while vehicle model is used to study on the car vibration to specifically understand the impact of vibration towards the model independently. In this study, a twelve degrees of freedom (12 DOF) human biodynamic model is incorporated with a two in-wheel electric car model to investigate the effect of vertical vibration towards the human brain based on different types of road profile and maneuver. MATLAB simulation environment is used to carry out the investigation, and it was established from the present study that the proposed model is able to provide significant insights on the impact experienced by the human brain to the skull based on the given vertical input of different road profile. The impact on the human brain to the skull is often associated with human alertness while driving where vibration exposure towards human driver influence the sleepiness level, human reaction times and lapses of attention which may lead to road accidents

Studies on ionics conduction properties of modification CMC-PVA based polymer blend electrolytes via impedance approach

In this study, the modification of cellulose derivative namely carboxymethyl cellulose (CMC) blended with polyvinyl alcohol (PVA) and doped with different content of NH4Br based solid polymer electrolytes (SPEs) prepared via solution casting method is investigated. The FTIR analysis demonstrated the interaction between CMC-PVA and NH4Br via COO−. The optimum ionic conductivity at ambient temperature is found to be 3.21 × 10−4 S/cm for the sample containing 20 wt% NH4Br with the lowest percentage of crystallinity and total weight loss. The conductivity-temperature relationship for the entire SPEs system obeys Arrhenius behaviour. Besides that, based on the Nyquist fitting analysis, it is shown that the ionic conductivity of the SPEs is primarily influenced by the ionic mobility as well as the ions diffusion coefficient. The H+ transference number obtained using non-blocking reversible electrode is 0.31, which further indicates that the conduction species is predominantly due to the cationic conduction

Keras Implementation in Detecting Intracranial Hemorrhage and Multiclass Classification of Subtypes via Transfer Learning and Classifiers Selection

The development of deep neural networks for medical imaging applications, especially the diagnosis of intracranial hemorrhage (ICH) from CT scans, is greatly aided by machine learning frameworks such as Keras. This work investigates a pipeline that uses Keras' neural modules to distinguish between CT scans of the normal head and those with ICH. Transfer learning models are then used to categorize ICH subtypes. An extensive analysis of current research and techniques demonstrates the effectiveness of deep learning in medical imaging and emphasizes how AI may improve radiologists' diagnostic precision. Using windowing techniques to improve diagnostic features, the study preprocesses pictures from the RSNA Intracranial Hemorrhage Detection dataset. The study assesses performance indicators such classification accuracy using SVM, k-NN, and Random Forest classifiers combined with built-in models from Keras, such as Xception and DenseNet. Findings show that the Xception-SVM pipeline performs exceptionally well in binary classification tasks, achieving 76.33% accuracy, while DenseNet201-SVM performs well in multiclass classification, achieving 60% accuracy. These results highlight how crucial it is to choose the right pipelines for certain classification jobs in order to achieve the best results possible when using medical image analysis. In order to improve diagnostic precision in identifying cerebral hemorrhages, future research directions include increasing classifier performance, investigating sophisticated preprocessing techniques, and fine-tuning models

The classification of Covid-19 cases through the employment of transfer learning on X-ray images

Covid-19 is a contagious disease that known to cause respirotary infection in humans. Almost 219 countries are effected by the outbreak of the latest coronavirus pandemic, exceed 100 millions of confirmed cases and about 2 million death recorded aound the world. This condition is alarming as some of the people who are infected with the virus show no symptoms of the disease. Due to the number of confirmed cases rapidly rising around the world, it is crucial find another method to diagnose the disease at the beginnings stage in order to control the spreading of the virus. Another alternative test from the main screening method is by using chest radiology image based detection which are X-ray or CT scan images. The aim of this research is to classify the Covid-19 cases by using the image classification technique.The dataset consist of 2000 images of chest X-ray images and have two classes which are Covid and Non-Covid. Each of the class consists of 1000 images.This research compare the performance of the various Transfer Learning models (VGG-16, VGG-19, and Inception V3) in extracting the feature from X-ray image combined with machine learning model (SVM, kNN, and Random Forest) as a classifier. The experiment result showed the VGG-19, VGG-16, and Inception V3 coupled with optimized SVM pipelines are comparably efficient in classifying the cases as compared to other pipelines evaluated in this reaseach and could archieved 99% acuuracy on the test datasets

The classification of FTIR plastic bag spectra via label spreading and stacking

Whereas plastics are a group of the most useful materials, widely used in all walks of life, the plastic waste that is produced daily poses a great threat towards wildlife and the planet as a whole. The use of biodegradable plastics is an important step in combating the plastic crisis. FTIR spectroscopy is a non-destructive method used for identifying different types of materials, however interpreting spectra produced by such spectrometers is both susceptible to human error, and time-consuming, not to mention that the industry suffers from a great of specialists, in the field of spectroscopy. Utilising machine learning as a method of filling the mentioned issue is suggested by this paper. Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. The pipelines studied seemed to show great predictivity at 100% classification accuracy acquired by the SVD-Stacked pipeline when data was sampled using an Agilent Cary 660 FTIR Spectrometer, and 99.18% by the same model when IDIR BP10 spectrometer was employed for sampling instead. The semi-supervised learning model (Label Spreading) seemed to achieve close enough accuracy at 99.82% in the case of the former dataset, and 97.54% for the latter, at a labelling rate of only 10% of the full datasets

Quantifying tensile properties of bamboo silicone biocomposite using Yeoh Model

The utilisation of bamboo has the potential of improving the properties of silicone. However, a thorough investigation has yet to be reported on the mechanical properties of bamboo silicone biocomposite. This study was carried out with the aim to quantify the tensile properties and assess the tensile behaviour of bamboo silicone biocomposite using Yeoh hyperelastic constitutive function. The specimens were prepared from the mix of bamboo particulate and pure silicone at various fibre composition ratio (0wt%, 1wt%, 3wt% and 5wt%) cured overnight at room temperature. A uniaxial tensile test was carried out by adopting the ASTM D412 testing standard. The Coefficient of Variation, CV, and the Coefficient of Determination, r2, were determined to assess the reliability of the experimental data and fitting model. The results of the determined Yeoh material constants for 5wt% specimen is found to be C1 = 12.0603×10-3 MPa, C2 = 8.7353×10-5 MPa and C3 = -11.6165×10-8 MPa, compared to pure silicone (0wt%) C1 = 5.6087×10-3 MPa, C2 = 8.6639×10-5 MPa and C3 = -7.6510×10-8 MPa. The results indicate that the bamboo fibre improves the stiffness of the silicone rubber by 115 percent. A low variance was exhibited by the experimental data with a CV value of less than 8 percent. The Yeoh Model demonstrated an excellent prediction of the elastic behaviour of bamboo silicone biocomposite with a fitting accuracy of more than 99.93 percent