Improved Gait Classification with Different Smoothing Techniques

Gait as a biometric has received great attention nowadays as it can offer human identification at a distance without any contact with the feature capturing device. This is motivated by the increasing number of synchronised closed-circuit television (CCTV) cameras which have been installed in many major towns, in order to monitor and prevent crime by identifying the criminal or suspect. This paper present a method to improve gait classification results by applying smoothing techniques on the extracted gait features. The proposed approach is consisted of three parts: extraction of human gait features from enhanced human silhouette, smoothing process on extracted gait features and classification by fuzzy k-nearest neighbours (KNN). The extracted gait features are height, width, crotch height, step-size of the human silhouette and joint trajectories. To improve the recognition rate, two of these extracted gait features are smoothened before the classification process in order to alleviate the effect of outliers. The proposed approach has been applied on a dataset of nine subjects walking bidirectionally on an indoor pathway with twelve different covariate factors. From the experimental results, it can be concluded that the proposed approach is effective in gait classification

Intracranial Hemorrhage Annotation for CT Brain Images

In this paper, we created a decision-making model to detect intracranial hemorrhage and adopted Expectation Maximization(EM) segmentation to segment the Computed Tomography (CT) images. In this work, basically intracranial hemorrhage is classified into two main types which are intra-axial hemorrhage and extra-axial hemorrhage. In order to ease classification, contrast enhancement is adopted to finetune the contrast of the hemorrhage. After that, k-means is applied to group the potential and suspicious hemorrhagic regions into one cluster. The decision-making process is to identify whether the suspicious regions are hemorrhagic regions or non-regions of interest. After the hemorrhagic detection, the images are segmented into brain matter and cerebrospinal fluid (CSF) by using expectation-maximization (EM) segmentation. The acquired experimental results are evaluated in terms of recall and precision. The encouraging results have been attained whereby the proposed system has yielded 0.9333 and 0.8880 precision for extra-axial and intra-axial hemorrhagic detection respectively, whereas recall rate obtained is 0.9245 and 0.8043 for extra-axial and intra-axial hemorrhagic detection respectively

Dynamic behaviours of damaged stability for floating energy storage unit after accidental collision

The transient dynamic behaviour of floating energy storage unit (FESU) is a result of coupling between three non-linear effects, which are sloshing of floodwater, wave loading, and FESU dynamics. The coupling of these effects would result in the catastrophic failure of the FESU in extreme conditions. Computational Fluid Dynamics (CFD) has shown that it holds great potential in solving the problem in the time domain, which is suitable for the transient stage. In this study, CFD simulation of damaged stability was conducted by using OpenFOAM to determine the dynamic response of FESU under the effects of floodwater and wave in transient flooding. OpenFOAM CFD simulation was conducted for the flooding of barge shaped FESU with different water inlet and air outlet sizes in still water condition followed by damaged stability in Stokes’ fifth-order beam wave and head wave condition. Dynamic responses of FESU, such as roll, pitch, heave, and floodwater volume flow rates were determined using the dynamic meshing solver of OpenFOAM. Simulation results showed similarity to experimental results within the time frame of 16 seconds. Reduction in water inlet area and air outlet area decreased the flooding time and flow rate of flood water. The amplitude of vibration of roll and pitch motion increased as the flood water volume was increased due to the force of floodwater exerted on the wall. Sloshing effects also caused the model to roll and pitch in secondary vibrational motion. Due to the coupling effect of the three non-linear criteria, the inflow and outflow of floodwater changed with time, which concludes that transient effects should not be ignored in the damaged stability assessment of FESU

EBV-encoded miRNAs target ATM-mediated response in nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a highly invasive epithelial malignancy that is prevalent in southern China and Southeast Asia. It is consistently associated with latent Epstein–Barr virus (EBV) infection. In NPC, miR‐BARTs, the EBV‐encoded miRNAs derived from BamH1‐A rightward transcripts, are abundantly expressed and contribute to cancer development by targeting various cellular and viral genes. In this study, we establish a comprehensive transcriptional profile of EBV‐encoded miRNAs in a panel of NPC patient‐derived xenografts and an EBV‐positive NPC cell line by small RNA sequencing. Among the 40 miR‐BARTs, predominant expression of 22 miRNAs was consistently detected in these tumors. Among the abundantly expressed EBV‐miRNAs, BART5‐5p, BART7‐3p, BART9‐3p, and BART14‐3p could negatively regulate the expression of a key DNA double‐strand break (DSB) repair gene, ataxia telangiectasia mutated (ATM), by binding to multiple sites on its 3'‐UTR. Notably, the expression of these four miR‐BARTs represented more than 10% of all EBV‐encoded miRNAs in tumor cells, while downregulation of ATM expression was commonly detected in all of our tested sequenced samples. In addition, downregulation of ATM was also observed in primary NPC tissues in both qRT‐PCR (16 NP and 45 NPC cases) and immunohistochemical staining (35 NP and 46 NPC cases) analysis. Modulation of ATM expression by BART5‐5p, BART7‐3p, BART9‐3p, and BART14‐3p was demonstrated in the transient transfection assays. These findings suggest that EBV uses miRNA machinery as a key mechanism to control the ATM signaling pathway in NPC cells. By suppressing these endogenous miR‐BARTs in EBV‐positive NPC cells, we further demonstrated the novel function of miR‐BARTs in inhibiting Zta‐induced lytic reactivation. These findings imply that the four viral miRNAs work co‐operatively to modulate ATM activity in response to DNA damage and to maintain viral latency, contributing to the tumorigenesis of NPC

Automated Classification and Annotation of Computed Tomography Brain Images

Brain hemorrhage detection is clinically crucial for the patients having head trauma and neurological disturbances. Early finding and accurate diagnosis of the brain abnormalities is one of the key contributions for the execution of the successful therapy and proper treatment. Multi-slice Computed Tomograph (CT) scans are widely employed in today’s examination of head traumas due to its effectiveness to disclose some abnormalities such as brain hemorrhages and so on. However, radiologists have to manually analyse the CT slices for the presence of brain hemorrhages. Due to the large volume of CT scan examinations, it is important to develop a computerised system that can assist the radiologists to automatically detect the presence of the brain abnormalities as well as automatically retrieve the images. This thesis presents an automated annotation and classification of the CT brain images. The main objective is to propose a new methodology to annotate and classify the different types of brain hemorrhages which are intra-axial, subdural and extradural hemorrhages. Besides, this thesis also aims to evaluate and investigate the effectiveness and suitability of different segmentation and classification techniques as well as introduce the new features for the classification

Unsupervised Abnormalities Extraction and Brain Segmentation

In this paper, we propose a methodology consists of several unsupervised clustering techniques to acquire a satisfactory segmentation of Computed Tomography (CT) brain images. The ultimate goal of segmentation is to obtain three segmented images, which are the abnormalities, cerebrospinal fluid (CSF) and brain matter respectively. The proposed approach contains of two phase-segmentation methods. In the first phase segmentation, the combination of k-means and fuzzy c-means(FCM) methods is implemented to partition the images into the binary images. From the binary images, a decision tree is then utilized to annotate the connected component into normal and abnormal regions. For the second phase segmentation, the obtained experimental results have shown that modified FCM with population-diameter independent(PDI) segmentation is more feasible and yield satisfactory results

Snake-Based Boundary Search for Segmentation of 3D Polygonal Model

Many existing 3D model segmentation methods require user input to accurately segment the virtual models. This paper applies snake-based (or contour) approach to automatically search for the boundary between the two connected functional features. The search is guided by the skeleton model. The proposed approach courageously modifies the geometric meshes with respect to each sectional skeleton to ensure the snake meets end to end to form a complete ring. The result of the ring is then interpolated to fit the local geometric feature model. This step eradicates the non-related feature and invalid rings. In the end, the created ring is reverted to its original coordinates so that the meshes are non-distorted. The entire process is done automatically without any user input. The proposed method is compared to two well-known methods: Shape Diameter method and Core Extraction method. The results generated by the proposed method turn out to be more accurate than the two existing methods

Predicting Diabetes Mellitus with Machine Learning Techniques

This study addresses the challenge of accurately identifying diabetes mellitus in individuals. Utilizing accessible online and real-world diagnostic data, we employ machine learning models, including Support Vector Machine, Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Deep Neural Network, on the PIMA Indian Diabetes and NHANES 1999-2016 datasets. Rigorous data pre-processing steps were conducted, handling null values, outliers, and imbalanced data together with data normalization. Our results reveal that the RF model achieves a 79% accuracy for binary classification on the PIMA Indian Diabetes dataset, using a 60:40 train-test split with BORUTA selected features. Meanwhile, the XGBoost model excels on the NHANES 1999-2016 dataset, achieving 92% accuracy for binary and 91% for multiclass classification respectively

Segmentation of CT head images

This paper presents the segmentation of the CT head images with different techniques. The system partitions the CT head images into four regions which are skull, calcifications, cerebrospinal fluid (CSF) and brain matter. The method consists of two phases. The first phase is to partition the skull, CSF and brain matter in which we applied the Expectation maximization (EM) algorithm for the segmentation. The second phase is to identify the calcifications where we used thresholding. The system has been tested with a number of real CT head images and has achieved promising results