A Design Construct of Developing Approaches to Measure Mental Health Conditions

Mental health is an important determinant of communities’ well-being, influenced not only by individual attributes, but also by social and organisational environments in which people work and live. Despite studies examining mental health status among specific populations, few attempts are evident that focus on solution designs for detecting and measuring impact of mental health conditions. In this study, we develop a construct utilising design science research principles for outlining common vocabulary around the problem, and solution design relevant to a mental health management system. For the case of IT professionals, the developed construct is informed through a social-media based dataset containing more than 65,000 cells and 100 attributes potentially identifying influencing factors. Machine learning techniques are applied to the dataset to discover new findings for this specific group. It is anticipated that the analysis reported in this study would contribute in developing other electronic health management systems both for communities and healthcare professionals

Gene selection for high dimensional data using k-means clustering algorithm and statistical approach

Microarray technology can measure thousands of genes which are useful for biologist to study and classify the cancer cells.However, this high dimensional data consists of large number of genes to be examined in regard of small samples size. Thus, selection of relevant genes is a challenging issue in microarray data analysis and has been a central research focus.This study proposed kmeans clustering algorithm to groups the relevant genes. Several statistical techniques such as Fisher criterion, Golub signal-to-noise, Mann Whitney rank and t-test have been used in deciding the clusters are well separated from one and others. Those genes with high discriminative score will later be used to train the k-NN classifier.The experimental results showed that the proposed gene selection methods able to identify differentially expressed genes with 0.86 ROC score

Image preprocessing in classification and identification of diabetic eye diseases

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity. © 2021, The Author(s)

A comparative study on gene selection methods for tissues classification on large scale gene expression data

Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously.However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”.Microarray data mainly involved thousands of genes, n in a very small size sample, p which complicates the data analysis process.For such a reason, feature selection methods also known as gene selection methods have become apparently need to select significant genes that present the maximum discriminative power between cancerous and normal tissues.Feature selection methods can be structured into three basic factions; a) filter methods; b) wrapper methods and c) embedded methods.Among these methods, filter gene selection methods provide easy way to calculate the informative genes and can simplify reduce the large scale microarray datasets.Although filter based gene selection techniques have been commonly used in analyzing microarray dataset, these techniques have been tested separately in different studies.Therefore, this study aims to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues.In this experiment, common classifiers, Support Vector Machine (SVM) is used to train the selected genes.These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset.This study has discovered that IG and SNR are more suitable to be used with SVM.Furthermore, this study has shown SVM performance remained moderately unaffected unless a very small size of genes was selected

A knowledge graph empowered online learning framework for access control decision-making

Knowledge graph, as an extension of graph data structure, is being used in a wide range of areas as it can store interrelated data and reveal interlinked relationships between different objects within a large system. This paper proposes an algorithm to construct an access control knowledge graph from user and resource attributes. Furthermore, an online learning framework for access control decision-making is proposed based on the constructed knowledge graph. Within the framework, we extract topological features to represent high cardinality categorical user and resource attributes. Experimental results show that topological features extracted from knowledge graph can improve the access control performance in both offline learning and online learning scenarios with different degrees of class imbalance status

Automatic breast lesion segmentation in phase preserved DCE-MRIs

We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method