Binary Feature Mask Optimization for Feature Selection

We investigate feature selection problem for generic machine learning (ML) models. We introduce a novel framework that selects features considering the predictions of the model. Our framework innovates by using a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same ML model during feature selection, unlike other feature selection methods where we need to train the ML model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the ML model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, no study has introduced a training-free framework for a generic ML model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and Multi-Layer Perceptron as our ML models. Additionally, we openly share the implementation code for our methods to encourage the research and the contributions in this area

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques

Assessment of factors that increase risk of falling in older women by four different clinical methods

Background: Women aged 65 years and over are at increased risk of falling. Falls in this age group increase the risk of morbidity and mortality. Aims: The aim of the present study was to find the most common factors that increase risk of falling in older women, by using four different assessment methods. Methods: 682 women, who attended a geriatric outpatient clinic and underwent comprehensive geriatric assessment, were included in the study. History of falling last year, the Timed Up and Go (TUG) test, Performance-Oriented Mobility Assessment (POMA), and 4-meter walking speed test were carried out on all patients. Results: The mean age (SD) of patients were 74.4 (8.5) years. 31.5% of women had a history of falling in the last year. 11%, 36.5%, and 33.3% of patients had a falling risk according to POMA, TUG and 4-meter walking speed test, respectively. We identified the following risk factors that increase risk of falling, according to these four methods: urinary incontinence, dizziness and imbalance, using a walking stick, frailty, dynapenia, higher Charlson comorbidity index and Geriatric Depression Scale score and lower Basic and Instrumental Activities of Daily Living scores (p<0.05). We found a significant correlation between all the assessment methods (p<0.001). Conclusion: There is a strong relationship between fall risk and dizziness, using a walking stick, dynapenia, high number of comorbidities, low functionality, and some geriatric syndromes such as depression, frailty, and urinary incontinence in older women. Therefore, older women should routinely be screened for these risk factors

Macroporous surgical mesh from a natural cocoon composite

Recently, traditional polymer-based surgical meshes have drawn unwanted attention as a result of host tissue complications arising from infection, biocompatibility, and mechanical compatibility. Seeking an alternative solution, we present a hierarchically structured nanofibrous surgical mesh derived from the naturally woven cocoon of the Japanese giant silkworm, termed MothMesh. We report that it displays nontoxicity, biocompatibility, suitable mechanical properties, and porosity while showing no adverse effect in animal trials and even appears to enhance cell proliferation. Hence, we assert that the use of this natural material may provide an effective and improved alternative to existing synthetic meshes