A New Ensemble-Based Intrusion Detection System for Internet of Things

The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios

Eliciting student feedback for course development: the application of a qualitative course evaluation tool among business research students

Student evaluations of teaching and learning are playing an increasingly important role in the delivery of high-quality, student-centred education. Insights into student perceptions of their learning experience provide important information that can be used to inform course design and development. The majority of course evaluations take the form of quantitative surveys, but research suggests that a reliance on survey data alone can be problematic from a teaching and learning perspective. Qualitative course evaluations have been cited as a viable alternative to quantitative evaluations, but less research has been conducted into their efficacy when compared to quantitative evaluations. The study on which this article reports attempted to contribute to addressing this shortcoming by describing and assessing a novel approach to eliciting qualitative feedback from students in a research methodology course at a higher education institution in South Africa. Conventional content analysis was used to analyse the qualitative feedback received from students. The qualitative course evaluation approach was then appraised in terms of the degree to which it has the potential to overcome the shortcomings associated with quantitative course evaluations and the extent to which the information gathered could be used to improve the design and delivery of the academic programme

Taxonomy of Cyber Crimes and legislation in Pakistan

We inhabit a rapidly developing world of shifting trends. Information Technology that was considered as a key contributor in progress of any country has evolved into a nightmare in form of Cyber Crimes. Despite proper legislation, most of such offences of less severe nature remain veiled in Pakistan. Deficient law enforcement and absence of an international treaty against Cyber Crimes helps Cyber Criminals skip unscathed. Study will focus on categorization of Cyber Crimes and legislation in Pakistan to cope with these crimes. We report some Cyber Crimes that still lie outside law jurisdictions in Pakistan and take a look at reported crimes in IT history of Pakistan

Machine learning based computer-aided diagnosis of liver tumours

Image processing plays a vital role in the early detection and diagnosis of Hepatocellular Carcinoma (HCC). In this paper, we present a computational intelligence based Computer-Aided Diagnosis (CAD) system that helps medical specialists detect and diagnose HCC in its initial stages. The proposed CAD comprises the following stages: image enhancement, liver segmentation, feature extraction and characterization of HCC by means of classifiers. In the proposed CAD framework, a Discrete Wavelet Transform (DWT) based feature extraction and Support Vector Machine (SVM) based classification methods are introduced for HCC diagnosis. For training and testing, the recorded biomarkers and the associated imaging data are fused. The classification accuracy of the proposed system is critically analyzed and compared with state-of-the-art machine learning algorithms. In addition, laboratory biomarkers are also used to cross-validate the diagnosis

Comparing Clinical Outcomes of COVID-19 and Influenza-Induced Acute Respiratory Distress Syndrome: A Propensity-Matched Analysis

Acute respiratory distress syndrome (ARDS) is one the leading causes of mortality and morbidity in patients with COVID-19 and Influenza, with only small number of studies comparing these two viral illnesses in the setting of ARDS. Given the pathogenic differences in the two viruses, this study shows trends in national hospitalization and outcomes associated with COVID-19- and Influenza-related ARDS. To evaluate and compare the risk factors and rates of the adverse clinical outcomes in patients with COVID-19 associated ARDS (C-ARDS) relative to Influenza-related ARDS (I-ARDS), we utilized the National Inpatient Sample (NIS) database 2020. Our sample includes 106,720 patients hospitalized with either C-ARDS or I-ARDS between January and December 2020, of which 103,845 (97.3%) had C-ARDS and 2875 (2.7%) had I-ARDS. Propensity-matched analysis demonstrated a significantly higher in-hospital mortality (aOR 3.2, 95% CI 2.5–4.2, p p < 0.001), higher likelihood of requiring vasopressors (aOR 1.7, 95% CI 2.5–4.2) and invasive mechanical ventilation (IMV) (aOR 1.6, 95% CI 1.3–2.1) in C-ARDS patients. Our study shows that COVID-19-related ARDS patients had a higher rate of complications, including higher in-hospital mortality and a higher need for vasopressors and invasive mechanical ventilation relative to Influenza-related ARDS; however, it also showed an increased utilization of mechanical circulatory support and non-invasive ventilation in Influenza-related ARDS. It emphasizes the need for early detection and management of COVID-19