Search

Background: As a result of the COVID-19 pandemic, various clinical intervention methods and AI methods have been employed in the detection, diagnosis, and prognosis of COVID-19 cases. However, limited instances of applying AI to the prognosis of COVID-19 cases in Africa have been reported in the literature. Thus, case studies on the application of machine learning to guide for decision-making on the treatment of COVID-19 cases in Africa are essential. Methods: We applied three machine learning (ML) algorithms: Deep Multi-layer Perceptron (Deep MLP), Extreme Boosted Trees (XGBoost) and Support Vector Machines (SVM) for predicting the outcome of intensive care patients of COVID-19 with comorbidities in a South African hospital. We compared the performance and interpretability of the three ML models when cross-validation (CV) and principal component analysis (PCA) were applied for the prognosis of COVID-19 mortality risk. Results: We found that Deep MLP had the best overall performance when CV and SMOTE were applied without PCA (F1=0.92; AUC = 0.94), followed by SVM (F1=0.83; AUC=0.82). We found that the performance of both SVM and MLP can be enhanced through CV without PCA. XGBoost (F1= 0.81; AUC = 0.79) performed best when none of CV, PCA or SMOTE was applied. XGBoost is not affected by CV and performs worse with PCA. From the model predictions, we identified Length of stay in the hospital, Duration in ICU, Time to ICU from Admission, Days discharged or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for the prediction of mortality or survival of the COVID-19 patients. We also found other variables: Age at admission, Pf Ratio (PaO2/FiO2 ratio), TropT, Ferritin, ventilation, CRP, and Symptom of Acute respiratory distress syndrome (ARDS) associated with the severity and fatality of COVID-19 cases. Conclusions: This study demonstrates how ML can be applied to identify variables that have prognostic value in the treatment and management of critically ill COVID-19 patients. The findings also reveal the effect of CV and PCA when predicting clinical outcomes of COVID-19 cases

Phishing is a cyber-attack that uses disguised email as a weapon and has been on the rise in recent times. Innocent Internet user if peradventure clicking on a fraudulent link may cause him to fall victim of divulging his personal information such as credit card pin, login credentials, banking information and other sensitive information. There are many ways in which the attackers can trick victims to reveal their personal information. In this article, we select important phishing URLs features that can be used by attacker to trick Internet users into taking the attacker’s desired action. We use two machine learning techniques to accurately classify our data sets. We compare the performance of other related techniques with our scheme. The results of the experiments show that the approach is highly effective in detecting phishing URLs and attained an accuracy of 97.8% with 1.06% false positive rate, 0.5% false negative rate, and an error rate of 0.3%. The proposed scheme performs better compared to other selected related work. This shows that our approach can be used for real-time application in detecting phishing URLs.

Wireless sensor networks have been widely applied in many areas due to their unique characteristics. These have exposed them to different types of active and passive attacks. In the literature, several solutions have been proposed to mitigate these attacks. Most of the proposed solutions are too complex to be implemented in wireless sensor networks considering the resource-constraint of sensor nodes. In this work, we proposed a hierarchical trust mechanism based on clustering approach to detect and prevent denial of service attacks in wireless sensor networks. The approach was validated through simulation using Network Simulator (NS2). The following metrics were used to evaluation the proposed scheme: packet delivery ratio, network lifetime, routing delay, overhead, and number of nodes. The proposed approach is capable of detecting compromised sensor nodes vulnerable to a denial of service attacks. Moreover, it is able to detect all sensed data that have been compromised during transmission to the base station. The results show that our method can effectively detect and defend against denial of service attacks in sensor wireless sensor networks.

The threat caused by the ubiquitous nature of the internet and quantum-computer-assisted cryptanalysis amongst the networked devices, organizations and users seeks a redress within the security community to apply innovative ways in the development of information communication protocols. Quantum teleportation (QT) has emerged as a technology that promise unconditional security and providing new ways to design and develop frameworks that operate based on the principles of quantum physics such as entanglement and no-cloning. This study applied an evaluation approach to empirically investigate the effect of QT on the improvement of networked device communication security on layer 2 using a simulated experiment. Software Defined Network's OpenFlow, Mininet-Wifi and Simulator for Quantum Networks and Channels (SQUANCH) were considered as our coding approach. The empirical evidence revealed that QT has the ability to make it harder for anyone trying to listen-in, eavesdrop or tap into data in-transit. QT despite its known challenges i.e., interference, freespace loss, transmission loss etc., when implemented properly within reasonable scope can improve security for data in-transit. This will become an advantage when designing frameworks to adopt quantum technology for implementation.

A generic framework for the implementation of secure quantum teleportation infrastructure is proposed and presented to enhance data transmission security for cloud and data centres operating on OSI layer 2 over the Wide Area Network. Quantum Teleportation (QT) and its mechanisms promises unconditional security through the use of unconventional methods. Currently there is a lack of generic framework with models and methods to guide the implementation of QT infrastructures to enable secure transmission of information. Design Science Research was undertaken to design and develop the artefact. The proposed generic secure QT framework artefact forms a step towards realizing an abstract and low-level architecture developments to deploy a full-scale quantum teleportation infrastructure rollout.

Wireless sensor networks (WSNs) have been recognized as one of the most essential technologies of the twenty-first century. The applications of WSNs are rapidly increasing in almost every sector because they can be deployed in areas where cable and power supply are difficult to use. In the literature, different methods have been proposed to minimize the energy consumption of sensor nodes to prolong WSNs utilization. In this article, we propose an efficient routing protocol for data transmission in WSNs; it is called energy-efficient hierarchical routing protocol for wireless sensor networks based on fog computing. Fog computing is integrated into the proposed scheme due to its capability to optimize the limited power source of WSNs and its ability to scale up to the requirements of the Internet of things applications. In addition, we propose an improved ant colony optimization algorithm that can be used to construct an optimal path for efficient data transmission for sensor nodes. The performance of the proposed scheme is evaluated in comparison with P-SEP, EDCF, and RABACO schemes. The results of the simulations show that the proposed approach can minimize sensor nodes’ energy consumption, data packet losses, and extends the network lifetime. We are aware that in WSNs, the certainty of the sensed data collected by a sensor node can vary due to many reasons such as environmental factors, drained energy, and hardware failures.

This paper discusses the system architecture of a cloud-native Weigh-In-Motion (WIM) system prototype. The prototype WIM system was developed to ingest Vehicular Telematics (VT) data from several Internet of Vehicles (IoV). It is a necessity to build a cloud-native system to serve VT data from IoV devices. There are some design issues to be considered before designing. This paper discusses how the system prototype was designed by considering the design aspects as a reactive system. Four major design solutions, 1) Kubernetes, 2) GlusterFS, 3) Kafka Cluster, 4) Akka Streams are used in the design. The explanations for choosing the solutions were also discussed.

Concept Knowledge (C-K) theory has been used in engineering and science-based research for more than a decade. Design of an Information Technology (IT) artefact is mostly pragmatic in nature. Design Science Research (DSR) methodology applied and studied in many Information Systems (IS) research. Many sub design decisions involved through the design of an IT artefact from a concept (idea) to a working prototype. A DSR artefact is based on a combination of decisions made during several sub-design stages. Artefacts are built based on the selection of elements in each sub-design space. Recording the design decisions on each sub-design space would be beneficial for future researchers. By knowing the design decisions on each sub-design space, researchers would be able to try different combinations of the design. C-K theory provides the ability to capture the design processes’ several sub-design spaces. In this paper, we discuss the DSR research methodology by looking at the stages proposed in the literature, and the application of C-K theory in an IT-based DSR. This paper also proposed a C-K theory-based protocol called Concept Tree for tracking and reporting artefact design steps. The application of C-K theory in DSR is exhibited using the implementation of the Concept Tree for a prototype design IT artefact.

The advent of the Internet in the late 1990s led to the increased flood of data termed Big Data. To derive meaningful value from big data, specialized tools and techniques are required. These tools and techniques are categorized under data management and data analysis. Under data analysis, predictive tools and techniques exists. These predictive tools and techniques use statistical models and machine learning algorithms to predict future events. Models are developed using probability theories such as Bayesian networks. However, development of probabilistic models require extreme technical expertise and it is a difficult task to model complex real-life situations. Thus, the emergence of probabilistic programming. The idea of probabilistic programming is new and its potential in AI and big data processing is important. This paper presents discussions on probabilistic reasoning and probabilistic programming with respect to big data. An investigation of the potential of probabilistic programming in big data is also presented by conducting a search through literature to find available big data solutions that use probabilistic programming. This search found one solution called InferSpark built on top of Apache Spark to process big data. This is an indication that more big data applications that uses the concept of probabilistic programming needs to be done.

There is a huge increase in the amount of generated data since the explosion of the Internet. This generated data which is usually collected in different formats and from multiple sources is popularly termed Big Data. Big data contains uncertainty. To handle uncertainty in big data, probabilistic reasoning is used to develop probabilistic models that specify generic knowledge in different topics. These models are used in conjunction with an inference algorithm to enable decision makers especially during uncertain situations. Extensive knowledge in fields such as statistics, machine learning and probability theories are employed in the development of these probabilistic models. Thus, it is usually a difficult undertaking. Probabilistic programming was introduced to simplify and enable development of complex models. Again, decision makers often need to use knowledge from historic data as well as current data to make cogent decisions. Thus, the necessity to unify processing of historic and real-time data with low latency. The Lambda architecture was introduced for this purpose. This paper presents a framework called Kognitor that simplifies the design and development of difficult models using probabilistic programming and Lambda architecture. Evaluation of this framework is also presented in this paper using a case study to highlight the crucial potential of probabilistic programming to achieve simplification of model development and enable real-time reasoning on big data. Thus, demonstrating the effectiveness of the framework. Finally, results of this evaluation are presented in this paper. The Kognitor framework can be used to steer effective and easier implementation of complicated real-life situations as probabilistic models. This will be beneficial in the big data processing domain and for decision makers. Kognitor ensures cost-effectiveness using contemporary big data tools and technology on commodity hardware. Kognitor framework will also be beneficial in academia with respect to the use of probabilistic programming.

Road Safety is a major concern around the world. Telematic solutions have been available for more than a decade, and several studies have been done in the use of telematics data in road safety. However, these studies are scattered on different topics. There is a need to find the best possible ways of using telematics data for safe driving. This paper presents the review made with the aim of finding the evidence on the effective use of telematics data for road safety. Summary of the data collection devices, sensors, features, algorithms, feedback types used are discussed in this paper.

Driving overloaded vehicle causes road infrastructural damages, accidents, air pollution by excessive fuel consumption, and unusual expenses. Measuring the gross weight of a vehicle on a particular road segment without interrupting the traffic flow is a problem worth researching, and its solutions have several economic benefits. This paper proposes an alternative way of finding an overloaded vehicle in motion, by introducing a novel approach in inferring the weight of a vehicle on a road segment using Telematics data and Machine Learning.