Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

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A systematic mapping study of performance analysis and modelling of cloud systems and applications

Cloud computing is a paradigm that uses utility-driven models in providing dynamic services to clients at all levels. Performance analysis and modelling is essential because of service level agreement guarantees. Studies on performance analysis and modelling are increasing in a productive manner on the cloud landscape on issues like virtual machines and data storage. The objective of this study is to conduct a systematic mapping study of performance analysis and modelling of cloud systems and applications. A systematic mapping study is useful in visualization and summarizing the research carried in an area of interest. The systematic study provided an overview of studies on this subject by using a structure, based on categorization. The results are presented in terms of research such as evaluation and solution, and contribution such as tools and method utilized. The results showed that there were more discussions on optimization in relation to tool, method and process with 6.42%, 14.29% and 7.62% respectively. In addition, analysis based on designs in terms of model had 14.29% and publication relating to optimization in terms of evaluation research had 9.77%, validation 7.52%, experience 3.01%, and solution 10.51%. Research gaps were identified and should motivate researchers in pursuing further research direction

Prediction of stroke disease with demographic and behavioural data using random forest algorithm

Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms to reduce its occurrence. However, these studies pay less attention to the predictors (both demographic and behavioural). Our study considers interpretability, robustness, and generalisation as key themes for deploying algorithms in the medical domain. Based on this background, we propose the use of random forest for stroke incidence prediction. Results from our experiment showed that random forest (RF) outperformed decision tree (DT) and logistic regression (LR) with a macro F1 score of 94%. Our findings indicated age and body mass index (BMI) as the most significant predictors of stroke disease incidence

An enhanced IoT-Based array of sensors for Monitoring Patients’ Health

This book promotes and facilitates exchanges of research knowledge and findings across different disciplines on the design and investigation of machine learning-based data analytics of IoT infrastructures

A BIMODAL BIOMETRIC BANK VAULT ACCESS CONTROL SYSTEM

The bank vault system has security as its most important aim. Banks could go bankrupt if the vault’s security system becomes compromised. In this paper, the use of bimodal biometrics (fingerprint and iris) is proposed as a means of ensuring the full integrity of the bank’s vault system, thus, further reducing the rate of compromise and theft within the bank’s vault system. A scanner captures the fingerprint and the iris of authorized users. The images of the fingerprint and iris captured by the scanner are segmented, normalized and made into templates that are stored in a database along with the particulars of the users. The accuracy of the system is measured in terms of sample acquisition error and recognition performance using False Accept Rate (FAR), False Identification Rate (FIR) and False Reject Rate (FRR). The result shows that the proposed system is very effective

Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated

A SMART AIR POLLUTION MONITORING SYSTEM

Air pollution affects our day to day activities and quality of life. It poses a threat to the ecosystem and the quality of life on the planet. The dire need to monitor air quality is very glaring, owing to increased industrial activities over the past years. People need to know the extent to which their activities affect air quality. This project proposes an air pollution monitoring system. The system was developed using the Arduino microcontroller. The air pollution monitoring system was designed to monitor and analyze air quality in real-time and log data to a remote server, keeping the data updated over the internet. Air quality measurements were taken based on the Parts per Million (PPM) metrics and analyzed using Microsoft Excel. The air quality measurements taken by the designed system was accurate. The result was displayed on the designed hardware’s display interface and could be accessed via the cloud on any smart mobile device

From Modeling to Code Generation: An Enhanced and Integrated Approach

Information system drives every aspect of human endeavor, and it is a major stakeholder in human existence. Systems with poor modeling suffer a lot from poor implementation down to poor performance due to lack of critical subjection and testing. Software modeling is, therefore, of paramount importance in order to achieve a reliable system. There has been a lot of works done in software modeling, and eventually, the Universal Modeling Language was formulated to create a standard for software modeling. Although there have been some development or modeling tools that can be used to model a software system and the design then converted to software codes that can then be perfected, none of these tools has considered security and integrated as a single tool. Therefore, this paper focuses on building an integrated system (all-encompassing system) for building UMLsec-based modeled systems that will convert UML diagrams to code. The system integrates Eclipse Mars incorporated with Papyrus modeling plug-ins and Eclipse Kepler with Java EE incorporated with CARiSMA plug-ins. These four tools were integrated together by an executable application built with NetBeans. The system was tested by modeling an e-government system from the class diagram to analysis and code generation