The novel severe contagious respiratory syndrome coronavirus called COVID-19 has caused the most significant global challenge and public health, after the pandemic of influenza outbreak of 1918. The adoption of the Internet of Things (IoT) and cloud computing in managing big data during an infectious disease outbreak has brought several opportunities. However, the required resources to collect such big data in a cloud-IoT platform are still a big challenge. Therefore, to efficiently manage an immense amount of data in an integrated Cloud-IoMT platform, this study recommends a framework for Cloud-IoMT-based big data analytics. The data gathered from diverse wearable sensors such as body temperature, glucose sensors, heartbeat sensor, and chest were conveyed via IoMT gadgets to the integrated cloud with the data analytics layer. The cloud database Hadoop MapReduce methods can be used to process vast amounts of data collected during COVID-19 monitoring and surveillance in parallel. For speedy tracking, evaluation, decision-making, and improved care approvals from doctors via IoMT devices to the advanced cloud and data analytics layer, data are gathered from diverse wearable sensors. The use of internal network by Cloud-IoMT-based big data analytics can help in tracking patient health conditions in real time globally, and this will reduce the workloads and pressure on the healthcare professionals, increase the accuracy of medical diagnosis, reduce healthcare costs, and lead to patient treatment satisfaction during the COVID-19 pandemic.

Cloud and IoMT-Based Big Data Analytics System During COVID-19 Pandemic

Malaria has been identified to be one of the most common diseases with a great public health problem globally and it is caused by mosquitos’ parasites. This prevails in developing nations where healthcare facilities are not enough for the patients. The technological advancement in medicine has resulted in the collection of huge volumes of data from various sources in different formats. A reliable and early parasite-based diagnosis, identification of symptoms, disease monitoring, and prescription are crucial to decreasing malaria occurrence in Nigeria. Hence, the use of deep and machine learning models is essentials to reduce the effect of malaria-endemic and for better predictive models. Therefore, this paper proposes a framework to predict malaria-endemic in Nigeria. To predict the malaria-endemic well, both environmental and clinical data were used using Kwara State as a case study. The study used a deep learning algorithm as a classifier for the proposed system. Three locations were selected from Irepodun Local Government Areas of Kwara State with 34 months periodic pattern. Each location reacted differently based on environmental factors. The findings indicate that both factors are significant in malaria prediction and transmission. The LSTM algorithm provides an efficient method for detecting situations of widespread malaria.

Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data.

Application of Artificial Intelligence and Big Data for Fighting COVID-19 Pandemic

The novel severe contagious respiratory syndrome coronavirus called (COVID-19) has caused the greatest global challenge and public health, after the pandemic of influenza outbreak of 1918. The adoption of the Internet of Medical Things (IoMT) and wearable sensor in managing patients during any infectious disease outbreak have brought several opportunities. Until now there has been a rapid increase in diverts research works to find a lasting solution to this worldwide threat. The edge and IoMT smart healthcare based is gaining impact to deal with COVID-19 in this digital technology era. The popularity of wearable devices enables a new perspective for the precaution of infectious diseases. Hence, for the continuous monitoring of patients, wearable and implantable body area network systems are very useful during the COVID-19 outbreak. Therefore, this paper presents the applicability of edge-IoMT-based systems in medicine to minimize the works load of medical practitioners, caregivers and help people live an independent life during the COVID-19 pandemic, and besides providing people with quality care. Also, an intelligent Edge-IoMT-based architecture was proposing for monitoring patients during the COVID-19 outbreak. The edge computing was used to secure the capture data from the patients for proper decision making. The proposed system can be used in real-time by the medical personnel to advises patients about their health condition and to suggest preventive measures in saving lives. The significant contributions of this chapter are (a) Designed an architecture for MIoT-based Big Data Analytics. (b) presented several applicability prospects compelled by edge-IoMT-based system for fighting COVID-19 pandemic (c) lastly, the applicability of the proposed system was presented, and future directions of the system were discussed.

Artificial Intelligence and an Edge-IoMT-Based System for Combating COVID-19 Pandemic

Prediction and Forecasting of Coronavirus Cases Using Artificial Intelligence Algorithm

In the process of clarifying whether a patient or patients is suffering from a disease or not, diagnosis plays a significant role. The procedure is quite slow and cumbersome, and some patients may not be able to pursue the final test results and diagnosis. The method in this paper comprises many fact-finding and data-mining methods. Artificial Intelligence techniques such as Neural Networks and Fuzzy Logic were fussed together in emerging the Coactive Neuro-Fuzzy Expert System diagnostic tool. The authors conducted oral interviews with the medical practitioners whose knowledge were captured into the knowledge based of the Fuzzy Expert System. Neuro-Fuzzy expert system diagnostic software was implemented with Microsoft Visual C# (C Sharp) programming language and Microsoft SQL Server 2012 to manage the database. Questionnaires were administered to the patients and filled by the medical practitioners on behalf of the patients to capture the prevailing symptoms. The study demonstrated the practical application of neuro-fuzzy method in diagnosis of malaria. The hybrid learning rule has greatly enhanced the proposed system performance when compared with existing systems where only the back-propagation learning rule were used for implementation. It was concluded that the diagnostic expert system developed is as accurate as that of the medical experts in decision making. DIAGMAL is hereby recommended to medical practitioners as a diagnostic tool for malaria.

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Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System

HR's purpose is to assign the best people to the right job at the right time, train and qualify them, and provide evaluation methods to track their performance and safeguard employees' perspective skills. These data are crucial for decision-makers, but collecting the best and most useful information from such large amounts of data is tough. HR employees no longer need to manually handle vast amounts of data with the advent of data mining. The basic purpose of data mining is to extract information from hidden patterns and trends in data to get near-optimal results. This study aims at comparing the performance of three techniques in the prediction of performance. The dataset undergoes preprocessing steps that include data cleaning, and data compression using PCA. After preprocessing, training and classification were done using Artificial Neural Network, Random Forest, and Decision tree algorithm. The result showed that Artificial Neural networks performed the best for the prediction of employee performance.

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Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

Diagnosis takes a definitive role in the course of determining about clarifying patients as either having or not having the disorder. This method is relatively sluggish and tedious. Various fact-finding and data-mining methods are part of the approach of this article. In the development of the Collaborative Neuro-Fuzzy Expert System diagnosis platform, Neural Networks and Fuzzy Logic, which are artificial intelligence methods, have been merged together. Oral interviews were conducted with medical professionals whose experience was caught in the Expertise Developed Fuzzy Proficient Scheme. With Microsoft Visual C # (C Sharp) Programming Language and Microsoft SQL (Structured Query Language) Server 2012 to handle the database, the Neuro-Fuzzy Expert Framework diagnostic software was introduced. To capture the predominant signs, questionnaires were administered to the patients and filled out by the doctors on behalf of the patients.

A joint neuro-fuzzy malaria diagnosis system

Signature is a biometric trait that has piqued the interest of researchers. This is due to its high rate of acceptability. Offline signature in particular, has been around for a while and hence its suitability as a biometric trait. This paper proposes an offline signature recognition system using a multiple algorithm approach. The system accepts handwritten signature, filters the signature and crops the signature region. The Local Binary Pattern (LBP) of the signature image is then obtained. After this, Grey Level Co-occurrence Matrix (GLCM) is applied. Statistical features are then extracted. The difference in the stored features and the extracted features was obtained. The output is compared with a threshold for discrimination. This research aims at improving the performance of offline signature recognition using its textural features. The designed system gave an FRR and FAR of 8.6%, 4.6% respectively for MYCT signature database and 8.8%, 5.2% for GPDS signature database.

A Multiple Algorithm Approach to Textural Features Extraction in Offline Signature Recognition

Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the increase in the rate of poor graduates, it is necessary to design a system that helps to reduce this menace as well as reduce the incidence of students having to repeat due to poor performance or having to drop out of school altogether in the middle of the pursuit of their career. It is therefore necessary to study each one as well as their advantages and disadvantages, so as to determine which is more efficient in and in what case one should be preferred over the other. The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits so as to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution. The model was developed based on certain selected variables as the input. It achieved an accuracy of over 92.3 percent, showing Artificial Neural Network potential effectiveness as a predictive tool and a selection criterion for candidates seeking admission to a university.

Jide Kehinde Adeniyi

Papers

Diagmal: A Malaria Coactive Neuro-Fuzzy Expert System

Comparison of the Performance of Machine Learning Techniques in the Prediction of Employee

A joint neuro-fuzzy malaria diagnosis system

A Multiple Algorithm Approach to Textural Features Extraction in Offline Signature Recognition

Prediction of Students performance with Artificial Neural Network using Demographic Traits.