Predictive Intelligent Decision Support Model in Forecasting of the Diabetes Pandemic Using a Reinforcement Deep Learning Approach

Abstract

Diabetes has since become global pandemicwhich must be diagnosed early enough if the patients are to survive a while longer. Traditional means of detection has its limitations and defects. The adoption of data mining tools and adaptation of machine intelligence is to yield an approach of predictive diagnosis that offers solution to task, which traditional means do not proffer low-cost-effective results. The significance thus, is to investigate data feats rippled with ambiguities and noise as well as simulate model tractability in order to yield a low-cost and robust solution. Thus, we explore a deep learning ensemble for detection of diabetes as a decision support. Model achieved a 95-percent accuracy, with a sensitivity of 0.98. It also agrees with other studies that age, obesity, environ-conditions and family relation to the first/second degrees are critical factors to be watched for type-I and type-II management. While, mothers with/without previous case of gestational diabetes is confirmed if there is: (a) history of babies with weight above 4.5kg at birth, (b) resistant to insulin showing polycystic ovary syndrome, and (c) have abnormal tolerance to insulin.

Pilot Study on Web Server HoneyPot Integration Using Injection Approach for Malware Intrusion Detection

“…The system properly set-up will achieve: (a) the honeypot can easily identify the system vulnerabilities as an adversary tries to access the honey-net and used them, (b) the honey-net will gathered data that seeks to identify which methods was employed by the adversary to capture data, (c) the honey-net will seek alternatives to such attack with various purposes aimed to capture, delete or alter data in the server, (d) knowing that some adversaries access a network via malicious script on a user browser and/or via malware -the honey-net will monitor, redirect any user to other sites via designated URLs and/or detect user source IP, (e) for connection logs recorded, the honey-net will show which attack was done more frequently on the web application, (f) honey-net will seeks to unveil the source IP address of the adversaries as it employs trace-back to track the source and origin of the attacks, (g) with the connection logs studied, it tells the net-administrator the pattern of attacks that were successful and those that failed, (h) the recorded data on the honeypot will also further show tools and techniques are employed by hackers (Ejeh et al, 2022;Ejim, 2017;Iskandarov, 2020;Ojugo & Ekurume, 2021b.…”

Folding Bicycle Prospective Buyer Prediction Model

“…Therefore, identifying the factors that increase customer churn is very important. In addition, the use of machine learning is ubiquitous from the classification of fake news [14] to the prediction of student performance [15], and it is not rare to be used for forecasting of the diabetes pandemic [16].…”

BEHeDaS: A Blockchain Electronic Health Data System for Secure Medical Records Exchange

“…Patient records and history are often not readily available to expert personnel to medical facilities other than where such records are created [5]. Other include: (a) issues of care coordination, (b) non-provision of telemedicine, as patients have access and control of their medical records [6]- [8], (c) corruption of patient records via tampering, stealing, or mishandling [9], and (d) patient record exchange with unauthorized medical experts with or without a patients' consent [10]- [12]. With EMRs -critical platform interoperability issues for data exchange, confidentiality, privacy, and security must be addressed urgently.…”