Looking at the higher learning institutions, there is no question that the current methods for paying student fees are inefficient, inconvenient, and wasteful of time. In addition, the rise in the number of students studying in higher learning institutions has led to long frustrating queues and overcrowding in most financial institutions during payment of student fees. This paper sought to design and implement a secure block chain-based payment system for higher learning institutions in developing countries. Students are to use the proposed payment system to pay tuition fees and other student fees to their respective higher educational institution. In addition, students are to use the proposed payment to pay for goods and services provided by the institution and other merchants in the institution’s premises. In this study, object oriented software development methodology was used to implement the proposed payment system. The proposed system consists of a mobile e-wallet, RESTful API, and blockchain as the core component of the API.

In recent times, Hacking has turn out to be more unfavorable than ever in all life fields, including the healthcare systems, with an increasing usage of information technology. By the expansion of technology development, the attacks number is too rising every few months in an exponential manner, which in turn makes the conventional IDS incapable to perceive. A healthcare system network intrusion detection method is proposed depending on the Google NET convolution neural network (Google NET). In healthcare system databases, intrusion detection (KDDs) can be seen as a search issue, which might be solved with the use of Google NET CNN algorithms. After pre-processing and characterizing the healthcare system data (including Electronic Health Records (EHR), Medical imaging data, Electronic Medical Records (EMR), etc.), the Google NET CNN model is used to simulate the intrusion into the healthcare system data. The low-level data intrusion is signified conceptually as the superior features with Google NET CNN, which in turn extracts the sample features separately, and by using MFO, network parameter is optimized (algorithm of optimization to meet the representation. At last, a sample test is conducted for the detection of healthcare system network intrusion behavior. The simulation outcome illustrate that the proposed technique has high accuracy on detection and a lower false-positive rate along with true positive rate.

Most of the profound learning applications that we find locally are typically outfitted towards fields like advertising, deals, finance, and so on We scarcely at any point read articles or discover assets about profound getting the hang of being utilized to secure these items, and the business, from malware and programmer assaults. While the enormous innovation organizations like Google, Facebook, Microsoft, and Sales force have effectively implanted profound learning into their items, the online protection industry is as yet playing make up for lost time. It’s a difficult field however one that needs our complete consideration. we momentarily present Deep Learning (DL) alongside a couple of existing Information Security (therefore alluded to as Information security analysts ) applications it empowers. We then, at that point profound plunge into the intriguing issue of unknown pinnacle traffic discovery and furthermore present a DL-based answer for distinguish TOR traffic.