ASRIC Journal of Natural Sciences 2021 v1-i1
ISSN: 2795-3610
EISSN: 2795-3610
ASRIC Journal of Natural Sciences 2021 v1-i1
Abstract – The heart is an important organ in human beings. Because even a slight error might result in weariness or death, diagnosing and forecasting cardiac disorders requires increased precision, perfection, and accuracy. There are innumerable heart-related deaths, and the number is increasing significantly every day. To address the issue, researchers use a variety of data mining and machine learning approaches to evaluate massive amounts of complex medical data, assisting healthcare providers in the prediction of heart disease.
Published: 2021-12-04
Articles
Using Machine Learning Techniques for predicting Email Spam
Hager Mohey Abohalfaya1, Someya Mohsen1
Email has become one of the most efficient and cost-effective methods of communication in recent years. However, as the number of email users grows, so does the number of spam emails. Email management has become a big and rising concern for both people and companies as a consequence of its sensitivity to abuse. Spam, or the unsolicited sending of unwanted email messages, is one example of misuse.
Geospatial ICT infrastructure for City Management in Africa: Case study of Ngaoundéré (Cameroon)
Tchotsoua Michel1, Ndjeuto Tchouli Innocent Prosper2, Petnga Nyamen Simon Pierre1, Mouhaman Issouhou2
The city of Ngaoundere is experiencing an accelerated and unplanned urban growth. Abundant rainfall accelerates flooding and erosion. Given its location near the Sahel, Ngaoundéré is also very exposed to the effects of climate change. With support from the World Bank, through the Global Mechanism for Disaster Reduction and Disaster Recovery Program and the Open Data for Resilience initiative of th
Borehole Water Quality within the Federal University of Technology, Akure, Nigeria
Samuel B. Akeju1, Ochuko M. Ojo2 and James R. Adewumi3
The aim of this study is to determine the quality of borehole water supplied within the Federal University of Technology, Akure, Nigeria in order to ascertain its suitability for drinking purpose. Water samples were procured from 16 boreholes within the Federal University of Technology, Akure. The physico-chemical parameters determined include turbidity, pH, total dissolved solids, chloride, nitra
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