{"title":"The Design of the IoT-based Smart Bin System","authors":"Emanuel N. Madihlaba, Vusumuzi Malele, P. Owolawi","doi":"10.59200/iconic.2022.020","DOIUrl":"https://doi.org/10.59200/iconic.2022.020","url":null,"abstract":"Waste management is one of the major problems facing the world today. The trash spillover from garbage/trash/dust bins and the waste handling facilities creates unnecessary waste that needs to be managed. For example, in certain areas, trash spillover generates pollution as well as various hazardous situations. In this regard, unmanaged trash may be the root cause of spreading various diseases as well as creating huge waste management and health issues. To overcome the challenges, as well as maintain public health and cleanliness, this paper proposes the design of an Internet-of-Things (IoTs) based garbage monitoring system that measures the level of garbage in the bins and alerts the relevant stakeholders. It uses an ultrasonic sensor, load sensor, Arduino board, GPS Module, buzzer, and a motor and trash bin (with lid) to design the system. A sensor network was used to collect the levels in the trash bins and remotely trigger the required controls using IoTs. An ultrasonic sensor is interfaced with an Arduino board to check the level of trash and display the data on a dashboard. The administrator will then execute the required notification and dispatch authorized action. In this regard, waste management will be efficient, and overspill managed.","PeriodicalId":225070,"journal":{"name":"International Conference on Intelligent and Innovative Computing Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114600245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Investigating the Barriers of ICT use in Teaching and Learning at Public Schools in South Africa","authors":"Pelonomi Ramafi","doi":"10.59200/iconic.2022.010","DOIUrl":"https://doi.org/10.59200/iconic.2022.010","url":null,"abstract":"Information and Communication Technology (ICT) has reshaped the field of pedagogy in many different ways. The increasing use of ICT tools in teaching and learning cannot be overlooked. Learners who are abreast with technology are independent and relieve overreliance on educators regardless of the geographical distance. ICT is important within the education sector as a means of enhancing the process of teaching and learning in a classroom. Despite the value of effective use of ICT tools, the South African government, having designed the curriculum to support effective ICT use in teaching and learning, and to some extent having provided ICT tools in public schools, there are still challenges in the effective adoption of ICT tools in teaching and learning. The purpose of this study is to investigate the barriers of ICT tools use in teaching and learning and propose guidelines in teaching and learning at public schools. The study used a mixed-method and data was collected from public school teachers through a questionnaire composed of both qualitative and quantitative variables. To gain further insight, educationalists were also interviewed. It was discovered that there are multiple challenges that hinder the adoption of ICT in public schools. Key challenges identified in the study include a lack of ICT relevant skills, lack of ICT infrastructure, poor IT support, lack of efficacy from the level of teachers, and attitude were amongst many other influencers to non compliance. The study recommends that the government adopt an ICT framework that will advocate for unison in ICT policy from all walks of government (district, provincial, and national) and also mandate and incentivize use of ICT by teachers in teaching and learning.","PeriodicalId":225070,"journal":{"name":"International Conference on Intelligent and Innovative Computing Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127469281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Malehlohonolo Yvonne Pitsane, Hope Mogale, JT Janse van Rensburg
{"title":"Improving Accuracy of Credit Card Fraud Detection Using Supervised Machine Learning Models and Dimension Reduction","authors":"Malehlohonolo Yvonne Pitsane, Hope Mogale, JT Janse van Rensburg","doi":"10.59200/iconic.2022.032","DOIUrl":"https://doi.org/10.59200/iconic.2022.032","url":null,"abstract":"Credit card fraud is a serious crime, and it is a common type of identity theft. Financial institutions and consumers are experiencing economical losses due to financial fraud caused by credit card transactions. Machine Learning Models can aid and alleviate credit card fraud by providing real time detection of credit card fraud before it takes place. The problem that arises with machine learning models is poor performance in terms of accuracy if the data objects in dataset have high dimensionality. In this paper we have tested and compared six machine learning models in detecting credit card fraud. Furthermore, dimension reduction techniques was used to improve the performance of these machine learning models. The results show improved accuracy on the machine learning models after applying dimension reduction and removing anomalies and imbalance.","PeriodicalId":225070,"journal":{"name":"International Conference on Intelligent and Innovative Computing Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115573484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}