Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051492
Cecilia Ajowho Adenusi, Olufunke Rebecca Vincent, Abayomi-Alli A., Olaniyi Mathew Olayiwola, Bakare Olawunmi Shamsudeen, Sayikanmi Titilayo Mary
Researchers and investors have been paying close attention to the application of Artificial Intelligence models to the economics, agriculture and other fields in recent years. This study uses a Multilayer Perceptron Artificial Neural Network to anticipate the effect of covid-19 on crude-oil prices, continuing the deep learning trend and also applied the use of time series model known as Autoregressive Integrated Moving Average (ARIMA) to validate the result gotten from MLP-ANN. The results produced accurately predicted crude oil prices, and covid-19 data was also analyzed, as well as the association between crude-oil prices and covid-19. Because of the substantial causative association between the coronavirus (number of confirmed cases), crude oil prices, this study is intriguing. Ten years forecast was done using both MLP-ANN and ARIMA and from result gotten, MLP-ANN has accuracy of 96% while ARIMA has 39% accuracy.
{"title":"PREDICTING THE UPSHOT OF COVID-19 ON CRUDE-OIL PRICES IN NIGERIA USING MLPARIMA MODEL","authors":"Cecilia Ajowho Adenusi, Olufunke Rebecca Vincent, Abayomi-Alli A., Olaniyi Mathew Olayiwola, Bakare Olawunmi Shamsudeen, Sayikanmi Titilayo Mary","doi":"10.1109/ITED56637.2022.10051492","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051492","url":null,"abstract":"Researchers and investors have been paying close attention to the application of Artificial Intelligence models to the economics, agriculture and other fields in recent years. This study uses a Multilayer Perceptron Artificial Neural Network to anticipate the effect of covid-19 on crude-oil prices, continuing the deep learning trend and also applied the use of time series model known as Autoregressive Integrated Moving Average (ARIMA) to validate the result gotten from MLP-ANN. The results produced accurately predicted crude oil prices, and covid-19 data was also analyzed, as well as the association between crude-oil prices and covid-19. Because of the substantial causative association between the coronavirus (number of confirmed cases), crude oil prices, this study is intriguing. Ten years forecast was done using both MLP-ANN and ARIMA and from result gotten, MLP-ANN has accuracy of 96% while ARIMA has 39% accuracy.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133804780","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051365
Jesufunbi Damilola Bolarinwa, O. R. Vincent, O. Ojo, M. Omeike, Abayomi Victor Opakunle, Olawale David Oyedeji
The coronavirus outbreak in 2020 has made it difficult to implement macroeconomic initiatives and has affected the economy in all countries in Africa. There has been a lot of concern regarding how to stabilize the economy at least to where it was before the coronavirus outbreak. There was increased governmental allocation to combat the spread and reduce COVID-19's impacts. This study evaluates the economic impacts of the COVID-19 pandemic on some African countries and examines the cognitive analysis as it affects the economy considering layoffs and other revenue losses, as well as a consistent recession and deterioration in the banking and economic sectors. A linear regression method was used in the analysis of this work. Although the pandemic affects every aspect of life and society at large, this study examines how it affects the nation's economy. It was recognized that numerous policy instruments, including those connected to health and social protection, fiscal policy, and financial, industrial, and trade policies, needed to be implemented for the economy to recover properly from the financial loss. The analysis of the data, shows that there was a reduction in the GDP of each country during the Covid-19 pandemic. It is predicted that adopting these technologies may minimize suffering among people and aid in the economy's recovery from recession and bankruptcy.
{"title":"A Cognitive Analysis of Covid-19 on the Africa Economy Using Linear Regression","authors":"Jesufunbi Damilola Bolarinwa, O. R. Vincent, O. Ojo, M. Omeike, Abayomi Victor Opakunle, Olawale David Oyedeji","doi":"10.1109/ITED56637.2022.10051365","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051365","url":null,"abstract":"The coronavirus outbreak in 2020 has made it difficult to implement macroeconomic initiatives and has affected the economy in all countries in Africa. There has been a lot of concern regarding how to stabilize the economy at least to where it was before the coronavirus outbreak. There was increased governmental allocation to combat the spread and reduce COVID-19's impacts. This study evaluates the economic impacts of the COVID-19 pandemic on some African countries and examines the cognitive analysis as it affects the economy considering layoffs and other revenue losses, as well as a consistent recession and deterioration in the banking and economic sectors. A linear regression method was used in the analysis of this work. Although the pandemic affects every aspect of life and society at large, this study examines how it affects the nation's economy. It was recognized that numerous policy instruments, including those connected to health and social protection, fiscal policy, and financial, industrial, and trade policies, needed to be implemented for the economy to recover properly from the financial loss. The analysis of the data, shows that there was a reduction in the GDP of each country during the Covid-19 pandemic. It is predicted that adopting these technologies may minimize suffering among people and aid in the economy's recovery from recession and bankruptcy.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132020438","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051541
Justin Onyarin Ogala, E. S. Mughele, S. Chiemeke
Nigerian researchers have shown that the lack of adequate mechanisms for fraud detection has impaired both providers and beneficiaries of this scheme. This work develops a fraud detection program for Nigeria's National Health Insurance Scheme (NHIS). Nigeria's National Health Insurance Scheme (NHIS) and Health Maintenance Organizations (HMOs) are the subjects of this study. The study was conducted using available data from NHIS-registered healthcare facilities and HMOs. Unified Modeling Language (UML) tools were used to create the framework. The framework was built with Apache Derby DB, Hadoop Distributed File System (HDFS), and Apache MapReduce as the big data processing platform. Using Apache Hive and Big Data Analytics, a system for detecting healthcare fraud is developed. This system used data from the Nigerian National Health Insurance Scheme (NHIS), which was broken down into three categories: enrolment, referral, and claim data. The analysis of current healthcare investigative methods is conducted, and a new framework is proposed.
{"title":"Fraud Detection System for Effective Healthcare Administration in Nigeria using Apache Hive and Big Data Analytics: Reflection on the National Health Insurance Scheme","authors":"Justin Onyarin Ogala, E. S. Mughele, S. Chiemeke","doi":"10.1109/ITED56637.2022.10051541","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051541","url":null,"abstract":"Nigerian researchers have shown that the lack of adequate mechanisms for fraud detection has impaired both providers and beneficiaries of this scheme. This work develops a fraud detection program for Nigeria's National Health Insurance Scheme (NHIS). Nigeria's National Health Insurance Scheme (NHIS) and Health Maintenance Organizations (HMOs) are the subjects of this study. The study was conducted using available data from NHIS-registered healthcare facilities and HMOs. Unified Modeling Language (UML) tools were used to create the framework. The framework was built with Apache Derby DB, Hadoop Distributed File System (HDFS), and Apache MapReduce as the big data processing platform. Using Apache Hive and Big Data Analytics, a system for detecting healthcare fraud is developed. This system used data from the Nigerian National Health Insurance Scheme (NHIS), which was broken down into three categories: enrolment, referral, and claim data. The analysis of current healthcare investigative methods is conducted, and a new framework is proposed.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132192070","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051577
H. E. Amhenrior, I. Oloma, Braimoh A. Ikharo
This paper presents a GSM and Wi-Fi based Low Cost Single phase Smart Prepaid Energy Meter with some functionalities such as keypad and screen implemented in embedded software. The design was borne out of the desire to ensure that smart prepaid meters are made available at a reduced cost. The methodology involves hardware and software. The hardware made use of integrated circuits (ICs) and discrete components. At the heart of the design is Atmega644P microcontroller which was interfaced to ADE7755 energy meter IC, GSM800L modem, Wi-Fi module, tamper switch, buzzer and other components. The microcontroller is used for monitoring and controlling the activities of the meter especially the ADE7755 used in producing pulses from consumption which are then measured and recorded by the microcontroller as Energy measurement. SIM800L was used to achieve communication between the meter and the web server. The Wi-Fi is used to access the meter for recharging. The software aspect involved the programming of the microcontroller using C++ and the use of a webpage with an associated web server for meter administration and control as well as meter token recharge validation. The prototype performed satisfactorily under different loads. Other functionalities such as tampering detection, token recharge, meter registration and meter information viewing on the webpage were successfully executed. Again, the cost of the meter is less than the cost of commercially available meters in Nigeria by N18,313.39. The GSM and Wi-Fi low cost smart energy meter designed and implemented worked satisfactorily and it is effective.
{"title":"Design and Implementation of a GSM and Wi-Fi Based Low Cost Smart Prepaid Energy Meter","authors":"H. E. Amhenrior, I. Oloma, Braimoh A. Ikharo","doi":"10.1109/ITED56637.2022.10051577","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051577","url":null,"abstract":"This paper presents a GSM and Wi-Fi based Low Cost Single phase Smart Prepaid Energy Meter with some functionalities such as keypad and screen implemented in embedded software. The design was borne out of the desire to ensure that smart prepaid meters are made available at a reduced cost. The methodology involves hardware and software. The hardware made use of integrated circuits (ICs) and discrete components. At the heart of the design is Atmega644P microcontroller which was interfaced to ADE7755 energy meter IC, GSM800L modem, Wi-Fi module, tamper switch, buzzer and other components. The microcontroller is used for monitoring and controlling the activities of the meter especially the ADE7755 used in producing pulses from consumption which are then measured and recorded by the microcontroller as Energy measurement. SIM800L was used to achieve communication between the meter and the web server. The Wi-Fi is used to access the meter for recharging. The software aspect involved the programming of the microcontroller using C++ and the use of a webpage with an associated web server for meter administration and control as well as meter token recharge validation. The prototype performed satisfactorily under different loads. Other functionalities such as tampering detection, token recharge, meter registration and meter information viewing on the webpage were successfully executed. Again, the cost of the meter is less than the cost of commercially available meters in Nigeria by N18,313.39. The GSM and Wi-Fi low cost smart energy meter designed and implemented worked satisfactorily and it is effective.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133683565","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051236
Kehinde Adebola Olatunji, A. Oguntimilehin, O. Adeyemo, O. Aweh, Adeola Ibukun Abiodun, O. Bello
One of the major illnesses combating human races is Skin disease. Some skin diseases if not detected and treated early can result into cancer - a killer disease or disfigure the bearer. Discovery of these diseases frequently relies on the expertise of the medical professionals and skin biopsy results, in which sometimes the accuracy and prediction is deficient and as well is time consuming. Misdiagnosis is very rampart because these diseases always look alike, and could possibly be mistaken for each other. Therefore, there is need for a computer-based system for skin disease identification and classification through images to improve the diagnostic accuracy as well as to handle the scarcity of human experts. The current research sought to classify three selected skin diseases (Benign keratosis, Actinic keratosis and Dermatofibroma) that could disfigure or lead to cancer if proper diagnosis is not given. A convolutional neural network method designed upon tensor flow framework was used for the classification of the diseases. At the end of the implementation, results from the proposed system exhibits disease identification accuracy of 72% for Benign keratosis, 77% for Actinic keratosis and 69% for Dermatofibroma.
{"title":"Skin Disease Classification using Deep Learning Methods","authors":"Kehinde Adebola Olatunji, A. Oguntimilehin, O. Adeyemo, O. Aweh, Adeola Ibukun Abiodun, O. Bello","doi":"10.1109/ITED56637.2022.10051236","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051236","url":null,"abstract":"One of the major illnesses combating human races is Skin disease. Some skin diseases if not detected and treated early can result into cancer - a killer disease or disfigure the bearer. Discovery of these diseases frequently relies on the expertise of the medical professionals and skin biopsy results, in which sometimes the accuracy and prediction is deficient and as well is time consuming. Misdiagnosis is very rampart because these diseases always look alike, and could possibly be mistaken for each other. Therefore, there is need for a computer-based system for skin disease identification and classification through images to improve the diagnostic accuracy as well as to handle the scarcity of human experts. The current research sought to classify three selected skin diseases (Benign keratosis, Actinic keratosis and Dermatofibroma) that could disfigure or lead to cancer if proper diagnosis is not given. A convolutional neural network method designed upon tensor flow framework was used for the classification of the diseases. At the end of the implementation, results from the proposed system exhibits disease identification accuracy of 72% for Benign keratosis, 77% for Actinic keratosis and 69% for Dermatofibroma.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127302744","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051602
Ralivat Haruna, A. Obiniyi, Muhammed Abdulkarim, A.A. Afolorunsho
As technology advances, the volume of textual material produced on the web has been steadily rising. It can take a lot of time and effort to extract useful information from textual data. Automatic text summarizing aims to create concise summaries that retain the most important parts of the source document. Transformer-based architectures have demonstrated excellently in Natural Language Processing (NLP), particularly when it comes to summarizing textual content. This paper presents a thorough analysis of the most recent advancements in transformer topologies for automatic text summarization, with a focus on the Bidirectional Autoregressive Transformer (BART). This paper highlights future directions for research on transformer-like models for autonomous text summarization, such as BART and BERT.
{"title":"Automatic Summarization of Scientific Documents Using Transformer Architectures: A Review","authors":"Ralivat Haruna, A. Obiniyi, Muhammed Abdulkarim, A.A. Afolorunsho","doi":"10.1109/ITED56637.2022.10051602","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051602","url":null,"abstract":"As technology advances, the volume of textual material produced on the web has been steadily rising. It can take a lot of time and effort to extract useful information from textual data. Automatic text summarizing aims to create concise summaries that retain the most important parts of the source document. Transformer-based architectures have demonstrated excellently in Natural Language Processing (NLP), particularly when it comes to summarizing textual content. This paper presents a thorough analysis of the most recent advancements in transformer topologies for automatic text summarization, with a focus on the Bidirectional Autoregressive Transformer (BART). This paper highlights future directions for research on transformer-like models for autonomous text summarization, such as BART and BERT.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127237901","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051271
T. M. Okediran, O. R. Vincent, A. O. Agbeyangi, A. Abayomi-Alli, O. Adeniran
House numbering is the act of assigning a unique number to each building in a street or area in order to make it easier to locate a specific building. Due to poor town and regional planning, street naming and house numbering are major challenges in Nigeria. The disadvantage is being unable to identify a specific house in a location. The purpose of this study is to use the Internet of Things (IoT) as a solution to address the issue of house numbering, specifically in the Ojo Local Government Area of Lagos State, by identifying houses on the street, numbering them, classifying the type of building, and storing the data in a database. The study employs a machine learning technique, the k-nearest neighbor classifier, to train and program the IoT device, with fifty houses serving as a case study. The work was tested using fifty houses to name the street, number the houses, and categorize them into five major groups. The use of Google Maps aided in determining the name and location of a street. The success rate was as high as 0.97 for training and testing data, indicating that the technique used is adequate to address street name and house numbering problems.
{"title":"Solving the House Numbering Problem in Nigeria: Internet of Things (IoT) As An Emerging Solution","authors":"T. M. Okediran, O. R. Vincent, A. O. Agbeyangi, A. Abayomi-Alli, O. Adeniran","doi":"10.1109/ITED56637.2022.10051271","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051271","url":null,"abstract":"House numbering is the act of assigning a unique number to each building in a street or area in order to make it easier to locate a specific building. Due to poor town and regional planning, street naming and house numbering are major challenges in Nigeria. The disadvantage is being unable to identify a specific house in a location. The purpose of this study is to use the Internet of Things (IoT) as a solution to address the issue of house numbering, specifically in the Ojo Local Government Area of Lagos State, by identifying houses on the street, numbering them, classifying the type of building, and storing the data in a database. The study employs a machine learning technique, the k-nearest neighbor classifier, to train and program the IoT device, with fifty houses serving as a case study. The work was tested using fifty houses to name the street, number the houses, and categorize them into five major groups. The use of Google Maps aided in determining the name and location of a street. The success rate was as high as 0.97 for training and testing data, indicating that the technique used is adequate to address street name and house numbering problems.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129662759","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051458
Donatus Uwadiae Irughe, Wilson Nwankwo, C. Nwankwo, Francis Uwadia
The rate of cyber-attacks on enterprise network is increasing daily, accounting for losses in billions of dollars yearly. Professor Soren Morgan once asserted that if it were measured as a country, then the damages from cybercrime alone estimated at $6 trillion globally in 2021, would be the world's third-largest economy after the U.S. and China. This study examines the implications of cyber resilience on the security of enterprise network operations with a view to ascertaining how organizations in different economic sectors adopt cyber resilience strategies towards protecting their network resources against data and privacy violations. Twenty organizations in three different sectors were studied. The findings showed that organizations in the two sectors: oil and gas, and finance and banking, demonstrate better cyber security infrastructure and the adoption of cyber resilience strategies respectively, whereas, organizations in the higher education sector did not show remarkable commitment to the adoption of good cyber security practices. Overall, all organizations appear rather slow in the implementation of multi-level resilience strategies.
{"title":"Resilience and Security on Enterprise Networks: A Multi-Sector Study","authors":"Donatus Uwadiae Irughe, Wilson Nwankwo, C. Nwankwo, Francis Uwadia","doi":"10.1109/ITED56637.2022.10051458","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051458","url":null,"abstract":"The rate of cyber-attacks on enterprise network is increasing daily, accounting for losses in billions of dollars yearly. Professor Soren Morgan once asserted that if it were measured as a country, then the damages from cybercrime alone estimated at $6 trillion globally in 2021, would be the world's third-largest economy after the U.S. and China. This study examines the implications of cyber resilience on the security of enterprise network operations with a view to ascertaining how organizations in different economic sectors adopt cyber resilience strategies towards protecting their network resources against data and privacy violations. Twenty organizations in three different sectors were studied. The findings showed that organizations in the two sectors: oil and gas, and finance and banking, demonstrate better cyber security infrastructure and the adoption of cyber resilience strategies respectively, whereas, organizations in the higher education sector did not show remarkable commitment to the adoption of good cyber security practices. Overall, all organizations appear rather slow in the implementation of multi-level resilience strategies.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127709165","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051209
Ayanseun S. Ayanboye, John E. Efiong, G. E. Alilu, Adedoyin I. Oyebade, B. Akinyemi
By establishing various simulated environments or specialized resources, a virtual environment enables users to interact with both the computing environment and the work of other users. Although these programs were created to limit attack vectors, the development of attack tools has made them great targets for cyberattacks. The Denial of Service (DoS) assault is the main consequence of successful attacks on the environment. In this study, the various security techniques for DoS attack in virtualized systems are evaluated, and the current state of intrusion detection, its processes, and potential future directions are covered.
{"title":"An Assessment of Security Techniques for Denial of Service Attack in Virtualized Environments","authors":"Ayanseun S. Ayanboye, John E. Efiong, G. E. Alilu, Adedoyin I. Oyebade, B. Akinyemi","doi":"10.1109/ITED56637.2022.10051209","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051209","url":null,"abstract":"By establishing various simulated environments or specialized resources, a virtual environment enables users to interact with both the computing environment and the work of other users. Although these programs were created to limit attack vectors, the development of attack tools has made them great targets for cyberattacks. The Denial of Service (DoS) assault is the main consequence of successful attacks on the environment. In this study, the various security techniques for DoS attack in virtualized systems are evaluated, and the current state of intrusion detection, its processes, and potential future directions are covered.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129941982","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051416
O. Boyinbode, Fiyinfoluwa G. Oyesanmi, Olumide Obe, O.F. Boyinbode
The lingering cases of building collapse in Nigeria and the attending rate of mortality call for urgent attention. Advances in Internet of Things (IoT) technology can be leveraged to facilitate the autonomous monitoring of buildings. In this paper, an IoT model was implemented using piezo electronic transducers and programmable microcontrollers to monitor structural health as well as a warning system to notify users when structural integrity becomes threatened. The data obtained from the sensors are stored on the cloud and can be used to develop a robust building information system.
{"title":"Implementation of Internet of Things for Structural Health Monitoring in Nigeria","authors":"O. Boyinbode, Fiyinfoluwa G. Oyesanmi, Olumide Obe, O.F. Boyinbode","doi":"10.1109/ITED56637.2022.10051416","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051416","url":null,"abstract":"The lingering cases of building collapse in Nigeria and the attending rate of mortality call for urgent attention. Advances in Internet of Things (IoT) technology can be leveraged to facilitate the autonomous monitoring of buildings. In this paper, an IoT model was implemented using piezo electronic transducers and programmable microcontrollers to monitor structural health as well as a warning system to notify users when structural integrity becomes threatened. The data obtained from the sensors are stored on the cloud and can be used to develop a robust building information system.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321909","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}
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