Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051610
U. Ibrahim, Moussa Boukar Mahatma, Muhammed Aliyu Suleiman
The idea of this paper is to present a framework for the development of the Hausa Speech recognition system. The framework comprises the creation of a dataset, and the development of acoustic, language, and speech models. The goal is to enhance speech recognition research for under-resourced languages such as the Hausa language. The paper presented work achieved so far by the researchers are creating and developing the Hausa dataset, acoustic model, language model and speech respectively. The dataset and models can be put together for the development of speech to text, text to speech, dictation application and speech to speech translator
{"title":"Framework for Hausa Speech Recognition","authors":"U. Ibrahim, Moussa Boukar Mahatma, Muhammed Aliyu Suleiman","doi":"10.1109/ITED56637.2022.10051610","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051610","url":null,"abstract":"The idea of this paper is to present a framework for the development of the Hausa Speech recognition system. The framework comprises the creation of a dataset, and the development of acoustic, language, and speech models. The goal is to enhance speech recognition research for under-resourced languages such as the Hausa language. The paper presented work achieved so far by the researchers are creating and developing the Hausa dataset, acoustic model, language model and speech respectively. The dataset and models can be put together for the development of speech to text, text to speech, dictation application and speech to speech translator","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"141 3 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":"124881541","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/ITED56637.2022.10051448
A. Abdulkarim, N. Faruk, Emmanuel Alozie, O. Sowande, Imam-Fulani Yusuf Olayinka, A. D. Usman, K. Adewole, A. Oloyede, H. Chiroma, Salisu Garba, A. Imoize, A. Musa, L. S. Taura
The demand for high-speed internet services is increasing due to emerging needs such as e-commerce, e-health, education, and other high-technology applications. Wireless communication networks have now become a necessity, especially with the introduction of the 5G networks which have the potential to provide extraordinary data rates with extremely low latency. The deployment and operation of 5G and beyond networks in built-up environments would require a complex and reliable radio propagation model that guides network engineers to achieve effective coverage estimation and appropriate base station placements. The inefficiency, and sometimes inconsistencies of deterministic and empirical path loss models necessitated the need to integrate machine learning models. Recently, different machine learning-based pathloss models have been developed to overcome drawbacks associated with conventional pathloss models due to their significant learning and prediction abilities. This paper aims to review path loss models relative to machine learning-based algorithms with a focus on models developed in the last 21 years (2000 to 2021) to study their network parameters and architectures, designs, and applicability, and proffer further research directions.
{"title":"Application of Machine Learning Algorithms to Path Loss Modeling: A Review","authors":"A. Abdulkarim, N. Faruk, Emmanuel Alozie, O. Sowande, Imam-Fulani Yusuf Olayinka, A. D. Usman, K. Adewole, A. Oloyede, H. Chiroma, Salisu Garba, A. Imoize, A. Musa, L. S. Taura","doi":"10.1109/ITED56637.2022.10051448","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051448","url":null,"abstract":"The demand for high-speed internet services is increasing due to emerging needs such as e-commerce, e-health, education, and other high-technology applications. Wireless communication networks have now become a necessity, especially with the introduction of the 5G networks which have the potential to provide extraordinary data rates with extremely low latency. The deployment and operation of 5G and beyond networks in built-up environments would require a complex and reliable radio propagation model that guides network engineers to achieve effective coverage estimation and appropriate base station placements. The inefficiency, and sometimes inconsistencies of deterministic and empirical path loss models necessitated the need to integrate machine learning models. Recently, different machine learning-based pathloss models have been developed to overcome drawbacks associated with conventional pathloss models due to their significant learning and prediction abilities. This paper aims to review path loss models relative to machine learning-based algorithms with a focus on models developed in the last 21 years (2000 to 2021) to study their network parameters and architectures, designs, and applicability, and proffer further research directions.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"105 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":"116415025","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/ITED56637.2022.10051345
Christiana Amaka Okoloegbo, U. F. Eze, G. Chukwudebe, O. Nwokonkwo
In recent years, difficulties related to cyberbullying have emerged as a result of the expansion of social media platforms and community interaction. Naive Bayes classifiers and other well-known models have been used successfully by several academics to create sentiment analysis systems for various use cases. Recent advances in the detection and management of multilingual cyberbullying actions on forums and social networking sites have built on the success of these sentiment analysis efforts. In order to reduce cybercrime in Nigeria, the study's goal is to create an improved Cyberbullying Detector (CD) that is interactive, affordable, and helps identify, monitor, and regulate cyberbullying. The application is the first of its kind in Nigeria to monitor and regulate cyberbullying on Twitter in Pidgin English and Igbo Language. A custom pidgin library was developed with comprehensive translations. The TextBlob library is appropriate for the study, which focuses on cyberbullying, in terms of sentiment prediction. From the sentiment analysis of Twitter data collected using SNScrape, the results show language-specific models that worked perfectly in flagging cyberbullying at manageable runs.
{"title":"Multilingual Cyberbullying Detector (CD) Application for Nigerian Pidgin and Igbo Language Corpus","authors":"Christiana Amaka Okoloegbo, U. F. Eze, G. Chukwudebe, O. Nwokonkwo","doi":"10.1109/ITED56637.2022.10051345","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051345","url":null,"abstract":"In recent years, difficulties related to cyberbullying have emerged as a result of the expansion of social media platforms and community interaction. Naive Bayes classifiers and other well-known models have been used successfully by several academics to create sentiment analysis systems for various use cases. Recent advances in the detection and management of multilingual cyberbullying actions on forums and social networking sites have built on the success of these sentiment analysis efforts. In order to reduce cybercrime in Nigeria, the study's goal is to create an improved Cyberbullying Detector (CD) that is interactive, affordable, and helps identify, monitor, and regulate cyberbullying. The application is the first of its kind in Nigeria to monitor and regulate cyberbullying on Twitter in Pidgin English and Igbo Language. A custom pidgin library was developed with comprehensive translations. The TextBlob library is appropriate for the study, which focuses on cyberbullying, in terms of sentiment prediction. From the sentiment analysis of Twitter data collected using SNScrape, the results show language-specific models that worked perfectly in flagging cyberbullying at manageable runs.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"29 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":"126755292","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/ITED56637.2022.10051334
Bartholomew Idoko, John Bush Idoko, Yusuf Zubair Mahmud Kazaure, Yahanasu Mohammed Ibrahim, Fatai A. Akinsola, Adereti Rasak Raji
In this paper, we designed a sophisticated security system that monitors a particular region (home or office) or a distinguished substance, evaluating occurrences within the scene. The system is implemented using gadgetry such as raspberry pi 2 model B, HC-SR501 sensor, Pi camera, mobile phone and a machine learning based python source code integrating the operations of these gadgets with those of SMS and image service clients (Nexmo and IMGUR) respectively. The unified function of these gadgets and services is basically to capture detected image within the scene and send it to a registered mobile phone in form of text message. The accuracy and response time of the proposed integrated system are very high and very low respectively.
{"title":"IoT Based Motion Detector Using Raspberry Pi Gadgetry","authors":"Bartholomew Idoko, John Bush Idoko, Yusuf Zubair Mahmud Kazaure, Yahanasu Mohammed Ibrahim, Fatai A. Akinsola, Adereti Rasak Raji","doi":"10.1109/ITED56637.2022.10051334","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051334","url":null,"abstract":"In this paper, we designed a sophisticated security system that monitors a particular region (home or office) or a distinguished substance, evaluating occurrences within the scene. The system is implemented using gadgetry such as raspberry pi 2 model B, HC-SR501 sensor, Pi camera, mobile phone and a machine learning based python source code integrating the operations of these gadgets with those of SMS and image service clients (Nexmo and IMGUR) respectively. The unified function of these gadgets and services is basically to capture detected image within the scene and send it to a registered mobile phone in form of text message. The accuracy and response time of the proposed integrated system are very high and very low respectively.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"16 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":"126810309","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/ITED56637.2022.10051510
S. Garba, M. Abdullahi, S. Bashir, O.A. Abisoye
Malaria is an infectious disease caused by a bite of an Anopheles Mosquito which has caused a lot of death. Diagnosis of malaria is made by examining a red blood cell of an infected patient using a microscope, which takes time and requires a qualified laboratory expert to examine, read and interpret the results obtained. Convolutional Neural Network (CNN) has played important role in image classification; however, it has exhibited some problems in consuming computing resources which is one of the limitations of CNN. To reduce this problem, this paper presented a Dilated Convolution Neural Network for malaria parasites detection and species classification using blood smear images. A direct classification was carried out to detect infected and uninfected malaria parasites. Subsequently, species classification was carried out using 3 convolutional layers and Convolution2D for convolution operation while a dilation rate of 2 was used for the convolution layers. The model was trained with a publicly available dataset of 27699 images with a performance accuracy of 99.9% for parasite detection and species classification of 99.9% for falciparum, 64.6% for Malarie, 39.1% for Ovale and 37.3% for Vivax.
{"title":"Implementation of Malaria Parasite Detection and Species Classification Using Dilated Convolutional Neural Network","authors":"S. Garba, M. Abdullahi, S. Bashir, O.A. Abisoye","doi":"10.1109/ITED56637.2022.10051510","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051510","url":null,"abstract":"Malaria is an infectious disease caused by a bite of an Anopheles Mosquito which has caused a lot of death. Diagnosis of malaria is made by examining a red blood cell of an infected patient using a microscope, which takes time and requires a qualified laboratory expert to examine, read and interpret the results obtained. Convolutional Neural Network (CNN) has played important role in image classification; however, it has exhibited some problems in consuming computing resources which is one of the limitations of CNN. To reduce this problem, this paper presented a Dilated Convolution Neural Network for malaria parasites detection and species classification using blood smear images. A direct classification was carried out to detect infected and uninfected malaria parasites. Subsequently, species classification was carried out using 3 convolutional layers and Convolution2D for convolution operation while a dilation rate of 2 was used for the convolution layers. The model was trained with a publicly available dataset of 27699 images with a performance accuracy of 99.9% for parasite detection and species classification of 99.9% for falciparum, 64.6% for Malarie, 39.1% for Ovale and 37.3% for Vivax.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"83 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":"126023808","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/ITED56637.2022.10051516
J. Ndunagu, Eyiyemi.Helen Aderemi, R. Jimoh, J. B. Awotunde
The majority of food commodities in Nigeria have seen persistent price instability. this is brought by elements like insecurity/insurgency, poor storage facilities, seasonal price changes, inconsistent government policies, COVID-19 containment measures, poor access to credit, technical inputs, lack of modern farm tools and implements. This study focused on comparing the prices of four different food items - beans, onion, tomato, and yam using the ARIMA model to forecast future prices. Two out of the six geopolitical zones of Nigeria were used for the study; the North-Central and North-West. The National Bureau of Statistics (NBS) provided the raw data between 2017 and 2018, and the items were weighed in kilograms (Kg). The data was extrapolated into a time series data by executing in R Studio. The stationarity of the series data was obtained by a Unit root Test using the KPSS test (If p<0.05 means the time series is stationary). Results from the forecasted values indicated that food commodities' prices increase with time, making ARIMA a good model for forecasting prices. It was recommended that necessary measures should be put in place to ameliorate the high cost of food prices being experienced in the country of Nigeria.
{"title":"Time Series: Predicting Nigerian Food Prices using ARIMA Model and R-Programming","authors":"J. Ndunagu, Eyiyemi.Helen Aderemi, R. Jimoh, J. B. Awotunde","doi":"10.1109/ITED56637.2022.10051516","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051516","url":null,"abstract":"The majority of food commodities in Nigeria have seen persistent price instability. this is brought by elements like insecurity/insurgency, poor storage facilities, seasonal price changes, inconsistent government policies, COVID-19 containment measures, poor access to credit, technical inputs, lack of modern farm tools and implements. This study focused on comparing the prices of four different food items - beans, onion, tomato, and yam using the ARIMA model to forecast future prices. Two out of the six geopolitical zones of Nigeria were used for the study; the North-Central and North-West. The National Bureau of Statistics (NBS) provided the raw data between 2017 and 2018, and the items were weighed in kilograms (Kg). The data was extrapolated into a time series data by executing in R Studio. The stationarity of the series data was obtained by a Unit root Test using the KPSS test (If p<0.05 means the time series is stationary). Results from the forecasted values indicated that food commodities' prices increase with time, making ARIMA a good model for forecasting prices. It was recommended that necessary measures should be put in place to ameliorate the high cost of food prices being experienced in the country of Nigeria.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"29 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":"128515667","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/ITED56637.2022.10051599
Sunday Abel, J. Tsado, O. Tola
This study demonstrates the use of a matrix converter to reduce electricity theft at the low distribution voltage end. Residential users' meter bypassing energy theft causes electric power distribution businesses in poor nations like Nigeria to lose a considerable amount of money. Direct tapping on distribution lines remains a persistent problem that needs to be utterly eliminated, even though smart metering systems have solved concerns linked to power theft at the meter. Because there is no need for a large, bulky de link electrolytic capacitor that increases system complexity, an indirect matrix converter is utilized because it ensures compactness and reliability. Design and simulation of the proposed system are based on the low voltage distribution network's frequency variation (10 Hz to 20 Hz). For the converter's design, a frequency of 10 Hz was used to produce a worst-case Total Harmonic Distortion (THD) of 204.99 %.
{"title":"Mitigation of Electricity Theft at Low Distribution Voltage End Using Matrix Converter","authors":"Sunday Abel, J. Tsado, O. Tola","doi":"10.1109/ITED56637.2022.10051599","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051599","url":null,"abstract":"This study demonstrates the use of a matrix converter to reduce electricity theft at the low distribution voltage end. Residential users' meter bypassing energy theft causes electric power distribution businesses in poor nations like Nigeria to lose a considerable amount of money. Direct tapping on distribution lines remains a persistent problem that needs to be utterly eliminated, even though smart metering systems have solved concerns linked to power theft at the meter. Because there is no need for a large, bulky de link electrolytic capacitor that increases system complexity, an indirect matrix converter is utilized because it ensures compactness and reliability. Design and simulation of the proposed system are based on the low voltage distribution network's frequency variation (10 Hz to 20 Hz). For the converter's design, a frequency of 10 Hz was used to produce a worst-case Total Harmonic Distortion (THD) of 204.99 %.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"69 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":"133339547","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/ITED56637.2022.10051473
Saheed Idowu, O. R. Vincent, Gboyega Festus Akinboro
The Internet of Things has greatly transformed Agriculture production, termed precision or smart farming, in the last few years, causing increased food production. The continuous increase in population has made sustainable food security an issue of concern beyond what regular agricultural practices can handle in today's economy. To tackle the problem of food insecurity and further increase yield in agricultural production, Information Technology tools began to find their application in both crop and animal production. This paper evaluates the use of the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAV) on the farm. In this paper, other research into IoT and UAV usage is reviewed and analyzed to present the importance of Information Technology in Agriculture.
{"title":"A Descriptive Evaluation of Unmanned Aerial Vehicles and Internet of Things for Agricultural Production: A Review","authors":"Saheed Idowu, O. R. Vincent, Gboyega Festus Akinboro","doi":"10.1109/ITED56637.2022.10051473","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051473","url":null,"abstract":"The Internet of Things has greatly transformed Agriculture production, termed precision or smart farming, in the last few years, causing increased food production. The continuous increase in population has made sustainable food security an issue of concern beyond what regular agricultural practices can handle in today's economy. To tackle the problem of food insecurity and further increase yield in agricultural production, Information Technology tools began to find their application in both crop and animal production. This paper evaluates the use of the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAV) on the farm. In this paper, other research into IoT and UAV usage is reviewed and analyzed to present the importance of Information Technology in Agriculture.","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":"129682660","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/ITED56637.2022.10051529
U. Orji, M. Ezema, Elochukwu A. Ukwandu, C. Ugwuishiwu, O. Ezugwu, Malachi. C. Egbugha
The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far.
{"title":"Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak","authors":"U. Orji, M. Ezema, Elochukwu A. Ukwandu, C. Ugwuishiwu, O. Ezugwu, Malachi. C. Egbugha","doi":"10.1109/ITED56637.2022.10051529","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051529","url":null,"abstract":"The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"56 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":"116899756","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/ITED56637.2022.10051269
Samuel Omaji, Ijegwa David Acheme, A. Makinde, Blessing Akogwu, Adamu Sani Yahaya, H. Alhakami, Wajdi Alhakami
In a vehicular energy network (VEN), an efficient transfer of energy among vehicles is realized while increasing the mobility of vehicles in a large geographic location. However, the security and privacy of vehicle owners are not fully explored in the existing literature. Today, because of the exponential rise in the number of vehicle owners in VEN, the problems of traffic congestion, energy consumption, etc., are created. The issues can be alleviated if certain information about vehicles such as the speed, energy consumption price, and location, is efficiently collected. Besides, effective communication is required for ensuring proper and authentic dissemination of traffic information among vehicles while preserving their data privacy. As a consequence, our study suggests a blockchain-based system for privacy preservation. In the proposed system, trust among vehicles is achieved using the Nash bargaining optimization method. The method is employed to maximize the payoffs of vehicles. Additionally, an improved super-increasing weighted sequence is used to preserve the privacy of vehicles by considering two essential parameters: energy consumption and price. Furthermore, the Paillier encryption mechanism is employed to securely transmit vehicles' information across the network. The proposed system has undergone a security study, which reveals that it is resistant to privacy and security-related threats. The performance of the proposed system shows that the system is efficient and reliable.
{"title":"A Real-time Privacy System for Electric Vehicles using Blockchain Technology","authors":"Samuel Omaji, Ijegwa David Acheme, A. Makinde, Blessing Akogwu, Adamu Sani Yahaya, H. Alhakami, Wajdi Alhakami","doi":"10.1109/ITED56637.2022.10051269","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051269","url":null,"abstract":"In a vehicular energy network (VEN), an efficient transfer of energy among vehicles is realized while increasing the mobility of vehicles in a large geographic location. However, the security and privacy of vehicle owners are not fully explored in the existing literature. Today, because of the exponential rise in the number of vehicle owners in VEN, the problems of traffic congestion, energy consumption, etc., are created. The issues can be alleviated if certain information about vehicles such as the speed, energy consumption price, and location, is efficiently collected. Besides, effective communication is required for ensuring proper and authentic dissemination of traffic information among vehicles while preserving their data privacy. As a consequence, our study suggests a blockchain-based system for privacy preservation. In the proposed system, trust among vehicles is achieved using the Nash bargaining optimization method. The method is employed to maximize the payoffs of vehicles. Additionally, an improved super-increasing weighted sequence is used to preserve the privacy of vehicles by considering two essential parameters: energy consumption and price. Furthermore, the Paillier encryption mechanism is employed to securely transmit vehicles' information across the network. The proposed system has undergone a security study, which reveals that it is resistant to privacy and security-related threats. The performance of the proposed system shows that the system is efficient and reliable.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"22 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":"133048464","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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