Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051179
C. Nwankwo, Wifred Adigwe, Wilson Nwankwo, Anazia E. Kizito, S. Konyeha, Francis Uwadia
Data breaches and information theft are among the major challenges confronting the legitimate information system user in the 21st century. In recent times, following the deepening of the digital space, mobile computing devices have become convenient resources to drive most day-to-day socioeconomic efforts. These devices are valuable as they host sensitive data, which if compromised may result to economic chaos. Consequently, strengthening access control measures becomes crucial. This study focuses on the strengths of the traditional password authentication mechanisms used on information systems. It identified the weaknesses and proposes a two-part improved password authentication model. The object-oriented methodology is adopted to analyse the components of the proposed password authentication cycle including the flow of activities, and the security relationships. Following a detailed analysis, we specify a simple algorithm to guide the implementation of the model.
{"title":"An Improved Password-authentication Model for Access Control in Connected Systems","authors":"C. Nwankwo, Wifred Adigwe, Wilson Nwankwo, Anazia E. Kizito, S. Konyeha, Francis Uwadia","doi":"10.1109/ITED56637.2022.10051179","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051179","url":null,"abstract":"Data breaches and information theft are among the major challenges confronting the legitimate information system user in the 21st century. In recent times, following the deepening of the digital space, mobile computing devices have become convenient resources to drive most day-to-day socioeconomic efforts. These devices are valuable as they host sensitive data, which if compromised may result to economic chaos. Consequently, strengthening access control measures becomes crucial. This study focuses on the strengths of the traditional password authentication mechanisms used on information systems. It identified the weaknesses and proposes a two-part improved password authentication model. The object-oriented methodology is adopted to analyse the components of the proposed password authentication cycle including the flow of activities, and the security relationships. Following a detailed analysis, we specify a simple algorithm to guide the implementation of the model.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"10 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113957607","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/ITED56637.2022.10051388
T. Oluwabunmi, O. Adenugba, I. A. Ayoade, J. Azeta, C. Bolu
Creating a reliable means of supplying water for irrigation in regions with little or no rainfall across a year is a form of precision agriculture. The compounding problem, however, is the inability to efficiently and conservatively manage the external water source to irrigate the soil. Most times, the amount of water supplied exceeds what crop roots need. To this end, An Autonomous Vehicle for Smart Irrigation System was developed to provide water, herbicide, pesticide, and water to agricultural cultivation to meet the demand for root crops crop roots. This project captures the design, simulation, development, and performance evaluation of the application of Autonomous Vehicles for Smart Irrigation using an Intelligent reprogrammable controller. The soil moisture sensors measure and transmit in real-time, the value of specific soil nutritional requirements to a receiver the autonomous vehicle dispense based on the requirement of soil nutrients to a specific location. With the use of transceivers, these moisture levels are then transmitted to an autonomous vehicle which is set in action when the moisture values are lower than what is required for the growth of the crops. The stress analysis of the Autonomous Vehicle was also carried out to optimize the working operation of the Autonomous Vehicle.
{"title":"Development of an Autonomous Vehicle for Smart Irrigation","authors":"T. Oluwabunmi, O. Adenugba, I. A. Ayoade, J. Azeta, C. Bolu","doi":"10.1109/ITED56637.2022.10051388","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051388","url":null,"abstract":"Creating a reliable means of supplying water for irrigation in regions with little or no rainfall across a year is a form of precision agriculture. The compounding problem, however, is the inability to efficiently and conservatively manage the external water source to irrigate the soil. Most times, the amount of water supplied exceeds what crop roots need. To this end, An Autonomous Vehicle for Smart Irrigation System was developed to provide water, herbicide, pesticide, and water to agricultural cultivation to meet the demand for root crops crop roots. This project captures the design, simulation, development, and performance evaluation of the application of Autonomous Vehicles for Smart Irrigation using an Intelligent reprogrammable controller. The soil moisture sensors measure and transmit in real-time, the value of specific soil nutritional requirements to a receiver the autonomous vehicle dispense based on the requirement of soil nutrients to a specific location. With the use of transceivers, these moisture levels are then transmitted to an autonomous vehicle which is set in action when the moisture values are lower than what is required for the growth of the crops. The stress analysis of the Autonomous Vehicle was also carried out to optimize the working operation of the Autonomous Vehicle.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"18 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":"134534987","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/ITED56637.2022.10051185
M. B. Akanbi, R. Jimoh, J. B. Awotunde
Biometric recognition systems provide security to systems by preventing unauthorized access. In recent times, research proved that unprotected biometric systems can be invaded by hackers. A spoofed biometric security can be a great risk as a compromised biometric template can be lost forever. It can also be used to gain unauthorized access into systems. There is the need to protect biometric template from hackers and possible misuse. This study presents various template protection schemes in literature. In addition, this paper reviewed fuzzy vault biocryptosystem template protection scheme by comparing some fuzzy vault schemes in literature based on their False acceptance rates (FAR) and Genuine acceptance rates (GAR) with different degrees of polynomial. The study also reported past studies with the lowest and the highest FAR as 0.00% and 0.87% respectively while the lowest and the highest GAR stand at 72% and 99% respectively.
{"title":"Biocryptosystems for Template Protection: A Survey of Fuzzy Vault","authors":"M. B. Akanbi, R. Jimoh, J. B. Awotunde","doi":"10.1109/ITED56637.2022.10051185","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051185","url":null,"abstract":"Biometric recognition systems provide security to systems by preventing unauthorized access. In recent times, research proved that unprotected biometric systems can be invaded by hackers. A spoofed biometric security can be a great risk as a compromised biometric template can be lost forever. It can also be used to gain unauthorized access into systems. There is the need to protect biometric template from hackers and possible misuse. This study presents various template protection schemes in literature. In addition, this paper reviewed fuzzy vault biocryptosystem template protection scheme by comparing some fuzzy vault schemes in literature based on their False acceptance rates (FAR) and Genuine acceptance rates (GAR) with different degrees of polynomial. The study also reported past studies with the lowest and the highest FAR as 0.00% and 0.87% respectively while the lowest and the highest GAR stand at 72% and 99% respectively.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"26 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":"134285747","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/ITED56637.2022.10051438
M. J. Musa, Y. Salihu, Moshood A. Yusuf, Z. Usman
Egg incubation is significant to modern agriculture; hence in this paper the design, implementation and integration of the Petroleum Liquid Gas (PLG) control into the existing XM-18 incubator controller will be presented. XM-18 controller is an automatic egg incubator controller, which has a reputable history of high % of hatchability and very low chick's mortality if operated on an uninterrupted AC power supply. However, the high rate of fluctuation of power supply in developing countries has resulted to the poor performance of the XM-18 controller. This hence makes it necessary to improve the XM-18 controller to suit the unique requirements of developing countries. The XM-18 circuit was improved using PIC12F683, ignition coil, spark plug, DC gas valve, and camp gas stove to accommodate the PLG and to automatically control the PLG as a heating source in the absence of the AC source. In so doing, the problem of power failure has been taking care by the standby PLG. With this improvement, the huge cost of running standby generator or setting of a power inverter plan is substituted with the low cost of the PLG control design. Furthermore, the huge labor associated with the use of kerosene source is now substituted with the autonomous control of the PLG. The improved LPG control incubator of 120 eggs capacity was compared with the Kerosene source incubator and incubator running on AC source and the hatchability of 65.3% and 72.22% respectively was obtained, while the hybrid of XM-18 & PLG gave a hatchability of 94.8%.
{"title":"Improved XM-18 Controller using Petroleum Liquid Gas-Based Automatic Heating Alternative for Egg Incubation in Developing Countries","authors":"M. J. Musa, Y. Salihu, Moshood A. Yusuf, Z. Usman","doi":"10.1109/ITED56637.2022.10051438","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051438","url":null,"abstract":"Egg incubation is significant to modern agriculture; hence in this paper the design, implementation and integration of the Petroleum Liquid Gas (PLG) control into the existing XM-18 incubator controller will be presented. XM-18 controller is an automatic egg incubator controller, which has a reputable history of high % of hatchability and very low chick's mortality if operated on an uninterrupted AC power supply. However, the high rate of fluctuation of power supply in developing countries has resulted to the poor performance of the XM-18 controller. This hence makes it necessary to improve the XM-18 controller to suit the unique requirements of developing countries. The XM-18 circuit was improved using PIC12F683, ignition coil, spark plug, DC gas valve, and camp gas stove to accommodate the PLG and to automatically control the PLG as a heating source in the absence of the AC source. In so doing, the problem of power failure has been taking care by the standby PLG. With this improvement, the huge cost of running standby generator or setting of a power inverter plan is substituted with the low cost of the PLG control design. Furthermore, the huge labor associated with the use of kerosene source is now substituted with the autonomous control of the PLG. The improved LPG control incubator of 120 eggs capacity was compared with the Kerosene source incubator and incubator running on AC source and the hatchability of 65.3% and 72.22% respectively was obtained, while the hybrid of XM-18 & PLG gave a hatchability of 94.8%.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"266 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":"125722856","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/ITED56637.2022.10051310
Olagoke Victoria Oluwadamilola, Adekitan Adetoun Abimbola, B. Hassan Tolulope, Vincent Olufunke Rebecca
The Electromagnetic (EM) geophysical method was used to assess the groundwater condition of the Oloruntedo community in Obantoko, Odeda Local Government of Ogun State, to delineate the most prolific aquifer unit within the study area. This study presents a heuristic evaluation of ground water yield. Six (6) EM transverses or profiles were established across the survey area. The survey was run close to existing boreholes and hand-dug wells to vividly imprint the fracture and the weathered zone using PQWT-TC150 geophysical water detector equipment. The hydrogeologic significance of the surveyed area delineates the groundwater architecture within the traverses. The results obtained from this study produced curve graphs and subsurface profile maps of each transverse location. It was observed from the result that EM profile 1, 3, and 6 has a high groundwater potential with varying aquifer depth, while EM profile 4 and 5 imprint a low groundwater potential zone.
{"title":"A Heuristic Evaluation of the State of Groundwater Using Electromagnetic Method","authors":"Olagoke Victoria Oluwadamilola, Adekitan Adetoun Abimbola, B. Hassan Tolulope, Vincent Olufunke Rebecca","doi":"10.1109/ITED56637.2022.10051310","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051310","url":null,"abstract":"The Electromagnetic (EM) geophysical method was used to assess the groundwater condition of the Oloruntedo community in Obantoko, Odeda Local Government of Ogun State, to delineate the most prolific aquifer unit within the study area. This study presents a heuristic evaluation of ground water yield. Six (6) EM transverses or profiles were established across the survey area. The survey was run close to existing boreholes and hand-dug wells to vividly imprint the fracture and the weathered zone using PQWT-TC150 geophysical water detector equipment. The hydrogeologic significance of the surveyed area delineates the groundwater architecture within the traverses. The results obtained from this study produced curve graphs and subsurface profile maps of each transverse location. It was observed from the result that EM profile 1, 3, and 6 has a high groundwater potential with varying aquifer depth, while EM profile 4 and 5 imprint a low groundwater potential zone.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"17 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":"128977325","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/ITED56637.2022.10051501
H. O. Salau, O.A. Abisoye, I. O. Oyefolahan, Solomon Adelowo Adepoju
Several alarming health challenges are urging medical experts and practitioners to research and develop new approaches to diagnose, detect and control the early spread of deadly diseases. One of the most challenging is Coronavirus Infection (Covid-19). Models have been proposed to detect and diagnose early infection of the virus to attain proper precautions against the Covid-19 virus. However, some researchers adopt parameter optimization to attain better accuracy on the Chest X-ray images of covid-19 and other related diseases. Hence, this research work adopts a hybridized cascaded feature extraction technique (Local Binary Pattern LBP and Histogram of Oriented Gradients HOG) and Convolutional Neural Network (CNN) for the deep learning classification model. The merging of LBP and HOG feature extraction significantly improved the performance level of the deep-learning CNN classifier. As a result, 95% accuracy, 92% precision, and 93% recall are attained by the proposed model.
{"title":"Enhanced Chest X-Ray Classification Model for Covid-19 Patients Using HOG and LBP","authors":"H. O. Salau, O.A. Abisoye, I. O. Oyefolahan, Solomon Adelowo Adepoju","doi":"10.1109/ITED56637.2022.10051501","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051501","url":null,"abstract":"Several alarming health challenges are urging medical experts and practitioners to research and develop new approaches to diagnose, detect and control the early spread of deadly diseases. One of the most challenging is Coronavirus Infection (Covid-19). Models have been proposed to detect and diagnose early infection of the virus to attain proper precautions against the Covid-19 virus. However, some researchers adopt parameter optimization to attain better accuracy on the Chest X-ray images of covid-19 and other related diseases. Hence, this research work adopts a hybridized cascaded feature extraction technique (Local Binary Pattern LBP and Histogram of Oriented Gradients HOG) and Convolutional Neural Network (CNN) for the deep learning classification model. The merging of LBP and HOG feature extraction significantly improved the performance level of the deep-learning CNN classifier. As a result, 95% accuracy, 92% precision, and 93% recall are attained by the proposed model.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"11 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120861291","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/ITED56637.2022.10051312
O. Awodele, Sheriff Alimi, O. Ogunyolu, O. Solanke, Seyi Iyawe, Foladoyin Adegbie
One of the core financial services that banks render to their customers is granting of loans with interest over a period. To minimize the risk of loan default which eventually may lead to bad debt; the banks use statistical models to determine the customer loan eligibility. There is a transition from the statistical models for predicting eligibility for bank loans to the use of machine learning models and several pieces of research have been carried out in this direction, but the accuracy is still a challenge. In our research work, we adopted a cascade of a pre-trained Deep Neural Network (DNN) and a Support Vector Machine (SVM) to realize a loan eligibility model. An 11-layer DNN with a sigmoid output layer was trained with a loan credit dataset obtained from Kaggle and the output layer was removed which then makes SoftMax with 64 outputs a new output layer. The DNN is then used to transform the original 11-feature dataset into a 64-feature high dimension dataset. An SVM with a polynomial kernel was trained on the original dataset and achieved an accuracy of 87% but the same SVM achieved an accuracy of 97.05% when trained with the transformed high dimension dataset obtained from the pre-trained DNN. In our study, our proposed prediction model has the best performance with regards to related reviewed works having accuracy of 97%. Our proposed prediction model has the best performance with regards to related reviewed works and it can be concluded that our machine learning mix-strategy is effective and can be adapted for a similar task.
{"title":"Cascade of Deep Neural Network And Support Vector Machine for Credit Risk Prediction","authors":"O. Awodele, Sheriff Alimi, O. Ogunyolu, O. Solanke, Seyi Iyawe, Foladoyin Adegbie","doi":"10.1109/ITED56637.2022.10051312","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051312","url":null,"abstract":"One of the core financial services that banks render to their customers is granting of loans with interest over a period. To minimize the risk of loan default which eventually may lead to bad debt; the banks use statistical models to determine the customer loan eligibility. There is a transition from the statistical models for predicting eligibility for bank loans to the use of machine learning models and several pieces of research have been carried out in this direction, but the accuracy is still a challenge. In our research work, we adopted a cascade of a pre-trained Deep Neural Network (DNN) and a Support Vector Machine (SVM) to realize a loan eligibility model. An 11-layer DNN with a sigmoid output layer was trained with a loan credit dataset obtained from Kaggle and the output layer was removed which then makes SoftMax with 64 outputs a new output layer. The DNN is then used to transform the original 11-feature dataset into a 64-feature high dimension dataset. An SVM with a polynomial kernel was trained on the original dataset and achieved an accuracy of 87% but the same SVM achieved an accuracy of 97.05% when trained with the transformed high dimension dataset obtained from the pre-trained DNN. In our study, our proposed prediction model has the best performance with regards to related reviewed works having accuracy of 97%. Our proposed prediction model has the best performance with regards to related reviewed works and it can be concluded that our machine learning mix-strategy is effective and can be adapted for a similar task.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"11 9 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":"126817728","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}
The advent of digitalization makes image security inevitable as a result of that, an asymmetric cryptography algorithm which is based on the improvement of double random phase encoding is proposed. This algorithm makes use of Gaussian random noise to boost the security of the existing method and was implemented on the MATLAB 2020a software application. The results obtained from the simulations show a slight decrease in the peak-to-signal ratio of 0.3304 and an increase in the mean square error when comparing the existing method with the enhanced method. An image encrypted using Enhanced Double Random Phase Encoding (EDRPE) has strong protection against intruders due to the introduction of the random Gaussian noise as one of the components of the masks in the encryption process. However, a slight noisy effect is produced on the recovered image which is not obvious to the receiver. The simulation result validates the algorithm's potential against security attacks but does not eliminate the presence of noise.
{"title":"Enhanced Optical Double Phase Image Encryption Using Random Gaussian Noise","authors":"K. Okokpujie, Damola Gideon Akinola, Innocent Nwokolo, Olisaemeka Isife, Oghorchukwuyem Obiazi, Oghenetega Owivri","doi":"10.1109/ITED56637.2022.10051261","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051261","url":null,"abstract":"The advent of digitalization makes image security inevitable as a result of that, an asymmetric cryptography algorithm which is based on the improvement of double random phase encoding is proposed. This algorithm makes use of Gaussian random noise to boost the security of the existing method and was implemented on the MATLAB 2020a software application. The results obtained from the simulations show a slight decrease in the peak-to-signal ratio of 0.3304 and an increase in the mean square error when comparing the existing method with the enhanced method. An image encrypted using Enhanced Double Random Phase Encoding (EDRPE) has strong protection against intruders due to the introduction of the random Gaussian noise as one of the components of the masks in the encryption process. However, a slight noisy effect is produced on the recovered image which is not obvious to the receiver. The simulation result validates the algorithm's potential against security attacks but does not eliminate the presence of noise.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"67 4 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":"124101927","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/ITED56637.2022.10051192
Abdul-Waliyi O. Muhammed, V. Oisamoje, H. E. Amhenrior, E. M. J. Evbogbai, V. K. Abanihi, Lawal O. Bello, C. Obasi
This paper presents “The Design and Implementation of an IoT Based Monitoring System for Home Energy consumption”. The system monitors appliances by providing real-time, energy usage, and other data and has the capability of wireless disconnection and connection of loads. The methodology consists of PZEM-004T meter module for consumption pulse measurement, which the ESP8266EX Microcontroller records. This microcontroller requests updates every second and manages unit consumption and other meter activities. This information is transmitted to the ThingSpeak cloud platform and later visualised with High-chart. The software aspect of this work is in two-fold namely the programming of the microcontroller in C++ to achieve the monitoring functionality of the system and the development of communication command using C++ and HTML. The result of the test carried out shows a range of 1 to 5seconds taken by the system in switching appliances depending on the network mode. The results show that the system is good for monitoring energy consumption in a home especially in dual mode - wireless and offline, as such, it allows users to use energy judiciously by providing real-time consumption data. The prototype of the IoT-based home energy monitoring system worked satisfactorily and it is dependable and efficient for use in homes.
{"title":"Design and Implementation of an IoT Based Home Energy Monitoring System","authors":"Abdul-Waliyi O. Muhammed, V. Oisamoje, H. E. Amhenrior, E. M. J. Evbogbai, V. K. Abanihi, Lawal O. Bello, C. Obasi","doi":"10.1109/ITED56637.2022.10051192","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051192","url":null,"abstract":"This paper presents “The Design and Implementation of an IoT Based Monitoring System for Home Energy consumption”. The system monitors appliances by providing real-time, energy usage, and other data and has the capability of wireless disconnection and connection of loads. The methodology consists of PZEM-004T meter module for consumption pulse measurement, which the ESP8266EX Microcontroller records. This microcontroller requests updates every second and manages unit consumption and other meter activities. This information is transmitted to the ThingSpeak cloud platform and later visualised with High-chart. The software aspect of this work is in two-fold namely the programming of the microcontroller in C++ to achieve the monitoring functionality of the system and the development of communication command using C++ and HTML. The result of the test carried out shows a range of 1 to 5seconds taken by the system in switching appliances depending on the network mode. The results show that the system is good for monitoring energy consumption in a home especially in dual mode - wireless and offline, as such, it allows users to use energy judiciously by providing real-time consumption data. The prototype of the IoT-based home energy monitoring system worked satisfactorily and it is dependable and efficient for use in homes.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"96 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":"117316584","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/ITED56637.2022.10051587
R. Jimoh, A. Oyelakin, I. S. Olatinwo, K. Y. Obiwusi, S. Muhammad-Thani, T. S. Ogundele, A. Giwa-Raheem, O. F. Ayepeku
People with malicious intent keep launching attacks in the internet through various means. These attackers are shifting their attacks to social sites such as twitter, facebook Instagram and the likes. One of attack methods is the use of spam in the social media platforms. Social network spam involves using unwanted content that appear on social networking sites such as facebook, twitter, instagram and related ones. Since attackers have shifted attention to using social media platforms for carrying out their nefarious activities there is a need to keep devising security measures to characterise social media based spam attacks. Thisstudy involves experimental evaluation of two ensemble learning models for twitter spam classification. The dataset employed in this study is a publicly available dataset on twitter spam studies. The dataset files are in four different groups, contain different twitter spam evidence. In each of the experimentation, each file in the whole dataset was used. Exploratory analysis of the datasets was carried out, one at a time. Thereafter, label encoding technique was used to handle the categorical feature. Then, two tree-based ensemble learning algorithms namely: Random Forest and Extra Trees algorithms were chosen to build the twitter spam detection models. Each of the set of dataset files was used for the training and testing of machine learning-based twitter spam detection models. The performances of the models built were evaluated and compared. The study revealed that the performances of the twitter spam detection models were promising. In all, the RF-based model recorded better performances in accuracy, precision, recall and f1-score compared to the results in the Extra Trees-based model.
{"title":"Experimental Evaluation of Ensemble Learning-Based Models for Twitter Spam Classification","authors":"R. Jimoh, A. Oyelakin, I. S. Olatinwo, K. Y. Obiwusi, S. Muhammad-Thani, T. S. Ogundele, A. Giwa-Raheem, O. F. Ayepeku","doi":"10.1109/ITED56637.2022.10051587","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051587","url":null,"abstract":"People with malicious intent keep launching attacks in the internet through various means. These attackers are shifting their attacks to social sites such as twitter, facebook Instagram and the likes. One of attack methods is the use of spam in the social media platforms. Social network spam involves using unwanted content that appear on social networking sites such as facebook, twitter, instagram and related ones. Since attackers have shifted attention to using social media platforms for carrying out their nefarious activities there is a need to keep devising security measures to characterise social media based spam attacks. Thisstudy involves experimental evaluation of two ensemble learning models for twitter spam classification. The dataset employed in this study is a publicly available dataset on twitter spam studies. The dataset files are in four different groups, contain different twitter spam evidence. In each of the experimentation, each file in the whole dataset was used. Exploratory analysis of the datasets was carried out, one at a time. Thereafter, label encoding technique was used to handle the categorical feature. Then, two tree-based ensemble learning algorithms namely: Random Forest and Extra Trees algorithms were chosen to build the twitter spam detection models. Each of the set of dataset files was used for the training and testing of machine learning-based twitter spam detection models. The performances of the models built were evaluated and compared. The study revealed that the performances of the twitter spam detection models were promising. In all, the RF-based model recorded better performances in accuracy, precision, recall and f1-score compared to the results in the Extra Trees-based model.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"70 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":"123455557","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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