Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051519
T. Olaleye, Adeola Olaleye, Emmanuel Ofoegbunam, Gbenga Abodunrin, Temitope Abioye, W. Ahiara
Internet of Things continues to redefine modus operandi across diverse socioeconomic and professional domains thereby generating an un-abating global discuss on the adoption and functionalities of smart devices. Since emotions play a critical role in decision making according to the psychological domain of emotion science, the paramount importance of periodic delineation of stakeholders' mood is imperative for policy makers. Whereas opinion mining analytics of IoT discussions have returned state-of-the-arts, there is need to address germane factors seldom factored into existing literatures. This study therefore consolidates on current frameworks through a bi-modal descriptive and content-based analytics of IoT ecosystem for detecting key mood domain and the BlueCheckCredibility status of IoT tweeters using Profile of Mood State and Nomogram-based analytics. With a 99.5% precision rate by Logistic regression model, social characteristic attributes of acquired ethnographic data points turns mutually exclusive to the credibility status of IoT opinion molders while tweet properties contributes higher discriminative tendencies for identifying negative IoT emotions. The impact of Internet of Things on data science is likewise unraveled through bi-gram content analytics to identify topical discussions encapsulated in the acquired tweet corpus.
{"title":"Opinion mining analytics of IoT ecosystem by Profile of Mood State with Logistic Regression","authors":"T. Olaleye, Adeola Olaleye, Emmanuel Ofoegbunam, Gbenga Abodunrin, Temitope Abioye, W. Ahiara","doi":"10.1109/ITED56637.2022.10051519","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051519","url":null,"abstract":"Internet of Things continues to redefine modus operandi across diverse socioeconomic and professional domains thereby generating an un-abating global discuss on the adoption and functionalities of smart devices. Since emotions play a critical role in decision making according to the psychological domain of emotion science, the paramount importance of periodic delineation of stakeholders' mood is imperative for policy makers. Whereas opinion mining analytics of IoT discussions have returned state-of-the-arts, there is need to address germane factors seldom factored into existing literatures. This study therefore consolidates on current frameworks through a bi-modal descriptive and content-based analytics of IoT ecosystem for detecting key mood domain and the BlueCheckCredibility status of IoT tweeters using Profile of Mood State and Nomogram-based analytics. With a 99.5% precision rate by Logistic regression model, social characteristic attributes of acquired ethnographic data points turns mutually exclusive to the credibility status of IoT opinion molders while tweet properties contributes higher discriminative tendencies for identifying negative IoT emotions. The impact of Internet of Things on data science is likewise unraveled through bi-gram content analytics to identify topical discussions encapsulated in the acquired tweet corpus.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"24 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":"131859370","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}
Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051347
A. Musa, M. Adio, N. Faruk
Evolution of technology has been rapid in recent years and with the wide acceptance of 5G/6G networks, Internet of Things (IoT), Machine Learning (ML), blockchain technologies, there must be a methodology of interconnecting them across large proximity or regions to centralized servers. Existing mobile technologies are not suitable to ensure communications between these devices, and this has necessitated the need to create Low Power Wide Area Network (LPWAN). The LPWAN, assuming a set of networks, is considered to have subsets including Sigfox, LoRaWAN, NB-IoT and others. For any real system, there is always collision between packets upon arrival, thereby subtracting and extracting the weaker ones. In this work, a Repetitive Interference Mitigation algorithm is proposed, with focus on power differences between User Equipment (UE) and guard subcarriers. This process ensures decryption of the packets that arrive at the same time. Usage of just one guard subcarriers and multiple iterations of the developed technique is suitable to ensure a very good system performance and has a throughput of 36% as compared with other methods.
{"title":"Development of Interference Mitigation Technique for Low Power Wide Area Network","authors":"A. Musa, M. Adio, N. Faruk","doi":"10.1109/ITED56637.2022.10051347","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051347","url":null,"abstract":"Evolution of technology has been rapid in recent years and with the wide acceptance of 5G/6G networks, Internet of Things (IoT), Machine Learning (ML), blockchain technologies, there must be a methodology of interconnecting them across large proximity or regions to centralized servers. Existing mobile technologies are not suitable to ensure communications between these devices, and this has necessitated the need to create Low Power Wide Area Network (LPWAN). The LPWAN, assuming a set of networks, is considered to have subsets including Sigfox, LoRaWAN, NB-IoT and others. For any real system, there is always collision between packets upon arrival, thereby subtracting and extracting the weaker ones. In this work, a Repetitive Interference Mitigation algorithm is proposed, with focus on power differences between User Equipment (UE) and guard subcarriers. This process ensures decryption of the packets that arrive at the same time. Usage of just one guard subcarriers and multiple iterations of the developed technique is suitable to ensure a very good system performance and has a throughput of 36% as compared with other methods.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"30 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":"124997412","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/ITED56637.2022.10051219
Marcus Musa Magaji, Abayomi Jegede, Nentawe Gurumdimma, M. Onoja, G. Aimufua, A. Oloyede
Network security personnel are expected to provide uninterrupted services by handling attacks irrespective of the modus operandi. Multiple defensive approaches to prevent, curtail, or mitigate an attack are the primary responsibilities of a security personnel. Considering the fact that, predicting security attacks is an additional technique currently used by most organizations to accurately measure the security risks related to overall system performance, several approaches have been used to predict network security attacks. However, high predicting accuracy and difficulty in analyzing very large amount of dataset and getting a reliable dataset seem to be the major constraints. The uncertain behavior would be subjected to verification and validation by the network administrator. KDDD CUPP 99 dataset and NSL KDD dataset were both used in the research. NSL KDD provides 0.997 average micro and macro accuracy, having average LogLoss of 0.16 and average LogLossReduction of 0.976. Log-Loss Reduction ranges from infinity to 1, where 1 and 0 represent perfect prediction and mean prediction respectively. Log-Loss reduction should be as close to 1 as possible for a good model. Log-Loss in the classification is an evaluation metrics that characterized the accuracy of a classifier. Log-loss is a measure of the performance of a classifier where the prediction input is a probability value between “0.00 to 1.00”. It should be as close to zero as possible. This paper proposes a FastTree Model for predicting network security incidents. Therefore, ML.NET Framework and FastTree Regression Technique have a high prediction accuracy and ability to analyze large datasets of normal, abnormal and uncertain behaviors.
{"title":"Fast Tree Model for Predicting Network Security Incidents","authors":"Marcus Musa Magaji, Abayomi Jegede, Nentawe Gurumdimma, M. Onoja, G. Aimufua, A. Oloyede","doi":"10.1109/ITED56637.2022.10051219","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051219","url":null,"abstract":"Network security personnel are expected to provide uninterrupted services by handling attacks irrespective of the modus operandi. Multiple defensive approaches to prevent, curtail, or mitigate an attack are the primary responsibilities of a security personnel. Considering the fact that, predicting security attacks is an additional technique currently used by most organizations to accurately measure the security risks related to overall system performance, several approaches have been used to predict network security attacks. However, high predicting accuracy and difficulty in analyzing very large amount of dataset and getting a reliable dataset seem to be the major constraints. The uncertain behavior would be subjected to verification and validation by the network administrator. KDDD CUPP 99 dataset and NSL KDD dataset were both used in the research. NSL KDD provides 0.997 average micro and macro accuracy, having average LogLoss of 0.16 and average LogLossReduction of 0.976. Log-Loss Reduction ranges from infinity to 1, where 1 and 0 represent perfect prediction and mean prediction respectively. Log-Loss reduction should be as close to 1 as possible for a good model. Log-Loss in the classification is an evaluation metrics that characterized the accuracy of a classifier. Log-loss is a measure of the performance of a classifier where the prediction input is a probability value between “0.00 to 1.00”. It should be as close to zero as possible. This paper proposes a FastTree Model for predicting network security incidents. Therefore, ML.NET Framework and FastTree Regression Technique have a high prediction accuracy and ability to analyze large datasets of normal, abnormal and uncertain behaviors.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"13 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":"129424558","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/ITED56637.2022.10051603
Adaora Obayi, Obinna Onyedeke, I. Uzo, Azuka Ijeomah
This paper presents a systematic review of android app respiratory system on smartphone. For some diseases, doctors have succeeded in inventing the necessary treatments that lasts for a short period, but in several cases, the treatment can stay for a lifetime. The goal of this system is to detect if a patient has any respiratory disease(s) by specifying the symptoms the patient encounters, schedules an appointement in the hospital for patient through the system to the linked specialist doctors to avoid contact in the case of Covid-19 patient. This research will help raise patient's awareness of the high risk of late discovery of having respiratory diseases (like Lung Cancer. corona virus etc), and also to develop a model that will help detect this disease early through mobile application. The focus of this review is to encourage medical institutions to adopt the health android app that can help patients in self-managing behavioral activities such as physical activities, using symptoms to determine the stage(early or critical) of the disease and drug suggestions with research evaluation using the app, this could help patients monitor and manage their health conditions.
{"title":"Android Application for Human Respiratory System Diagnosis: A Systematic Review","authors":"Adaora Obayi, Obinna Onyedeke, I. Uzo, Azuka Ijeomah","doi":"10.1109/ITED56637.2022.10051603","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051603","url":null,"abstract":"This paper presents a systematic review of android app respiratory system on smartphone. For some diseases, doctors have succeeded in inventing the necessary treatments that lasts for a short period, but in several cases, the treatment can stay for a lifetime. The goal of this system is to detect if a patient has any respiratory disease(s) by specifying the symptoms the patient encounters, schedules an appointement in the hospital for patient through the system to the linked specialist doctors to avoid contact in the case of Covid-19 patient. This research will help raise patient's awareness of the high risk of late discovery of having respiratory diseases (like Lung Cancer. corona virus etc), and also to develop a model that will help detect this disease early through mobile application. The focus of this review is to encourage medical institutions to adopt the health android app that can help patients in self-managing behavioral activities such as physical activities, using symptoms to determine the stage(early or critical) of the disease and drug suggestions with research evaluation using the app, this could help patients monitor and manage their health conditions.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"301 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":"122014703","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/ITED56637.2022.10051589
C. Nwankwo, Francis Uwadia, W. Nwankwo, Wifred Adigwe, P. Chinedu, Emmanuel Ojei
In recent times, cybercrimes, kidnapping, and ritual killings are being enabled through the use and abuse of social media technologies and students are becoming cheap targets. Consequently, this study seeks to investigate the imperative of electronic communication styles among students via social media channels vis-a-vis the users' resilience before and during communication on social media to ensure that the message is routed to the intended recipient. In this study, we adopted the case study approach and 3500 students were drawn from different academic programmes in a known tertiary institution in Southern Nigeria. Validly completed questionnaires from 1000 students were analyzed. Findings revealed that 96% of the students who use social media are not concerned with any form of security screening before sending messages on social media networks via their smartphones.
{"title":"Privacy and Security of Content: A Study of User-resilience and Pre-checks on Social Media","authors":"C. Nwankwo, Francis Uwadia, W. Nwankwo, Wifred Adigwe, P. Chinedu, Emmanuel Ojei","doi":"10.1109/ITED56637.2022.10051589","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051589","url":null,"abstract":"In recent times, cybercrimes, kidnapping, and ritual killings are being enabled through the use and abuse of social media technologies and students are becoming cheap targets. Consequently, this study seeks to investigate the imperative of electronic communication styles among students via social media channels vis-a-vis the users' resilience before and during communication on social media to ensure that the message is routed to the intended recipient. In this study, we adopted the case study approach and 3500 students were drawn from different academic programmes in a known tertiary institution in Southern Nigeria. Validly completed questionnaires from 1000 students were analyzed. Findings revealed that 96% of the students who use social media are not concerned with any form of security screening before sending messages on social media networks via their smartphones.","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":"116972017","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/ITED56637.2022.10051295
I. Yau, S. Sani, A. D. Usman, A. Tekanyi, A. M. Abba, D. Gambo
The use of Plastic Optical Fiber (POF) cable has the potential to enhance broadband transmission, particularly in the indoor access network. The POF cable offers a cost-effective solution because it is much easier to install and less expensive than glass optical fiber. The biggest drawback of the POF cable is intermodal dispersion, which reduces the link bandwidth of a 100 m length of cable to approximately 40 MHz. Numerous studies are being conducted to enhance the bandwidth-length product of POF. This work seeks to minimize the intermodal dispersion of the cable by finding optimal materials for the core and cladding of the cable with the improved bandwidth-length product. The results obtained indicate that the optimal core and cladding materials have refractive indices of 1.4865 and 1.4756, respectively. The intermodal dispersion per unit length is found to be 36.169 ps/m. The bandwidth of 100 m of the improved POF cable is therefore 121.65 MHz. A RoF communication system based on the developed POF is designed using the Optisystem16 software tool. The Bit Error Rate (BER) performance of the system in terms of quality factor is evaluated. A maximum achievable POF cable length of 117 m is obtained for a transmission data rate of 1 Gbps with an acceptable quality factor of 7.0.
{"title":"Enhancing the Transmission Performance of Step Index Plastic Optical Fiber","authors":"I. Yau, S. Sani, A. D. Usman, A. Tekanyi, A. M. Abba, D. Gambo","doi":"10.1109/ITED56637.2022.10051295","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051295","url":null,"abstract":"The use of Plastic Optical Fiber (POF) cable has the potential to enhance broadband transmission, particularly in the indoor access network. The POF cable offers a cost-effective solution because it is much easier to install and less expensive than glass optical fiber. The biggest drawback of the POF cable is intermodal dispersion, which reduces the link bandwidth of a 100 m length of cable to approximately 40 MHz. Numerous studies are being conducted to enhance the bandwidth-length product of POF. This work seeks to minimize the intermodal dispersion of the cable by finding optimal materials for the core and cladding of the cable with the improved bandwidth-length product. The results obtained indicate that the optimal core and cladding materials have refractive indices of 1.4865 and 1.4756, respectively. The intermodal dispersion per unit length is found to be 36.169 ps/m. The bandwidth of 100 m of the improved POF cable is therefore 121.65 MHz. A RoF communication system based on the developed POF is designed using the Optisystem16 software tool. The Bit Error Rate (BER) performance of the system in terms of quality factor is evaluated. A maximum achievable POF cable length of 117 m is obtained for a transmission data rate of 1 Gbps with an acceptable quality factor of 7.0.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"30 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":"127491052","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/ITED56637.2022.10051365
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.
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