Pub Date : 2022-11-01DOI: 10.1109/ITED56637.2022.10051316
Seyi Fanifosi, S. Ike, E. Buraimoh, I. Davidson
This study modelled a power distribution system with and without incorporating a Dynamic Voltage Restorer (DVR). It investigated the performance of DVR operation in load voltage compensation under voltage sags and swells conditions. This was to provide insight into how DVR will enhance the reliability and quality of power delivered to the end-user with sensitive equipment in the face of voltage disturbances in the system. An industrial 33kV BEDC Electricity Distribution Plc distribution feeder was modelled in MATLAB SIMULINK. DVR is a series-connected custom power device compensating for distribution system voltage sags and swells. The simulation result attests to the performance of the DVR configuration in mitigating voltage disturbances in a typical distribution feeder under both balanced and unbalanced fault (sags/swells) conditions in a medium-level distribution system and enhancing voltage quality and reliability at the customer side to a statutory voltage level.
{"title":"33kV Distribution Feeder Line Sag and Swell Mitigation using Customized DVR","authors":"Seyi Fanifosi, S. Ike, E. Buraimoh, I. Davidson","doi":"10.1109/ITED56637.2022.10051316","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051316","url":null,"abstract":"This study modelled a power distribution system with and without incorporating a Dynamic Voltage Restorer (DVR). It investigated the performance of DVR operation in load voltage compensation under voltage sags and swells conditions. This was to provide insight into how DVR will enhance the reliability and quality of power delivered to the end-user with sensitive equipment in the face of voltage disturbances in the system. An industrial 33kV BEDC Electricity Distribution Plc distribution feeder was modelled in MATLAB SIMULINK. DVR is a series-connected custom power device compensating for distribution system voltage sags and swells. The simulation result attests to the performance of the DVR configuration in mitigating voltage disturbances in a typical distribution feeder under both balanced and unbalanced fault (sags/swells) conditions in a medium-level distribution system and enhancing voltage quality and reliability at the customer side to a statutory voltage level.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"61 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":"124833074","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/ITED56637.2022.10051500
George Oludare Gbadebo, J. Alhassan, O. A. Ojerinde
Onion (Allium Cepa) is one of the most important vegetable and commercial plants that is being grown all around the world for more than 3000 years. Just like several other crop plants, Onion plants too can be attacked by pests and diseases of various kind, this attacks do give rise to low yields, bad quality and of course shortages of this important plants. Visual observation and analysis for detection of onion leaf diseases, if handed over to computing, using Machine Learning techniques, is more efficient, fast, cost saving, consistent, more reliable and highly accurate compare to what any human disease-expert eyes can offer. This work makes use of the prepared datasets of onion leaf digital images, after image preprocessing, some features were extracted/selected using Grey Level Co-occurrence Matrix (GLCM) and Particle Swarm Optimization (PSO) algorithms, the selected/extracted features then fed into classifier algorithms for eventual classification into healthy or unhealthy onion leaf.
{"title":"Detection of Onion Leaf Disease Using Hybridized Feature Extraction and Feature Selection Approach","authors":"George Oludare Gbadebo, J. Alhassan, O. A. Ojerinde","doi":"10.1109/ITED56637.2022.10051500","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051500","url":null,"abstract":"Onion (Allium Cepa) is one of the most important vegetable and commercial plants that is being grown all around the world for more than 3000 years. Just like several other crop plants, Onion plants too can be attacked by pests and diseases of various kind, this attacks do give rise to low yields, bad quality and of course shortages of this important plants. Visual observation and analysis for detection of onion leaf diseases, if handed over to computing, using Machine Learning techniques, is more efficient, fast, cost saving, consistent, more reliable and highly accurate compare to what any human disease-expert eyes can offer. This work makes use of the prepared datasets of onion leaf digital images, after image preprocessing, some features were extracted/selected using Grey Level Co-occurrence Matrix (GLCM) and Particle Swarm Optimization (PSO) algorithms, the selected/extracted features then fed into classifier algorithms for eventual classification into healthy or unhealthy onion leaf.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"60 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":"126174713","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/ITED56637.2022.10051486
I. Odun-Ayo, A. O. Agbeyangi, L. A. Odeniyi
As in every other region of the world, cloud computing has undeniable advantages for the ICT industry. The development of information technology solutions, like cloud computing, has an impact on a variety of industries, including education, healthcare, banking, manufacturing, and finance, as well as agriculture, government, and communication. It is causing a fundamental shift in the design of computers, software, and tools, as well as, naturally, in how we store, handle, distribute, and use information. It is undeniable that West Africa has faced many obstacles in the development of information technology (IT), from cyber threats to a lack of adequate IT infrastructure. As a result, some West African nations have not yet fully embraced cloud computing. This research shows that about 27% of communication in West Africa is done through cloud computing, 25% in Information Technology, 22% in marketing, 14% in banking and 12% in the Government. Although some countries in western Africa have not yet embraced cloud computing, it has been estimated that the number of countries doing so could propel regional growth and development to a new level. This is because the nations that have embraced cloud computing have a significant influence on the rest of the region.
{"title":"A Critical Analysis of Cloud Computing Adoption in Selected West African Countries","authors":"I. Odun-Ayo, A. O. Agbeyangi, L. A. Odeniyi","doi":"10.1109/ITED56637.2022.10051486","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051486","url":null,"abstract":"As in every other region of the world, cloud computing has undeniable advantages for the ICT industry. The development of information technology solutions, like cloud computing, has an impact on a variety of industries, including education, healthcare, banking, manufacturing, and finance, as well as agriculture, government, and communication. It is causing a fundamental shift in the design of computers, software, and tools, as well as, naturally, in how we store, handle, distribute, and use information. It is undeniable that West Africa has faced many obstacles in the development of information technology (IT), from cyber threats to a lack of adequate IT infrastructure. As a result, some West African nations have not yet fully embraced cloud computing. This research shows that about 27% of communication in West Africa is done through cloud computing, 25% in Information Technology, 22% in marketing, 14% in banking and 12% in the Government. Although some countries in western Africa have not yet embraced cloud computing, it has been estimated that the number of countries doing so could propel regional growth and development to a new level. This is because the nations that have embraced cloud computing have a significant influence on the rest of the region.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"35 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":"126194317","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/ITED56637.2022.10051472
Sheriff Alimi, A. Adenowo, A. Kuyoro, A. Oludele
Automating the process of malaria diagnosis is very crucial; malaria is a deadly disease with an annual infection rate between 300–500 million and a death rate of 1 million yearly. The diagnosis approach is manual and is subject to human error. In this current work, we automate the process of diagnosis and provide results in quantitative form with a diagnostic tool deployed on a web server to eradicate limiting the access to the service to a physical location. The input to the developed diagnostic tool is a Giemsa-stain blood image which undergoes image processing using Otsu segmentation to identify regions of the red blood cells, and a trained SVM classifier iterate through the red blood cells to determine the infected ones. The trained SVM achieved accuracy and precision of 88% and 87% against the validation dataset. The count of infected red blood cells against total red blood cells in the image is used to compute the quantitative result which is the level of severity and number of infected cells per uL of blood, based on the World Health Organization (WHO) standard. A couple of Giemsa-stain blood images were uploaded for diagnosis, our web-based diagnostic tool achieved 90.55%, 85.7% and 100% for average count (both total red blood cells and total infected red blood cells in the processed Giemsa-stain blood images) accuracy, severity classification accuracy and negative test accuracy respectively. The system's average time to complete a diagnosis is 2.2824 seconds, this is a very short time which will create a near-real-time experience for the users of the service.
{"title":"Quantitative Approach to Automated Diagnosis of Malaria from Giemsa-Thin Blood Stain using Support Vector Machine","authors":"Sheriff Alimi, A. Adenowo, A. Kuyoro, A. Oludele","doi":"10.1109/ITED56637.2022.10051472","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051472","url":null,"abstract":"Automating the process of malaria diagnosis is very crucial; malaria is a deadly disease with an annual infection rate between 300–500 million and a death rate of 1 million yearly. The diagnosis approach is manual and is subject to human error. In this current work, we automate the process of diagnosis and provide results in quantitative form with a diagnostic tool deployed on a web server to eradicate limiting the access to the service to a physical location. The input to the developed diagnostic tool is a Giemsa-stain blood image which undergoes image processing using Otsu segmentation to identify regions of the red blood cells, and a trained SVM classifier iterate through the red blood cells to determine the infected ones. The trained SVM achieved accuracy and precision of 88% and 87% against the validation dataset. The count of infected red blood cells against total red blood cells in the image is used to compute the quantitative result which is the level of severity and number of infected cells per uL of blood, based on the World Health Organization (WHO) standard. A couple of Giemsa-stain blood images were uploaded for diagnosis, our web-based diagnostic tool achieved 90.55%, 85.7% and 100% for average count (both total red blood cells and total infected red blood cells in the processed Giemsa-stain blood images) accuracy, severity classification accuracy and negative test accuracy respectively. The system's average time to complete a diagnosis is 2.2824 seconds, this is a very short time which will create a near-real-time experience for the users of the service.","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":"115201147","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/ITED56637.2022.10051483
R. O. Oveh, M. Adewunmi, G. Aziken
Ovarian cancer is the cancerous growth that begins in the ovaries. It has been identified as the most common cause of cancer related death around the world. It is known for its complexity and low survival rate due to late diagnosis and ineffective early detection mechanism. The mutation of p53 tumour suppressor gene is prevalent in High Grade Serious Ovarian Cancer (HGSOC). In this paper BERTopic Topic modelling an unsupervised machine learning technique was used to extract the keywords p53 and ovarian cancer from PubMed database using the Entrez Global Query Cross-Database Search System. The resulting data was then processed using the regex approach and the Natural Language Tool Kit (NLTK). The result showed useful insight in p53 ovarian cancer topic areas.
{"title":"BERTopic Modelling with P53 in Ovarian Cancer","authors":"R. O. Oveh, M. Adewunmi, G. Aziken","doi":"10.1109/ITED56637.2022.10051483","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051483","url":null,"abstract":"Ovarian cancer is the cancerous growth that begins in the ovaries. It has been identified as the most common cause of cancer related death around the world. It is known for its complexity and low survival rate due to late diagnosis and ineffective early detection mechanism. The mutation of p53 tumour suppressor gene is prevalent in High Grade Serious Ovarian Cancer (HGSOC). In this paper BERTopic Topic modelling an unsupervised machine learning technique was used to extract the keywords p53 and ovarian cancer from PubMed database using the Entrez Global Query Cross-Database Search System. The resulting data was then processed using the regex approach and the Natural Language Tool Kit (NLTK). The result showed useful insight in p53 ovarian cancer topic areas.","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":"115345491","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/ITED56637.2022.10051224
Oguntimilehin A, A.A. Oyefiade, K. A. Olatunji, O. Abiola, S.E Obamiyi, B. Badeji-Ajisafe
The increase in number of road users, dangerous driving, bad roads and bad weather among others have increased road accidents, resulting in significant loss of lives and properties mostly due to inadequate emergency services. In 2021, the World Health Organization (WHO) estimated that about 1.3 million lives are lost due to road mishaps yearly. The major factor that increases mortality after an accident occurs is the delay in emergency response. The system developed in this study provides a solution to this problem by leveraging on the Internet of Things (IoT) technology. The system consists of a hardware subsystem installed in a vehicle and a web application for emergency service operations. A microcontroller interacts with a vibration sensor, a tilt sensor, a flame sensor, GPS module and a network module for internet connection. An accident is detected when the vibration sensor detects a vibration greater than the defined threshold value. The microcontroller determines the orientation of the vehicle through the tilt sensor, checks for fire from the flame sensor and gets the vehicle's location from the GPS module. The microcontroller delays sending the information to the web application for 45 seconds so the driver can reset the system if an accident is falsely detected, after which the information about the accident is sent to the web application and the closest hospitals to the accident scene are identified. The hardware subsystem was programmed with $mathbf{C}++$ and the web application was developed using Hypertext Markup Language (HTML), Hypertext Preprocessor (PHP) and MySQL.
{"title":"Internet of Things (Iot) Enabled Automobile Accident Detection and Reporting System *","authors":"Oguntimilehin A, A.A. Oyefiade, K. A. Olatunji, O. Abiola, S.E Obamiyi, B. Badeji-Ajisafe","doi":"10.1109/ITED56637.2022.10051224","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051224","url":null,"abstract":"The increase in number of road users, dangerous driving, bad roads and bad weather among others have increased road accidents, resulting in significant loss of lives and properties mostly due to inadequate emergency services. In 2021, the World Health Organization (WHO) estimated that about 1.3 million lives are lost due to road mishaps yearly. The major factor that increases mortality after an accident occurs is the delay in emergency response. The system developed in this study provides a solution to this problem by leveraging on the Internet of Things (IoT) technology. The system consists of a hardware subsystem installed in a vehicle and a web application for emergency service operations. A microcontroller interacts with a vibration sensor, a tilt sensor, a flame sensor, GPS module and a network module for internet connection. An accident is detected when the vibration sensor detects a vibration greater than the defined threshold value. The microcontroller determines the orientation of the vehicle through the tilt sensor, checks for fire from the flame sensor and gets the vehicle's location from the GPS module. The microcontroller delays sending the information to the web application for 45 seconds so the driver can reset the system if an accident is falsely detected, after which the information about the accident is sent to the web application and the closest hospitals to the accident scene are identified. The hardware subsystem was programmed with $mathbf{C}++$ and the web application was developed using Hypertext Markup Language (HTML), Hypertext Preprocessor (PHP) and MySQL.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"90 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":"128322198","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/ITED56637.2022.10051568
Prosper Chiemezuo Noble-Nnakenyi, Kehinde Adebola Olatunji, O. B. Abiola, A. Oguntimilehin, O. Adeyemo, Gbemisola Babalola
Medication or surgical treatment is the techniques used for people diagnosed with epilepsy, but these procedures are not completely effective. Nevertheless, therapeutic method can be employed in the prediction of the seizure at an early stage. This is because it has been made known through research that the irregular activity in the brain begins a few minutes before the seizure start, the condition normally referred to as preictal state, which is known as a preictal state. Different Deep learning algorithms have been applied to detect seizures in Electroencephalogram (EEG) data. Though, several of the Epileptic Seizures (ES) prediction models have suffered from a lack of reliability and reproducibility due to the flaw in setting up a model to classify seizure prediction. The use of deep learning techniques is proposed to set up an ensemble model that will predict epileptic seizures. In the proposed method, Scalp EEG signals are used and they were gotten from the following repositories, TUG EEG Corpus, CHB-MIT, and GitHub EEG Repository later preprocessed. Univariate features were extracted from the preprocessed signal using signal mapping. The three deep learning techniques, Sparse Autoencoder (SAE), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) are independently trained with the data obtained from the feature extraction process. Majority Voting and Fusion Function are used to develop the ensemble model. 200 subjects of scalp EEG dataset were fed into the proposed system to test for scalability, the results successfully show an achievement of an average accuracy, sensitivity, and specificity of 97.4%, 96.1%, and 98% respectively.
{"title":"Predicting Epileptic Seizures using Ensemble Method","authors":"Prosper Chiemezuo Noble-Nnakenyi, Kehinde Adebola Olatunji, O. B. Abiola, A. Oguntimilehin, O. Adeyemo, Gbemisola Babalola","doi":"10.1109/ITED56637.2022.10051568","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051568","url":null,"abstract":"Medication or surgical treatment is the techniques used for people diagnosed with epilepsy, but these procedures are not completely effective. Nevertheless, therapeutic method can be employed in the prediction of the seizure at an early stage. This is because it has been made known through research that the irregular activity in the brain begins a few minutes before the seizure start, the condition normally referred to as preictal state, which is known as a preictal state. Different Deep learning algorithms have been applied to detect seizures in Electroencephalogram (EEG) data. Though, several of the Epileptic Seizures (ES) prediction models have suffered from a lack of reliability and reproducibility due to the flaw in setting up a model to classify seizure prediction. The use of deep learning techniques is proposed to set up an ensemble model that will predict epileptic seizures. In the proposed method, Scalp EEG signals are used and they were gotten from the following repositories, TUG EEG Corpus, CHB-MIT, and GitHub EEG Repository later preprocessed. Univariate features were extracted from the preprocessed signal using signal mapping. The three deep learning techniques, Sparse Autoencoder (SAE), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) are independently trained with the data obtained from the feature extraction process. Majority Voting and Fusion Function are used to develop the ensemble model. 200 subjects of scalp EEG dataset were fed into the proposed system to test for scalability, the results successfully show an achievement of an average accuracy, sensitivity, and specificity of 97.4%, 96.1%, and 98% respectively.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"37 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":"128709648","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/ITED56637.2022.10051291
T. Moses, B. T. Adeleke, O. Abiodun
In the contemporary information era, smart education is seen as an unavoidable option and an important educational trend development. Information and Communication Technology (ICT) is becoming a crucial instrument for smart education, and it is incumbent on governments worldwide to rethink students' approaches to a smart classroom. Relying on published works, this article evaluated three important developments with the status of Nigeria for a successful smart education environment, which are information technology tools, funding for smart education and cultural/behavioral differences of learners. The study concluded that, while information technology tools are available in most Nigerian schools, they are not suitable for smart classrooms. Funding for smart education is still limited, and students' and instructors' attitudes toward learning with technology must improve in order to create a genuinely smart classroom.
{"title":"Measuring Smart Education Readiness: A case of Nigeria","authors":"T. Moses, B. T. Adeleke, O. Abiodun","doi":"10.1109/ITED56637.2022.10051291","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051291","url":null,"abstract":"In the contemporary information era, smart education is seen as an unavoidable option and an important educational trend development. Information and Communication Technology (ICT) is becoming a crucial instrument for smart education, and it is incumbent on governments worldwide to rethink students' approaches to a smart classroom. Relying on published works, this article evaluated three important developments with the status of Nigeria for a successful smart education environment, which are information technology tools, funding for smart education and cultural/behavioral differences of learners. The study concluded that, while information technology tools are available in most Nigerian schools, they are not suitable for smart classrooms. Funding for smart education is still limited, and students' and instructors' attitudes toward learning with technology must improve in order to create a genuinely smart classroom.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"57 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":"128997337","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/ITED56637.2022.10051435
Sani Galadima Garba, A. Obiniyi, Musa Adeku Ibrahim, B. I. Ahmad
Implementing conventional cryptography like Advance Encryption Standard (AES) requires hardware resources beyond what constrained devices like RFID tags can offer and still perform their primary function. This limitation gave rise to lightweight cryptography to secure constrained devices. The block cipher is the branch of the cryptography scheme that is mostly considered for lightweight cryptography. A key component of the block cipher largely responsible for its security, implementation cost, and efficiency is the Substitution Box (S-box). Most of the time spent in block cipher development is used to find the best S-box with high resistance against known cryptanalysis attacks. However, finding the optimal S-box among the huge possible permutations has always been challenging. The wrong choice of S-box has led to the exploit of some cryptography (cipher). This paper focuses on finding an optimal 4-bit x 4-bit S-box for the lightweight block cipher that will guarantee the cipher security against differential and linear cryptanalysis. We achieved our aim by considering research findings from 1990 to date, to determine the optimal S-box properties and their best values. The S-box properties include and are not limited to differential uniformity, Linearity, and “BOGI Applicability”. Differential uniformity measures resistance to differential attack. S-box Linearity measures resistance to linear cryptanalysis attack. And “BOGI-Applicable S-box” determines if an S-box can implement the “BOGI Strategy”. The “BOGI Strategy” is a strategy that synchronizes the design of a block cipher permutation layer with its S-box to eliminate the S-box weakness. The concluded best values for the S-box characteristics were incorporated into an algorithm and implemented using the C++ programming language. Sample optimal S-boxes were generated using the suggested metric values. The generated S-boxes comply with the “BOGI strategy”, which eliminates the S-box weaknesses that cryptanalysts would otherwise have exploited.
{"title":"Towards Finding An Optimal S-box For Lightweight Block Cipher","authors":"Sani Galadima Garba, A. Obiniyi, Musa Adeku Ibrahim, B. I. Ahmad","doi":"10.1109/ITED56637.2022.10051435","DOIUrl":"https://doi.org/10.1109/ITED56637.2022.10051435","url":null,"abstract":"Implementing conventional cryptography like Advance Encryption Standard (AES) requires hardware resources beyond what constrained devices like RFID tags can offer and still perform their primary function. This limitation gave rise to lightweight cryptography to secure constrained devices. The block cipher is the branch of the cryptography scheme that is mostly considered for lightweight cryptography. A key component of the block cipher largely responsible for its security, implementation cost, and efficiency is the Substitution Box (S-box). Most of the time spent in block cipher development is used to find the best S-box with high resistance against known cryptanalysis attacks. However, finding the optimal S-box among the huge possible permutations has always been challenging. The wrong choice of S-box has led to the exploit of some cryptography (cipher). This paper focuses on finding an optimal 4-bit x 4-bit S-box for the lightweight block cipher that will guarantee the cipher security against differential and linear cryptanalysis. We achieved our aim by considering research findings from 1990 to date, to determine the optimal S-box properties and their best values. The S-box properties include and are not limited to differential uniformity, Linearity, and “BOGI Applicability”. Differential uniformity measures resistance to differential attack. S-box Linearity measures resistance to linear cryptanalysis attack. And “BOGI-Applicable S-box” determines if an S-box can implement the “BOGI Strategy”. The “BOGI Strategy” is a strategy that synchronizes the design of a block cipher permutation layer with its S-box to eliminate the S-box weakness. The concluded best values for the S-box characteristics were incorporated into an algorithm and implemented using the C++ programming language. Sample optimal S-boxes were generated using the suggested metric values. The generated S-boxes comply with the “BOGI strategy”, which eliminates the S-box weaknesses that cryptanalysts would otherwise have exploited.","PeriodicalId":246041,"journal":{"name":"2022 5th Information Technology for Education and Development (ITED)","volume":"283 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":"126925976","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/ITED56637.2022.10051548
A. Ìyàndá, Omolara Aminat Ogungbe, A. Aderibigbe
In Nigeria, prose format is used to present and perform analysis on chest x-ray reports and this often results in delayed response from the clinicians. Therefore, with a view to developing a system for analyzing chest x-ray reports for diagnosing cardiomegaly, linear support vector machine algorithm was utilized to formulate an adaptable model with a train-test split of 70:30 for six hundred and fifty (650) de-identified patients' information. Attributes relevant to cardiomegaly from the collected dataset were extracted using Term frequency/inverse document frequency technique. This work provides an adequate requirement for diagnosis design with accuracy of 93.69%. Its implementation in software application has the potential to reduce delay in attending to patients and can also help the clinicians focus on the findings from chest x-ray reports.
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