Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082729
Norman Nelufule, A. Kock
This study examines the application of infant iris biometrics as a method of identification for newborns and young children. Infant iris photos were collected using an IriShield-USB BK 2121U camera at a variety of locations, including public clinics and preschools. Before image segmentation, the quality of acquired images was evaluated. Subsequently, the traits were retrieved and matched online and the matching results were good enough to distinguish between minors two years old and older. However, the system did not show adequate recognition performance for infants under the age of two years. This approach can be used successfully to track the stability of iris traits from conception to death, as well as to identify minors from birth.
本研究探讨了婴儿虹膜生物识别技术作为新生儿和幼儿识别方法的应用。使用IriShield-USB BK 2121U相机在不同地点(包括公共诊所和幼儿园)收集婴儿虹膜照片。在图像分割之前，对获取的图像质量进行评价。随后，对这些性状进行检索和在线匹配，匹配结果足以区分两岁以上的未成年人。然而，该系统对两岁以下的婴儿没有表现出足够的识别性能。这种方法可以成功地用于跟踪虹膜特征从受孕到死亡的稳定性，以及从出生开始识别未成年人。
{"title":"Infant Iris Biometric Recognition System: Can the iris be used for a secure infant recognition system?","authors":"Norman Nelufule, A. Kock","doi":"10.1109/ICTAS56421.2023.10082729","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082729","url":null,"abstract":"This study examines the application of infant iris biometrics as a method of identification for newborns and young children. Infant iris photos were collected using an IriShield-USB BK 2121U camera at a variety of locations, including public clinics and preschools. Before image segmentation, the quality of acquired images was evaluated. Subsequently, the traits were retrieved and matched online and the matching results were good enough to distinguish between minors two years old and older. However, the system did not show adequate recognition performance for infants under the age of two years. This approach can be used successfully to track the stability of iris traits from conception to death, as well as to identify minors from birth.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131358971","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082749
Norman Nelufule, Y. Moolla, Cynthia Sthembile Ntshangase, A. Kock
One of the first recognised and commonly used biometric modalities for men is the fingerprint, which is frequently used to register adults at home and in traffic centres. Fingerprint biometrics for babies, in particular, are not commonly used or approved. The infant recognition system discussed in this article is tested in infants as early as six weeks of age using a prototype infant fingerprint capture device. To compare and contrast the identification performance of the prototype fingerprint scanner with the traditional fingerprint scanner, the same error rates, standard deviations, and Failure to Acquire were calculated. The results of this investigation point to the possibility of registering newborns as early as six weeks using a baby's fingerprint.
{"title":"Biometric Recognition of Infants Using Fingerprints: Can the infant fingerprint be used for secure authentication?","authors":"Norman Nelufule, Y. Moolla, Cynthia Sthembile Ntshangase, A. Kock","doi":"10.1109/ICTAS56421.2023.10082749","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082749","url":null,"abstract":"One of the first recognised and commonly used biometric modalities for men is the fingerprint, which is frequently used to register adults at home and in traffic centres. Fingerprint biometrics for babies, in particular, are not commonly used or approved. The infant recognition system discussed in this article is tested in infants as early as six weeks of age using a prototype infant fingerprint capture device. To compare and contrast the identification performance of the prototype fingerprint scanner with the traditional fingerprint scanner, the same error rates, standard deviations, and Failure to Acquire were calculated. The results of this investigation point to the possibility of registering newborns as early as six weeks using a baby's fingerprint.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114925647","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082751
Leonardo Ayavaca-Vallejo, D. Avila-Pesantez
Integrating Internet of Things (IoT) technology in smart homes has brought about significant automation and convenience advancements. Smart Home Internet of Things (SHIoT) is a technology where various intelligent devices communicate and share information, providing home comfort and convenience. However, with the increasing connectedness of devices, the potential for cyber-attacks and data breaches also increases. The increasing popularity and adoption of Smart Home IoT devices have brought about new challenges in cybersecurity. This systematic literature review aims to identify and analyze the current state of cybersecurity in SHIoT, including the threats and vulnerabilities faced by these devices and the existing countermeasures for securing them. A total of 50 studies published between 2016 and 2022 were evaluated, identifying several critical threats to SHIoT devices, including unsecured communications, lack of device security, and weak authentication mechanisms. Vulnerabilities in these devices include weak or hard-coded passwords, insecure software updates, and a lack of encryption. Finally, the countermeasures establish encryption, firewall, secure communication protocols, and authentication and access control to improve global security.
将物联网(IoT)技术集成到智能家居中，带来了显著的自动化和便利性进步。智能家居物联网(Smart Home Internet of Things, SHIoT)是指各种智能设备之间进行通信和信息共享，为家庭提供舒适和便利的技术。然而，随着设备连接程度的提高，网络攻击和数据泄露的可能性也在增加。智能家居物联网设备的日益普及和采用给网络安全带来了新的挑战。本系统的文献综述旨在识别和分析SHIoT的网络安全现状，包括这些设备面临的威胁和漏洞以及现有的保护措施。我们对2016年至2022年间发表的50项研究进行了评估，确定了SHIoT设备面临的几个关键威胁，包括不安全的通信、缺乏设备安全性和薄弱的认证机制。这些设备中的漏洞包括弱密码或硬编码密码、不安全的软件更新以及缺乏加密。最后，建立加密、防火墙、安全通信协议、认证和访问控制等对策来提高全局安全性。
{"title":"Smart Home IoT Cybersecurity Survey: A Systematic Mapping","authors":"Leonardo Ayavaca-Vallejo, D. Avila-Pesantez","doi":"10.1109/ICTAS56421.2023.10082751","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082751","url":null,"abstract":"Integrating Internet of Things (IoT) technology in smart homes has brought about significant automation and convenience advancements. Smart Home Internet of Things (SHIoT) is a technology where various intelligent devices communicate and share information, providing home comfort and convenience. However, with the increasing connectedness of devices, the potential for cyber-attacks and data breaches also increases. The increasing popularity and adoption of Smart Home IoT devices have brought about new challenges in cybersecurity. This systematic literature review aims to identify and analyze the current state of cybersecurity in SHIoT, including the threats and vulnerabilities faced by these devices and the existing countermeasures for securing them. A total of 50 studies published between 2016 and 2022 were evaluated, identifying several critical threats to SHIoT devices, including unsecured communications, lack of device security, and weak authentication mechanisms. Vulnerabilities in these devices include weak or hard-coded passwords, insecure software updates, and a lack of encryption. Finally, the countermeasures establish encryption, firewall, secure communication protocols, and authentication and access control to improve global security.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133713633","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082735
Lumka Thami P. Salamntu, Frank Makoza
South African state-owned enterprises (SOEs) often adopt business process management (BPM) to improve service delivery. BPM supports effective and efficient business processes. However, BPM adoption does not always yield positive results, and the known benefits associated with BPM are not always realised. This paper explores the factors that affect BPM adoption, particularly in South African SOEs. The study used the grounded theory method of literature review. The findings showed five main factors that can affect BPM adoption in the context of South African SOEs. The BPM adoption factors were culture, the BPM office, technology and infrastructure, leadership, and people. The presence of a BPM office to manage organizational processes was critical to the success of BPM adoption. Good technology and infrastructure are also important when adopting BPM. Finally, top management support and employee participation are required for the BPM initiative to be successful.
{"title":"Exploring factors that affect Business Process Management (BPM) adoption in South African State-Owned Enterprises","authors":"Lumka Thami P. Salamntu, Frank Makoza","doi":"10.1109/ICTAS56421.2023.10082735","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082735","url":null,"abstract":"South African state-owned enterprises (SOEs) often adopt business process management (BPM) to improve service delivery. BPM supports effective and efficient business processes. However, BPM adoption does not always yield positive results, and the known benefits associated with BPM are not always realised. This paper explores the factors that affect BPM adoption, particularly in South African SOEs. The study used the grounded theory method of literature review. The findings showed five main factors that can affect BPM adoption in the context of South African SOEs. The BPM adoption factors were culture, the BPM office, technology and infrastructure, leadership, and people. The presence of a BPM office to manage organizational processes was critical to the success of BPM adoption. Good technology and infrastructure are also important when adopting BPM. Finally, top management support and employee participation are required for the BPM initiative to be successful.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124350103","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082744
Elliot Mbunge, M. Sibiya, Sam Takavarasha, R. Millham, Garikayi B. Chemhaka, Benhildah Muchemwa, T. Dzinamarira
Diarrhoea continues to be a major public health burden and cause of death among children under 5 years in many developing countries. Rotavirus vaccination, hygiene practices, clean water, and health promotion are among the preventive measures implemented to improve child health. Nevertheless, tackling diarrhoea also requires the integration of ensemble machine learning (ML) into health systems to improve child health. However, the integration of ensemble classifiers into health systems in many developing countries is still nascent. Therefore, this study applied SMOTE, SMOTEEN and SMOTETomek class imbalance approaches and ensemble ML classifiers to predict diarrhoea. Ensemble methods significantly improve the performance of conventional ML classifiers. The study revealed that the ExtraTrees classifier achieved a high recall of 96.3%, accuracy of 94.3%, precision of 93.8%, and F1-score of 95% when predicting diarrhoea with SMOTEENN as compared to SMOTE and SMOTETomek. The performance of the HistGradientBoosting classifier also improved and achieved a high recall of 95.2%, accuracy of 91.5%, precision of 90.4%, and F1-score of 92.7%. The paper also shows that ensemble methods are increasingly becoming state-of-the-art solutions for multiple challenges encountered with ML algorithms such as overfitting, computationally intensive, underfitting and representation. The paper also demonstrates how ensemble methods are becoming state-of-the-art solutions to multiple problems that arise with ML algorithms. There is a need to develop data-driven applications that incorporate ensemble methods to model and predict diarrhoea to assist policymakers to craft interventions aimed to improve child health.
{"title":"Implementation of ensemble machine learning classifiers to predict diarrhoea with SMOTEENN, SMOTE, and SMOTETomek class imbalance approaches","authors":"Elliot Mbunge, M. Sibiya, Sam Takavarasha, R. Millham, Garikayi B. Chemhaka, Benhildah Muchemwa, T. Dzinamarira","doi":"10.1109/ICTAS56421.2023.10082744","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082744","url":null,"abstract":"Diarrhoea continues to be a major public health burden and cause of death among children under 5 years in many developing countries. Rotavirus vaccination, hygiene practices, clean water, and health promotion are among the preventive measures implemented to improve child health. Nevertheless, tackling diarrhoea also requires the integration of ensemble machine learning (ML) into health systems to improve child health. However, the integration of ensemble classifiers into health systems in many developing countries is still nascent. Therefore, this study applied SMOTE, SMOTEEN and SMOTETomek class imbalance approaches and ensemble ML classifiers to predict diarrhoea. Ensemble methods significantly improve the performance of conventional ML classifiers. The study revealed that the ExtraTrees classifier achieved a high recall of 96.3%, accuracy of 94.3%, precision of 93.8%, and F1-score of 95% when predicting diarrhoea with SMOTEENN as compared to SMOTE and SMOTETomek. The performance of the HistGradientBoosting classifier also improved and achieved a high recall of 95.2%, accuracy of 91.5%, precision of 90.4%, and F1-score of 92.7%. The paper also shows that ensemble methods are increasingly becoming state-of-the-art solutions for multiple challenges encountered with ML algorithms such as overfitting, computationally intensive, underfitting and representation. The paper also demonstrates how ensemble methods are becoming state-of-the-art solutions to multiple problems that arise with ML algorithms. There is a need to develop data-driven applications that incorporate ensemble methods to model and predict diarrhoea to assist policymakers to craft interventions aimed to improve child health.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122414417","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082753
Luthfiya. Essop, Alveen Singh, J. Wing
A chatbot is a domain-specific conversational interface that mimics human assistance for users of various systems. Recently chatbots have received much research interest in supporting university administrative operations. However, rapid and large-scale implementation of chatbots in university administration systems remains challenged. Extant literature reflects on this challenge from various perspectives including, technical, managerial and socio-technical lenses. This paper heralds a somewhat overlooked perspective namely, the processes and techniques for concise and rigorous evaluation of these chatbots. The distinctiveness of this paper lies in the tri-perspectives of anthropomorphism, usability and user experience which converge to provide a stronger lens for chatbot evaluation particularly in a university administration setting. Recent studies primarily devise heuristic methods that tend to evaluate chatbots in silos such as, user interface, usability or the conversation ability and quality. There is a noticeable lack of research that attempts combination of these seemingly complex areas of chatbot evaluation. This paper postulates improved rigour of evaluation if coverage is expanded to usability, anthropomorphism, acceptance, usage, and user interface. The aim of this paper is therefore, to design a novel evaluation instrument tailored for a university administration chatbot. This is achieved by implementing the well-known Unified Technology Acceptance and Use of Technology framework as the architectural underpinning. Constituent components of the instrument derive from recent literature and emerging trends in frequently asked questions-based chatbot evaluation. The major contribution stems from the identification and insertion of key and overlooked evaluation perspectives which culminate in a more rigorous and a more encompassing evaluation questionnaire.
{"title":"Developing a comprehensive evaluation questionnaire for university FAQ administration chatbots","authors":"Luthfiya. Essop, Alveen Singh, J. Wing","doi":"10.1109/ICTAS56421.2023.10082753","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082753","url":null,"abstract":"A chatbot is a domain-specific conversational interface that mimics human assistance for users of various systems. Recently chatbots have received much research interest in supporting university administrative operations. However, rapid and large-scale implementation of chatbots in university administration systems remains challenged. Extant literature reflects on this challenge from various perspectives including, technical, managerial and socio-technical lenses. This paper heralds a somewhat overlooked perspective namely, the processes and techniques for concise and rigorous evaluation of these chatbots. The distinctiveness of this paper lies in the tri-perspectives of anthropomorphism, usability and user experience which converge to provide a stronger lens for chatbot evaluation particularly in a university administration setting. Recent studies primarily devise heuristic methods that tend to evaluate chatbots in silos such as, user interface, usability or the conversation ability and quality. There is a noticeable lack of research that attempts combination of these seemingly complex areas of chatbot evaluation. This paper postulates improved rigour of evaluation if coverage is expanded to usability, anthropomorphism, acceptance, usage, and user interface. The aim of this paper is therefore, to design a novel evaluation instrument tailored for a university administration chatbot. This is achieved by implementing the well-known Unified Technology Acceptance and Use of Technology framework as the architectural underpinning. Constituent components of the instrument derive from recent literature and emerging trends in frequently asked questions-based chatbot evaluation. The major contribution stems from the identification and insertion of key and overlooked evaluation perspectives which culminate in a more rigorous and a more encompassing evaluation questionnaire.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127577851","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082748
Timothy T. Adeliyi, Alveen Singh, O. Aroba
The Internet's pervasiveness has resulted in major shifts in the television ecosphere, where IPTV subscribers are now able to stream their favourite TV channels without having to consider time or location. Channel surfing is the practice of quickly scanning through various television channels in search of something interesting to watch. Due to the large number of TV channels available to IPTV subscribers, these subscribers may have difficulty matching their channel interests. This study aims to use machine learning models to analyze IPTV subscribers' channel surfing behaviours and predict contributing factors that lead to the rapid change of IPTV channels. Logitboost was benchmarked with six machine learning models in analyzing IPTV subscribers' channel surfing behaviour. Consequently, eight well-known performance evaluation metrics were used to compare the effectiveness of the machine learning models. The result presented shows that Logitboost outperformed the other six machine learning models. Consequently, the study identified four significant features that contribute to the channel surfing behaviour of IPTV subscribers which includes gender, peak hour, age, and genre. The findings show that over 40% of channel switching occurrences occur in less than 10 seconds, indicative that user attentiveness is very unpredictable. The study further discovered significant gender variations in channel genre viewing behaviours during peak hours.
{"title":"Analysing Channel Surfing Behaviour of IPTV Subscribers Using Machine Learning Models","authors":"Timothy T. Adeliyi, Alveen Singh, O. Aroba","doi":"10.1109/ICTAS56421.2023.10082748","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082748","url":null,"abstract":"The Internet's pervasiveness has resulted in major shifts in the television ecosphere, where IPTV subscribers are now able to stream their favourite TV channels without having to consider time or location. Channel surfing is the practice of quickly scanning through various television channels in search of something interesting to watch. Due to the large number of TV channels available to IPTV subscribers, these subscribers may have difficulty matching their channel interests. This study aims to use machine learning models to analyze IPTV subscribers' channel surfing behaviours and predict contributing factors that lead to the rapid change of IPTV channels. Logitboost was benchmarked with six machine learning models in analyzing IPTV subscribers' channel surfing behaviour. Consequently, eight well-known performance evaluation metrics were used to compare the effectiveness of the machine learning models. The result presented shows that Logitboost outperformed the other six machine learning models. Consequently, the study identified four significant features that contribute to the channel surfing behaviour of IPTV subscribers which includes gender, peak hour, age, and genre. The findings show that over 40% of channel switching occurrences occur in less than 10 seconds, indicative that user attentiveness is very unpredictable. The study further discovered significant gender variations in channel genre viewing behaviours during peak hours.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130861420","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082723
John Batani, M. Maharaj
Under-five mortality remains a global health concern as many countries have failed to achieve the United Nations Millennium Development Goal 4 (MDG 4). Children under five (under-fives) continue to perish to preventable deaths globally. Zimbabwe is amongst the Sub-Saharan African countries that failed to achieve the MDG 4 on under-five mortality. Regardless of evidence from other regions that emerging technologies help eliminate preventable deaths among under-fives, Zimbabwe's adoption of such technologies in public health facilities remains nascent. The country has introduced some digital health technologies in public facilities, but they are not specific to paediatric care. Likewise, research on digital health in Zimbabwe has paid little attention to paediatric care. Therefore, this study proposes a framework for adopting and utilizing emerging technologies to reduce under-five mortality in Zimbabwe's public health facilities. The pragmatism philosophy guided the study. It employed a sequential exploratory mixed-methods design to explore factors affecting the adoption of emerging technologies in Zimbabwe's public health facilities, and the perceived role of emerging technologies, with the aim of designing a technology adoption framework. Future studies could focus on integrating the existing digital health systems in Zimbabwe to harness the data generated to enhance paediatric care through utilizing such data in paediatric care information systems.
{"title":"A Framework for the Adoption of Emerging Technologies to Reduce Under-Five Mortality in Zimbabwe","authors":"John Batani, M. Maharaj","doi":"10.1109/ICTAS56421.2023.10082723","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082723","url":null,"abstract":"Under-five mortality remains a global health concern as many countries have failed to achieve the United Nations Millennium Development Goal 4 (MDG 4). Children under five (under-fives) continue to perish to preventable deaths globally. Zimbabwe is amongst the Sub-Saharan African countries that failed to achieve the MDG 4 on under-five mortality. Regardless of evidence from other regions that emerging technologies help eliminate preventable deaths among under-fives, Zimbabwe's adoption of such technologies in public health facilities remains nascent. The country has introduced some digital health technologies in public facilities, but they are not specific to paediatric care. Likewise, research on digital health in Zimbabwe has paid little attention to paediatric care. Therefore, this study proposes a framework for adopting and utilizing emerging technologies to reduce under-five mortality in Zimbabwe's public health facilities. The pragmatism philosophy guided the study. It employed a sequential exploratory mixed-methods design to explore factors affecting the adoption of emerging technologies in Zimbabwe's public health facilities, and the perceived role of emerging technologies, with the aim of designing a technology adoption framework. Future studies could focus on integrating the existing digital health systems in Zimbabwe to harness the data generated to enhance paediatric care through utilizing such data in paediatric care information systems.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124939360","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082743
Douglas Emmanuel Ikiomoye, N. Linge, S. Hill
In recent years, legacy networks have evolved to incorporate the use of programmability features with the aim of improving performance and resource utilisation. In achieving this goal, packets need to be monitored and classified. In this study, an optimal monitoring tool is used in capturing the packets or flows in an emulated Software Defined Wide Area Network using GNS3. The network architecture is implemented using two hosts communicating to a server integrated with a machine learning (ML) model (python based) to classify real network packets. The ML model is achieved using the Decision Tree algorithm based on python programming. The proposed implementation ensures the ML algorithm efficiently classifies and segments various packets in the network in a database structure. This testbed can be effectively implemented in a real network scenario, and packet data can be captured and analysed into a database structure which can be used for further analysis such as congestion window or throughput for improving network performance and resource utilisation.
{"title":"Analysis of SD-WAN Packets using Machine Learning Algorithm","authors":"Douglas Emmanuel Ikiomoye, N. Linge, S. Hill","doi":"10.1109/ICTAS56421.2023.10082743","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082743","url":null,"abstract":"In recent years, legacy networks have evolved to incorporate the use of programmability features with the aim of improving performance and resource utilisation. In achieving this goal, packets need to be monitored and classified. In this study, an optimal monitoring tool is used in capturing the packets or flows in an emulated Software Defined Wide Area Network using GNS3. The network architecture is implemented using two hosts communicating to a server integrated with a machine learning (ML) model (python based) to classify real network packets. The ML model is achieved using the Decision Tree algorithm based on python programming. The proposed implementation ensures the ML algorithm efficiently classifies and segments various packets in the network in a database structure. This testbed can be effectively implemented in a real network scenario, and packet data can be captured and analysed into a database structure which can be used for further analysis such as congestion window or throughput for improving network performance and resource utilisation.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127693618","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 : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082734
Elliot Mbunge, S. Fashoto, Benhildah Muchemwa, R. Millham, Garikayi B. Chemhaka, M. Sibiya, T. Dzinamarira, Jolly Buwerimwe
Despite continuous persistent efforts to enhance child health through, among other things, universal access to care, child mortality remains a significant public health concern on a global scale. Child mortality is attributed to several factors including birth asphyxia/trauma, demographic and socioeconomic factors, preterm birth and intrapartum-related complications, pneumonia, preventable and treatable diseases, congenital anomalies, poor access to quality healthcare, poor hygiene and nutrition, and sanitation among others. In many sub-Saharan African nations, including Zimbabwe, the use of machine learning techniques to predict child mortality is still in its infancy. Therefore, this study applied machine learning algorithms decision trees, random forest, logistic regression and XGBoost to develop child mortality predictive models that utilize nationally representative demographic and health survey data. The logistic regression classifier achieved an accuracy of 74%, random forest 72%, Decision tree 72%, and XGBoost a high accuracy of 81%. All under-five predictive models achieved a precision of 95 %. However, logistic regression achieved a recall of 76%, random forest 74%, Decision tree 74%, and XGBoost 84%. Logistic Regression achieved F1-score of 84%, random forest 83%, Decision tree 83% and 89% for XGBoost. The XGBoost outperformed other under-five predictive models. Integrating such models into health information systems can significantly assist policymakers and healthcare professionals to improve the health status of children, access to quality care and most importantly, improve preventive measures, immunization programmes, policies, and decision-making to improve child health. Understanding the risk factors can assist in designing intervention programmes aimed at improve child health while reducing child mortality.
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