Emerging Technologies for Developing Countries: 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2023
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series
v.503 |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (231 Seiten) |
| ISBN: | 9783031358838 |
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| 490 | 0 | |a Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series |v v.503 | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Intro -- Preface -- Organization -- Contents -- Education in the 4IR Era -- Reinforcement Learning in Education: A Multi-armed Bandit Approach -- 1 Introduction -- 1.1 Recommender Systems -- 1.2 Multi-armed Bandits and Markov Decision Processes -- 2 Methods -- 2.1 MAB Student Intervention Recommendation Framework -- 2.2 Simulation Parameters -- 2.3 MAB Algorithms -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Assessing Institutional Readiness for the Fourth Industrial Revolution: Using Learning Analytics to Improve Student Experiences -- 1 Introduction -- 2 Decision-Making in Higher Education -- 3 Challenges with Decision-Making -- 4 Decision-Making Approaches -- 4.1 Data-Driven Decision-Making -- 4.2 Decision-Support Systems -- 4.3 Learning Analytics -- 5 Theoretical Framework -- 6 Methodology -- 6.1 Participants -- 6.2 Procedure -- 7 Results and Discussion -- 7.1 Personal Beliefs and Perspectives -- 7.2 Institutional Capabilities/Readiness - Systems in Place -- 8 Conclusion, Limitations and Recommendations -- References -- M-learning During COVID-19: A Systematic Literature Review -- 1 Introduction -- 2 Methodology -- 2.1 First Stage. Collecting the Relevant Papers -- 2.2 Second Stage. Screening Using Inclusion and Exclusion Criteria -- 2.3 Third Stage. Data Extraction -- 3 Classification Framework -- 3.1 A Summary of the Selected Papers -- 3.2 Structuring the Findings -- 4 Analysis and Discussion of Findings -- 4.1 Context -- 4.2 Opportunities (M-learning Resources) -- 4.3 Opportunities (Related to Students) -- 4.4 Opportunities (Related to Educators) -- 4.5 Opportunities (Related to Contents of M-learning) -- 4.6 Resources Challenges -- 4.7 Educator Readiness Challenges -- 4.8 Challenges Facing Students -- 4.9 Learning Content Challenges -- 4.10 Origin -- 5 Research Agenda -- 5.1 Context -- 5.2 Opportunities | |
| 505 | 8 | |a 5.3 Student Challenges -- 5.4 Educator Readiness -- 5.5 Learning Content -- 5.6 Origin -- 6 Conclusions and Implications of the Study -- References -- Opportunities for Driving Efficiencies and Effectiveness -- Archiving 4.0: Dataset Generation and Facial Recognition of DRC Political Figures Using Machine Learning -- 1 Introduction -- 2 Background and Literature -- 2.1 Face Detection and Face Recognition -- 2.2 Transfer Learning -- 2.3 Evaluation Metrics -- 3 Methodology and Data Acquisition -- 3.1 Methodology -- 3.2 Data Collection and Preparation -- 4 Performance Evaluation -- 4.1 Face Detection -- 4.2 Classification -- 4.3 Discussion of Results -- 5 Conclusion -- References -- On the Machine Learning Models to Predict Town-Scale Energy Consumption in Burkina Faso -- 1 Introduction -- 2 Related Work -- 3 Data and Exploratory Analysis -- 3.1 Dataset -- 3.2 Exploratory Analysis -- 4 Modeling Framework -- 5 Results and Discussion -- 6 Conclusion -- References -- Application of Latent Dirichlet Allocation Topic Model in Identifying 4IR Research Trends -- 1 Introduction -- 1.1 An Overview of the Fourth Industrial Revolution -- 1.2 Topic Modeling -- 1.3 Latent Dirichlet Allocation (LDA) -- 1.4 Dynamic Topic Models -- 2 Data and Methods -- 2.1 Data Sources -- 2.2 Text Pre-processing -- 2.3 Bibliometric Analysis -- 2.4 Analysis of the WoS Text Data Using Topic Models -- 2.5 Analyze Tccc-Announce Text Using Longer Phrases -- 3 Results -- 3.1 LDA Topic Models for WoS -- 3.2 Fourth Industrial Revolution (4IR) Trends -- 3.3 Predict Top LDA Topics of Tccc Emails Documents -- 4 Discussion and Conclusion -- 4.1 LDA Topic Models for Decision Support -- 4.2 Further Work -- References -- A Conceptual Model for the Digital Inclusion of SMMEs in the Informal Sector in South Africa - The Use of Blockchain Technology to Access Loans -- 1 Introduction | |
| 505 | 8 | |a 2 Literature Review -- 2.1 A Brief Overview of the Informal Sector in South Africa -- 2.2 Challenges in the Informal Sector: Lack of Credit Access -- 2.3 Blockchain Technology -- 3 Theoretical Framework -- 3.1 Supply Side of Digital Inclusion -- 3.2 Demand Side of Digital Inclusion -- 4 Conceptual Model -- 5 Conclusion, Limitations and Contribution of Study -- References -- Key 4IR Baseline Architectures -- Multiple Mobile Robotic Formation Control Based on Differential Flatness -- 1 Introduction -- 2 System Description -- 3 Motion Planning and Control -- 4 Simulation and Results -- 5 Conclusion -- References -- A Comparison of Publish-Subscribe and Client-Server Models for Streaming IoT Telemetry Data -- 1 Introduction -- 1.1 WebSocket -- 1.2 MQTT -- 2 Related Works -- 3 Methodology -- 4 Results -- 4.1 Throughput -- 4.2 Round Trip Time (RTT) -- 4.3 System Load -- 5 Conclusion and Further Work -- References -- Fourth Industrial Revolution Research Outputs in Africa: A Bibliometric Review -- 1 Introduction -- 2 Material and Methods -- 3 Results -- 3.1 The Fourth Industrial Revolution Research Trends in Africa -- 3.2 VOSviewer Visualization Maps -- 4 Discussion -- 4.1 State of Fourth Industrial Revolution Research in Africa -- 4.2 4IR Trending Technologies and Research Trends in Africa -- 4.3 Research Gaps and Future Research Directions -- 5 Conclusion -- References -- Modelling DDoS Attacks in IoT Networks Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Pre-processing and Labelling -- 3.2 Supervised Learning -- 3.3 Unsupervised Learning -- 3.4 Semi-supervised Learning -- 3.5 Statistical Models -- 3.6 Prediction -- 4 Implementation -- 4.1 Metrics -- 4.2 Supervised Learning -- 4.3 Unsupervised Learning -- 4.4 Semi-supervised Learning -- 4.5 Statistical Models -- 4.6 Prediction -- 5 Conclusion -- References | |
| 505 | 8 | |a Application of 4IR in Environment and Agriculture Monitoring -- Towards a Microservice-Based Middleware for a Multi-hazard Early Warning System -- 1 Introduction -- 2 Background -- 2.1 Microservices and Containers Management -- 2.2 Container Orchestration -- 3 Proposed Experimental Framework -- 3.1 Cluster Setup -- 3.2 Results -- 4 Conclusion -- References -- Indigenous Knowledge Mobile Based Application that Quantifies Farmers' Season Predictions with the Help of Scientific Knowledge -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 NDVI -- 3.2 NDWI -- 3.3 NDMI -- 3.4 Mobile Application Development -- 3.5 Season Prediction -- 3.6 Certainty Level Computation -- 3.7 Next Season Prediction -- 3.8 SES Model -- 3.9 Downscaling of Predictions -- 4 SES Model Evaluation -- 5 Conclusion -- References -- Weed Identification in Plant Seedlings Using Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 System Architecture -- 3.2 System Methodology -- 3.3 Performance Metrics -- 4 Results and Discussion -- 4.1 Results -- 4.2 Comparative Performance Evaluation -- 4.3 Weed Detection -- 4.4 Discussion -- 5 Conclusions -- References -- Author Index | |
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| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Masinde, Muthoni |t Emerging Technologies for Developing Countries |d Cham : Springer International Publishing AG,c2023 |z 9783031358821 |
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Datensatz im Suchindex
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| author | Masinde, Muthoni |
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| contents | Intro -- Preface -- Organization -- Contents -- Education in the 4IR Era -- Reinforcement Learning in Education: A Multi-armed Bandit Approach -- 1 Introduction -- 1.1 Recommender Systems -- 1.2 Multi-armed Bandits and Markov Decision Processes -- 2 Methods -- 2.1 MAB Student Intervention Recommendation Framework -- 2.2 Simulation Parameters -- 2.3 MAB Algorithms -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Assessing Institutional Readiness for the Fourth Industrial Revolution: Using Learning Analytics to Improve Student Experiences -- 1 Introduction -- 2 Decision-Making in Higher Education -- 3 Challenges with Decision-Making -- 4 Decision-Making Approaches -- 4.1 Data-Driven Decision-Making -- 4.2 Decision-Support Systems -- 4.3 Learning Analytics -- 5 Theoretical Framework -- 6 Methodology -- 6.1 Participants -- 6.2 Procedure -- 7 Results and Discussion -- 7.1 Personal Beliefs and Perspectives -- 7.2 Institutional Capabilities/Readiness - Systems in Place -- 8 Conclusion, Limitations and Recommendations -- References -- M-learning During COVID-19: A Systematic Literature Review -- 1 Introduction -- 2 Methodology -- 2.1 First Stage. Collecting the Relevant Papers -- 2.2 Second Stage. Screening Using Inclusion and Exclusion Criteria -- 2.3 Third Stage. Data Extraction -- 3 Classification Framework -- 3.1 A Summary of the Selected Papers -- 3.2 Structuring the Findings -- 4 Analysis and Discussion of Findings -- 4.1 Context -- 4.2 Opportunities (M-learning Resources) -- 4.3 Opportunities (Related to Students) -- 4.4 Opportunities (Related to Educators) -- 4.5 Opportunities (Related to Contents of M-learning) -- 4.6 Resources Challenges -- 4.7 Educator Readiness Challenges -- 4.8 Challenges Facing Students -- 4.9 Learning Content Challenges -- 4.10 Origin -- 5 Research Agenda -- 5.1 Context -- 5.2 Opportunities 5.3 Student Challenges -- 5.4 Educator Readiness -- 5.5 Learning Content -- 5.6 Origin -- 6 Conclusions and Implications of the Study -- References -- Opportunities for Driving Efficiencies and Effectiveness -- Archiving 4.0: Dataset Generation and Facial Recognition of DRC Political Figures Using Machine Learning -- 1 Introduction -- 2 Background and Literature -- 2.1 Face Detection and Face Recognition -- 2.2 Transfer Learning -- 2.3 Evaluation Metrics -- 3 Methodology and Data Acquisition -- 3.1 Methodology -- 3.2 Data Collection and Preparation -- 4 Performance Evaluation -- 4.1 Face Detection -- 4.2 Classification -- 4.3 Discussion of Results -- 5 Conclusion -- References -- On the Machine Learning Models to Predict Town-Scale Energy Consumption in Burkina Faso -- 1 Introduction -- 2 Related Work -- 3 Data and Exploratory Analysis -- 3.1 Dataset -- 3.2 Exploratory Analysis -- 4 Modeling Framework -- 5 Results and Discussion -- 6 Conclusion -- References -- Application of Latent Dirichlet Allocation Topic Model in Identifying 4IR Research Trends -- 1 Introduction -- 1.1 An Overview of the Fourth Industrial Revolution -- 1.2 Topic Modeling -- 1.3 Latent Dirichlet Allocation (LDA) -- 1.4 Dynamic Topic Models -- 2 Data and Methods -- 2.1 Data Sources -- 2.2 Text Pre-processing -- 2.3 Bibliometric Analysis -- 2.4 Analysis of the WoS Text Data Using Topic Models -- 2.5 Analyze Tccc-Announce Text Using Longer Phrases -- 3 Results -- 3.1 LDA Topic Models for WoS -- 3.2 Fourth Industrial Revolution (4IR) Trends -- 3.3 Predict Top LDA Topics of Tccc Emails Documents -- 4 Discussion and Conclusion -- 4.1 LDA Topic Models for Decision Support -- 4.2 Further Work -- References -- A Conceptual Model for the Digital Inclusion of SMMEs in the Informal Sector in South Africa - The Use of Blockchain Technology to Access Loans -- 1 Introduction 2 Literature Review -- 2.1 A Brief Overview of the Informal Sector in South Africa -- 2.2 Challenges in the Informal Sector: Lack of Credit Access -- 2.3 Blockchain Technology -- 3 Theoretical Framework -- 3.1 Supply Side of Digital Inclusion -- 3.2 Demand Side of Digital Inclusion -- 4 Conceptual Model -- 5 Conclusion, Limitations and Contribution of Study -- References -- Key 4IR Baseline Architectures -- Multiple Mobile Robotic Formation Control Based on Differential Flatness -- 1 Introduction -- 2 System Description -- 3 Motion Planning and Control -- 4 Simulation and Results -- 5 Conclusion -- References -- A Comparison of Publish-Subscribe and Client-Server Models for Streaming IoT Telemetry Data -- 1 Introduction -- 1.1 WebSocket -- 1.2 MQTT -- 2 Related Works -- 3 Methodology -- 4 Results -- 4.1 Throughput -- 4.2 Round Trip Time (RTT) -- 4.3 System Load -- 5 Conclusion and Further Work -- References -- Fourth Industrial Revolution Research Outputs in Africa: A Bibliometric Review -- 1 Introduction -- 2 Material and Methods -- 3 Results -- 3.1 The Fourth Industrial Revolution Research Trends in Africa -- 3.2 VOSviewer Visualization Maps -- 4 Discussion -- 4.1 State of Fourth Industrial Revolution Research in Africa -- 4.2 4IR Trending Technologies and Research Trends in Africa -- 4.3 Research Gaps and Future Research Directions -- 5 Conclusion -- References -- Modelling DDoS Attacks in IoT Networks Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Pre-processing and Labelling -- 3.2 Supervised Learning -- 3.3 Unsupervised Learning -- 3.4 Semi-supervised Learning -- 3.5 Statistical Models -- 3.6 Prediction -- 4 Implementation -- 4.1 Metrics -- 4.2 Supervised Learning -- 4.3 Unsupervised Learning -- 4.4 Semi-supervised Learning -- 4.5 Statistical Models -- 4.6 Prediction -- 5 Conclusion -- References Application of 4IR in Environment and Agriculture Monitoring -- Towards a Microservice-Based Middleware for a Multi-hazard Early Warning System -- 1 Introduction -- 2 Background -- 2.1 Microservices and Containers Management -- 2.2 Container Orchestration -- 3 Proposed Experimental Framework -- 3.1 Cluster Setup -- 3.2 Results -- 4 Conclusion -- References -- Indigenous Knowledge Mobile Based Application that Quantifies Farmers' Season Predictions with the Help of Scientific Knowledge -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 NDVI -- 3.2 NDWI -- 3.3 NDMI -- 3.4 Mobile Application Development -- 3.5 Season Prediction -- 3.6 Certainty Level Computation -- 3.7 Next Season Prediction -- 3.8 SES Model -- 3.9 Downscaling of Predictions -- 4 SES Model Evaluation -- 5 Conclusion -- References -- Weed Identification in Plant Seedlings Using Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 System Architecture -- 3.2 System Methodology -- 3.3 Performance Metrics -- 4 Results and Discussion -- 4.1 Results -- 4.2 Comparative Performance Evaluation -- 4.3 Weed Detection -- 4.4 Discussion -- 5 Conclusions -- References -- Author Index |
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| id | DE-604.BV049876310 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-06T15:18:34Z |
| institution | BVB |
| isbn | 9783031358838 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035215760 |
| oclc_num | 1394922511 |
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| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (231 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series |
| spelling | Masinde, Muthoni Verfasser aut Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings 1st ed Cham Springer International Publishing AG 2023 ©2023 1 Online-Ressource (231 Seiten) txt rdacontent c rdamedia cr rdacarrier Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Series v.503 Description based on publisher supplied metadata and other sources Intro -- Preface -- Organization -- Contents -- Education in the 4IR Era -- Reinforcement Learning in Education: A Multi-armed Bandit Approach -- 1 Introduction -- 1.1 Recommender Systems -- 1.2 Multi-armed Bandits and Markov Decision Processes -- 2 Methods -- 2.1 MAB Student Intervention Recommendation Framework -- 2.2 Simulation Parameters -- 2.3 MAB Algorithms -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Assessing Institutional Readiness for the Fourth Industrial Revolution: Using Learning Analytics to Improve Student Experiences -- 1 Introduction -- 2 Decision-Making in Higher Education -- 3 Challenges with Decision-Making -- 4 Decision-Making Approaches -- 4.1 Data-Driven Decision-Making -- 4.2 Decision-Support Systems -- 4.3 Learning Analytics -- 5 Theoretical Framework -- 6 Methodology -- 6.1 Participants -- 6.2 Procedure -- 7 Results and Discussion -- 7.1 Personal Beliefs and Perspectives -- 7.2 Institutional Capabilities/Readiness - Systems in Place -- 8 Conclusion, Limitations and Recommendations -- References -- M-learning During COVID-19: A Systematic Literature Review -- 1 Introduction -- 2 Methodology -- 2.1 First Stage. Collecting the Relevant Papers -- 2.2 Second Stage. Screening Using Inclusion and Exclusion Criteria -- 2.3 Third Stage. Data Extraction -- 3 Classification Framework -- 3.1 A Summary of the Selected Papers -- 3.2 Structuring the Findings -- 4 Analysis and Discussion of Findings -- 4.1 Context -- 4.2 Opportunities (M-learning Resources) -- 4.3 Opportunities (Related to Students) -- 4.4 Opportunities (Related to Educators) -- 4.5 Opportunities (Related to Contents of M-learning) -- 4.6 Resources Challenges -- 4.7 Educator Readiness Challenges -- 4.8 Challenges Facing Students -- 4.9 Learning Content Challenges -- 4.10 Origin -- 5 Research Agenda -- 5.1 Context -- 5.2 Opportunities 5.3 Student Challenges -- 5.4 Educator Readiness -- 5.5 Learning Content -- 5.6 Origin -- 6 Conclusions and Implications of the Study -- References -- Opportunities for Driving Efficiencies and Effectiveness -- Archiving 4.0: Dataset Generation and Facial Recognition of DRC Political Figures Using Machine Learning -- 1 Introduction -- 2 Background and Literature -- 2.1 Face Detection and Face Recognition -- 2.2 Transfer Learning -- 2.3 Evaluation Metrics -- 3 Methodology and Data Acquisition -- 3.1 Methodology -- 3.2 Data Collection and Preparation -- 4 Performance Evaluation -- 4.1 Face Detection -- 4.2 Classification -- 4.3 Discussion of Results -- 5 Conclusion -- References -- On the Machine Learning Models to Predict Town-Scale Energy Consumption in Burkina Faso -- 1 Introduction -- 2 Related Work -- 3 Data and Exploratory Analysis -- 3.1 Dataset -- 3.2 Exploratory Analysis -- 4 Modeling Framework -- 5 Results and Discussion -- 6 Conclusion -- References -- Application of Latent Dirichlet Allocation Topic Model in Identifying 4IR Research Trends -- 1 Introduction -- 1.1 An Overview of the Fourth Industrial Revolution -- 1.2 Topic Modeling -- 1.3 Latent Dirichlet Allocation (LDA) -- 1.4 Dynamic Topic Models -- 2 Data and Methods -- 2.1 Data Sources -- 2.2 Text Pre-processing -- 2.3 Bibliometric Analysis -- 2.4 Analysis of the WoS Text Data Using Topic Models -- 2.5 Analyze Tccc-Announce Text Using Longer Phrases -- 3 Results -- 3.1 LDA Topic Models for WoS -- 3.2 Fourth Industrial Revolution (4IR) Trends -- 3.3 Predict Top LDA Topics of Tccc Emails Documents -- 4 Discussion and Conclusion -- 4.1 LDA Topic Models for Decision Support -- 4.2 Further Work -- References -- A Conceptual Model for the Digital Inclusion of SMMEs in the Informal Sector in South Africa - The Use of Blockchain Technology to Access Loans -- 1 Introduction 2 Literature Review -- 2.1 A Brief Overview of the Informal Sector in South Africa -- 2.2 Challenges in the Informal Sector: Lack of Credit Access -- 2.3 Blockchain Technology -- 3 Theoretical Framework -- 3.1 Supply Side of Digital Inclusion -- 3.2 Demand Side of Digital Inclusion -- 4 Conceptual Model -- 5 Conclusion, Limitations and Contribution of Study -- References -- Key 4IR Baseline Architectures -- Multiple Mobile Robotic Formation Control Based on Differential Flatness -- 1 Introduction -- 2 System Description -- 3 Motion Planning and Control -- 4 Simulation and Results -- 5 Conclusion -- References -- A Comparison of Publish-Subscribe and Client-Server Models for Streaming IoT Telemetry Data -- 1 Introduction -- 1.1 WebSocket -- 1.2 MQTT -- 2 Related Works -- 3 Methodology -- 4 Results -- 4.1 Throughput -- 4.2 Round Trip Time (RTT) -- 4.3 System Load -- 5 Conclusion and Further Work -- References -- Fourth Industrial Revolution Research Outputs in Africa: A Bibliometric Review -- 1 Introduction -- 2 Material and Methods -- 3 Results -- 3.1 The Fourth Industrial Revolution Research Trends in Africa -- 3.2 VOSviewer Visualization Maps -- 4 Discussion -- 4.1 State of Fourth Industrial Revolution Research in Africa -- 4.2 4IR Trending Technologies and Research Trends in Africa -- 4.3 Research Gaps and Future Research Directions -- 5 Conclusion -- References -- Modelling DDoS Attacks in IoT Networks Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Pre-processing and Labelling -- 3.2 Supervised Learning -- 3.3 Unsupervised Learning -- 3.4 Semi-supervised Learning -- 3.5 Statistical Models -- 3.6 Prediction -- 4 Implementation -- 4.1 Metrics -- 4.2 Supervised Learning -- 4.3 Unsupervised Learning -- 4.4 Semi-supervised Learning -- 4.5 Statistical Models -- 4.6 Prediction -- 5 Conclusion -- References Application of 4IR in Environment and Agriculture Monitoring -- Towards a Microservice-Based Middleware for a Multi-hazard Early Warning System -- 1 Introduction -- 2 Background -- 2.1 Microservices and Containers Management -- 2.2 Container Orchestration -- 3 Proposed Experimental Framework -- 3.1 Cluster Setup -- 3.2 Results -- 4 Conclusion -- References -- Indigenous Knowledge Mobile Based Application that Quantifies Farmers' Season Predictions with the Help of Scientific Knowledge -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 NDVI -- 3.2 NDWI -- 3.3 NDMI -- 3.4 Mobile Application Development -- 3.5 Season Prediction -- 3.6 Certainty Level Computation -- 3.7 Next Season Prediction -- 3.8 SES Model -- 3.9 Downscaling of Predictions -- 4 SES Model Evaluation -- 5 Conclusion -- References -- Weed Identification in Plant Seedlings Using Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 System Architecture -- 3.2 System Methodology -- 3.3 Performance Metrics -- 4 Results and Discussion -- 4.1 Results -- 4.2 Comparative Performance Evaluation -- 4.3 Weed Detection -- 4.4 Discussion -- 5 Conclusions -- References -- Author Index Artificial intelligence Bagula, Antoine Sonstige oth Erscheint auch als Druck-Ausgabe Masinde, Muthoni Emerging Technologies for Developing Countries Cham : Springer International Publishing AG,c2023 9783031358821 |
| spellingShingle | Masinde, Muthoni Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings Intro -- Preface -- Organization -- Contents -- Education in the 4IR Era -- Reinforcement Learning in Education: A Multi-armed Bandit Approach -- 1 Introduction -- 1.1 Recommender Systems -- 1.2 Multi-armed Bandits and Markov Decision Processes -- 2 Methods -- 2.1 MAB Student Intervention Recommendation Framework -- 2.2 Simulation Parameters -- 2.3 MAB Algorithms -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Assessing Institutional Readiness for the Fourth Industrial Revolution: Using Learning Analytics to Improve Student Experiences -- 1 Introduction -- 2 Decision-Making in Higher Education -- 3 Challenges with Decision-Making -- 4 Decision-Making Approaches -- 4.1 Data-Driven Decision-Making -- 4.2 Decision-Support Systems -- 4.3 Learning Analytics -- 5 Theoretical Framework -- 6 Methodology -- 6.1 Participants -- 6.2 Procedure -- 7 Results and Discussion -- 7.1 Personal Beliefs and Perspectives -- 7.2 Institutional Capabilities/Readiness - Systems in Place -- 8 Conclusion, Limitations and Recommendations -- References -- M-learning During COVID-19: A Systematic Literature Review -- 1 Introduction -- 2 Methodology -- 2.1 First Stage. Collecting the Relevant Papers -- 2.2 Second Stage. Screening Using Inclusion and Exclusion Criteria -- 2.3 Third Stage. Data Extraction -- 3 Classification Framework -- 3.1 A Summary of the Selected Papers -- 3.2 Structuring the Findings -- 4 Analysis and Discussion of Findings -- 4.1 Context -- 4.2 Opportunities (M-learning Resources) -- 4.3 Opportunities (Related to Students) -- 4.4 Opportunities (Related to Educators) -- 4.5 Opportunities (Related to Contents of M-learning) -- 4.6 Resources Challenges -- 4.7 Educator Readiness Challenges -- 4.8 Challenges Facing Students -- 4.9 Learning Content Challenges -- 4.10 Origin -- 5 Research Agenda -- 5.1 Context -- 5.2 Opportunities 5.3 Student Challenges -- 5.4 Educator Readiness -- 5.5 Learning Content -- 5.6 Origin -- 6 Conclusions and Implications of the Study -- References -- Opportunities for Driving Efficiencies and Effectiveness -- Archiving 4.0: Dataset Generation and Facial Recognition of DRC Political Figures Using Machine Learning -- 1 Introduction -- 2 Background and Literature -- 2.1 Face Detection and Face Recognition -- 2.2 Transfer Learning -- 2.3 Evaluation Metrics -- 3 Methodology and Data Acquisition -- 3.1 Methodology -- 3.2 Data Collection and Preparation -- 4 Performance Evaluation -- 4.1 Face Detection -- 4.2 Classification -- 4.3 Discussion of Results -- 5 Conclusion -- References -- On the Machine Learning Models to Predict Town-Scale Energy Consumption in Burkina Faso -- 1 Introduction -- 2 Related Work -- 3 Data and Exploratory Analysis -- 3.1 Dataset -- 3.2 Exploratory Analysis -- 4 Modeling Framework -- 5 Results and Discussion -- 6 Conclusion -- References -- Application of Latent Dirichlet Allocation Topic Model in Identifying 4IR Research Trends -- 1 Introduction -- 1.1 An Overview of the Fourth Industrial Revolution -- 1.2 Topic Modeling -- 1.3 Latent Dirichlet Allocation (LDA) -- 1.4 Dynamic Topic Models -- 2 Data and Methods -- 2.1 Data Sources -- 2.2 Text Pre-processing -- 2.3 Bibliometric Analysis -- 2.4 Analysis of the WoS Text Data Using Topic Models -- 2.5 Analyze Tccc-Announce Text Using Longer Phrases -- 3 Results -- 3.1 LDA Topic Models for WoS -- 3.2 Fourth Industrial Revolution (4IR) Trends -- 3.3 Predict Top LDA Topics of Tccc Emails Documents -- 4 Discussion and Conclusion -- 4.1 LDA Topic Models for Decision Support -- 4.2 Further Work -- References -- A Conceptual Model for the Digital Inclusion of SMMEs in the Informal Sector in South Africa - The Use of Blockchain Technology to Access Loans -- 1 Introduction 2 Literature Review -- 2.1 A Brief Overview of the Informal Sector in South Africa -- 2.2 Challenges in the Informal Sector: Lack of Credit Access -- 2.3 Blockchain Technology -- 3 Theoretical Framework -- 3.1 Supply Side of Digital Inclusion -- 3.2 Demand Side of Digital Inclusion -- 4 Conceptual Model -- 5 Conclusion, Limitations and Contribution of Study -- References -- Key 4IR Baseline Architectures -- Multiple Mobile Robotic Formation Control Based on Differential Flatness -- 1 Introduction -- 2 System Description -- 3 Motion Planning and Control -- 4 Simulation and Results -- 5 Conclusion -- References -- A Comparison of Publish-Subscribe and Client-Server Models for Streaming IoT Telemetry Data -- 1 Introduction -- 1.1 WebSocket -- 1.2 MQTT -- 2 Related Works -- 3 Methodology -- 4 Results -- 4.1 Throughput -- 4.2 Round Trip Time (RTT) -- 4.3 System Load -- 5 Conclusion and Further Work -- References -- Fourth Industrial Revolution Research Outputs in Africa: A Bibliometric Review -- 1 Introduction -- 2 Material and Methods -- 3 Results -- 3.1 The Fourth Industrial Revolution Research Trends in Africa -- 3.2 VOSviewer Visualization Maps -- 4 Discussion -- 4.1 State of Fourth Industrial Revolution Research in Africa -- 4.2 4IR Trending Technologies and Research Trends in Africa -- 4.3 Research Gaps and Future Research Directions -- 5 Conclusion -- References -- Modelling DDoS Attacks in IoT Networks Using Machine Learning -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Data Pre-processing and Labelling -- 3.2 Supervised Learning -- 3.3 Unsupervised Learning -- 3.4 Semi-supervised Learning -- 3.5 Statistical Models -- 3.6 Prediction -- 4 Implementation -- 4.1 Metrics -- 4.2 Supervised Learning -- 4.3 Unsupervised Learning -- 4.4 Semi-supervised Learning -- 4.5 Statistical Models -- 4.6 Prediction -- 5 Conclusion -- References Application of 4IR in Environment and Agriculture Monitoring -- Towards a Microservice-Based Middleware for a Multi-hazard Early Warning System -- 1 Introduction -- 2 Background -- 2.1 Microservices and Containers Management -- 2.2 Container Orchestration -- 3 Proposed Experimental Framework -- 3.1 Cluster Setup -- 3.2 Results -- 4 Conclusion -- References -- Indigenous Knowledge Mobile Based Application that Quantifies Farmers' Season Predictions with the Help of Scientific Knowledge -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 NDVI -- 3.2 NDWI -- 3.3 NDMI -- 3.4 Mobile Application Development -- 3.5 Season Prediction -- 3.6 Certainty Level Computation -- 3.7 Next Season Prediction -- 3.8 SES Model -- 3.9 Downscaling of Predictions -- 4 SES Model Evaluation -- 5 Conclusion -- References -- Weed Identification in Plant Seedlings Using Convolutional Neural Networks -- 1 Introduction -- 2 Related Works -- 3 Materials and Methods -- 3.1 System Architecture -- 3.2 System Methodology -- 3.3 Performance Metrics -- 4 Results and Discussion -- 4.1 Results -- 4.2 Comparative Performance Evaluation -- 4.3 Weed Detection -- 4.4 Discussion -- 5 Conclusions -- References -- Author Index Artificial intelligence |
| title | Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings |
| title_auth | Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings |
| title_exact_search | Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings |
| title_full | Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings |
| title_fullStr | Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings |
| title_full_unstemmed | Emerging Technologies for Developing Countries 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings |
| title_short | Emerging Technologies for Developing Countries |
| title_sort | emerging technologies for developing countries 5th eai international conference africatek 2022 bloemfontein south africa december 5 7 2022 proceedings |
| title_sub | 5th EAI International Conference, AFRICATEK 2022, Bloemfontein, South Africa, December 5-7, 2022, Proceedings |
| topic | Artificial intelligence |
| topic_facet | Artificial intelligence |
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