International Conference on Artificial Intelligence and its Applications最新文献

{"title":"Smart Water Metering System (SWMS) Adoption: A Systematic Literature Review","authors":"Nwakego Joy Okoli","doi":"10.59200/icarti.2023.025","DOIUrl":"https://doi.org/10.59200/icarti.2023.025","url":null,"abstract":"Water is fundamental for the economy and for our lives. Managing water scarcity remains one of the most pressing challenges across the globe. Smart Water Meter Systems (SWMS) are Internet of Things (IoT) innovations that have been around for over a decade and several studies on the use of smart water metering data for water management have been conducted. However, they are dispersed across various topics. For sustainable implementation of SWMS, it is necessary to review the existing practical adoption of the system. This paper is a review aimed at finding practical evidence on SWMS adoption by water utilities. A systematic literature review search of relevant databases was conducted using relevant search strings. Papers for the study were chosen based on predefined inclusion and exclusion criteria. The main findings of the paper show real-world examples of water utilities that have successfully implemented SWMS, the key benefits of SWMS, and the challenges to SWMS adoption. The study identifies areas where future research can be pursued to better realize SWMS's full potential in promoting sustainable water management.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282060","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}

{"title":"Enhancing Grain Moisture Prediction with Artificial Neural Networks and Computational Fluid Dynamic","authors":"Cubaka Birhakahwa Kelvin, L. Tartibu","doi":"10.59200/icarti.2023.026","DOIUrl":"https://doi.org/10.59200/icarti.2023.026","url":null,"abstract":"Drying cereals is a vital process to reduce moisture content, enabling efficient storage. This study investigates the convective drying behaviour of cereals through numerical simulations and feedforward neural networks. Key parameters considered include temperature, ambient air speed, relative humidity, porosity, intrinsic permeability, density, thermal capacity, thermal conductivity, and initial relative humidity of stored grains. Training data were generated using numerical methods, solving heat and mass transfer equations based on Whitaker's model within COMSOL Multiphysics® 5.6. Simulation results reveal that moisture in cereals gradually equilibrates with the ambient environment, commencing from the exposed surfaces. The artificial neural network (ANN) demonstrates remarkable predictive accuracy using 100 data points, yielding an overall correlation coefficient of 0.99458 and a mean squared error (MSE) of 3.132 ×10-4. The combination of these two methods offers distinct advantages, with ANN saving computational time and numerical simulations not requiring initial samples. This combined approach proves promising for grain moisture prediction, though results must undergo rigorous validation to ensure reliability and accuracy.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282085","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}

{"title":"A Machine Learning Based Framework for Collecting and Using Social Media for Real-time Terrorist Attacks Prediction","authors":"Lossan Bonde, Severin Dembele","doi":"10.59200/icarti.2023.013","DOIUrl":"https://doi.org/10.59200/icarti.2023.013","url":null,"abstract":"Terrorism has become a global plague causing insecurity and jeopardizing the development of many countries. In the past few years, terrorism has exploded in Burkina Faso, affecting education, national security, health, and the economy. There is a great need for solutions to detect and stop terrorist attacks before they occur. This research project seeks to use Artificial Intelligence (AI) to mine social media and detect probable future terrorist attacks. This article describes the design of a framework, its partial implementation, and an experiment to validate the technique. The system consists of five steps: taking social media as input, converting it to text, validating it, extracting essential information, predicting its class, and storing it in a dataset. The modest size of the manually produced dataset utilized in the original experiment is a key drawback of the work discussed in this research. The modest size proved inconvenient for Deep Learning algorithms, which operate best with massive datasets. When we complete the entire system, inserting increased data from social media into the dataset will resolve this limitation. The other limitation is the partial implementation of the framework, which does not provide a comprehensive picture of the proposed approach. Our future works will address the remainder parts of the proposed framework.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282291","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}

{"title":"Analysing Health Insurance Customer Dataset to Determine Cross-Selling Potential","authors":"Khulekani Mavundla, Surendra Thakur","doi":"10.59200/icarti.2023.031","DOIUrl":"https://doi.org/10.59200/icarti.2023.031","url":null,"abstract":"Health insurance cross-selling refers to the practice of offering additional or complementary insurance products to existing policyholders. Insurance providers leverage cross-selling, offering customers additional policies like dental or life insurance when they already have a basic health insurance plan. This study is conducted to focus on the application of machine learning techniques to predict health insurance cross-selling opportunities among South African customers. The aim of this study is to develop a cross-selling predictive machine learning model that can assist health insurance companies to identify potential customers for cross-selling probabilities. To achieve this goal, a quantitative research methodology is adopted, focusing on extracting a comprehensive dataset of health insurance consumer information and employing various machine learning algorithms using the Python programming language, including Random Forest, K-Nearest Neighbours, XGBoost classifier, and Logistic Regression algorithms to build the cross-selling predictive machine learning model. The experimental results obtained demonstrate the accuracy scores of four different machine learning algorithms trained using 1,000,000 customer dataset with 17 features, logistic regression is considered as the top-performing model. It achieved an accuracy score of 0.83 and an F1 score of 0.91. The analysis indicates that customers aged 30-60, with prior insurance, and longer service history are more likely to buy additional health insurance products. The findings of this research can help health insurers boost revenue by improving their customer targeting and retention strategies.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282321","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}

{"title":"Measuring Misinformation Trends on Social Media in South Africa using Machine Learning","authors":"J. Mtsweni, Lungisani Ndlovu, Sthembile Mthethwa, N. Mkuzangwe","doi":"10.59200/icarti.2023.018","DOIUrl":"https://doi.org/10.59200/icarti.2023.018","url":null,"abstract":"Misinformation, disinformation, malinformation, and/or fake news have gained attention for good and bad in South Africa, especially since the COVID-19 pandemic. The research-based and non-research-based interventions to tackle misinformation have also been slowly gaining traction, particularly through fact checkers, fake news reporting systems such as those by real411, research on automated systems to detect fake news online using machine learning, sentiment analysis of fake news, tagging of fake news data, and so on. Nevertheless, the spread of misinformation and/or fake news still represents a serious threat and challenge to social media platform owners, citizens, lawmakers, governments, and businesses alike. We hypothesized that the awareness, engagement, influence, and impact levels of misinformation on citizens, politicians, journalists, and lawmakers are relatively low, especially in South Africa. However, no sufficient research has been done in this area to understand engagements, awareness, and reporting of fake news online. This research uses open-source intelligence and selected machine learning techniques to analyse publicly collected social media data to monitor and measure the awareness and engagements of fake news in South Africa over a period of 30 days. The research further identifies key drivers of spreading or reporting misinformation online. We conclude that misinformation engagements on social media in South Africa are active, but only in affluent regions and influenced by mobile device users, who are mostly male. The study recommends further research that may support raising misinformation awareness and positive engagements on social media.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281677","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}

{"title":"Reflections on Feature Engineering and Design Using Causal Machine Learning (CML) for African Swine Fever (ASF) Diagnosis","authors":"Steven Lububu, Boniface Kabaso","doi":"10.59200/icarti.2023.004","DOIUrl":"https://doi.org/10.59200/icarti.2023.004","url":null,"abstract":"Feature engineering is a crucial step in the process of machine learning, where raw data is transformed into meaningful features that can effectively represent the underlying patterns and relationships in the data. The goal is to improve the performance of machine learning models by providing them with more informative and meaningful input features. Automated feature engineering techniques, such as genetic algorithms, can also be used to automatically generate and optimise features. These methods search a space of potential features and select or create features based on their impact on the model's performance. Overall, feature engineering plays a crucial role in machine learning by enabling models to exploit the most relevant and informative aspects of the data, thereby improving their accuracy, robustness, and interpretability. This paper reports empirical studies aimed at demonstrating which types of technical features are best suited to establish relationships between ASF viruses and clinical symptoms to accurately diagnose ASF disease. Various machine learning models such as neural networks, decision trees, random forests, linear regression, and Bayesian regression accept ASF features and provide predictions. The experiment demonstrates the extent to which the machine learning model can establish correlations between ASF viruses and clinical symptoms by independently analysing the required feature. The focus is on establishing relationships between ASF viruses and clinical symptoms for diagnosis. Data from the European Union Reference Laboratory for African swine fever (ASF) was collected for the study. This paper provides essential information on ASF datasets based on the interpretation of results obtained by using appropriate samples and validated tests in combination with information from laboratory tests on ASF disease epidemiology, scenario, clinical signs, and lesions caused by different virulence. The study proposes to use causal ML to establish relationships between ASF viruses and symptoms to improve the accuracy of the ASF disease. In this study, the performance and validation of the models were measured using metrics such as R-squared, mean absolute error (MAE) and mean square error (MSE).","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"182 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282062","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}

{"title":"The Integration of Automated Business Models and the Participants within the Workforce in Implementing Digital Transformation","authors":"Marphil Mokoena, G. Baduza","doi":"10.59200/icarti.2023.010","DOIUrl":"https://doi.org/10.59200/icarti.2023.010","url":null,"abstract":"As technologies are constantly evolving, businesses are seeking out different methods they could use to leverage their competitive advantage; however, there’s often the downside of innovation posing the risk of human replacement. Technological advances tend to decrease the need for additional human labour because autonomous systems can perform tasks without human intervention. This potentially threatens the job security of participants in the workforce. To help adopt the research concept of Automating Business Models, they’ll require buy-in from the workforce population. Acquiring the adoption of automated business models from the workforce will be essential for the business to facilitate Digital Transformation within their organisation, which is the context of this study. This study uses scoping review methods to analyse the existing literature to support and identify the key themes that impact digital transformation. The key themes identified are used to assist in the integration of automated business models with the Participants within the Workforce to facilitate Digital Transformation through several jobs, job quality, social protection, wages and income, social dialogue, and industrial relations. Technology is used to help automate business models to help innovate firms, business development is essential to compete with competitors and bring value to customers, industry life cycles explain the need to transform before firms become displaced digitally, firms should always aim to keep stakeholders satisfied to create value, and economic growth is necessary for the improvement of society’s standard of living.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282217","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}

{"title":"Fifth Generation Non-Standalone to Standalone Wireless Architecture Reconstruction Solution and Implementation: South Africa Context","authors":"Ahmed S. Omar, KingsleyA. Ogudo","doi":"10.59200/icarti.2023.003","DOIUrl":"https://doi.org/10.59200/icarti.2023.003","url":null,"abstract":"The wireless mobile communication industry has evolved immensely over the past five decades, starting from the voice-centric 1G technology to the current data-centric 5G technology because of the increasing data traffic and the need for higher data speed rates. To provide the initial experience of 5G technology, many network operators worldwide started their deployment with the Non-Standalone (NSA) solution to mainly reduce the cost and time. 5G NSA solution already provides lower latency, higher network capacity, and bandwidth compared to the legacy network, however, there is growing oncern about its reliance on the 4G core network, capability, and the long-term migration plan to 5G Standalone (SA) network solution. This paper discusses a brief review of 5G evolution, standardization formulation, deployment strategies, security enhancement, challenges, and presents the reconstruction of 5G NSA to SA solution implementation. Furthermore, a 5G field test will be performed to analyze the user experience under the NSA NR in South Africa, and key practical challenges facing the South African MNOs from the perspective of business and technical challenges will be discussed with recommendations.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"44 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281766","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}

{"title":"Explainable AI modelling of Comorbidity in Pregnant Women and Children with Tropical Febrile Conditions","authors":"K. Attai, C. Akwaowo, Daniel Asuquo, Nne-eka Esubok, Udeme-Abasi Nelson, Emem Dan, O. Obot, Constance Amannah, Faith-Michael Uzoka","doi":"10.59200/icarti.2023.022","DOIUrl":"https://doi.org/10.59200/icarti.2023.022","url":null,"abstract":"Febrile diseases often exhibit overlapping symptoms, posing a challenge for their differential diagnosis. This challenge is particularly critical in pregnant women and children, where early and accurate diagnosis is vital to mitigate the elevated risk of maternal mortality prevalent in tropical and subtropical regions. Despite the commonality of fever as a symptom, the diverse range of potential co-morbidities necessitates an exploration of associated illnesses. This study employs the eXtreme Gradient Boosting (XGBoost) machine learning algorithm to classify febrile diseases' co-morbidities in pregnant women and children under 5 years. The dataset, comprising 1,350 records from selected health facilities across Niger-delta states in Nigeria, contributes to informed decision-making by physicians, ultimately enhancing healthcare provision. Evaluation results demonstrate the classifier's high precision (0.995) and recall (1.00) for the children dataset, while precision and recall of 1.00 are achieved for the pregnant women dataset. To facilitate model explanation and result interpretation, an eXplainable Artificial Intelligence (XAI) approach, specifically the SHapley Additive exPlanations (SHAP) method, is applied. The summary plot highlights upper and lower respiratory tract infections and malaria as the predominant diseases co-morbidities in children. In contrast, pregnant women exhibit upper and lower urinary tract infections, and malaria as the highest-ranking diseases co-morbidity. These results underscore the potential of ML techniques in accurately classifying febrile conditions' co-morbidities, contributing to the reduction of adverse health outcomes. The study's findings offer valuable insights for healthcare providers, enabling them to deliver more targeted and effective care to these vulnerable populations, thereby enhancing overall well-being.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139281875","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}

{"title":"Identifying Research Topics in Explainable Artificial Intelligence (xAI): the Value Add of Natural Language Processing Techniques","authors":"J. V. Biljon, E. V. D. Poel, Malesela Motlhape, Hulisani Mafune","doi":"10.59200/icarti.2023.007","DOIUrl":"https://doi.org/10.59200/icarti.2023.007","url":null,"abstract":"Explainable AI (xAI) refers to the concept and set of techniques that aim to make artificial intelligence (AI) systems more transparent and understandable to humans. The increased use of AI models in complex tasks and critical domains drives the current focus on xAI to enable humans to comprehend and trust the decisions made by AI systems. The fast-growing literature base on xAI means researchers need support in identifying the core research areas and current research patterns effectively, efficiently and with provenance. Our research design includes a systematic literature review (SLR) of xAI publications whereafter the same dataset was analyzed using two NLP techniques, namely Latent Derelict Allocation (LDA) and Encoder Representations from Transformers (BERT). The wordlists of possible xAI topics created from LDA and BERT were independently labelled by three xAI researchers and an xAI expert selected the most appropriate label. These results were then triangulated with the results from the SLR to gain new insights into xAI research topics and trends.","PeriodicalId":374836,"journal":{"name":"International Conference on Artificial Intelligence and its Applications","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139282003","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}