Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Awotunde, Joseph Bamidele; Jimoh, Rasheed Gbenga; Oladipo, Idowu Dauda; AbdulRaheem, Muyideen

doi:10.1007/978-3-030-69143-1_4

Cited by 19 publications

(4 citation statements)

References 50 publications

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“…In another field setting, SVM based on Radial Basic Function attained an accuracy of 0.93 on credit risk prediction (Alabi et al, 2020). Additionally, previous research in various disciplines in the study of Maskeliunas et al 2020, Awotunde et al 2020, and Mebawondu 2020 have proven the overall effectiveness of SVM.…”

Section: Supervised Machine Learningmentioning

confidence: 87%

Enhancing Conversions and Lead Scoring in Online Professional Education

Yim,

Khaw,

Lim

et al. 2024

IJOMFA

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This study seeks to enhance lead conversion for online professional education providers by using supervised machine learning algorithms for lead conversion targeting and lead scoring, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Random Forst, Bagging, Boosting, and Stacking. A lead dataset was used to train and test the machine-learning models. The Recursive Feature Elimination (RFE) is used to establish a precise lead profile. The performance of the trained lead conversion models was evaluated and compared using the 10-Folds cross-validation method based on accuracy, precision, recall, and F1-score. The results show that Stacking is the best model with an accuracy of 0.9233, precision of 0.9391, and F1-score of 0.8939. Meanwhile, the Logistic Regression-based lead scoring model demonstrated promising potential for automating lead scoring. The results of the Logistic Regression-based lead scoring model achieved an accuracy of 0.9019, recall of 0.9019, precision of 0.9015, and F1-score of 0.9014. The optimal lead scoring threshold is 0.20, which stroked the optimal trade-off balance between accuracy, sensitivity, and specificity.

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Section: Supervised Machine Learningmentioning

confidence: 87%

Enhancing Conversions and Lead Scoring in Online Professional Education

Yim,

Khaw,

Lim

et al. 2024

IJOMFA

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show abstract

“…For example, automated mosquito traps can use AI algorithms to identify specific mosquito species that carry diseases causing dengue or malaria (Kaur et al., 2022). These traps use a combination of visual and chemical cues to lure mosquitoes, and AI methods can analyse the data from the traps to optimize their effectiveness (Awotunde et al., 2021; Santosh et al., 2020). AI can also be used to control the spread of insect‐borne diseases through the use of techniques originally developed for the purpose of precision agriculture.…”

Section: Disease Vector Controlmentioning

confidence: 99%

Artificial neuronal networks are revolutionizing entomological research

Hartbauer

2024

J Applied Entomology

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The application of artificial intelligence (AI) in entomological research has gained significant attention in recent years. This review summarizes the current state of research on the potential of AI methods in various subfields of entomology, such as behavioural biology, biodiversity research, climate change research, pest management, and disease vector control. In some cases, AI‐based species identification methods based on deep learning neuronal network models have been shown to outperform traditional morphological identification methods in terms of accuracy and speed. Behavioural biology research has been enhanced through the use of AI‐based tracking systems that can classify insect behaviour and movement patterns. Habitat modelling has also been improved with the use of AI, allowing for the creation of more accurate models that can predict insect distribution and abundance. Climate change and biodiversity research have benefited from AI‐driven tools that can analyse large datasets and predict the impact of environmental changes on insect populations. In agriculture, pest management has been revolutionized by AI methods, with the development of smart traps and monitoring systems that can detect and identify pest species in real‐time, enabling targeted control measures. Disease vector control has also been improved through the use of AI‐based predictive models, which can identify areas at risk of disease transmission and aid in the development of effective control strategies. In conclusion, AI methods have the potential to revolutionize many aspects of entomological research. Future research should focus on developing more sophisticated AI tools and integrating them into entomological research to address even more complex ecological questions.

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“…Another work by Awotunde et al, [4] proposed a framework to predict malaria-endemic in selected geographical locations such as Nigeria. In their work, long short-term memory (LSTM) classifier was employed with Satellite and clinical data.…”

Section: B Long-short Term Memory Network (Lstm)mentioning

confidence: 99%

Artificial Intelligence System for Malaria Diagnosis

Barracloug,

Were,

Mwangakala

et al. 2024

IJACSA

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Malaria threats have remained one of the major global health issues over the past decades specifically in lowmiddle income countries. 70% of the Kenya population lives in malaria endemic zones and the majority have barriers to access health services due to factors including lack of income, distance, and social culture. Despite various research efforts using blood smears under a microscope to combat malaria with advantages, this method is time consuming and needs skillful personnel. To effectively solve this issue, this study introduces a new method integrating InfoGainAttributeEval feature selection techniques and parameter tuning method based on Artificial Intelligence and Machine Learning (AIML) classifiers with features to diagnose types of malaria more accurately. The proposed method uses 100 features extracted from 4000 samples. Sets of experiments were conducted using Artificial Neural Network (ANNs), Naïve Bayes (NB), Random Forest (RF) classifiers and Ensemble methods (Meta Bagging, Random Committee Meta, and Voting). Naïve Bayes has the best result. It achieved 100% accuracy and built the model in 0.01 second. The results demonstrate that the proposed method can classify malaria types accurately and has the best result compared to the reported results in the field.

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Prediction of Malaria Fever Using Long-Short-Term Memory and Big Data

Cited by 19 publications

References 50 publications

Enhancing Conversions and Lead Scoring in Online Professional Education

Enhancing Conversions and Lead Scoring in Online Professional Education

Artificial neuronal networks are revolutionizing entomological research

Artificial Intelligence System for Malaria Diagnosis

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