Recurrent Neural Network Architectures for Analysing Biomedical Data Sets

Khalaf, Mohammed; Hussain, Abir Jaafar; Keight, Robert; Al‐Jumeily, Dhiya; Keenan, Russell; Chalmers, Carl; Fergus, Paul; Salih, Wafaa Mahdi; Hamed, Dhafar; Idowu, Ibrahim Olatunji

doi:10.1109/dese.2017.12

Cited by 16 publications

(4 citation statements)

References 21 publications

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“…Holdout technique is applied in our study for the purpose of evaluating how the statistical analysis can generalise to an independent data sets [25]. The total proportion of the flood data sets is divided into training, validation, and testing phases.…”

Section: Methodsmentioning

confidence: 99%

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Khalaf

Hussain

Al‐Jumeily

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models.

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Section: Methodsmentioning

confidence: 99%

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Khalaf

Hussain

Al‐Jumeily

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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“…The sequence needs to meet the size requirement, otherwise the sequences will be filled until the specified value. However, the excess will be barred if the sequence size is more than the specified value [62].…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Intelligent Locking System using Deep Learning for Autonomous Vehicle in Internet of Things

H¹,

Ithnin²,

Wahab³

et al. 2021

IJACSA

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“…Each input vector corresponds to an object x, characterized by a set of features, whereas y describes the class label assigned to x. In this respect, the classification process employed to label training and testing data sets is also known as a descriptive classifier, i.e., a method to discover the class label for various inputs [27], [28]. To apply classification in the target research domain, it is vital to identify distinctive feature patterns from un-labelled datasets during the testing process.…”

Section: Classification Of Flood Sensor Datamentioning

confidence: 99%

IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

Khalaf

Alaskar²,

Hussain

et al. 2020

IEEE Access

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River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively. INDEX TERMS Internet of Things, ensemble machine learning, flood sensor data, long-short term memory.

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Recurrent Neural Network Architectures for Analysing Biomedical Data Sets

Cited by 16 publications

References 21 publications

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Intelligent Locking System using Deep Learning for Autonomous Vehicle in Internet of Things

IoT-Enabled Flood Severity Prediction via Ensemble Machine Learning Models

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