Energy Management Model for Mobile Ad hoc Network using Adaptive Information Weight Bat Algorithm

Olusesi, Ayobami Taiwo; Olaniyan, Olatayo Moses; Omodunbi, Bolaji Abigail; Wahab, Wajeed Bolanle; Adetunji, Olusogo Julius; Olukoya, Bamidele Musiliu

doi:10.1016/j.prime.2023.100255

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…Reinforcement learning is a science of decision making and sub division of the machine learning algorithms that apply artificial intelligence system to learn through trial and error using feedback from its actions [15]. Different predictive models have been developed with the application of these described machine learning algorithms, for instance [4,16,17,55,56]. The recent studies encourage ensemble techniques of two or more algorithms and have been employed in different disciplines for better predictive performance.…”

Section: Introductionmentioning

confidence: 99%

Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques

Olaniyan,

Adetunji,

Fasanya

2023

AJERD

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Lumpy skin diseases virus (LSDV) is a dangerous and contagious diseases that are mostly common in Sub-Saharan African, South Eastern Europe, South Asia and as well as Middle East, China. LSDV is transmitted through blood sucking insects which are double stranded DNA virus and belong to the family of Capri poxvirus genus family. The recent study proved and clarified that lumpy skin diseases viruses (LSDV) affected mostly cattle and buffalo in Africa, Asia and Europe with population of 29 966, 8 837 and 2 471 outbreaks respectively, between the years 2005 – 2021. Different machine learning approaches have been adopted for the prediction of lumpy skin diseases. An enhanced model was developed to improve the predictive performance of existing model and also, compared the performance of stacked ensemble of single classifiers with respect to optimized artificial neural network. The implementation was done with python 3.7 on Core i5, 16G RAM Intel hardware. The single classifiers are decision tree (DT), k-nearest neighbor, random forest (RF) and support vector machine (SVM). A feature wiz feature selection technique was adopted on lumpy skin diseases dataset coupled with the parameters tuning of the model before classification. Both stacked ensemble and optimized artificial neural network model outperformed the existing model. Stacked ensemble model gives accuracy, precision, f1-score and recall of 97.69%, 98.44%, 98.93% and 98.68% respectively. The results also showed that optimized artificial neural networks of 200 epochs outperformed stacked ensemble classifiers with accuracy of 98.89% and 98.66% of training and validation respectively. The developed model in a real world would assist in reducing the occurrence of lumpy skin diseases.

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Section: Introductionmentioning

confidence: 99%

Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques

Olaniyan,

Adetunji,

Fasanya

2023

AJERD

Self Cite

View full text Add to dashboard Cite

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“…It is often employed in artificial intelligence that are used for discovering of new properties by learning from training dataset [12]. Methods of machine learning classification techniques are sub divided into different categories; these classes include supervised learning, unsupervised learning, deep learning and dimensionality reduction [7,72]. One of the application areas of machine learning is the deep learning, which is frequently adopted in core areas of mathematics, engineering and computer science [13].…”

Section: Introductionmentioning

confidence: 99%

“…One of the application areas of machine learning is the deep learning, which is frequently adopted in core areas of mathematics, engineering and computer science [13]. Different studies had shown the impacts of machine learning and artificial intelligence in image processing, intelligent system development, natural language processing, security and signal processing [12,71,72]. Flooding is a condition in which surface water exceeds the retentive capacity of the dry land [7,8,14,15].…”

Section: Introductionmentioning

confidence: 99%

Flood Image Classification using Convolutional Neural Networks

Adetunji,

Adeyanju,

Esan

et al. 2023

AJERD

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Flood disaster is a natural disaster that leads to loss of lives, properties damage, devastating effects on the economy and environment; therefore, there should be effective predictive measures to curb this problem. Between the years 2002- 2023, flood has caused death of over 200,000 people globally and occurred majorly in resource poor countries and communities. Different machine learning approaches have been developed for the prediction of floods. This study develops a novel model using convolutional neural networks (CNN) for the prediction of floods. Important parameters such as standard deviation and variance were incorporated in the parameters tuned CNN model that performed flood images feature extraction and classification for better predictive performance. The enhanced model was assessed with accuracy and loss measurement and compared with the existing model. The model leverage on the unique features of region of Interest aligns to resolve the issues of misalignments caused by the use of region of Interest pooling engaged in the traditional Faster-RCNN. The techniques and the developed system were implemented using a Python-based integrated development environment called “Anaconda Navigator” on Intel Core i5 with 8G Ram hardware of Window 10 operating system. The developed model achieved optimal accuracy at 200 epochs with 99.80% and corresponding loss of 0.0890. The results confirmed that predictive performance of a model can be improved by incorporating standard deviation and variance on model, coupled with its parameters tunning approach before classification.

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Energy Management Model for Mobile Ad hoc Network using Adaptive Information Weight Bat Algorithm

Cited by 2 publications

References 29 publications

Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques

Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques

Flood Image Classification using Convolutional Neural Networks

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