Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Abiodun, Oludare Isaac; Kiru, Muhammad Ubale; Omolara, Abiodun Esther; Dada, Kemi Victoria; Umar, Abubakar Malah; Linus, Okafor Uchenwa; Arshad, Humaira; Kazaure, Abdullahi Aminu; Gana, Usman M.

doi:10.1109/access.2019.2945545

Cited by 445 publications

(211 citation statements)

References 167 publications

(131 reference statements)

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“…Pattern recognition processes has three main tasks: data pre-processing, data representation and decision making. A simple feed-forward neural network can be trained for pattern recognition tasks such as image processing and biometric identification [13]. ANNs can be described as non-linear machine learning methods.…”

Section: Methods and Techniquesmentioning

confidence: 99%

A hybrid appliance identification method by using grey relational artificial neural network

Güven¹,

Kocaoğlu²

2020

J. mechatron., artif. intell., eng.

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Nowadays, everything is getting smarter such as mobile phones, cars, watches and home appliances. Our powerlines are also getting smarter. There are many smart grid and smart home applications. Designing of recognition devices to identify appliances for these smart networks is a new task to do it. There are many different approaches on recognition and identification these power consumer devices and appliance. This study aims to develop an effective method that does not require any additional hardware. This method has been developed by using powerline parameters such as current, phase angle, voltage, active and reactive power. These data have been classified and normalized by using a validation method and grey relational analysis to train an artificial neural network. This neural network was trained by using power parameters of many different common appliances like heater, coffee machine, television, radio, lamp, computer, fan, refrigerator etc. This identification algorithm can be used within a low-cost embedded system for collecting appliance information over a powerline to provide info for smart homes and smart grids.

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Section: Methods and Techniquesmentioning

confidence: 99%

A hybrid appliance identification method by using grey relational artificial neural network

Güven¹,

Kocaoğlu²

2020

J. mechatron., artif. intell., eng.

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“…However, the generalization capabilities of ANN-based models [32] cannot be assured. More generic and stable approaches are therefore required to solve these problems.…”

Section: Related Workmentioning

confidence: 99%

Windows PE Malware Detection Using Ensemble Learning

et al. 2021

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In this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naïve Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.

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“…This method gave a highly robust and accurate result, but it did not focus on textual information. Abiodun et al 28 did a comprehensive review of PR, which showed ANN was the best comparable method using in statistical, fuzzy, structural techniques to address the PR problems. Abiodun et al 29 did a survey based on the ANN contribution, which accuracy, latency, volume, processing speed, performance, fault tolerance, and scalability was high.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid features and optimization‐driven recurrent neural network for glaucoma detection

Ajesh¹,

Ravi²

2020

Int J Imaging Syst Tech

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Glaucoma is considered as the main source of irrevocable loss of vision. The earlier diagnosis of glaucoma is essential to provide earlier treatment and to reduce vision loss. The fundus images are transfigured in the ophthalmology and are used to visualize the structures of the optic disc. However, accuracy is considered as a major constraint. To increase accuracy, an effective optimization‐driven classifier is developed for glaucoma detection. The proposed Jaya‐chicken swarm optimization (Jaya‐CSO) is employed for training the recurrent neural network (RNN) for glaucoma detection. The proposed Jaya‐CSO is designed by integrating the Jaya algorithm with the chicken swarm optimization (CSO) technique for tuning the weights of the RNN classifier. The method utilized optic disc features, statistical features, and blood vessel features for the determination of the glaucomatous region. The features obtained from the optic disc, blood vessels, and the fundus image is formulated as a feature vector. Finally, the glaucoma classification is done using RNN using the feature vector such that the RNN is trained using the proposed Jaya‐CSO. The proposed Jaya‐CSO outperformed other existing models with maximal accuracy of 0.97, the specificity of 0.97, and sensitivity of 0.97, respectively.

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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

Cited by 445 publications

References 167 publications

A hybrid appliance identification method by using grey relational artificial neural network

A hybrid appliance identification method by using grey relational artificial neural network

Windows PE Malware Detection Using Ensemble Learning

Hybrid features and optimization‐driven recurrent neural network for glaucoma detection

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