Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

AL‐Dhief, Fahad Taha; Baki, Marina Mat; Latiff, N. M. Abdul; Malik, Nik Noordini Nik Abd; Salim, Naseer Sabri; Albader, Musatafa Abbas Abbood; Mahyuddin, Nor Muzlifah; Mohammed, Mazin Abed

doi:10.1109/access.2021.3082565

Cited by 44 publications

(13 citation statements)

References 46 publications

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“…The evaluation measurements have been used in order to evaluate the proposed GWO-ELM approach regarding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), recall, accuracy, specificity, G-mean, precision, F-measure, and MCC. Equations (11–17) ( 44 – 46 ) depict these evaluation measurements.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Albadr

Ayob

Tiun

et al. 2022

Front. Public Health

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Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

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Section: Experiments and Resultsmentioning

confidence: 99%

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Albadr

Ayob

Tiun

et al. 2022

Front. Public Health

Self Cite

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show abstract

“…These measurements are accuracy, specificity, precision, recall (sensitivity), f-measure, G-mean, and execution time (sec). These evaluation measures have computed as shown in ( 3)-( 8) [34]- [36]. Where: TP denotes to true positive, TN denotes to true negative, FP and FN refer to false positive and false negative, respectively.…”

Section: Resultsmentioning

confidence: 99%

Ear recognition system using random forest and histograms of oriented gradients techniques

Mutar

Ahmed

Alsemawi

et al. 2022

IJEECS

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In recent years, systems of ear recognition are considered a significant topic of research in the biometrics field. In such systems, the models of machine learning represent a principal part in order to recognise humans’ identities by using their ear images. In this paper, a system of ear recognition is proposed by using random forest (RF) and histograms of oriented gradients (HOG) techniques. The HOG is used to extract features from ear images. Subsequently, these extracted features will be fed to the RF classifier to classify the ear images with respect to the classes. In this study, the ear images have been selected from the Indian Institute of Technology Delhi, second version (IITD II). The performance of the proposed system has evaluated by using different evaluation measures such as accuracy, specificity, and G-mean. The experimental results show that the proposed system for ear recognition obtains accuracy up to 99.69%. Furthermore, this system archives 99.84% and 80.78% for specificity and G-mean, respectively. The proposed system has the ability to identify persons through their ear images effectively.

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“…This section evaluated the performance of our proposed O-LAR mechanism against the existing LAR, D-LAR, and LEPR schemes using simulated experiments. The simulations of the proposed and existing routing protocols were conducted for different scenarios with varying number of UAVs and their speed through the NS-2.35 simulator [38][39][40][41][42][43][44][45].At the beginning of FANET scenario, all UAVs (05-25) are randomly distributed in the area of 1×1km 2 with a transmission range of each UAV is maximum as 250m and used IEEE 802.11g as a mac layer wireless standard. The speed of each UAV is varying from 20 to 100m/sec.…”

Section: Simulation Setup and Results Discussionmentioning

confidence: 99%

3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

et al. 2021

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A Flying Ad-hoc Networks (FANETs) is an autonomous technology that creates a selforganized wireless network via Unmanned Arial Vehicles (UAVs). In this network, all UAVs can communicate within a restricted range of wireless communication in the absence of fixed infrastructure. As a result of high mobility, the limited energy, and the communication range of UAVs, network forming, and deformation between them are very frequent that causes packet delivery failure. Therefore, a stable route is always needed to ensure effective data dissemination between source and destination in FANETs. Since it has drastically changing network topology, therefore, to maintain the stable route during packet transmission, there is a need for a suitable routing protocol. This paper proposes an Optimized Location-Aided Routing (O-LAR) protocol which is the modified version of Location-Aided Routing (LAR) protocol. Our protocol's novelty comes from the fact that it established an optimal route between UAVs for information dissemination towards their respective destination UAV by considering weight function. A weighted function is used to decide the best next-hop node selection based on the parameters like residual energy, distance, and UAV movement direction. The performance of the O-LAR is evaluated mathematically and simulated through the NS-2 simulator. The empirical results attest that O-LAR improves the link duration, network lifetime, packet delivery ratio, and average throughput compared with the state-of-the-art protocols: LEPR, D-LAR, and LAR. Further, the proposed scheme reduces the number of next-hops, routing overhead and end-to-end delay compared to the state-of-the-art protocols.

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Voice Pathology Detection and Classification by Adopting Online Sequential Extreme Learning Machine

Cited by 44 publications

References 46 publications

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Ear recognition system using random forest and histograms of oriented gradients techniques

3D Location Oriented Routing in Flying Ad-Hoc Networks for Information Dissemination

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