Multi-Objective Differential Evolution of Evolving Spiking Neural Networks for Classification Problems

Saleh, Abdulrazak Yahya; Shamsuddin, Siti Mariyam; Hamed, Haza Nuzly Abdull

doi:10.1007/978-3-319-23868-5_25

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…The ESNN model demonstrates its simplicity and performance capabilities with its fast one-pass learning algorithm where no data re-training is required. However, determining a suitable parameter value is crucial for ESNN implementation, thus guaranteeing the best output [26]. A comparative study between ESNN, MLP, and SVM has been performed where ESNN achieved a better accuracy of 97.1% on the VidTimit dataset [27][28][29].…”

Section: Related Workmentioning

confidence: 99%

Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

Sabri¹,

Hamed²,

İbrahim³

et al. 2022

Computers, Materials &Amp; Continua

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Adolescent Idiopathic Scoliosis (AIS) is a deformity of the spine that affects teenagers. The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation. Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human's back. Currently, detecting the curve of the spine is manually performed, making it a time-consuming task. To overcome this issue, it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS. This research proposes a new integration of ESNN and Feature Extraction (FE) methods and explores the architecture of ESNN for the AIS classification model. This research identifies the optimal Feature Extraction (FE) methods to reduce computational complexity. The ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial. A comparison between the conventional classifier (Support Vector Machine (SVM), Multi-layer Perceptron (MLP) and Random Forest (RF)) with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia (HUKM). This dataset consists of various photogrammetry images of the human back with different types of Malaysian AIS patients to solve the scoliosis problem. The process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale images. This is then followed by feature extraction, normalization, and classification. The experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS classification. This demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.

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Section: Related Workmentioning

confidence: 99%

Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

Sabri¹,

Hamed²,

İbrahim³

et al. 2022

Computers, Materials &Amp; Continua

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“…10 shows a summary of the comparative results. Note that none of the algorithms that presented in Table XI (MEPGANf1-f3 [12], MLP-BP [13], ISO-FLANN [13], NN-CAPSO [14], NN-GSA [14], NN-ICA [14], NN-BP [15], NN-MVO [15], MODE-ESNN [16], DPM [16], SAE-MR Over all, NN-GSO algorithm is better or at least competitive for breast cancer, diabetes, heart, hepatitis, appendicitis and Alzheimer. On the other hand, NN-GSO performs comparable for the liver, disorders, thyroid and dermatology datasets (with respect to classification accuracy) comparing to other algorithms.…”

Section: B Mean Squared Error Analysismentioning

confidence: 99%

A New Hybrid Intelligent System for Prediction of Medical Diseases

Qasem¹,

Alsaidan²

2018

ijacsa

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Abstract-This paper proposes a hybrid intelligent system as medical decision support tool for data classification based on the Neural Network, Galactic Swarm Optimization (NN-GSO), and the classification model. The goal of the hybrid intelligent system is to take the advantages and reduce the disadvantages of the constituent models. The system is capable of learning from data sets and reach great classification performance. Consequently, various algorithms have been developed that include Neural Network based on Galactic Swarm Optimization (NN-GSO), Neural Network based on Particle Swarm Optimization (NN-PSO) and Neural Network based on Genetic Algorithm (NN-GA) to improve NN structure and accuracy rates. For the evaluation process, the hybrid intelligent system has used multiple of benchmark medical data sets to evaluate the effectiveness. These benchmarks were gotten from the UCI Repository of Machine Learning. The three-performance metrics were calculated are accuracy, sensitivity and specificity. These metrics are useful for medical applications. The proposed algorithm was tested on various data sets which represent binary and multi-class medical diseases problems. The proposed algorithm performance analyzed and compared with others using k-fold cross validation. The significance tests results have proven that the proposed algorithm is effective to solve neural networks with good generalization ability and network structure for medical diseases detection.

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