Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

Pu, Xiaorong; Chen, Shanxiong; Yu, Xianping; Zhang, Le

doi:10.1155/2018/2943290

Cited by 14 publications

(9 citation statements)

References 31 publications

(38 reference statements)

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“…Where implies the count of input nodes, denotes the connection weight from ℎ node in input layer to ℎnode in a hidden layer, represents the ith input, and stands for threshold of ℎ hidden node. The simulation of a hidden node is determined below [13]:…”

Section: Mlpc Modelmentioning

confidence: 99%

Improvement in Tongue Color Image Analysis for Disease Identification Using Deep Learning Based Depthwise Separable Convolution Model

Rajakumaran¹,

Sasikala²

2021

INDJCSE

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Disease diagnosis using tongue color image is a traditional non-invasive method widely employed to determine the status of the patient's internal organ. The elimination of dependencies on subjective and expert knowledge assessment for tongue diagnosis might considerably raise the scope of wide utilization of tongue diagnosis over the globe, including Western medicine. Computer based tongue diagnosis connected to light estimation, color correction, tongue segmentation, image analysis, geometry analysis, etc is a proficient method to diagnose diseases. This paper introduces a new Deep Learning with Depthwise Separable Convolution (Xception) Model called DLXM for tongue color image analysis. The presented model involves data augmentation and bilateral filtering (BF) based noise removal at the preprocessing stage. Additionally, the DLXM is applied for feature extraction process. At last, the bagging classifier (BC)and multilayer perceptron classifier (MLPC) models are employed to categorize the feature vectors into distinct types of diseases. The performance of the presented model is evaluated against benchmark tongue image dataset and the results depicted the effectual classification performance on the applied images. The experimental values notified that the DLXM-MLPC model has outperformed the compared methods by achieving a higher precision, recall, accuracy, and F1-Score of 97.35%, 97.01%, 97.01%, and 96.77% respectively.

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Section: Mlpc Modelmentioning

confidence: 99%

Improvement in Tongue Color Image Analysis for Disease Identification Using Deep Learning Based Depthwise Separable Convolution Model

Rajakumaran¹,

Sasikala²

2021

INDJCSE

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“…Classification datasets like balloon, iris, heart, and vehicle were utilized for performance evaluation. The comparative analysis of the experimental results of Chaotic BBO was done with standard BBO, GSA, and PSO (Pu et al, 2018).…”

Section: A Hybrid Cpsogsa For Training Mlpmentioning

confidence: 99%

A hybrid constriction coefficient-based particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

Rather

Bala

2020

IJICC

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PurposeIn this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.Design/methodology/approachIn this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.FindingsThe experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.Originality/valueThe CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

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“…[20] offered Bacterial Foraging to prevent security threats on the flow of big-data information. [108] developed a novel hybrid bio-inspired algorithm using a Multilayer Perceptron (MLP) to handle big data security. However, even with significant advantages, bio-inspired algorithms (such as Bacterial Foraging, Swarm approach, and MLP) are often not suitable for scalability-vulnerable considering fault tolerance, and agility.…”

Section: G Virtualizationmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

Cited by 14 publications

References 31 publications

Improvement in Tongue Color Image Analysis for Disease Identification Using Deep Learning Based Depthwise Separable Convolution Model

Improvement in Tongue Color Image Analysis for Disease Identification Using Deep Learning Based Depthwise Separable Convolution Model

A hybrid constriction coefficient-based particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

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