scholarly journals Grey wolf optimization algorithm for hierarchical document clustering

2021 ◽  
Vol 24 (3) ◽  
pp. 1744
Author(s):  
Ayad Mohammed Jabbar ◽  
Ku Ruhana Ku-Mahamud
Keyword(s):  
Document Clustering ◽  
Text Clustering ◽  
Search Space ◽  
Learning Approaches ◽  
Grey Wolf ◽  
External Evaluation ◽  
Grey Wolf Optimization ◽  
Noise Data ◽  
F Measure ◽  
Better Than

In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as rand, purity, F-measure, and entropy, indicates that the proposed M-GWO algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on purity metric, 75% better than all algorithms using F-measure metric, and 100% based on entropy metric).

A Grey Wolf Optimizer for Text Document Clustering

2018 ◽  
Vol 29 (1) ◽  
pp. 814-830 ◽  
Author(s):  
Hasan Rashaideh ◽  
Ahmad Sawaie ◽  
Mohammed Azmi Al-Betar ◽  
Laith Mohammad Abualigah ◽  
Mohammed M. Al-laham ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Document Clustering ◽  
Text Clustering ◽  
The Other ◽  
Grey Wolf Optimizer ◽  
Data Sets ◽  
Complex Data ◽  
Grey Wolf ◽  
Clustering Methods ◽  
F Measure

Abstract Text clustering problem (TCP) is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer (GWO) for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main issue to be addressed is how to split text documents on the basis of GWO into homogeneous clusters that are sufficiently precise and functional. Specifically, TCP-GWO, or referred to as the document clustering algorithm, used the average distance of documents to the cluster centroid (ADDC) as an objective function to repeatedly optimize the distance between the clusters of the documents. The accuracy and efficiency of the proposed TCP-GWO was demonstrated on a sufficiently large number of documents of variable sizes, documents that were randomly selected from a set of six publicly available data sets. Documents of high complexity were also included in the evaluation process to assess the recall detection rate of the document clustering algorithm. The experimental results for a test set of over a part of 1300 documents showed that failure to correctly cluster a document occurred in less than 20% of cases with a recall rate of more than 65% for a highly complex data set. The high F-measure rate and ability to cluster documents in an effective manner are important advances resulting from this research. The proposed TCP-GWO method was compared to the other well-established text clustering methods using randomly selected data sets. Interestingly, TCP-GWO outperforms the comparative methods in terms of precision, recall, and F-measure rates. In a nutshell, the results illustrate that the proposed TCP-GWO is able to excel compared to the other comparative clustering methods in terms of measurement criteria, whereby more than 55% of the documents were correctly clustered with a high level of accuracy.

Automated Early Detection of Astrocytomas Tumor Based on Optimized Adaptive Cluster with Super Pixel Model

2021 ◽  
Vol 30 (06n08) ◽  
Author(s):  
V. K. Deepak ◽  
R. Sarath
Keyword(s):  
Brain Tumor ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Grey Wolf ◽  
Dice Coefficient ◽  
Grey Wolf Optimization ◽  
Brain Tumor Segmentation ◽  
Better Than

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue.

Self-improved grey wolf optimization for estimating carrier frequency offset in SCM-OFDM systems

2020 ◽  
Vol 16 (1) ◽  
pp. 53-73
Author(s):  
Rashmi N. ◽  
Mrinal Sarvagya
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Carrier Frequency Offset ◽  
State Of The Art ◽  
Frequency Division ◽  
Grey Wolf ◽  
Ofdm System ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Transmitted Signal ◽  
Better Than

Purpose The purpose of this paper is to demonstrate a proficiency for accomplishing optimal CFO and keep down the error among the received and transmitted signal. Orthogonal frequency-division multiplexing (OFDM) is considered as an attractive modulation scheme that could be adopted in wireless communication systems owing to its reliability in opposition to multipath interruptions under different subchannels. Carrier frequency offset (CFO) establishes inter-carrier interference that devastates the orthogonality between the subcarriers and fluctuates the preferred signal and minimizes the effectual signal-to-noise ratio (SNR). This results in corrupted system performance. For sustaining the subcarriers’ orthogonality, timing errors and CFOs have to be approximated and sufficiently compensated for. Single carrier modulation (SCM) is a major feature for efficient OFDM system. Design/methodology/approach This paper introduces a novel superposition coded modulation-orthogonal frequency-division multiplexing (SCM-OFDM) system with optimal CFO estimation using advanced optimization algorithm. The effectiveness of SCM-OFDM is validated by correlating the transmitted and received signal. Hence, the primary objective of the current research work is to reduce the error among the transmitted and received signal. The received signal involves CFO, which has to be tuned properly to get the signal as closest as possible with transmitted signal. The optimization or tuning of CFO is done by improved grey wolf optimization (GWO) called GWO with self-adaptiveness (GWO-SA). Further, it carries the performance comparison of proposed model with state-of-the-art models with the analysis on bit error rate (BER) and mean square error (MSE), thus validating the system’s performance. Findings From the analysis, BER of the proposed and conventional schemes for CFO at 0.25 was determined, where the adopted scheme at 10th SNR was 99.6 per cent better than maximum likelihood, 99.6 per cent better than least mean square (LMS), 99.3 per cent better than particle swarm optimization (PSO), 75 per cent better than genetic algorithm (GA) and 25 per cent better than GWO algorithms. Moreover, MSE at 1st SNR, the proposed GWO-SA scheme, is 4.62 per cent better than LMS, 60.1 per cent better than PSO, 37.82 better than GA and 67.85 per cent better than GWO algorithms. Hence, it is confirmed that the performance of SCM-OFDM system with GWO-SA-based CFO estimation outperformed the state-of-the-art techniques. Originality/value This paper presents a technique for attaining optimal CFO and to minimize the error among the received and transmitted signal. This is the first work that uses GWO-SA for attaining optimal CFO.

Liver Disease Detection Using Grey Wolf Optimization and Random Forest Classification

2021 ◽  
pp. 130-160
Author(s):  
Singaravelan Shanmugasundaram ◽  
Parameswari M.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Cutting Edge ◽  
Learning Approaches ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Metavir Score

Utilizing machine learning approaches as non-obtrusive strategies is an elective technique in organizing perpetual liver infections for staying away from the downsides of biopsy. This chapter assesses diverse machine learning methods in expectation of cutting-edge fibrosis by joining the serum bio-markers and clinical data to build up the order models. An imminent accomplice of patients with incessant hepatitis C was separated into two sets—one classified as gentle to direct fibrosis (F0-F2) and the other ordered as cutting-edge fibrosis (F3-F4) as per METAVIR score. Grey wolf optimization, random forest classifier, and decision tree procedure models for cutting-edge fibrosis chance expectation were created. Recipient working trademark bend investigation was performed to assess the execution of the proposed models.

scholarly journals Improved grey wolf algorithm for optimization problems

2021 ◽  
Vol 22 (3) ◽  
pp. 1573
Author(s):  
Hafiz Maaz Asgher ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali ◽  
Muhammad Aamir
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Research Work ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Grey Wolf Optimization ◽  
Whale Optimization

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.

Optimal Text Document Clustering Enabled by Weighed Similarity Oriented Jaya With Grey Wolf Optimization Algorithm

2021 ◽  
Author(s):  
Gugulothu Venkanna ◽  
Dr K F Bharati
Keyword(s):  
Optimization Algorithm ◽  
Document Clustering ◽  
Similarity Index ◽  
Scientific Development ◽  
Similarity Function ◽  
Grey Wolf ◽  
Text Document ◽  
Clustering Model ◽  
Proposed Model ◽  
Better Than

Abstract Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which overcomes challenges in clustering. The two most important challenges in clustering are clustering accuracy and quality. For this reason, this paper intends to present an ideal clustering model for text document using term frequency–inverse document frequency, which is considered as feature sets. Here, the initial centroid selection is much concentrated which can automatically cluster the text using weighted similarity measure in the proposed clustering process. In fact, the weighted similarity function involves the inter-cluster, and intra-cluster similarity of both ordered and unordered documents, which is used to minimize weighted similarity among the documents. An advanced model for clustering is proposed by the hybrid optimization algorithm, which is the combination of the Jaya Algorithm (JA) and Grey Wolf Algorithm (GWO), and so the proposed algorithm is termed as JA-based GWO. Finally, the performance of the proposed model is verified through a comparative analysis with the state-of-the-art models. The performance analysis exhibits that the proposed model is 96.56% better than genetic algorithm, 99.46% better than particle swarm optimization, 97.09% superior to Dragonfly algorithm, and 96.21% better than JA for the similarity index. Therefore, the proposed model has confirmed its efficiency through valuable analysis.

An effective digit recognition model using enhanced convolutional neural network based chaotic grey wolf optimization

2021 ◽  
pp. 1-11
Author(s):  
P. Preethi ◽  
R. Asokan ◽  
N. Thillaiarasu ◽  
T. Saravanan
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Domain Knowledge ◽  
Handwriting Recognition ◽  
Research Work ◽  
Recognition System ◽  
Learning Approaches ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Processing Power

Classical Handwriting recognition systems depend on manual feature extraction with a lot of previous domain knowledge. It’s difficult to train an optical character recognition system based on these requirements. Deep learning approaches are at the centre of handwriting recognition research, which has yielded breakthrough results in recent years. However, the rapid growth in the amount of handwritten data combined with the availability of enormous processing power necessitates an increase in recognition accuracy and warrants further investigation. Convolutional Neural Networks (CNNs) are extremely good at perceiving the structure of handwritten characters in ways that allow for the automatic extraction of distinct features, making CNN the best method for solving handwriting recognition problems. In this research work, a novel CNN has built to modify the network structure with Orthogonal Learning Chaotic Grey Wolf Optimization (CNN-OLCGWO). This modification is adopted for evolutionarily optimizing the number of hyper-parameters. This proposed optimizer predicts the optimal values from the fitness computation and shows better efficiency when compared to various other conventional approaches. The ultimate target of this work is to endeavour a suitable path towards digitalization by offering superior accuracy and better computation. Here, MATLAB 2018b has been used as the simulation environment to measure metrics like accuracy, recall, precision, and F-measure. The proposed CNN- OLCGWO offers a superior trade-off in contrary to prevailing approaches.

scholarly journals Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rashid Naseem ◽  
Bilal Khan ◽  
Muhammad Arif Shah ◽  
Karzan Wakil ◽  
Atif Khan ◽  
...  
Keyword(s):  
Liver Disease ◽  
Early Stage ◽  
Optimal Solution ◽  
Classification Algorithms ◽  
Early Prediction ◽  
Learning Approaches ◽  
Aged People ◽  
The World ◽  
F Measure ◽  
Better Than

In the recent era, a liver syndrome that causes any damage in life capacity is exceptionally normal everywhere throughout the world. It has been found that liver disease is exposed more in young people as a comparison with other aged people. At the point when liver capacity ends up, life endures just up to 1 or 2 days scarcely, and it is very hard to predict such illness in the early stage. Researchers are trying to project a model for early prediction of liver disease utilizing various machine learning approaches. However, this study compares ten classifiers including A1DE, NB, MLP, SVM, KNN, CHIRP, CDT, Forest-PA, J48, and RF to find the optimal solution for early and accurate prediction of liver disease. The datasets utilized in this study are taken from the UCI ML repository and the GitHub repository. The outcomes are assessed via RMSE, RRSE, recall, specificity, precision, G-measure, F-measure, MCC, and accuracy. The exploratory outcomes show a better consequence of RF utilizing the UCI dataset. Assessing RF using RMSE and RRSE, the outcomes are 0.4328 and 87.6766, while the accuracy of RF is 72.1739% that is also better than other employed classifiers. However, utilizing the GitHub dataset, SVM beats other employed techniques in terms of increasing accuracy up to 71.3551%. Moreover, the comprehensive outcomes of this exploration can be utilized as a reference point for further research studies that slight assertion concerning the enhancement in extrapolation through any new technique, model, or framework can be benchmarked and confirmed.

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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