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Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第29-30期79卷 22027-22045页

作者： Qaddoura, Raneem Al Manaseer, Waref Abushariah, Mohammad A. M. Alshraideh, Mohammad Aref Philadelphia Univ Informat Technol Amman Jordan Univ Jordan King Abdullah II Sch Informat Technol Dept Comp Sci Amman Jordan Univ Jordan King Abdullah II Sch Informat Technol Dept Comp Informat Syst Amman Jordan

Image segmentation is a popular technique that is used for extracting information from images, which has also gained a lot of interest lately due to its importance in different scientific fields such as the medical field. This paper proposes a novel image segmentation technique using Expectation-Maximization (EM) clustering algorithm and grasshopper Optimizer algorithm (GOA). The proposed technique and the concept of image segmentation are effectively applied on dental radiography datasets that are collected from 120 patients with an age between 6 to 60 years old. To validate the proposed technique, a comparison in terms of purity and entropy measures is conducted against K-means, X-means, EM, and Farthest First algorithms. Based on our experimental results, the proposed technique using EM and GOA achieved the best results compared to other algorithms for all three datasets in terms of entropy and purity. The best results were obtained using the second dataset, which achieved purity value of 0.7126 and entropy value of 0.3083. Further, the proposed technique also outperforms U-net and Random Forest algorithms for the selected datasets.

关键词： Image segmentation Expectation-Maximization algorithm grasshopper optimization algorithm Dental radiography Anatomical segmentation and classification

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Analysis and Design of Auxiliary Array With Intelligent optimization for Interference Suppression of Vehicular Communication

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第6期73卷 8295-8305页

作者： Guo, Yu Wang, Hengfeng Wu, Hao Ge, Songhu Yang, Zhong Meng, Jin Naval Univ Engn Natl Key Lab Electromagnet Energy Wuhan 430000 Peoples R China

The auxiliary array is the key to improving the interference suppression performance in vehicular communication, especially for various operating frequencies of radio communication and unknown directions of interference. In this paper, the effects of the auxiliary array parameters on the interference suppression performance are analyzed, and the maximum acceptable area model is proposed. After deriving and simplifying the analytical solutions of the weights and the signal to interference plus noise ratio (SINR) after interference suppression, the efficient acceptable rule inherits suppression requirements of the communication radio, significantly reducing computational complexity. The proposed model focuses on maximizing the acceptable area in view of this rule. The gain of the auxiliary antenna is also obtained by the proposed model. Based on theoretical analysis, we designed the auxiliary structure, which can fully absorb the advantages of the loading and matching technology and avoid the limitations of the independent strategy. However, the structure parameters, including the topology and components of the antenna, are complex and interactive. Therefore, the intelligent method named the homogeneous grasshopper optimization algorithm was adopted to consider these parameters comprehensively. Finally, it leads to a remarkable improvement in the optimization efficiency for not only the spacing but also the antenna parameters. Experiments based on communication radio demonstrate that the proposed model is very efficient and promising for auxiliary array optimization, and the implemented antenna achieves ideal radiation characteristics and high gain in a wide frequency band.

关键词： optimization Signal to noise ratio Antennas Interference suppression Adaptive arrays Broadband antennas Loaded antennas Auxiliary array interference suppression maximum acceptable area signal to interference noise ratio grasshopper optimization algorithm

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A new approach to energy-aware routing in the Internet of Things using improved grasshopper Metaheuristic algorithm with Chaos theory and Fuzzy Logic

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第4期82卷 5133-5159页

作者： Mir, Masoomeh Yaghoobi, Mahdi Khairabadi, Maryam Islamic Azad Univ Neyshabour Branch Neyshabour Iran Islamic Azad Univ Mashhad Branch Mashhad Razavi Khorasan Iran

In most Internet of Things (IoT) applications, network nodes are limited in terms of energy source. Therefore, the need for innovative methods to eliminate energy loss which shortens the life of networks is fully felt in such networks. One of the optimization techniques of energy consumption on the Internet of things is efficient energy routing that the required energy can be reduced by choosing an optimal path. In this paper, an informed or efficient energy approach is proposed for routing on the Internet of Things in which focus is on the sleep-wake schedule of nodes;therefore, a new optimization algorithm called chaos fuzzy grasshopper optimization algorithm was used. In chaos fuzzy grasshopper algorithm, the initial population of grasshoppers is generated by Lorenz chaos theory and the input and output parameters of the algorithm are adjusted by fuzzy approach. To evaluate the efficiency of the proposed method, three criteria of evaluation of remaining energy, network life and coverage rate were used. Investigating the findings in two different scenarios (efficiency over time and efficiency per number of different nodes) showed that the proposed method always is better than the base methods in all scenarios and for all performance evaluation criteria. So that in the study of the death of 30% of nodes which indicates the life of the network, results showed that the proposed method of the paper (FLGOA) has 9% better efficiency than FGOA, 12% better than GOA and 16% better than GSO. Also, the findings about the remaining energy of the network showed that the proposed method has 16% better efficiency than FGOA method, 21% better than GOA and 22% better than GSO. Finally, studies in the coverage rate evaluation criterion showed that the proposed method has 12% coverage rate better than FGOA method, 15% better than GOA and 16% better than GSO.

关键词： Routing Internet of Things Chaos Theory Fuzzy Logic grasshopper optimization algorithm

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Hybrid model to optimize object-based land cover classification by meta-heuristic algorithm:an example for supporting urban management in Ha Noi,Viet Nam

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International Journal of Digital Earth 2019年第10期12卷 1118-1132页

作者： Quang-Thanh Bui Manh Pham Van Nguyen Thi Thuy Hang Quoc-Huy Nguyen Nguyen Xuan Linh Pham Minh Hai Tran Anh Tuan Pham Van Cu Center for Applied Research in Remote sensing and GIS(CARGIS) VNU University of ScienceHa NoiViet Nam VNU University of Science Ha NoiViet Nam Vietnam Institute of Geodesy and Cartography Ha NoiViet Nam Ministry of Education and Training Ha NoiViet Nam

This study proposed a novel object-based hybrid classification model named GMNN that combines grasshopper optimization algorithm(GOA)and the multiple-class Neural network(MNN)for urban pattern detection in Hanoi,*** bands of SPOT 7 image and derivable NDVI,NDWI were used to generate image segments with associated attributes by PCI Geomatics *** segments were classified into four urban surface types(namely water,impervious surface,vegetation and bare soil)by the proposed ***,three training and validation datasets of different sizes were used to verify the robustness of this *** all tests,the overall accuracies of the classification were approximately 87%,and the Area under Receiver Operating Characteristic curves for each land cover type was ***,the performance of this model was examined by comparing several statistical indicators with common benchmark *** results showed that GMNN out-performed established methods in all comparable *** results suggested that our hybrid model was successfully deployed in the study area and could be used as an alternative classification method for urban land cover *** a broader sense,classification methods will be enriched with the active and fast-growing contribution of metaheuristic algorithms.

关键词： Urban remote sensing object-based classification neural network grasshopper optimization algorithm

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MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

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APPLIED INTELLIGENCE 2018年第8期48卷 2268-2283页

作者： Tharwat, Alaa Houssein, Essam H. Ahmed, Mohammed M. Hassanien, Aboul Ella Gabel, Thomas Frankfurt Univ Appl Sci Fac Comp Sci & Engn D-60318 Frankfurt Germany Suez Canal Univ Fac Engn Ismailia Egypt Minia Univ Fac Comp & Informat Al Minya Egypt Cairo Univ Fac Comp & Informat Giza Egypt SRGE Cairo Egypt

grasshopper optimization algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective grasshopper optimization algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archive is then employed for defining the social behavior of the GOA in the multi-objective search space. To evaluate and verify the effectiveness of the MOGOA, a set of standard unconstrained and constrained test functions are used. Moreover, the proposed algorithm was compared with three well-known optimization algorithms: Multi-Objective Particle Swarm optimization (MOPSO), Multi-Objective Ant Lion Optimizer (MOALO), and Non-dominated Sorting Genetic algorithm version 2 (NSGA-II);and the obtained results show that the MOGOA algorithm is able to provide competitive results and outperform other algorithms.

关键词： Multi-objective optimization grasshopper optimization algorithm Pareto optimal solutions Evolutionary algorithm Constrained optimization Unconstrained optimization

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Enhanced Marathi Speech Recognition Facilitated by grasshopper Optimisation-Based Recurrent Neural Network

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Computer Systems Science & Engineering 2022年第11期43卷 439-454页

作者： Ravindra Parshuram Bachate Ashok Sharma Amar Singh Ayman AAly Abdulaziz HAlghtani Dac-Nhuong Le School of Computer Science and Engineering Lovely Professional UniversityPunjab144001India Department of Computer Science and IT University of JammuJammu and Kashmir180006India School of Computer Applications Lovely Professional UniversityPunjab144001India Department of Mechanical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia School of Computer Science Duy Tan UniversityDanang550000Vietnam Institute of Research and Development Duy Tan UniversityDanang550000Vietnam

Communication is a significant part of being human and living in the *** kinds of languages and their variations are there;thus,one person can speak any language and cannot effectively communicate with one who speaks that language in a different *** application fields such as education,mobility,smart systems,security,and health care systems utilize the speech or voice recognition models ***,various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional *** is necessary to understand the speech recognition field,in which the major concentrated stages are feature extraction and *** paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network(RNN).Here,the preprocessing of the input signal is performed by smoothing and median *** preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech *** optimized RNN classifier is used for speech recognition after completing the feature extraction task,where the optimization of hidden neurons in RNN is performed by the grasshopper optimization algorithm(GOA).Finally,the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

关键词： Deep learning grasshopper optimization algorithm recurrent neural network speech recognition word error rate

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Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence algorithm-Based Feature Selection Methods

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SENSORS 2023年第15期23卷 6939页

作者： Zhang, Yue Sun, Maoxun Xia, Chunming Zhou, Jie Cao, Gangsheng Wu, Qing Nantong Univ Sch Mech Engn Nantong 226019 Peoples R China Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China

Pattern recognition of lower-limb movements based on mechanomyography (MMG) signals has a certain application value in the study of wearable rehabilitation-training devices. In this paper, MMG feature selection methods based on a chameleon swarm algorithm (CSA) and a grasshopper optimization algorithm (GOA) are proposed for the pattern recognition of knee and ankle movements in the sitting and standing positions. Wireless multichannel MMG acquisition systems were designed and used to collect MMG movements from four sites on the subjects thighs. The relationship between the threshold values and classification accuracy was analyzed, and comparatively high recognition rates were obtained after redundant information was eliminated. When the threshold value rose, the recognition rates from the CSA fluctuated within a small range: up to 88.17% (sitting position) and 90.07% (standing position). However, the recognition rates from the GOA drop dramatically when increasing the threshold value. The comparison results demonstrated that using a GOA consumes less time and selects fewer features, while a CSA gives higher recognition rates of knee and ankle movements.

关键词： feature selection chameleon swarm algorithm grasshopper optimization algorithm mechanomyography pattern recognition

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A hybrid equilibrium optimizer algorithm for multi-level image segmentation

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第4期18卷 4648-4678页

作者： Qi, Hong Zhang, Guanglei Jia, Heming Xing, Zhikai Northeast Forestry Univ Sch Informat & Comp Engn Harbin Heilongjiang Peoples R China Sanming Univ Sch Informat Engn Sanming Fujian Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Peoples R China

Threshlod image segmentation is a classic method of color image segmentation. In this paper, we propose a hybrid equilibrium optimizer algorithm for multi-level image segmentation. When multi-level threshold method calculates the neighborhood mean and median of color image, it takes huge challenge to find the optimal threshold. We use the proposed method to optimize the multi-level threshold method and get the optimal threshold from the color image. In order to test the performance of the proposed method, we select the CEC2015 dataset as the benchmark function. The result shows that the proposed method improves the optimization ability of the original algorithm. And then, the classic images and wood fiber images are taken as experimental objects to analyze the segmentation result. The experimental results show that the proposed method has a good performance in Uniformity measure, Peak Signal-to-Noise Ratio and Feature Similarity Index and CPU time.

关键词： wood fiber image segmentation 3DOtsu equilibrium optimizer algorithm grasshopper optimization algorithm hybrid optimization algorithm

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Modified fractional least mean square-based control scheme for dynamic voltage restorer to improve power quality

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ELECTRICAL ENGINEERING 2024年第4期106卷 5069-5087页

作者： Deshpande, Chinmay V. Chilipi, Rajasekharareddy Arya, Sabha Raj SVNIT DoEE Ichchhanath Surat 395007 Gujarat India

The paper introduces a modified fractional least mean square (M-FLMS) filter-based control scheme designed for optimal control of dynamic voltage restorers. This scheme accurately estimates fundamental components from the supply voltage and performs exceptionally well, even under grid voltage anomalies. In this study, the original fractional least mean square (FLMS) control structure undergoes modifications incorporating an adjustable gain parameter, significantly enhancing its performance during voltage distortions. The resultant M-FLMS control scheme achieves swift convergence, reduced steady-state errors, and improved computational efficiency. Comparative analysis between the least mean square and FLMS control algorithms validates the superiority of the proposed M-FLMS approach, demonstrating faster settling times and decreased peak overshoot. Additionally, the grasshopper optimization algorithm is employed to efficiently tune load voltage and DC-link voltage proportional-integral controller gains, further augmenting the control scheme's effectiveness. The paper evaluates the suggested control scheme's effectiveness in both steady-state and dynamic behaviors through MATLAB-based simulations, offering a comprehensive assessment of its capabilities.

关键词： Adaptive algorithm Fractional least mean square grasshopper optimization algorithm Modified-fractional least mean square Power quality improvement Riemann-Liouville derivative Voltage unbalance

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Face Expression Recognition with the optimization based Multi-SVNN Classifier and the Modified LDP Features

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年 62卷 43-55页

作者： Revina, I. Michael Emmanuel, W. R. Sam Manonmaniam Sundaranar Univ Nesamony Mem Christian Coll Dept Comp Sci Tirunelveli 627012 Tamil Nadu India

Facial expression recognition (FER) is the interesting research area that enables us to recognize the expression of the human face in the day-to-day life. Most of the traditional methods fail to recognize the expressions accurately as the expressions are based on the movements of the parts in the human face. The paper proposes the effective method of FER using the proposed Whale- grasshopper optimization algorithm based Multi-Support Vector Neural Network (W-GOA-based MultiSVNN). The features from the facial image is extracted using the Scale-Invariant Feature Transform (SIFT) and the proposed Scatter Local Directional Pattern (SLDP). The extracted features are classified using the proposed classifier to recognize the expression of the face. The proposed method of facial recognition enhances the recognition accuracy. The experimentation of the proposed algorithm is performed using the databases, such as Cohn-Kanade AU-Coded Expression Database and The Japanese Female Facial Expression (JAFFE) Database. The proposed algorithm outperforms the existing methods in terms of the accuracy, TPR, and FPR and the values are found to be 0.96, 0.96, and 0.009, respectively. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Facial expression recognition MultiSVNN classifier grasshopper optimization algorithm Whale optimization algorithm Local Directional Pattern

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