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Audio classification using grasshopper-ride optimization algorithm-based support vector machine

IET CIRCUITS DEVICES & SYSTEMS 2021年第5期15卷 434-447页

作者： Singh, Suryabhan Pratap Jaiswal, Umesh Chandra Madan Mohan Malaviya Univ Technol Dept Comp Sci & Engn Gorakhpur Uttar Pradesh India Madan Mohan Malaviya Univ Technol Dept Informat Technol & Comp Applicat Gorakhpur Uttar Pradesh India

The accurate and robust detection of the audio has been widely grown as the speech technology in the area of audio forensics, speech recognition, and so on. However, in real time, it is a challenge to deal with the massive data arriving from distributed sources. Thus, the study introduces a method that effectively deals with the data from the distributed sources using the map-reduce framework (MRF). The map and reduce function in MRF aim at feature extraction and audio classification. The robust classification using the proposed grasshopper-ride optimization algorithm-based support vector machine (G-ROA-based SVM) uses the features, such as multiple kernel Mel frequency cepstral coefficients, spectral flux, spectral kurtosis, and delta-amplitude modulation spectrogram. The proposed G-ROA is the integration of ROA and grasshopper optimization algorithm in tuning the optimal weights of SVM and also, the kernel function in SVM is modified using the Gaussian radial basis function, Gaussian kernel, and polynomial kernels. The experimentation of the proposed method is done using two datasets, namely TUT sound event 2017 dataset and ESC dataset. TUT sound event 2017 dataset consists of eight audio recordings from a single acoustic scene. ESC dataset consists of three parts and 252,400 recordings. The analysis reveals that the proposed audio classification acquired the maximal accuracy of 0.96, minimal false alarm rate, and false rejection rate of 0.022 and 0.0119, respectively.

关键词： audio signal processing feature extraction signal classification G-ROA-based SVM robust detection robust classification map-reduce framework accurate detection distributed sources speech recognition support vector machines radial basis function networks audio forensics audio recordings TUT sound event 2017 dataset grasshopper optimization algorithm audio recording audio classification grasshopper-ride optimization algorithm-based support vector machine multiple kernel Mel frequency cepstral coefficients cepstral analysis

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Using grasshopper optimization algorithm to Solve 0-1 Knapsack Computation Resources Allocation Problem in Mobile Edge Computing

Using Grasshopper Optimization Algorithm to Solve 0-1 Knapsa...

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第34届中国控制与决策会议

作者： Zijun Su Guannan He Zhonghua Li College of Automation Guangdong University of Technology School of Electronics and Information Technology Sun Yat-sen University IEEE School of Intelligent Systems Engineering Sun Yat-sen University

In a mobile edge computing(MEC) system,the MEC server can't provide sufficient computation resources to support the offloading operations of all concurrent tasks from many mobile *** this case,the computation resources of MEC server should be allocated to those important *** paper establishes a computation resources allocation model based on 0-1 knapsack problem,and proposes a grasshopper optimization algorithm for computation resources allocation(GOA-CRA).On the basis of the novel binary grasshopper optimization algorithm(NBGO A),the proposed GOA-CRA adds a position correction step where three strategies(i.e.,random correction,maximum correction and minimum correction) are realized,respectively.A series of numerical experiments are investigated among these three correction *** results indicate that the proposed GOA-CRA is an effective and efficient algorithm for computation resources allocation problem in MEC system.

关键词： Mobile edge computing grasshopper optimization algorithm Computation resources allocation 0-1 knapsack problem

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A hybrid grasshopper optimization algorithm for skin lesion segmentation and melanoma classification using deep learning

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Healthcare Analytics 2024年 5卷 100326-100326页

作者： Thapar, Puneet Rakhra, Manik Alsaadi, Mahmood Quraishi, Aadam Deka, Aniruddha Naga Ramesh, Janjhyam Venkata Department of Computer Science and Engineering Lovely Professional University Punjab India Department of Computer Science Al-Maarif University College Al Anbar 31001 Iraq Intervention Treatment Institute Houston TX United States Department of Computer Science and Engineering Faculty of Engineering Assam Downtown University India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist. Andhra Pradesh 522302 India

Skin cancer can be detected through visual examination and confirmed through dermoscopic analysis and various diagnostic tests. This is because visual observation enables early detection of unique skin images by artificial intelligence. Promising outcomes are shown by several Convolution Neural Network (CNN)–based skin lesion classification systems that employ tagged skin images. This study suggests a practical approach for identifying skin cancers using dermoscopy pictures, improving specialists' ability to distinguish benign from malignant tumors. The Swarm Intelligence (SI) approach used dermoscopy photographs to locate lesions on the skin areas Region of interest (ROI). The grasshopper optimization technique produced the best segmentation outcomes. The Speed-Up Robust Features (SURF) approach is applied to extract features based on these findings. Two groups were created using the ISIC-2017, ISIC-2018, and PH-2 databases to categorize skin tumors. With an estimated accuracy in classification of 98.52%, preciseness of 96.73%, and Matthews Correlation Coefficient (MCC) of 97.04%, the suggested classification and segmentation methodologies have been evaluated for classification efficacy, specificity, sensitivity, F-measure, preciseness, the MCC, the dice coefficient, and Jaccard's index. In every performance indicator, the method we suggest outperformed state-of-the-art methods. © 2024 The Authors

关键词： Dermoscopic analysis grasshopper optimization algorithm Melanoma classification Skin cancer Speeded-up robust features Swarm intelligence

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Comparison of grasshopper and Whale optimization algorithm for Design of FACTS Controller with Power System Stabilizer 5

Comparison of Grasshopper and Whale Optimization Algorithm f...

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5th IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC)

作者： Sahu, Preeti Ranjan Hota, Prakash Kumar Panda, Sidhartha Veer Surendra Sai Univ Technol Dept Elect Engn Burla 768018 India

ISBN: (纸本)9781728106465

Recently, grasshopper optimization algorithms (GOA) and Whale optimization algorithm (WOA) technique have significant consideration among different meta-heuristic optimization techniques. GOA is a newly proposed nature inspired algorithm based on social behavior of grasshopper. The WOA technique is also comparatively modern meta-heuristic method, which mimics the behavior of humpback whales. Both the optimization techniques are popular in industry and science mainly because of their simplicity, avoidance of local optima and gradient free mechanism. These two methods are assumed to discover an appropriate solution considering a given objective work. The major goal of this controller design is to increase the stability of power system. Here, both GOA and WOA techniques are employed to optimize the parameters of the designed static synchronous series compensator (SSSC) controller with power system stabilizer (PSS). The proposed conventional lead-lag SSSC controller with PSS are optimized by using both GOA and WOA optimization techniques and tested under single-machine infinite bus system. At last, the simulation results of both GOA and WOA techniques are verified by considering different loading conditions.

关键词： grasshopper optimization algorithm Whale optimization algorithm Flexible AC Transmission System Controller Lead-Lag Controller Power System Stability

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Hybrid grasshopper Whale optimization algorithm Based on Faster Recurrent Convolutional Neural Network for Intrusion Detection System in Internet of Things

Hybrid Grasshopper Whale Optimization Algorithm Based on Fas...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Suvarna Sunil Goutham, V. Prathima, Y. Unnisa, Raheem Aruna, T.M. Malla Reddy Engineering College for Women It Department Hyderabad India Nalla Narasimha Reddy Group of Institutions Cse Department Hyderabad India Cmr Engineering College Department of Computer Science and Engineering Hyderabad India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Yelahanka Bengaluru India

ISBN: (纸本)9798350313413

Intrusion Detection System (IDS) is one of the general Deep Learning (DL) techniques which are utilized to find and identify the outliers to prevent adversarial attacks, fraud, intrusions in Internet of Things (IoT). The proposed hybrid optimization technique known as GWOA by integrating the grasshopper optimization algorithm (GOA) and the Whale optimization algorithm (WOA). For better IDS performance, this GWOA can be utilized into the Faster Recurrent Convolutional Neural Network (FRCNN) model. This proposed method utilized the BoT-IoT dataset to assess the efficacy of this GWOA-based strategy. The main objective was to improve the IDS's balance between exploration and exploitation. This system makes use of the FRCNN to categorize incursions precisely and effectively, improving the overall detection capabilities. When compared to measures of evaluation like precision, recall, accuracy, and f1-score, the suggested GWOA-based FRCNN model's results show a considerable improvement. Comparing the model to other intrusion detection methods, such as Deep Neural Network (DNN), Long Short-Term Memory (LSTM) and IDS-SIoEL. It attains values of 93.77% for accuracy, 86.66% for precision, 95.87% for recall, and 91.03% for f1-score. © 2023 IEEE.

关键词： Deep Learning Faster Recurrent-Convolutional Neural Network grasshopper optimization algorithm Internet of Things Intrusion Detection System Whale optimization algorithm

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MNASreID: grasshopper optimization based neural architecture search for motorcycle re-identification

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-14页

作者： Rampavan, Medipelly Ijjina, Earnest Paul Natl Inst Technol Dept Comp Sci & Engn Warangal 506004 Telangana India

Object re-identification (reID) plays a pivotal role in traffic surveillance systems for matching objects like people, cars, and motorcycles across multiple cameras. This is an active area of research in both industry and academia due to the ever-growing population and need for smart surveillance, public safety, and traffic management. Most current reID methods use deep convolutional neural networks as the backbone that are manually designed, which does not have the optimum settings as the network complexity increases. This paper introduces MNASreID, an automated approach for designing deep convolutional neural networks designed specifically for motorcycle reID. Key contributions include proposing a NAS based optimization framework and designing a comprehensive search space covering backbone architectures and hyperparameters. grasshopper optimization algorithm used as NAS search strategy to find the optimal DNN model. Experimental results on two motorcycle datasets, MoRe and BPReID, demonstrate MNASreID's ability to automatically identify efficient DNN models for reID tasks. Comparative evaluation against existing algorithms reveals significant performance enhancements. Specifically, MNASreID achieves a notable improvement of +1.14% and +1.24% in r1 and mAP metrics, respectively, on the MoRe dataset. On the BPReID dataset, it outperforms existing approaches by +26.82% and +29.56% in r1 and mAP metrics, respectively.

关键词： Object re-identification Neural architecture search grasshopper optimization algorithm Auto reID

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Enhancing sarcasm detection through grasshopper optimization with deep learning based sentiment analysis on social media

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1785-1791页

作者： Srinu, Nidamanuri Sivaraman, K. Sriram, M. School of Computing Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai 600073 India

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning grasshopper optimization algorithm Natural language processing Sarcasm detection Sentiment analysis

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Multilevel Image Segmentation using Hybrid grasshopper optimization and k-means algorithm 6

Multilevel Image Segmentation using Hybrid Grasshopper Optim...

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6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Shahrian, Masoud Momtaz, Amir Keyvan Islamic Azad Univ Fac Engn Comp Grp Tehran North Branch Tehran Iran Civil Aviat Technol Coll CATC Grp Elect Tehran Iran

ISBN: (纸本)9781728186290

Image Segmentation has been considered as challenging task in image processing field. In this paper we proposed a new method for image segmentation purpose. Our proposed method is based on combining grasshopper optimization algorithm (GOA) and k-means clustering. grasshopper optimization algorithm (GOA), inspired by nature, divides the search process into two parts of exploration and utilization. The proposed algorithm in terms of mathematics models the behavior of grasshopper groups in nature for solving the problems of optimization. We evaluate the proposed algorithm function based on eight-image segmentation and compare the obtained results of the tests of PSNR scale and SSIM measurement with four moth-flame whale algorithms by harmony search and social spider. The results indicate that the proposed algorithm with a simple threshold gives us a more optimized response regarding other state of arts in terms of both objective and subjective criteria.

关键词： Image processing Image segmentation grasshopper optimization algorithm k-means clustering

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optimization of Railway Bogie Snubber Spring with grasshopper algorithm 3rd

Optimization of Railway Bogie Snubber Spring with Grasshoppe...

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3rd Conference on Data Engineering and Communication Technology (ICDECT)

作者： Neve, Abhishek G. Kakandikar, Ganesh M. Kulkarni, Omkar Nandedkar, V. M. MAEERS MIT Dept Mech Engn Pune 411038 Maharashtra India Shri Guru Gobind Singhji Inst Engn & Technol Dept Prod Engn Nanded India

ISBN: (纸本)9789811510977;9789811510960

Swarm intelligence is a branch which deals in research that models the population of interacting agents or swarms that are self-organizing in nature. grasshopper optimization algorithm is a modern algorithm for optimization which is inspired from the swarm-based nature. This algorithm simulates the behaviour of the grasshopper in nature and models that mathematically for solving optimization problems. grasshopper optimization algorithm is used for the optimization of mechanical components and systems. Snubber spring is a kind of helical spring which is a part of suspension system in railway bogie. In this work, the design of snubber spring is optimized by using grasshopper optimization algorithm. The suspension system of railway bogie consists of inner spring, outer spring, and snubber spring. optimization is done for the weight minimization of snubber spring. Wire diameter, number of active turns and mean coil diameter are the design parameters for the optimization. These parameters are optimized by using grasshopper optimization algorithm according to bounds, loading, and boundary conditions. The optimized parameters are validated experimentally and also by using a software. The spring is modelled in CATIA V5 and analyzed in ANSYS 17.0. The comparison of results is done and is validated with results experimentally in which the spring is tested on universal testing machine for compression test.

关键词： Swarm-based optimization grasshopper optimization algorithm Weight minimization Snubber spring

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Water distribution network calibration for unreported leak localization with consideration of uncertainties

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2025年第1期22卷 399-418页

作者： Moasheri, R. Ghazizadeh, M. Jalili Kohanali, R. Ahmadi Shahid Beheshti Univ Fac Civil Water & Environm Engn Tehran Iran

Leakage in water distribution networks precipitates both water wastage and the ingress of pollutants. The localization of leaks, a formidable challenge within water demand management, has spurred an examination of hydraulic simulation-based methodologies as a more economically feasible and time-efficient alternative to conventional methods. This paper introduces a framework for precisely determining the location of leaks within a water distribution network, leveraging the grasshopper optimization algorithm. The approach meticulously compares simulated data with pressure field information. Acknowledging the intrinsic uncertainties pertaining to hydraulic model parameters-such as elevations, nodal base demand, and pipe roughness coefficients in real-world water distribution networks-the developed method incorporates perturbation analysis for judicious parameter selection. Monte Carlo simulation is then employed to apply these parameters in the simulation process systematically. The efficacy of the method is demonstrated by applying it to benchmark water distribution networks (specifically, Poulakis and Balerma) under various leakage scenarios, achieving accuracy levels of up to 99%. Introducing uncertainty into the simulation process results in a maximum 20% reduction in method accuracy. Real-world implementation successfully and accurately localizes leakage, affirming the practical applicability of the proposed method for water utilities.

关键词： grasshopper optimization algorithm K-means clustering Leakage detection Monte Carlo simulation Water distribution networks Water loss

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