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Efficient adaptive deep neural network model for securing demand side management in IoT enabled smart grid

RENEWABLE ENERGY FOCUS 2022年第0期42卷 277-284页

作者： Sivarajan, S. Jebaseelan, S. D. Sundarsingh Sathibama Inst Sci & Technol Chennai Tamil Nadu India Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai Tamil Nadu India

Electricity distribution reliability is critical for global safety, healthcare and the economy. The smart grid is defined as the combination of sensors and modern technology into the power system, which innovates the electricity generation, distribution, surveillance, and control model. Several issues must be addressed in order for the smart grid to be useful. The smart grid's security is both a difficult task and a critical con -cern. An Adaptive Deep Neural Network (ADNN) will be developed in this study to secure demand-side management in an IoT-enabled smart grid. The proposed ADNN combines Deep Neural Networks (DNN) and the squirrel search algorithm (SSA). The SSA is used in the DNN to improve its performance by selecting optimal weighting factors. The proposed methodology will be used to ensure that energy is used efficiently based on its priority level. It will also be used to prevent intrusions into the smart grid. A pro-posed classifier will classify the intrusion or dishonest entities. To optimize energy utilization and empower security, this proposed multi-agent energy management system (MEMS) and autonomous interface system (AIS) are being developed. Performance metrics such as energy consumption, load demand power, and generated power will be used to evaluate the proposed methodology, which will be implemented in MATLAB. Particle Swarm Optimization (PSO) and Whale Optimization algorithm (WOA) will be used to compare the proposed methodology to existing methods. (c) 2022 Published by Elsevier Ltd.

关键词： Demand-side management Smart grid Security Adaptive deep neural network squirrel search algorithm Internet of Things

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Aspect term extraction and optimized deep learning for sentiment classification

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SOCIAL NETWORK ANALYSIS AND MINING 2024年第1期14卷 221页

作者： Adilakshmi, Konda Srinivas, Malladi Anuradha, K. Srilakshmi, V. Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Prades India Gokaraju Rangaraju Inst Engn & Technol GRIET Dept CSE Hyderabad 500090 India

Sentiment or opinion largely relies on public commentary, where reflections are either positive or negative. Sentiment classification automates the process of determining the orientation of a subject based on text, which aims to classify documents by expressed views. This task faces significant challenges due to issues, like negation, ambiguity, and complex language structures. In this work, a new squirrel search Mayfly algorithm_Hierarchical Deep Learning for Text (SSMA_HDLTex) is established for sentiment classification using aspect term extraction and optimized deep learning. Primarily, the document on Amazon review is taken as input and later it is applied to the process of tokenization. In this process, Bidirectional Encoder Representations from Transformers is employed to partition the sentence into tokens. Later, Aspect Term Extraction is effectuated. At last, sentiment classification is done by employing HDLTex which is trained by utilizing the presented SSMA method. The novel SSMA method is devised by integrating the squirrel search algorithm as well as the Mayfly algorithm. The proposed SSMA_HDLTex has attained maximal and precision, F-measure, and recall, of 0.936, 0.937, and 0.941correspondingly. This innovative approach significantly enhances the accuracy and reliability of sentiment classification tasks.

关键词： squirrel search algorithm Mayfly optimization algorithm Bidirectional encoder representations from transformers squirrel search Mayfly algorithm Hierarchical deep learning for text

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Hybrid grasshopper and ant lion algorithms to improve imperceptibility, robustness, and convergence rate for the video steganography

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2023年第3期26卷 324-336页

作者： Gupta, Sahil Garg, Naresh Kumar Maharaja Ranjit Singh Punjab Tech Univ Dept Elect & Commun Engn Bathinda Punjab India Maharaja Ranjit Singh Punjab Tech Univ Dept Comp Sci & Engn Bathinda Punjab India

The need for securing multimedia content from being intercepted is a prominent research issue. This work proposes an optimised video steganography model that improves imperceptibility and robustness by extracting keyframes and calculating the optimal scaling factor. The squirrel search algorithm (SSA) is used to extract keyframes since it ensures distinct position updation processes through Levy flying and predator features, whilst the grasshopper optimisation and ant lion optimisation algorithms are hybridised to compute the optimal value of the scaling factor. In terms of imperceptibility and robustness, the simulation results suggest that the proposed approach outperforms existing data-hiding models. It also discovers the optimal scaling factor in under ten iterations, indicating that the fastest convergence rate is possible.

关键词： ant lion optimisation ALO grasshopper optimisation singular value decomposition SVD video steganography imperceptibility robustness PSNR mean square error MSE squirrel search algorithm SSA

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HIAS: Hybrid Intelligence Approach for Soil Classification and Recommendation of Crops 1st

HIAS: Hybrid Intelligence Approach for Soil Classification a...

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1st International Conference on Electronic Governance with Emerging Technologies (EGETC)

作者： Palvannan, S. Deepak, Gerard Natl Inst Technol Dept Met & Mat Engn Tiruchirappalli India Natl Inst Technol Dept Comp Sci & Engn Tiruchirappalli India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Bengaluru India

ISBN: (纸本)9783031229497;9783031229503

Agriculture is the largest industry in the world, and it is vital to any country's economic development. Farmers' failure to select the appropriate crop for cultivation is a significant and severe mistake that results in poor agricultural output. For the surplus of crop production, a soil classification and crop recommendation system were developed, which used a semantically driven hybrid intelligence method. In this paper, the Hybrid Intelligence Approach for Soil (HIAS) modelwas presented and tested with several baseline models using southern South Australian Soil datasets to evaluate the proper prediction. Besides, ontology is generated from the summarised content of agricultural and geological eBooks. Textbooks and then the query and Soil dataset are pre-processed. The semantic similarity is calculated using Jaccard similarity, Cosine similarity, and SemantoSim measure under the squirrel search algorithm to ensure that the relevant result is optimised and set entities formed from a set of recommendations are optimised. The term enrichment is done usingMediaWiki andWikiData, and then the LSTM classifier is used for classification. Finally, the precision, accuracy, recall, F-measure, and FNR for the soil classification and crop recommendation system are evaluated. The precision percentage of 99.78% and the lowest FNR of 0.01 is obtained from the HIAS model.

关键词： Crop recommendation LSTM Soil classification Semantic intelligence squirrel search algorithm

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Initial steam flow prediction and control optimization for waste incineration plants based on machine learning 35

Initial steam flow prediction and control optimization for w...

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35th Chinese Control and Decision Conference (CCDC)

作者： Dung, Jinxi Liu, Wen Zhong, Biao Yu, Zhaosheng Zhang, Xikui Zou, Qihong Ma, Xiaogian South China Univ Technol Sch Elect Power Guangzhou 510640 Peoples R China Guangdong Prov Key Lab Efficient & Clean Energy U Guangzhou 510640 Peoples R China Grandtop Fushun Environm Protect Energy Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9798350334722

As the demand for electricity in modern cities continues to grow, the construction of smart power plants has become a key focus for the sustainable development of our power plants. Daily power plant operations generate a large amount of operational data, mostly monitored for normality without being further explored. With the development of artificial intelligence, machine learning can be used to fit the relationships between the various parameters simply and efficiently. Currently, thermal power generation is China's primary form of power generation. In this paper, we take a waste incineration plant as an example and use XGBoost and the SSA (squirrel search algorithm) to predict and optimize the initial steam flow of the plant.

关键词： Waste incineration plant Initial steam flow Machine Learning XGBoost squirrel search algorithm

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Fault diagnosis of aircraft power generation system based on ISSA-SVM 14

Fault diagnosis of aircraft power generation system based on...

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14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

作者： Wang, Zhiliang Wu, Yuhan Liu, Hongxu Fan, Xiaodong Zhao, Yang Wang, Li Beijing Electro-Mechanical Engineering Institute Beijing China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350301359

Addressing the challenge of dealing effectively with multiple variables and complex nonlinear relationships in aircraft power generation systems, which are typically difficult to handle using traditional linear methods, this paper presents an enhanced fault diagnosis method. The method combines the squirrel search algorithm and support vector machine (ISSA-SVM) to improve the accuracy of fault classification. The ISSA is enhanced by incorporating the periodic variational perturbation formula, optimizing the performance of the fault classification model. To validate the effectiveness of the proposed method, MATLAB/Simulink simulation experiments are performed using a fault dataset of the power generation system. The experimental results demonstrate that the diagnostic accuracy of the proposed method is significantly high. © 2023 IEEE.

关键词： Aircraft power generation system Fault diagnosis squirrel search algorithm support vector machine

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A method based on VMD improved by SSA for leak location of water distribution

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DIGITAL SIGNAL PROCESSING 2024年 145卷

作者： Yu, Zhi Tang, Bo Chen, Wei Huang, Danguang Xu, Lei China JiLiang Univ Coll Metrol & Measurement Engn Hangzhou Zhejiang Peoples R China Ningbo Water Meter Grp Co Ltd Ningbo Zhejiang Peoples R China

Leak location using cross-correlation of acoustic signals collected by acceleration sensors is easily disturbed by the environmental noises resulting in inaccurate identification of its location, especially at low SNR. Aiming at this problem, an adaptive signal denoising algorithm based on squirrel search algorithm referred to as improved variational mode decomposition (SSA-VMD) is proposed to improve the accuracy of leak location. First, a fitness function based on the ratio of the envelope entropy to the kurtosis of the power spectrum of intrinsic mode functions (IMF) is established. Second, the leak signal is decomposed into IMFs using VMD with optimized parameters searched using the squirrel search algorithm. Then, a new method combining kurtosis analysis with crest factor and impulse factor is applied to select effective IMF components to reconstruct the leak signal at low SNR effectively. Finally, location search based on cross-correlation is performed using the reconstructed signal. Simulation and experiments results show that the proposed method can effectively suppress noise and reduce the error of leak location. The average relative leak location error of this method is within 2%, which proves the feasibility and effectiveness of the proposed adaptive signal noise reduction method.

关键词： Leak location Variational mode decomposition squirrel search algorithm Cross-correlation

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squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi-texture feature descriptors for digital image forgery detection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第8期36卷 e7965-e7965页

作者： Priya, G. Nirmala Kumar, K. Suresh Suganthi, N. Muppidi, Satish Rajalakshmi Inst Technol Dept Elect & Commun Engn Chennai India Saveetha Engn Coll Dept Informat Technol Chennai India SRM Inst Sci & Technol Dept Comp Sci & Engn Ramapuram Campus Chennai India GMR Inst Technol Dept Comp Sci & Engn Rajam Andhra Pradesh India

The ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a process known as copy move forgery. The two main types of copy move forgery prediction are keypoint-based and block-based techniques. While the block-based approach takes more time, the keypoint-based model performs poorly. The goal of this research was to develop a method for detecting image counterfeiting called the squirrel Henry Gas Solubility Optimization algorithm-based Deep Maxout Network (SHGSO-based DMN). Here, Steerable Pyramid Transform (SPT) decomposition is done in order to obtain quantity of multi-scale and multi-oriented sub bands. For the purpose of detecting forgeries, the Deep Maxout Network which was constructed using the SHGSO model is deployed. With accuracy of 0.9507, True Negative Rate (TNR) of 0.9568, and True Positive Rate (TPR) of 0.9517, the new method outperformed other current forgery detection techniques.

关键词： Deep Maxout Network forgery detection Henry Gas Solubility Optimization local directional texture pattern squirrel search algorithm

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Quantum squirrel inspired algorithm for gene selection in methylation and expression data of prostate cancer

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APPLIED SOFT COMPUTING 2021年 105卷 107221-107221页

作者： Ghosh, Manosij Sen, Sagnik Sarkar, Ram Maulik, Ujjwal Jadavpur Univ Comp Sci & Engn Dept Kolkata India

Prostate cancer is the second most common type of cancer among men after skin cancer In this work, we present a comprehensive view on genomic and epigenomic changes following the incremental biological functionality. For gene selection, a new Feature Selection algorithm called Quantum squirrel inspired Feature Selection is proposed here. While exploring the feature space, the proposed algorithm exploits the benefits of squirrel search algorithm (a recently proposed swarm intelligence algorithm) along with Quantum mechanics. Moreover, a modified version of the end of winter concept is used to achieve effective dimension reduction capacity. Quantum squirrel inspired Feature Selection is executed on both expression and methylation data of prostate cancer. The major challenge in gene selection is to bring down the number of selected features without compromising on accuracy. The proposed algorithm consistently achieves this goal and outperforms other state-of-the-art algorithms. The proposed algorithm has steadily attained 100% accuracy while selecting a much lower number of features (around 4), which is a major improvement over others. The top selected genes are biologically validated in terms of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontologies (GO), which further demonstrates the usefulness of the proposed method. The genes selected by Quantum squirrel inspired Feature Selection show an association with prostate carcinoma and most are known biomarkers. A few novel biomarkers selected by proposed algorithm have also been detailed in this work. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Expression data Methylation data Prostate cancer squirrel search algorithm Quantum mechanics Feature Selection

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Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network

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PROCESSES 2024年第1期12卷 191页

作者： Bai, Wangwang Jin, Mengxue Li, Wanwei Zhao, Juan Feng, Bin Xie, Tuo Li, Siyao Li, Hui Econ & Tech Res Inst State Grid Gansu Power Co Lanzhou 730050 Peoples R China State Grid Changzhi Power Supply Co Changzhi 046011 Peoples R China Northwest Power Design Inst Co Ltd China Power Engn Consultant Grp Xian 710075 Peoples R China Xian Univ Technol Sch Elect Engn Xian 710048 Peoples R China

Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step prediction model combining variational mode decomposition (VMD) with sequence-to-sequence (Seq2Seq) is proposed. Firstly, the wind power sequence is smoothed using VMD and the decomposition parameters of VMD are optimized by using the squirrel search algorithm (SSA) to effectively optimize the decomposition effect. Then, the subsequence obtained from decomposition, together with the original wind power data, is reconstructed into multivariate time series features. Finally, a Seq2Seq model is constructed, and convolutional neural networks (CNNs) with bidirectional gate recurrent units (BiGRUs) are used to learn the coupling and timing relationships of the input data and encode them. The gate recurrent unit (GRU) is decoded to achieve continuous power prediction. Based on the actual operating data of a wind farm, a case analysis is conducted. Experimental results show that SSA-VMD can effectively optimize the decomposition effect, and the subsequences obtained with its decomposition are highly accurate when applied to predictions. The Seq2Seq model has better multi-step prediction results than traditional prediction methods, and as the prediction step size increases, the advantages are more obvious.

关键词： convolutional neural network multi-step prediction of wind power sequence-to-sequence squirrel search algorithm variational mode decomposition

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