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Energy Conscious squirrel search based Task Scheduling Mechanism in Fog Environment

TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2024年第10期35卷

作者： Singh, Raj Mohan Sikka, Geeta Awasthi, Lalit Kumar Dr B R Ambedkar Natl Inst Technol Dept Comp Sci & Engn Jalandhar Punjab India Natl Inst Technol Dept Comp Sci & Engn Delhi India Natl Inst Technol Dept Comp Sci & Engn Pauri Uttarakhand India

The swift explosion of Internet of Things (IoT) devices in the past few years has resulted in a deluge of data that requires timely processing. However, transmitting such massive amounts of data in and out from cloud leads to high latency and network bandwidth. Consequently, the field of fog computing has turned out as a feasible platform for utilizing resources within the vicinity of IoT devices for providing services in a timely manner. The resource constrained trait of fog nodes demands judicious management of resources in satisfying the requirements of IoT applications. This makes energy efficiency a core issue in fog networks. This article addresses the issue of energy efficiency by proposing an energy conscious squirrel search algorithm (EcSSA) for scheduling tasks in fog environment. A salient feature of EcSSA lies in the novel objective function that aims to optimize the energy consumption by minimizing the cumulative active time of all nodes in the network. Simulation studies conducted as part of this work demonstrate that the proposed approach significantly improves average energy consumption, average makespan, and carbon dioxide emission rate in comparison to the existing algorithms. We propose an energy conscious task scheduling approach (EcSSA) for fog computing environments by making use of a recent nature inspired approach called squirrel search algorithm (SSA). The objective function to minimize the energy consumption was achieved by minimizing cumulative active time of all nodes in the ***

关键词： energy efficiency fog computing metaheuristic squirrel search algorithm task scheduling

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Reconfigured single- and double-diode models for improved modelling of solar cells/modules

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SCIENTIFIC REPORTS 2025年第1期15卷 1-19页

作者： Celik, Emre Karayel, Mehmet Maden, Dincer Abdel-Salam, Mahmoud Ozturk, Nihat Kaplan, Orhan Tejani, Ghanshyam G. Sharma, Sunil Kumar Baljon, Mohammed Duzce Univ Engn Fac Dept Elect & Elect Engn Duzce Turkiye Cankiri Karatekin Univ Vocat Sch Dept Elect & Energy Cankiri Turkiye Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Gazi Univ Technol Fac Dept Elect & Elect Engn Ankara Turkiye Yuan Ze Univ Dept Ind Engn & Management Taoyuan 320315 Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Majmaah Univ Coll Comp & Informat Sci Dept Informat Syst Majmaah 11952 Saudi Arabia Majmaah Univ Coll Comp & Informat Sci Dept Comp Engn Majmaah 11952 Saudi Arabia

Proper modeling of PV cells/modules through parameter identification based on the real current-voltage (I-V) data is important for the efficiency of PV systems. Most related works have concentrated on the classical single-diode model (SDM) and double-diode model (DDM) and their parameter extraction by various metaheuristic algorithms. In order to render more accurate and representative modeling, this paper adds a small resistance in series with the diodes in SDM and DDM. The new models are named reconfigured SDM (Reconfig-SDM) and reconfigured DDM (Reconfig-DDM), and they have not been studied so far as we know. A squirrel search algorithm (SSA) is employed to globally find the parameters of the new models. The performance achieved is experimentally tested on both a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module located at D & uuml;zce University in T & uuml;rkiye. A vivid comparison of experimental findings, observation, and analysis clearly demonstrates that the proposed Reconfig-SDM and Reconfig-DDM tuned by the SSA have better capacity and effectiveness for modeling PV devices than some cutting-edge approaches. Specifically, compared with the best-performing approach in the literature, Reconfig-SDM and Reconfig-DDM could reduce the error rate up to 0.37% and 2.58% for the solar cell, and 3.21% and 29.0% for the solar module.

关键词： PV modeling I-V characteristic Reconfigured SDM Reconfigured DDM Parameter identification squirrel search algorithm

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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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False Data Injection Detection in Power System Based on LOSSA-AdaBoostDT

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PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 2025年第3期10卷 55-64页

作者： Xi, Lei Tian, Xilong He, Miao Cheng, Chen China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China China Three Gorges Univ Hubei Prov Key Lab Operat & Control Cascaded Hydro Yichang 443002 Peoples R China Qianjiang Power Supply Co State Grid Hubei Elect Power Co Ltd Qianjiang 433100 Peoples R China Jingzhou Power Supply Co State Grid Hubei Elect Power Co Ltd Jingzhou 434000 Peoples R China Southern Power Grid Co Ltd Leliu Power Supply Stn Foshan Supply Bur Foshan 528000 Peoples R China

The attack of false data injection can contaminate the measurements acquired from the supervisory control and data acquisition (SCADA) system, which can seriously endanger the safety and stability of power system operations. The conventional machine learning attack detection methods use a single strong classifier and are difficult to solve the problem of overfitting, making them lack of generalization ability. On the other hand, most existing dimension reduction approaches based on feature extraction can change the original physical meanings of measurements. Here, a novel method is proposed based on feature selection and ensemble learning to solve the above problems. squirrel search algorithm combines Latin hypercube sampling and opposition-based learning to form an improved algorithm with strong global search ability for feature selection. This avoids the problem of feature extraction changing the original physical meanings of measurements. Besides, the classifier based on adaptive boosting decision tree ensemble learning algorithm with stronger generalization ability is used to distinguish the false data injection. Simulation results using the IEEE 14-bus and IEEE 57-bus test systems verify the proposed method with higher performance of detection compared with other widely adopted methods.

关键词： False data injection squirrel search algorithm adaptive boosting decision tree SCADA

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Research on photovoltaic energy storage micro-grid systems based on improved sliding mode control

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IET RENEWABLE POWER GENERATION 2023年第3期17卷 750-761页

作者： Fu, Changxin Zhang, Lixin Li, Huaisheng Shihezi Univ Coll Mech & Elect Engn Shihezi 832000 Xinjiang Peoples R China Inst Agr Sci 9th Div Xinjiang Prov Tacheng 834700 Peoples R China

In islanded microgrid systems, PV power generation efficiency and energy loss of storage battery are the current research trends. Due to the intermittent and fluctuating characteristics of PV power generation, various loads connected to the DC microgrid system would also bring DC bus voltage low-frequency fluctuations and other problems. In order to solve the above problems, an maximum power point tracking (MPPT) tracking strategy (sliding mode control based on squirrel search algorithm) is proposed in this study;meanwhile, a sliding mode control strategy based on an improved reaching law is designed. The components of the PV energy storage system and the control method are mainly focused on, and the PV energy storage system is optimized by improving the sliding mode control. The proposed control algorithm is verified and analyzed by MATLAB/Simulink simulation platform. Compared with the double integral sliding mode control algorithm MPPT tracking case, sliding mode control based on squirrel search algorithm shows excellent performance (short response time, good tracking maximum power point effect etc.). In addition, the sliding mode control based on the proposed reaching law reduces the system jitter, and effectively reduces jitter and vibration of the PV energy storage battery.

关键词： Matlab sliding mode control strategy DC microgrid system Solar power stations and photovoltaic power systems DC-DC power convertors PV energy storage system maximum power point trackers PV energy storage battery variable structure systems maximum power point tracking tracking strategy energy storage control method DC bus voltage low-frequency fluctuations improved sliding mode control double integral sliding mode control distributed power generation Power system control power generation control control algorithm Control of electric power systems photovoltaic energy storage microgrid systems power grids PV power generation energy loss Multivariable control systems squirrel search algorithm Power electronics, supply and supervisory circuits Power engineering computing power engineering computing photovoltaic power systems islanded microgrid systems Distributed power generation good tracking maximum power point effect etc jitter

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Enhancing Diabetic Retinopathy Detection Through Machine Learning with Restricted Boltzmann Machines

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第9期14卷 573-585页

作者： Naramala, Venkateswara Rao Kumar, B. Anjanee Rao, Vuda Sreenivasa Mishra, Annapurna Hannan, Shaikh Abdul El-Ebiary, Yousef A. Baker Manikandan, R. RVR & JC Coll Engn CSE AI&ML Guntur Andhra Pradesh India Dadi Inst Engn & Technol Elect & Commun Engn Anakapalle Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram AP India Silicon Inst Technol Elect & Commun Engn Bhubaneswar 751024 India Albaha Univ Dept Comp Sci & Informat Technol Albaha Saudi Arabia UniSZA Univ Fac Informat & Comp Terengganu Malaysia Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai 600062 Tamil Nadu India

Diabetes is a potentially sight-threatening condition that can lead to blindness if left undetected. Timely diagnosis of diabetic retinopathy, a persistent eye ailment, is critical to prevent irreversible vision loss. However, the traditional method of diagnosing diabetic retinopathy through retinal testing by ophthalmologists is labor-intensive and time-consuming. Additionally, early identification of glaucoma, indicated by the Cup-to-Disc Ratio (CDR), is vital to prevent vision impairment, yet its subtle initial symptoms make timely detection challenging. This research addresses these diagnostic challenges by leveraging machine learning and deep learning techniques. In particular, the study introduces the application of Restricted Boltzmann Machines (RBM) to the domain. By extracting and analyzing multiple features from retinal images, the proposed model aims to accurately categorize anomalies and automate the diagnostic process. The investigation further advances with the utilization of a U-network model for optic segmentation and employs the squirrel search algorithm (SSA) to fine-tune RBM hyperparameters for optimal performance. The experimental evaluation conducted on the RIM-ONE DL dataset demonstrates the efficacy of the proposed methodology. A comprehensive comparison of results against previous prediction models is carried out, assessing accuracy, cross-validation, and Receiver Operating Characteristic (ROC) metrics. Remarkably, the proposed model achieves an accuracy value of 99.2% on the RIM-ONE DL dataset. By bridging the gap between automated diagnosis and ophthalmological practice, this research contributes significantly to the medical field. The model's robust performance and superior accuracy offer a promising avenue to support healthcare professionals in enhancing their decision -making processes, ultimately improving the quality of care for patients with retinal anomalies.

关键词： Optic disc (OD) Optic cup (OC) U-network restricted Boltzmann machines squirrel search algorithm

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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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An effective parameter tuned deep belief network for detecting anomalous behavior in sensor-based cyber-physical systems

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THEORETICAL COMPUTER SCIENCE 2022年 931卷 142-151页

作者： Narayanan, S. Krishna Dhanasekaran, S. Vasudevan, V. Kalasalingam Acad Res & Educ Dept Comp Sci & Engn Srivilliputtur Tamil Nadu India

Cyber-physical system (CPS) integrates cyber systems and the corporal world to perform critical processes that are begun from the developments in digital electronics. The sensor placed in CPS is used to monitor and control processes that are vulnerable to anomalies (reliability failures and cyber-attacks). For the detection of anomalies and prevention of destructive issues, Abnormality Detection System (ADS) is introduced. But ADS suffered from a false alarm rate and missed detection rate, which has resulted in the degraded performance in CPS applications. A novel deep learning approach for detecting Anomalous Behavior in sensor-based CPS is presented in this study. Preprocessing is used in the conceptual scheme to remove noisy data. CPS irregularity is then detected using a DL -based deep belief network (DBN) framework. Furthermore, the number of hidden layers, number of iterations, period frequency, and learning rate of DBN are all tuned using a squirrel search algorithm (SSA). Several tests were conducted with the help of data from device CPS to validate the performance of the SSA-DBN model. To assure the SSA-DBN framework's appropriate detection accuracy, a complete comparable study is conducted, and the produced extensive experiments confirmed the framework's outstanding quality on the applicable information over the comparable approaches.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Cyber -physical system Deep learning Anomaly detection squirrel search algorithm

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Cat-squirrel Optimization algorithm for VM Migration in a Cloud Computing Platform

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2022年第1期18卷 1-23页

作者： Kumar, Ashok C. Sivakumar, P. PSNA Coll Engn & Technol Kothandaraman Nagar Tamil Nadu India PSG Coll Technol Coimbatore India

This paper introduces an approach for the VM migration based on an optimization algorithm named CS in cloud. Selecting the provider is carried out with the usage of multiple constraints, like delay, bandwidth, cost, and load. Subsequently, the effective searching criteria are computed for finding the optimal service on the basis of fitness constraints. The searching criteria are formulated as optimization problems, which are tackled using CS. The proposed CS is designed by integrating CSO with the SSA such that the fitness function is evaluated for the optimal VM migration by considering several parameters, such as delay, cost, bandwidth, and load. Thus, the cloud manager will perform the migration of VM in cloud based on the proposed CS-based VM migration approach. The performance of the CS-based VM migration is evaluated in terms of delay, cost, and load. The proposed CS-based VM migration method achieves the minimal delay of 0.146, minimal cost of 0.052, and the minimal load of 0.182.

关键词： Cat Swarm Optimization Cloud Computing Migration Factor squirrel search algorithm Virtual Machine Migration

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Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition

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China Communications 2022年第2期19卷 47-61页

作者： Miao Shi Chao Wang Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie Department of Mechanical Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China College of Civil Engineering and Architecture Anhui Polytechnic UniversityAnhuiWuhu 241000PR China Department of Computer Science and Technology Anhui University of TechnologyAnhuiMa’anshan 243002PR China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterAnhuiHefei230000PR China Department of Management science and Engineering Anhui University of TechnologyAnhuiMa’anshan 243002PR China

Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and *** Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing *** are two parameters in VMD that have a great influence on the result of signal ***,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based *** original seasonal monitoring condition in SSA is *** feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring ***-based learning is introduced to reposition the position of the population in this *** is applied to optimize the important parameters of ***-VMD model is established to remove ocular artifacts from EEG *** have verified the effectiveness of our proposal in a public dataset compared with other *** proposed method improves the SNR of the dataset from-2.03 to 2.30.

关键词： ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning

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