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Enhancing load frequency control and automatic voltage regulation in Interconnected power systems using the walrus optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Dev, Ark Bhatt, Kunalkumar Mondal, Bappa Kumar, Vineet Kumar, Vineet Bajaj, Mohit Tuka, Milkias Berhanu Parul Univ Parul Inst Engn & Technol Dept Elect Engn Vadodara Gujarat India GMR Inst Technol Dept Mech Engn Vizianagaram Andhra Pradesh India Madanapalle Inst Technol & Sci Dept Elect & Elect Engn Madanapalle Andhra Pradesh India Graph Era Dept Elect Engn Dehra Dun 248002 India AL Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Univ Business & Technol Coll Engn Jeddah 21448 Saudi Arabia Addis Ababa Sci & Technol Univ Coll Engn Sustainable Energy Ctr Excellency Dept Elect & Comp Engn Addis Ababa Ethiopia

This paper introduces the walrus optimization algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations.

关键词： walrus optimization algorithm Load frequency control Automatic voltage regulation FO-PID controller Power system stability Metaheuristic optimization Frequency regulation Voltage stability

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Improved Extraction of Parameters of Solar PV Cell Diode Model Using Marine walrus Inspired optimization algorithm

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IEEE ACCESS 2024年 12卷 181217-181231页

作者： Dev, Ark Kumar, Vineet Pathak, Diwaker Tummala, Ayyarao S. L. V. Sharma, Sachin Kuppan, Vasudevan Kumar, Vineet Parul Univ Parul Inst Engn & Technol Dept Elect Engn Vadodara 391760 Gujarat India Teerthanker Mahaveer Univ Fac Engn Dept Elect Engn Moradabad 244001 Uttar Pradesh India GMR Inst Technol Dept Elect & Elect Engn Rajam 532127 Andhra Pradesh India Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 Karnataka India Madanapalle Inst Technol & Sci Dept Elect & Elect Engn Madanapalle 517325 Andhra Pradesh India

Accurate parameter estimation is essential for optimizing the performance of solar photovoltaic (PV) models. Traditional methods, such as deterministic approaches, often face challenges due to the inherent nonlinearity of PV systems, resulting in high computational demands and difficulty in accurately extracting key parameters. These methods frequently rely on approximations for the objective functions, which may compromise accuracy. To address these limitations, a novel application of walrus optimization algorithm (WaOA) is introduced for precise parameter extraction in solar PV cell diode models. Inspired by the social and foraging behavior of walruses, WaOA improves performance by globally expanding the search space and incorporates the Newton-Raphson method to refine objective function accuracy. The algorithm is validated on single diode (SDM), double diode (DDM), and triple diode models (TDM), demonstrating superior performance compared to other optimization techniques such as artificial hummingbird optimization (AHO), brown bear optimization (BO), war strategy optimization (WSO), and other hybrid algorithms. The proposed design resulted in faster convergence (about 200 iterations) and significant RMSE improvements of minimum 22.2%, 42.8%, and 29.8% for SDM, DDM, and TDM respectively compared to mentioned optimization approaches. The proposed design is validated across all diode models under various irradiance conditions, confirming its robustness and adaptability.

关键词： optimization Accuracy Computational modeling Convergence Mathematical models Metaheuristics Time division multiplexing Resistance Particle swarm optimization Estimation Diode models optimization parameter estimation root mean square error (RMSE) walrus optimization algorithm

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A walrus optimization-enhanced long short-term memory model for credit fraud detection in banking

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International Journal of Information Technology (Singapore) 2025年 1-17页

作者： Pillai, Sanjaikanth E. Vadakkethil Somanathan Nadella, Geeta Sandeep Meduri, Karthik Priyadharsini, Naveena A. Bhuvanesh, A. Kumar, Deepak School of Electrical Engineering and Computer Science University of North Dakota Grand Forks ND United States Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electronics and Communication Engineering Arunachala College of Engineering for Women Tamil Nadu Kanyakumari India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Abstract: This paper presents an innovative framework for enhancing credit fraud detection in banking by combining Autoencoder (AE), Long Short-Term Memory (LSTM) networks, and the walrus optimization algorithm (WOA). The framework begins with the Autoencoder, which is utilized to pre-process high-dimensional transactional data. This step effectively reduces noise, extracts key features, and retains essential information, ensuring that the data fed into the model is of high quality. The LSTM network is then employed to capture the temporal dependencies and sequential patterns present in transaction data, enabling the model to detect fraudulent activities based on the order and timing of events. To further enhance performance, the walrus optimization algorithm (WOA) is used to fine-tune the hyperparameters of the AE-LSTM model, optimizing both detection accuracy and computational efficiency. The integration of these techniques offers a comprehensive and scalable solution to the challenges of detecting fraudulent transactions in large, dynamic datasets typical in modern banking systems. By leveraging the strengths of each component—AE for data preprocessing, LSTM for sequential analysis, and WOA for optimization—the proposed model achieves significant improvements in fraud detection capabilities. This approach not only improves the detection of fraud in real-time banking transactions but also provides a more efficient and effective alternative to traditional fraud detection methods, addressing both accuracy and processing speed in a unified framework. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Autoencoder Banking credit fraud Long short term memory Transactions walrus optimization algorithm

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Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women

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SENSORS 2025年第4期25卷 1166-1166页

作者： Panjwani, Bharti Yadav, Jyoti Mohan, Vijay Agarwal, Neha Agarwal, Saurabh Shri Madhwa Vadiraja Inst Technol & Management Dept Comp Sci & Engn Bantakal 574115 Karnataka India Netaji Subhas Univ Technol Dept Instrumentat & Control Engn Sect 3 New Delhi 110078 Delhi India Manipal Acad Higher Educ Dept Mechatron Manipal Inst Technol Manipal 576104 Karnataka India Yeungnam Univ Sch Chem Engn Gyongsan 38541 South Korea Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea

Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide;however, due to a lack of public awareness, as well as the expensive testing involved in the identification of PCOS, 70% of cases go undiagnosed. Therefore, the primary objective of this study is to design an expert machine learning (ML) model for the early diagnosis of PCOS based on initial symptoms and health indicators;two datasets were amalgamated and preprocessed to accomplish this goal, resulting in a new symptomatic dataset with 12 attributes. An ensemble learning (EL) model, with seven base classifiers, and a deep learning (DL) model, as the meta-level classifier, are proposed. The hyperparameters of the EL model were optimized through the nature-inspired walrus optimization (WaO), cuckoo search optimization (CSO), and random search optimization (RSO) algorithms, leading to the WaOEL, CSOEL, and RSOEL models, respectively. The results obtained prove the supremacy of the designed WaOEL model over the other models, with a PCOS prediction accuracy of 92.8% and an area under the receiver operating characteristic curve (AUC) of 0.93;moreover, feature importance analysis, presented with random forest (RF) and Shapley additive values (SHAP) for positive PCOS predictions, highlights crucial clinical insights and the need for early intervention. Our findings suggest that patients with features related to obesity and high cholesterol are more likely to be diagnosed as PCOS positive. Most importantly, it is inferred from this study that early PCOS identification without expensive tests is possible with the proposed WaOEL, which helps clinicians and patients make better informed decisions, identify comorbidities, and reduce the harmful long-term effects of PCOS.

关键词： polycystic ovary syndrome detection ensemble learning deep learning walrus optimization algorithm cuckoo search algorithm

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Identification of source of image degradation using a new hybrid dense F-LSTM network and image restoration

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Pawar, Poonam Yogesh Ainapure, Bharati Sanjay Vishwakarma Univ Fac Sci & Technol Dept Comp Engn Survey 234 Pune 411048 Maharashtra India

Image degradation has been garnering huge interest from researchers in the field of image processing and computer vision as images may contain several degradations that lead to different types of blurring, noise, and distortions. Hence, a new hybrid optimized network named DenseNet Fused Long Short Term Memory (Dense F-LSTM) is proposed for the identification of the source of image degradation. Initially, the input image with noise and blur is taken from the database and is subjected to noisy pixel identification, and identification of image source degradation, where the noise pixel identification is performed by Deep Convolutional Neural Network (DCNN), and the image source degradation is employed by proposed Dense F-LSTM. The identified noisy pixel is fed to the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter for noisy correction for obtaining the denoised images. Once the image source is identified, the identified source of image degradation and the denoised image is used for performing deblurring. Here, deblurring is done by kernel estimation, which is optimally selected employing Jaya walrus optimization (JWaO). The JWaO is formed by the integration of the Jaya algorithm (JA) and walrus optimization algorithm (WaOA). Both the denoised image and deblurred image are fused to gain the final output image. The devised Dense F-LSTM method provided superior identification performance and achieved a Second-Derivative-like Measure of Enhancement (SDME) is 59.956, Peak Signal Noise Ratio (PSNR) is 49.924 dB and Structural Similarity Index Measure (SSIM) is 0.979, and Mean Squared Error (MSE) of 0.2.

关键词： Deep convolutional neural network Jaya algorithm walrus optimization algorithm Long short term memory DenseNet

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Green federated learning empowered drug manufacturing mechanism for the pharmaceutical industry

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COMPUTING 2025年第3期107卷 1-24页

作者： Malathy, N. Lavanya, A. Shree, S. Pooja Kumaripriya, R. Mepco Schlenk Engn Coll Dept Informat Technol Sivakasi India

The Internet of Medical Things (IoMT) emerged as a result of the close connection between the IoT which is the Internet of Things and the medical field. In the pharmaceutical industries, drug production is carried out by deploying IoMT by assessing the data gathered through smart devices by utilizing AI-powered systems. However, the inherent design weaknesses of conventional AI technology could result in the leakage of drugs' private information. A privacy-preserved global model can be produced through federated learning(FL). Despite this, FL continues to be susceptible to inference attacks, and energy consumption is a further concern. For this constraint, we could use green federated learning a novel and crucial research area where carbon footprint is an evaluation criterion for AI, alongside accuracy, convergence, speed, and other necessary metrics. In this paper, to address the above-mentioned consequences, an energy-conserved and privacy-enhanced technique incorporating Green FL which involves optimizing FL features by walrus optimization algorithm(WaOA) and making design choices to minimize the carbon emissions consistent with competitive performance for IoMT is proposed. The proposed work shows improved performance with 91% global model accuracy, reduced carbon emissions, and better privacy in drug manufacturing. Furthermore, participants received rewards based on data quality, similarity, and richness, as validated through simulation trials. The findings indicate a convergence accuracy of up to 90% for local models and an increase in participant incentives proportional to data quality. These results confirm the effectiveness of the approach in balancing privacy, accuracy, and energy efficiency in the drug manufacturing Industry.

关键词： Machine learning Federated learning Green federated learning walrus optimization algorithm Artificial intelligence Internet of medical things Pharmaceutical industry

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Machine learning-driven prediction of average localization error in wireless sensor networks

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年第4期16卷 1468-1484页

作者： Zhang, Lan Zhao, Yunfeng North China Inst Aerosp Engn Sch Remote Sensing & Informat Engn Langfang 065000 Hebei Peoples R China

The exact localization of sensor nodes is one of the important issues in Wireless Sensor Networks (WSNs) for different applications. However, traditional methods of localization may suffer from several types of errors. This research examines a machine learning (ML) approach for predicting Average Localization Error (ALE) in WSNs. This study applies two powerful ML models: K-nearest neighbors Regression (KNNR) and Light Gradient Boosting Machine (LGBM). KNNR is light and easy to interpret, while LGBM has the capability to model complex relationships among features. Furthermore, an optimizer in the form of the walrus optimization algorithm (WaOA) is utilized to boost the performance of the model. WaOA is a nature-inspired algorithm that is efficient in fine-tuning the parameters of ML models to improve their prediction accuracy. The LGWO model performed better on the test set, with an RMSE value of 0.066 and an R2 of 0.980, compared with other traditional models, such as KNN, at 0.131 and 0.915, respectively. During the testing phase, the LGWO model demonstrated the highest performance based on the Mean Squared Error (MSE) metric, achieving a value of 0.004, while the KNWO model ranked third with an MSE value of 0.015. Similarly, in the validation phase, the LGWO model achieved the best performance in terms of the Relative Absolute Error (RAE) metric, with a value of 2.799. The second-best performance in the validation phase was observed with the LGBM model, which recorded an RAE value of 3.931. In terms of the minimum prediction error and best accuracy within the entire training, validation, and testing processes, the LGWO model proves robust and reliable.

关键词： Machine learning Average localization error K-Nearest neighbors regression Light gradient boosting machine walrus optimization algorithm

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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities

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SCIENTIFIC REPORTS 2025年第1期15卷 1-26页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Alghamdi, Bandar M. Aboalela, Rania Alsaadi, Naif Maghrabi, Louai A. Allehaibi, Khalid H. King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Elect & Comp Engn Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Rabigh Informat Syst Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Engn Rabigh Dept Ind Engn Jeddah 21589 Saudi Arabia Univ Business & Technol Coll Engn Dept Software Engn Jeddah Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Comp Sci Dept Jeddah 21589 Saudi Arabia

With the fast growth of artificial intelligence (AI) and a novel generation of network technology, the Internet of Things (IoT) has become global. Malicious agents regularly utilize novel technical vulnerabilities to use IoT networks in essential industries, the military, defence systems, and medical diagnosis. The IoT has enabled well-known connectivity by connecting many services and objects. However, it has additionally made cloud and IoT frameworks vulnerable to cyberattacks, production cybersecurity major concerns, mainly for the growth of trustworthy IoT networks, particularly those empowering smart city systems. Federated Learning (FL) offers an encouraging solution to address these challenges by providing a privacy-preserving solution for investigating and detecting cyberattacks in IoT systems without negotiating data privacy. Nevertheless, the possibility of FL regarding IoT forensics remains mostly unexplored. Deep learning (DL) focused cyberthreat detection has developed as a powerful and effective approach to identifying abnormal patterns or behaviours in the data field. This manuscript presents an Advanced Artificial Intelligence with a Federated Learning Framework for Privacy-Preserving Cyberthreat Detection (AAIFLF-PPCD) approach in IoT-assisted sustainable smart cities. The AAIFLF-PPCD approach aims to ensure robust and scalable cyberthreat detection while preserving the privacy of IoT users in smart cities. Initially, the AAIFLF-PPCD model utilizes Harris Hawk optimization (HHO)-based feature selection to identify the most related features from the IoT data. Next, the stacked sparse auto-encoder (SSAE) classifier is employed for detecting cyberthreats. Eventually, the walrus optimization algorithm (WOA) is used for hyperparameter tuning to improve the parameters of the SSAE approach and achieve optimal performance. The simulated outcome of the AAIFLF-PPCD technique is evaluated using a benchmark dataset. The performance validation of the AAIFLF-PPCD

关键词： Federated Learning Privacy preserving Artificial Intelligence Cyberthreat Smart cities IoT walrus optimization algorithm

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Optimal scheduling and energy management of a multi-energy microgrid with electric vehicles incorporating decision making approach and demand response

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SCIENTIFIC REPORTS 2025年第1期15卷 1-29页

作者： Xiao, Genfu Liu, Huan Nabatalizadeh, Javad Jinggangshan Univ Coll Mech & Elect Jian 343009 Jiangxi Peoples R China Jinggangshan Univ Coll Elect & Informat Engn Jian 343009 Jiangxi Peoples R China Islamic Azad Univ Dept Elect Engn Ardabil Branch Ardebil Iran

Multi-Energy Microgrids (ME-MGs) represent an integrated and advanced energy system, playing a vital role in delivering optimal and sustainable energy solutions in modern societies. These systems combine various energy sources, such as electricity, heat, and storage systems, to ensure efficient resource management and operation. One of the primary challenges in managing ME-MGs is reducing operational costs and emissions while addressing uncertainties. This study investigates the optimization and energy management (EM) in ME-MGs through the application of the Multi-Objective walrus optimization algorithm (MOWaOA) combined with fuzzy decision-making techniques. The main objective of the research is to minimize operational costs and emissions in the face of uncertain conditions. To achieve this goal, multiple scenarios were analyzed, including EM without considering demand response and electric vehicles, EM with the inclusion of these factors, and EM under uncertain conditions. The results demonstrated that integrating electric vehicles and demand response into microgrid EM led to a 15.6% reduction in operational costs and a 12.8% decrease in emissions compared to scenarios where these factors were excluded. Furthermore, when uncertainties were accounted for, operational costs increased by 2.1% and emissions rose by 1.2%. This increase emphasizes the significance of employing more precise management techniques and advanced strategies to effectively address uncertainties in ME-MGs.

关键词： Multi-energy Microgrid Energy Management walrus optimization algorithm Fuzzy decision-making Electric vehicles Demand response Uncertainty Emission Reduction Operational cost reduction

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Accurate prediction of hyaluronic acid concentration under temperature perturbations using near-infrared spectroscopy and deep learning

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 317卷 124396页

作者： Tian, Weilu Zang, Lixuan Ijaz, Muhammad Dong, Zaixing Zhang, Shudi Gao, Lele Li, Meiqi Nie, Lei Zang, Hengchang Shandong Univ Sch Pharmaceut Sci NMPA Key Lab Technol Res & Evaluat Pharmaceut Prod Jinan 250012 Peoples R China Shandong Univ Natl Glycoengn Res Ctr Jinan 250012 Peoples R China Shandong Univ Key Lab Chem Biol Minist Educ Jinan 250012 Peoples R China Shandong Univ Sch Pharmaceut Sci Dept Pharmacol Jinan 250012 Peoples R China Northeastern Univ Sch Mat Sci & Engn Shenyang 110819 Peoples R China

Accurate prediction of the concentration of a large number of hyaluronic acid (HA) samples under temperature perturbations can facilitate the rapid determination of HA 's appropriate applications. Near -infrared (NIR) spectroscopy analysis combined with deep learning presents an effective solution to this challenge, with current research in this area being scarce. Initially, we introduced a novel feature fusion method based on an intersection strategy and used two-dimensional correlation spectroscopy (2DCOS) and Aquaphotomics to interpret the interaction information in HA solutions reflected by the fused features. Subsequently, we created an innovative, multi -strategy improved walrus optimization algorithm (MIWaOA) for parameter optimization of the deep extreme learning machine (DELM). The final constructed MIWaOA-DELM model demonstrated superior performance compared to partial least squares (PLS), extreme learning machine (ELM), DELM, and WaOA-DELM models. The results of this study can provide a reference for the quantitative analysis of biomacromolecules in complex systems.

关键词： Near -infrared spectroscopy Deep extreme learning machine Feature fusion Aquaphotomics walrus optimization algorithm

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