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检索条件"主题词=Whale Optimization Algorithms"

共 393 条记录，以下是301-310 订阅

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Credit Rating Prediction Based on WOA-Cross-Validation-LightGBM

Credit Rating Prediction Based on WOA-Cross-Validation-Light...

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International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Zichang Sun Shandong University Weihai China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

One of the key challenges faced by banks in the context of continuous growth in customer loan demand is how to effectively execute customer credit ratings. This study introduces SMOTE to solve the problem of data imbalance and utilizes RFE_XG-Boost for feature importance assessment to screen out the key features that have a significant impact on credit ratings. Based on these selected features, the LightGBM model is used to predict customer credit ratings. To further optimize the model performance, this paper applies the whale optimization Algorithm (WOA) to adjust the parameters of the LightGBM model and combines with the 5-fold cross-validation technique to enhance the model prediction accuracy. Using this method, the study significantly improves the accuracy of the model prediction. The experimental results confirm that the LightGBM model, which combines SMOTE, RFE_XGBoost feature selection and WOA, shows high accuracy and practicality in credit rating prediction. The methodology of this study provides an efficient solution for bank credit rating and lays the foundation for future research in similar areas.

关键词： Radio frequency Technological innovation Accuracy Predictive models Prediction algorithms Feature extraction whale optimization algorithms

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Intelligent and Optimized Deep Learning Techniques for Analyzing the IoMT-Based Remote Patient Monitoring Systems

Intelligent and Optimized Deep Learning Techniques for Analy...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Sayyed Johar G R Manjula Dept of AIML JNNCE-Shivamogga Visveswaraya Technological University Belagavi India Dept of Data Science JNNCE-Shivamogga Visveswaraya Technological University Belagavi

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The IoMT has dramatically empowered the process of remote patient monitoring (RPM) through much better real-time data collection and health analysis. The real struggle lies in categorizing and evaluating the patient’s health-related datas due to its complexity and unbalanced nature. This study proposes an Intelligent W-DQN model based on a whale-optimized Deep-Q-Network (W-DQN) to identify and categorize pathological information, an LSTM-QDA model for recognizing temporal patterns and reducing dimensions, and a PathoDimClassifier for selecting features to address imbalanced data and noise issues effectively. optimization techniques, including the whale optimization Algorithm and the Adam optimizer, are applied to improve accuracy rates while decreasing training duration and minimizing errors. The approach seeks to enhance health monitoring in time by providing accurate categorization and prompt medical interventions to ultimately enhance patient care results.

关键词： Training Patient monitoring Pathology Accuracy Scientific computing Noise whale optimization algorithms Data models Real-time systems Pattern recognition

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Considering the Green Environmental Value Attribute of Electricity: A Deep Learning Model for Green Electricity Price Forecasting

Considering the Green Environmental Value Attribute of Elect...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Ran Ding Haixiang Xu Yiming Yao Wenyu Zhang Bin Wang Xiaochuan Xu Hongyong Liu State Grid Jibei Electric Power Co. Ltd Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Against the backdrop of the "dual carbon" goals, the green electricity trading market is gradually developing. Currently, the formation mechanism of green electricity prices is complex, environmental premiums are not unified, and the impact paths are not clear, making the prediction of green electricity prices extremely challenging. Therefore, this paper proposes a deep learning model for forecasting green electricity prices that takes into account the green environmental value attributes of electricity. Firstly, the mechanism of green electricity price formation is studied to explore the impact paths of its influencing factors; then, a quantification index system for green electricity environmental value is constructed; subsequently, the LSTM neural network parameters are optimized using the whale optimization Algorithm (WOA) to build the forecasting model; finally, a simulation experiment is conducted in a green electricity market in North China, and the results show that the proposed model has a prediction accuracy of 91.7%, achieving accurate forecasting of green electricity prices, which can provide reference for market trading entities to formulate strategies.

关键词： Deep learning Accuracy Electricity Green products Predictive models Prediction algorithms whale optimization algorithms Power markets Forecasting Long short term memory

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Noise reduction method based on WOA-VMD-FE for fault arc acoustic signals in distribution networks

Noise reduction method based on WOA-VMD-FE for fault arc aco...

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Intelligent Power and Systems (ICIPS), International Conference on

作者： Ying Yang Wenmin Wang Zhengyun Liu Jiahao Wang Cheng Zeng Qi Feng Haojie Wei Kai Chen State Grid Hubei Power Transmission and Transformation Engineering Co. State Grid Corporation of China Wu Han China College of Electrical Engineering and New Energy China Three Gorges University Yi Chang China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

Arc acoustic signals are an effective means of characterizing faults in distribution networks, but their acquisition is often hindered by environmental limitations and various types of noise interference. To address the issue of misjudgment in fault detection due to noise and the challenge of selecting parameters for the Variational Mode Decomposition (VMD) algorithm, this paper proposes a denoising method based on the whale optimization Algorithm (WOA) for arcing acoustic signals in distribution network faults. Firstly, the whale optimization Algorithm is used to optimize the penalty factor α and the decomposition modulus K of VMD, thereby determining the optimal decomposition parameters. Secondly, fuzzy entropy is employed to evaluate each Intrinsic Mode Function (IMF) signal decomposed by VMD, enabling the distinction between the noisy components and the effective signal parts. The Sparrow Search Algorithm, Particle Swarm optimization, Genetic Algorithm, and the whale optimization-based VMD parameter searching method are compared. The results demonstrate that the proposed method significantly improves the accuracy and speed of VMD parameter optimization compared to the Sparrow, Particle Swarm, and Genetic algorithms. Effectively filtering out ambient noise while preserving the original characteristics of the signal to a great extent.

关键词： Heuristic algorithms Noise Noise reduction Distribution networks Acoustics whale optimization algorithms Entropy Particle swarm optimization optimization Genetic algorithms

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Internet Abnormal Traffic Detection Based on WOA-BiLSTM-Attention

Internet Abnormal Traffic Detection Based on WOA-BiLSTM-Atte...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Lin Mei Department of Basic Education Shandong University of Engineering and Vocational Technology Jinan China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

With the rise of the Internet, network application coverage is becoming more and more extensive, and a large amount of data has brought security issues to the network environment. In order to detect abnormal network traffic, this research builds a model based on the WOA using BiLSTM-Attention, and conducts empirical research using certain network traffic data as an example. The research results show that the root mean square error (RMSE) of WOA-BiLSTM-Attention is 79.1746, which is better than the traditional prediction model and is more suitable for predicting Internet traffic. Its prediction results can provide certain reference significance for abnormal traffic detection and abnormal data detection in other fields in the future.

关键词： Deep learning Neural networks Telecommunication traffic Predictive models whale optimization algorithms Data models Internet

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Super Twisting SMC Based 5L-NPC for Medium Voltage Grid Connected PV-Battery System

Super Twisting SMC Based 5L-NPC for Medium Voltage Grid Conn...

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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Kalpana Beura Omar Al Zaabi Mohamed Alkhatib Electrical & Communication Engg. COE UAE University Al Ain Abu Dhabi UAE APEC Electrical Engg. Dept. Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350368864

ISBN: (纸本)9798350368871

Medium voltage drives, particularly those for induction motors, are regularly using in various industrial tasks. The efficient management of induction motors is crucial in nuclear power plants. Induction motors are often controlled using direct torque control (DTC) to achieve smoother speed torque characteristics in comparison to other available control methods. Nevertheless, traditional DTC encounters numerous challenges due to the need to uphold a consistent reference flux across all speed ranges. Therefore, this paper presents the implementation of two innovative super twisting sliding mode (STSM) controllers in a novel DTC system. The induction motor is driven by a 5L-NPC: five level neutral point clamped inverter operating in three phases. The connection to the dc-link is made via a 36-pulse converter. Additionally, a photovoltaic (PV) system is added with a battery bank to ensure uninterrupted power supply in off-grid situations. A constant dc-link voltage is maintained through the use of a dc bidirectional circuit that links the dc-bus with the battery. The suggested regulation of the dc bidirectional converter has the potential to minimize the oscillations and ripples in the dc-link caused by the 36 pulse converter when the grid voltages are unbalanced. In order to identify best vector location, whale optimization methodology has been implemented. This paper presents practical responses by implementing hardware-in-the-loop (HIL) using OPAL-RT circuits. The results obtained from HIL are examined across multiple case studies.

关键词： Induction motor drives Space vector pulse width modulation Torque Torque control Medium voltage whale optimization algorithms Vectors Regulation Batteries Voltage control

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An Interoperability Test Case Generation Method for State Transition and Parameter Combination Coverage

An Interoperability Test Case Generation Method for State Tr...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Changan Wei Ji Xu Zhong Bo Yunlong Sheng Feixuan Wang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

This paper addresses the critical role of interoperability testing for launch and control electronic information systems and introduces a novel test case generation method leveraging the whale optimization Algorithm (WOA). With the increasing complexity of launch missions, ensuring seamless coordination between these systems is of paramount importance. By constructing state transition models and optimizing parameter combinations, WOA can efficiently generate high-coverage test cases that validate system interoperability. Experimental results demonstrate that WOA significantly outperforms traditional algorithms, such as Particle Swarm optimization (PSO) and Genetic Algorithm (GA), in terms of the number of test cases generated, convergence speed, coverage rate, and fitness value. This method presents a robust solution for enhancing the testing efficiency and reliability of launch and control electronic information systems in the context of complex mission scenarios.

关键词： Reliability theory Control systems Solids whale optimization algorithms Particle swarm optimization Interoperability Testing Information systems Genetic algorithms Convergence

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Secure and Efficient Data Transmission for IoT Devices in Untrusted Environments

Secure and Efficient Data Transmission for IoT Devices in Un...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Upma Jain Smitha K R.S.S. Raju Battula Arindam Sarkar Natasha Martin Natrayan L Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Artificial Intelligence and Machine Learning BGS Institute of Technology Adichunchanagiri University India Department of Computer Applications Aditya University India Department of Applied Electronics and Instrumentation Engineering Heritage Institute of Technology Kolkata India Department of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Mechanical Engineering Saveetha School of Engineering SIMATS India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

One technological trend that will not go away in the modern world is the Internet of Things (IoT). With the development of the Internet of Things, the world is becoming smarter. The efficacy, flexibility, and simplicity of deployment of IoT make it the preferred choice for most real-time applications. But energy efficiency and security are the big obstacles for the Internet of Things. Presented here are safe, energy-efficient solutions for the Internet of Things (IoT), a problem whose significance this study acknowledges. Since routing is the primary energy consumer, this study presents secure routing strategies that are both energy efficient and reliable. Three distinct safe data transmission techniques for the Internet of Things (IoT) are presented in this paper, all of which aim to reduce energy usage during routing. The first part of this study lays out a clustering-and secure route selection-based architecture for safe and energy-efficient data transmission with the Internet of Things (IoT). The W-HHO technique, which is based on whale optimization, is used to identify the secure routes, and fuzzy-based secure clustering is used for that purpose. Encryption protocols that work in tandem with cypher block chaining ensure the safety of sensitive information. Using industry-standard metrics like packet delivery rate, throughput, energy efficiency, network longevity, etc., we assess how well each solution performs. When compared to the current methods, the outcomes produced by the presented works demonstrate promising performance.

关键词： Performance evaluation Software algorithms Clustering algorithms Routing Throughput Energy efficiency whale optimization algorithms Routing protocols Internet of Things Data communication

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Investigations of PAPR reduction methods in FBMC System

Investigations of PAPR reduction methods in FBMC System

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Rashmi Rani Samantaray Siddappaji Department of Electronics and Communication VTU HKBK College Of Engineering Bengaluru India Department of Electronics and Communication BMS College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The primary concern of the 5G radio network has sparked interest in the development of new waveforms beyond 4G (5th generation networks). Filter Bank Multicarrier (FBMC) waveforms have recently been suggested as the desired one for 5G networks. It has both advantages and disadvantages. One of the biggest drawback of the FBMC broadcast signal is its high Peak to Average Power Ratio (PAPR). It has been widely used in wireless communication to achieve high speed and data rates. A high PAPR system results in substantial distortion of the signal because of the nonlinear power amplifier. Therefore, methods that effectively reduce PAPR are necessary for the realization of the FBMC system. The FBMC system uses the some common techniques as the OFDM system. Comparative analysis of various approaches such as PTS, SLM, Companding, etc are performed by considering their simulation outcomes when it comes to effective spectrum utilization, improved PAPR, along with minimum complexity.

关键词： Wireless communication 5G mobile communication Nonlinear distortion Metaheuristics Power amplifiers Peak to average power ratio whale optimization algorithms Complexity theory Radio networks Particle swarm optimization

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Design and Analysis of an Adaptive QoS Aware Approach for Supporting Multiple Services using Meta Heuristic Enhanced whale optimization Algorithm over Hybrid Cloud Environment (EWOC)

Design and Analysis of an Adaptive QoS Aware Approach for Su...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Premnadh Katkam P. Anbalagan V.V.S.S.S. Balaram Department of Computer Science and Engineering Annamalai University Annamalainagar Dept. of Computer Science and Engineering Annamalai University Annamalainagar Sreenidhi Institute of Science and Technology Yamnampet Ghatkesar Hyderabad

ISBN: (纸本)9781665460859

Though numerous research works have discussed about approaches towards providing QoS over cloud environment for variable services, research community suggest that the need for an adaptive QoS approach with quantifiable resources is a challenge. Hybrid cloud is currently adopted by industry with need for consistent QoS and variable security features. Variable number of users connected over cloud demand consistent resources such as network link capacity, distributed storage and dynamic processing capabilities for execution of service based on variable cloud. This paper focuses on identification and provisioning of demandable resources over a session for a specific task. The proposed EWOC approach works on an enhanced whale optimization algorithm for QoS supportive sessions in use. This paper models an adaptive resource assignment over PSO, SA scheduling algorithms and their QoS aspects.

关键词： Adaptation models Scheduling algorithms Heuristic algorithms Quality of service Throughput whale optimization algorithms Delays

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