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Enhanced Arabic Feature Selection using an Improved whale optimization Algorithm

Enhanced Arabic Feature Selection using an Improved Whale Op...

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Information Technology (ACIT)

作者： Meryeme Hadni Mounir Gouiouez Hassane Hjiaj Meryeme Amane FSJESK Cadi Ayyad University Marrakech Morocoo PSCS Laboratory SIDI Mohamed Ben Abdellah University Fez Morocco Department of Mathematics Faculty of Science Abdelmalek Essaadi University ADSES Laboratory Sidi Mohammed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Research in the field of Natural Language Processing for Arabic documents that incorporate metaheuristic techniques remain limited, primarily due to the intricate nature of the language, which encompasses challenges related to its structure, morphology, syntactic analysis, grammatical conventions, and derivational processes. This study introduces a novel approach for the processing of Arabic documents, which aims to enhance the whale optimization algorithm. This approach functions as a supervised learning technique, emulating the behaviors of whales, including bubble netting and spiral movements toward their targets. The proposed methodology represents an advancement of the traditional whale optimization algorithm, characterized by its adaptability and efficiency. It effectively integrates exploration and exploitation strategies, drawing inspiration from the natural behaviours of humpback whales while also incorporating the beneficial attributes of chaotic functions. This adaptability positions the method to address complex optimization challenges across various fields. Beside, the experiment show that enhanced method has a better result by using Firefly Algorithm classifier and the TF-IDFC-RF feature extraction.

关键词： Spirals Supervised learning Metaheuristics Morphology Syntactics Feature extraction whale optimization algorithms Classification algorithms Information technology

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Research on Personalized Recommendation Based on Improved whale optimization Algorithm

Research on Personalized Recommendation Based on Improved Wh...

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Jun Jiang Wenyang Qing Jun Hu Fazhong Hou School of Medical Humanities and Information Management Hunan University of Medicine Huaihua China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In order to solve the problem of insufficient information mining and low degree of personalization due to the single data dimension of cognitive diagnosis test question recommendation, this paper proposes a personalized exercise question recommendation method based on multi-dimensional data cognitive diagnosis optimization model. Firstly, the improved whale optimization algorithm with adaptive probability distribution is used to solve the problem that the initial clustering center of the K-means algorithm is sensitive and easy to fall into the local optimum, and secondly, the optimized k-means algorithm was used to cluster the exercises to generate a specific group of test questions. Then, the students' answers to specific test groups were recorded to form a multi-dimensional test question set. Finally, based on the multi-dimensional question bank, the method proposed in this paper and the traditional GDINA cognitive model algorithm are used to test the average accuracy of student sampling, and the results show that the algorithm proposed in this paper has stronger information mining ability and is more suitable for accurate recommendation of personalized test question resources matching learning needs and abilities.

关键词： Information science Accuracy optimization models Clustering algorithms whale optimization algorithms Probability distribution Data models Data mining optimization Periodic structures

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A Fault Forecasting Approach Using Two-Dimensional optimization

A Fault Forecasting Approach Using Two-Dimensional Optimizat...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Mohsen Tajgardan Reza Khoshkangini Abbas Orand Mahdi Rabbani Faculty of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Computer Science and Media Technology Internet of Things and People (IOTAP) Malmö University Malmo Sweden Arriver Software AB Qualcomm Company Linköping Sweden Canadian Institute for Cybersecurity University of New Brunswick NB Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Data preparation is crucial in every machine learning approach, particularly when applied to detecting claims in the automotive industry. The challenge of managing highdimensional feature spaces and imbalanced data becomes even more pronounced with the proliferation of IoT devices, which generate vast amounts of data over time. Machine learning models trained on such imbalanced datasets often yield unreliable and inaccurate predictions. Thus, addressing these issues during the data pre-processing phase is critical. In this paper, we introduce a novel two-dimensional optimization (TDO) strategy to tackle the problem of imbalanced data in fault detection, specifically in the context of IoT-enhanced automotive systems. We leverage a heuristic optimization technique known as the Genetic Algorithm to simultaneously reduce both the number of data point tuples and the feature space. Additionally, we evaluate the effectiveness of two-dimensional reduction using Particle Swarm optimization (PSO) and whale optimization algorithms. Our empirical results, derived from data collected from thousands of IoT-equipped vehicles, demonstrate the promise of our proposed methods.

关键词： Industries Fault detection Machine learning Predictive models whale optimization algorithms Internet of Things Particle swarm optimization optimization Automotive engineering Genetic algorithms

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Metaheuristic Approaches for Energy Efficiency and Network Lifetime optimization in Wireless Sensor Networks

Metaheuristic Approaches for Energy Efficiency and Network L...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： A.S. Sai Puneeth Theja S. Swarnalatha B. Shoban Babu Department of ECE Sri Venkateswara University College of Engineering Tirupati A.P. India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This paper focuses on traditional and natureinspired metaheuristic algorithms, such as Low-Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm optimization (PSO) in Wireless Sensor Networks (WSNs). The evaluation focuses on key parameters like network lifetime, node clustering, and energy consumption levels. Results indicate that PSO consistently outperform the LEACH algorithm in these metrics However, the Adaptive whale optimization Algorithm (AWOA) is competitive with the PSO algorithm with its results leaning towards on the better side. The study complements related research on the application of swarm intelligence in WSN by focusing on routing optimization, energy aware protocols and centralized clustering of nodes.

关键词： Measurement Wireless sensor networks Protocols Visual analytics Metaheuristics Clustering algorithms Routing Energy efficiency whale optimization algorithms Particle swarm optimization

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Beluga whale optimization for Effective Energy Management in Microgrids with Electric Vehicle Charging in Smart Distribution Networks

Beluga Whale Optimization for Effective Energy Management in...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Narendra Pothula Nagasrisaihari Sunkara Vanitha R Thiyagesan M H. Mickle Aancy Balasubbareddy Mallala Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad Telangana India Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of Master of Business Administration Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telanagana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

EM in MGs with EV charging refers to the special mainstream of providing electricity and continuously managing and balancing energy supply, load demand and integrating prosumer elements to maintain proper grid stability and efficiency. There are several disadvantages in the incorporation of EVs in to MGs and smart distribution networks. They are the high initial investment costs which accompany infrastructure improvements like updating charging stations and smart grid technologies may hinder implementation. In this paper, we present a new approach of Beluga whale optimization (BWO) incorporating electric vehicle charging stations (EVCS) for EM in MGs. The primary task that the given approach addresses is the optimization of system utilization and LCOE reduction. The BWO is applied for the performance enhancement of load scheduling of MGs with incorporated EV charging in smart distribution grids. The structure of BWO approach is also established on MATLAB platform, and their performance is compared with other algorithms including Artificial Neural Network (ANN), Shell Game optimization (SGO), Multi-Objective Improved arithmetic optimization Algorithm (MOIAOA) and Young's double Slit-Experiment Optimizer (YDSEO). The presented BWO method obtains LCOE equal to 22,173$/kWh to prove its efficiency over existing methodologies for planning and designing energy systems.

关键词： Energy consumption Renewable energy sources Electricity Artificial neural networks Distribution networks Power system stability Electric vehicle charging whale optimization algorithms MATLAB Energy management

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optimization of Gas Sensor Array in Electronic Nose Using whale optimization Algorithm for Tuberculosis Detection

Optimization of Gas Sensor Array in Electronic Nose Using Wh...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Dava Aulia Riyanarto Sarno Kelly Rossa Sungkono Muhammad Rivai Sheva Aulia Dwi Sunaryono Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Information System Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

A lung infected with tuberculosis contains volatile organic compounds (VOCs) caused by Mycobacterium tuberculosis. An exhaled breath analysis approach can detect biomarkers of tuberculosis. An electronic nose can be proposed as a device for the early diagnosis of tuberculosis. The primary problem with this device is to specify the optimal number of gas sensors while maintaining high performance. The whale optimization algorithm (WOA) is an optimization method that uses the hunting mechanism of humpback whales, including bubble-net feeding and spiral movement. This algorithm is straightforward to implement, uncomplicated, and converges quickly. This study aimed to determine the optimal number of gas sensors in an electronic nose for differentiating between patients with tuberculosis and healthy controls through exhaled breath using WOA. The implementation was performed using Python programming and Jupyter Notebook for data processing, optimization, and classification. The experimental results demonstrate that the WOA efficiently selected the optimal gas sensor combinations in a short optimization time. Furthermore, two gas sensors were selected, and the support vector machine (SVM) successfully distinguished between tuberculosis and healthy controls with accuracy, precision, recall, and f1-score above 98%.

关键词： Support vector machines Volatile organic compounds Technological innovation Spirals Machine learning algorithms Tuberculosis Electronic noses whale optimization algorithms Gas detectors Sensor arrays

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Enhancing the Puma Optimizer Algorithm for optimization Problems

Enhancing the Puma Optimizer Algorithm for Optimization Prob...

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International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakkayaphop Pravesjit Krittika Kantawong School of Information and Communication Technology University of Phayao Phayao Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper proposes an enhanced Puma Optimizer Algorithm for solving optimization problems. The Rao Algorithm is incorporated and refined within the exploration phase of the Puma Optimizer, incorporating novel procedures to improve position updates. The proposed algorithm is evaluated on six benchmark functions. An evaluation results show in comparison to Differential Evolution (DE), Intersection Mutation Differential Evolution (IMDE), the whale optimization Algorithm (WOA), and the Sand Cat Swarm optimization Algorithm (SCSO). Computational results demonstrate that the proposed algorithm achieves optimal solutions for two of the six benchmark functions. Furthermore, it consistently outperforms the other four algorithms across the test suite. These findings highlight the potential for further advancements in the Puma Optimizer and demonstrate its effectiveness in addressing continuous step functions, multimodal functions, and discontinuous step functions, achieving performance that is competitive with or surpasses existing state-of-the-art methods.

关键词： Benchmark testing whale optimization algorithms Particle swarm optimization optimization

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Semantic QoS-aware Resource Allocation for IoV Computation Offloading using Layer Enhanced whale optimization Algorithm

Semantic QoS-aware Resource Allocation for IoV Computation O...

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International Conference on Computer and Communications (ICCC)

作者： Caitong Tang Ningbo Zhang Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In the Internet of Vehicles, computation task offloading is essential to meet increasing computational demands. This paper proposes a resource allocation algorithm based on an enhanced whale optimization Algorithm (WOA) for semantic communication, dynamically allocating resources based on the semantic quality of tasks. We develop a Semantic-Aware Layer Enhanced whale optimization Algorithm (LEWOA) that improves multi-dimensional resource allocation by considering the Quality of Service (QoS) of semantic data. Simulations using the YOLO-V8 framework and HRNet are performed to validate the superiority of the proposed algorithm.

关键词： Heuristic algorithms Simulation Semantic segmentation Quality of service Semantic communication whale optimization algorithms Resource management Vehicle dynamics optimization Internet of Vehicles

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Improving the Application of whale optimization Algorithm in PID Parameter optimization for Linear Motor Control System of Wire Bonding Machine XY Motion Platform

Improving the Application of Whale Optimization Algorithm in...

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International Conference on (ICEPT) Electronic Packaging Technology

作者： Bo Xie Quan Zhou Jingmin Yang School of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou P.R. China

ISBN: (数字)9798350353808

ISBN: (纸本)9798350353815

This study addresses the challenge of optimizing PID controller parameters in the linear motor drive system of wire bonding machines' XY motion platforms. An innovative approach is proposed, leveraging an Enhanced whale optimization Algorithm (IWOA) to enhance optimization efficiency, precision, and stability. By integrating adaptive weight strategies and random differential mutation tactics, the IWOA dynamically balances global search and local refinement, significantly improving optimization outcomes. The investigation begins with establishing a comprehensive mathematical model of the linear motor, followed by extensive simulations conducted using MATLAB. Results demonstrate that the optimized IWOA outperforms classical Particle Swarm optimization (PSO), Genetic algorithms (GA), and standard WOA in PID parameter tuning, showcasing superior convergence properties. To further enhance system performance, a feedback-feedforward hybrid control system is introduced to minimize tracking errors in system response. Empirical validation confirms the effectiveness of this approach in improving trajectory tracking accuracy and positional precision of the platform. Practical implementation within an industrial context, particularly in the control system of a welding machine, underscores the remarkable advantages of IWOA in PID parameter tuning. This research not only enhances optimization outcomes but also significantly elevates the overall operational performance of wire bonding machines' XY motion platforms.

关键词： Motor drives Trajectory tracking Welding Control systems Motors whale optimization algorithms Wire

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optimization study of professional recognition based on big data

Optimization study of professional recognition based on big ...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Xue Huang Chongqing Hailian Technical and Vocational College Chongqing China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

With the continuous development and deepening of the Internet, cloud computing, SG and other information technologies, big data has gradually penetrated into every aspect of all industries, and colleges and universities, as an information-intensive service industry, are the key areas for the application of big data technology. Under the background of big data, colleges and universities integrate traditional business with big data technology to provide differentiated and accurate statistics. In this paper, an optimization study on the survey of professional recognition is conducted by using big data technology. A multi-strategy improved hybrid whale optimization algorithm and ant colony optimization algorithm are proposed and used to optimize support vector regression and random forest model parameters. The construction of the optimization algorithm found that building a channel system of big data can improve the professional recognition of colleges and universities, and the use of big data technology improves the core management level. Finally, it is used in the actual data set to verify the effectiveness of the algorithm to optimize the model parameters.

关键词： Surveys Support vector machines Industries Ant colony optimization Scientific computing Big Data whale optimization algorithms Data models Information technology optimization

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