Network-Computation in Neural Systems最新文献_第2页

Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model. 用于人群异常检测的优化深度最大值：基于优化的混合模型

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-02-01 Epub Date: 2024-09-20 DOI: 10.1080/0954898X.2024.2392772

Rashmi Chaudhary, Manoj Kumar

{"title":"Optimized deep maxout for crowd anomaly detection: A hybrid optimization-based model.","authors":"Rashmi Chaudhary, Manoj Kumar","doi":"10.1080/0954898X.2024.2392772","DOIUrl":"10.1080/0954898X.2024.2392772","url":null,"abstract":"Monitoring Surveillance video is really time-consuming, and the complexity of typical crowd behaviour in crowded situations makes this even more challenging. This has sparked a curiosity about computer vision-based anomaly detection. This study introduces a new crowd anomaly detection method with two main steps: Visual Attention Detection and Anomaly Detection. The Visual Attention Detection phase uses an Enhanced Bilateral Texture-Based Methodology to pinpoint crucial areas in crowded scenes, improving anomaly detection precision. Next, the Anomaly Detection phase employs Optimized Deep Maxout Network to robustly identify unusual behaviours. This network's deep learning capabilities are essential for detecting complex patterns in diverse crowd scenarios. To enhance accuracy, the model is trained using the innovative Battle Royale Coalesced Atom Search Optimization (BRCASO) algorithm, which fine-tunes optimal weights for superior performance, ensuring heightened detection accuracy and reliability. Lastly, using various performance metrics, the suggested work's effectiveness will be contrasted with that of the other traditional approaches. The proposed crowd anomaly detection is implemented in Python. On observing the result showed that the suggested model attains a detection accuracy of 97.28% at a learning rate of 90%, which is much superior than the detection accuracy of other models, including ASO = 90.56%, BMO = 91.39%, BES = 88.63%, BRO = 86.98%, and FFLY = 89.59%.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"148-173"},"PeriodicalIF":1.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation. HCAR-AM 坚果叶网：基于混合卷积的自适应 ResNet，采用注意力机制，通过自适应分割检测坚花叶病。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-02-01 Epub Date: 2024-11-17 DOI: 10.1080/0954898X.2024.2424248

Annamalai Thiruvengadam Madhavi, Kamal Basha Rahimunnisa

{"title":"HCAR-AM ground nut leaf net: Hybrid convolution-based adaptive ResNet with attention mechanism for detecting ground nut leaf diseases with adaptive segmentation.","authors":"Annamalai Thiruvengadam Madhavi, Kamal Basha Rahimunnisa","doi":"10.1080/0954898X.2024.2424248","DOIUrl":"10.1080/0954898X.2024.2424248","url":null,"abstract":"Estimating the optimal answer is expensive for huge data resources that decrease the functionality of the system. To solve these issues, the latest groundnut leaf disorder identification model by deep learning techniques is implemented. The images are collected from traditional databases, and then they are given to the pre-processing stage. Then, relevant features are drawn out from the preprocessed images in two stages. In the first stage, the preprocessed image is segmented using adaptive TransResunet++, where the variables are tuned with the help of designed Hybrid Position of Beluga Whale and Cuttle Fish (HP-BWCF) and finally get the feature set 1 using Kaze Feature Points and Binary Descriptors. In the second stage, the same Kaze feature points and the binary descriptors are extracted from the preprocessed image separately, and then obtain feature set 2. Then, the extracted feature sets 1 and 2 are concatenated and given to the Hybrid Convolution-based Adaptive Resnet with Attention Mechanism (HCAR-AM) to detect the ground nut leaf diseases very effectively. The parameters from this HCAR-AM are tuned via the same HP-BWCF. The experimental outcome is analysed over various recently developed ground nut leaf disease detection approaches in accordance with various performance measures.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"38-78"},"PeriodicalIF":1.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing. 云计算任务调度的深度学习和优化多目标。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-02-01 Epub Date: 2024-08-20 DOI: 10.1080/0954898X.2024.2391395

Dinesh Komarasamy, Siva Malar Ramaganthan, Dharani Molapalayam Kandaswamy, Gokuldhev Mony

{"title":"Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.","authors":"Dinesh Komarasamy, Siva Malar Ramaganthan, Dharani Molapalayam Kandaswamy, Gokuldhev Mony","doi":"10.1080/0954898X.2024.2391395","DOIUrl":"10.1080/0954898X.2024.2391395","url":null,"abstract":"In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine's (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"79-108"},"PeriodicalIF":1.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net. 使用基于改进型 U-Net 的快速卷积神经网络诊断彩色眼底图像中的出血。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-02-01 Epub Date: 2024-02-12 DOI: 10.1080/0954898X.2024.2310687

Rathinavelu Sathiyaseelan, Krishnamoorthy Ranganathan, Ramesh Ramamoorthy, M Pedda Chennaiah

{"title":"Haemorrhage diagnosis in colour fundus images using a fast-convolutional neural network based on a modified U-Net.","authors":"Rathinavelu Sathiyaseelan, Krishnamoorthy Ranganathan, Ramesh Ramamoorthy, M Pedda Chennaiah","doi":"10.1080/0954898X.2024.2310687","DOIUrl":"10.1080/0954898X.2024.2310687","url":null,"abstract":"Retinal haemorrhage stands as an early indicator of diabetic retinopathy, necessitating accurate detection for timely diagnosis. Addressing this need, this study proposes an enhanced machine-based diagnostic test for diabetic retinopathy through an updated UNet framework, adept at scrutinizing fundus images for signs of retinal haemorrhages. The customized UNet underwent GPU training using the IDRiD database, validated against the publicly available DIARETDB1 and IDRiD datasets. Emphasizing the complexity of segmentation, the study employed preprocessing techniques, augmenting image quality and data integrity. Subsequently, the trained neural network showcased a remarkable performance boost, accurately identifying haemorrhage regions with 80% sensitivity, 99.6% specificity, and 98.6% accuracy. The experimental findings solidify the network's reliability, showcasing potential to alleviate ophthalmologists' workload significantly. Notably, achieving an Intersection over Union (IoU) of 76.61% and a Dice coefficient of 86.51% underscores the system's competence. The study's outcomes signify substantial enhancements in diagnosing critical diabetic retinal conditions, promising profound improvements in diagnostic accuracy and efficiency, thereby marking a significant advancement in automated retinal haemorrhage detection for diabetic retinopathy.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"198-219"},"PeriodicalIF":1.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139725025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review on real time implementation of soft computing techniques in thermal power plant. 火力发电厂软计算技术实时应用综述。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-02-01 Epub Date: 2024-11-27 DOI: 10.1080/0954898X.2024.2429721

Love Kumar Thawait, Mukesh Kumar Singh

{"title":"A review on real time implementation of soft computing techniques in thermal power plant.","authors":"Love Kumar Thawait, Mukesh Kumar Singh","doi":"10.1080/0954898X.2024.2429721","DOIUrl":"10.1080/0954898X.2024.2429721","url":null,"abstract":"Thermal Power Plant is a common power plant that generates power by fuel-burning to produce electricity. Being a significant component of the energy sector, the Thermal Power Plant faces several issues that lead to reduced productivity. Conventional researchers have tried using different mechanisms for improvising the production of Thermal Power Plants in varied dimensions. Due to the diverse dimensions considered by existing works, the present review endeavours to afford a comprehensive summary of these works. To achieve this, the study reviews articles in the range (2019-2023) that are allied with the utility of SC methodologies (encompassing AI-ML (Machine Learning) and DL (Deep Learning) in enhancing the productivity of Thermal Power Plants by various dimensions. The conventional AI-based approaches are comparatively evaluated for effective contribution in improvising Thermal Power Plant production. Following this, a critical assessment encompasses the year-wise distribution and varied dimensions focussed by traditional studies in this area. This would support future researchers in determining the dimensions that have attained limited and high focus based on which appropriate research works can be performed. Finally, future suggestions and research gaps are included to offer new stimulus for further investigation of AI in Thermal Power Plants.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-37"},"PeriodicalIF":1.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142734747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication. 用于 5g v2x 通信的 Q-learning 和模糊逻辑多层多接入边缘聚类。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-02-01 Epub Date: 2024-03-06 DOI: 10.1080/0954898X.2024.2309947

Sangeetha Alagumani, Uma Maheswari Natarajan

{"title":"Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication.","authors":"Sangeetha Alagumani, Uma Maheswari Natarajan","doi":"10.1080/0954898X.2024.2309947","DOIUrl":"10.1080/0954898X.2024.2309947","url":null,"abstract":"The 5th generation (5 G) network is required to meet the growing demand for fast data speeds and the expanding number of customers. Apart from offering higher speeds, 5 G will be employed in other industries such as the Internet of Things, broadcast services, and so on. Energy efficiency, scalability, resiliency, interoperability, and high data rate/low delay are the primary requirements and obstacles of 5 G cellular networks. Due to IEEE 802.11p's constraints, such as limited coverage, inability to handle dense vehicle networks, signal congestion, and connectivity outages, efficient data distribution is a big challenge (MAC contention problem). In this research, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-pedestrian (V2P) services are used to overcome bandwidth constraints in very dense network communications from cellular tool to everything (C-V2X). Clustering is done through multi-layered multi-access edge clustering, which helps reduce vehicle contention. Fuzzy logic and Q-learning and intelligence are used for a multi-hop route selection system. The proposed protocol adjusts the number of cluster-head nodes using a Q-learning algorithm, allowing it to quickly adapt to a range of scenarios with varying bandwidths and vehicle densities.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"174-197"},"PeriodicalIF":1.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140040908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligent based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system.

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-01-30 DOI: 10.1080/0954898X.2025.2453620

Kerlin Sara Wilson, K K Saravanan

{"title":"Artificial intelligent based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system.","authors":"Kerlin Sara Wilson, K K Saravanan","doi":"10.1080/0954898X.2025.2453620","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2453620","url":null,"abstract":"Brain-controlled robotic arm systems are designed to provide a method of communication and control for individuals with limited mobility or communication abilities. These systems can be beneficial for people who have suffered from a spinal cord injury, stroke, or neurological disease that affects their motor abilities. The ability of a person to control a robotic arm to reach and grasp multiple objects using their brain signals. This technology involves the use of an electroencephalogram (EEG) cap that captures the electrical activity in the user's brain, which is then processed by an artificial intelligent to translate it into commands that control the movements of the robotic arm. With this technology, individuals who are unable to move their limbs due to paralysis or other conditions can still perform daily activities such as feeding themselves, drinking from a glass, or grasping objects. In this paper, we propose an artificial intelligent-based control strategy for reach and grasp of multi-objects using brain-controlled robotic arm system. The proposed control strategy consists of threefold process: feature extraction, feature optimization, and control strategy classification. Initially, we design an improved ResNet pre-trained architecture for deep feature extraction from the given EEG signal.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-29"},"PeriodicalIF":1.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance analysis of image retrieval system using deep learning techniques. 基于深度学习技术的图像检索系统性能分析。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-01-20 DOI: 10.1080/0954898X.2025.2451388

Selvalakshmi B, Hemalatha K, Kumarganesh S, Vijayalakshmi P

{"title":"Performance analysis of image retrieval system using deep learning techniques.","authors":"Selvalakshmi B, Hemalatha K, Kumarganesh S, Vijayalakshmi P","doi":"10.1080/0954898X.2025.2451388","DOIUrl":"https://doi.org/10.1080/0954898X.2025.2451388","url":null,"abstract":"The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results. These problems are overcome through the proposed methodologies stated in this work. In this paper, the General Image (GI) and Medical Image (MI) are retrieved using deep learning architecture. The proposed system is designed with feature computation module, Retrieval Convolutional Neural Network (RETCNN) module, and Distance computation algorithm. The distance computation algorithm is used to compute the distances between the query image and the images in the datasets and produces the retrieval results. The average precision and recall for the proposed RETCNN-based CBIRS is 98.98% and 99.15% respectively for GI category, and the average precision and recall for the proposed RETCNN-based CBIRS are 99.04% and 98.89% respectively for MI category. The significance of these experimental results is used to produce the higher image retrieval rate of the proposed system.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-21"},"PeriodicalIF":1.1,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel efficient data storage and data auditing in cloud environment using enhanced child drawing development optimization strategy. 采用增强的儿童绘图开发优化策略，在云环境中实现了一种新的高效数据存储和数据审计。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-01-17 DOI: 10.1080/0954898X.2024.2443622

Aruna Kari Balakrishnan, Arunachalaperumal Chellaperumal, Sudha Lakshmanan, Sureka Vijayakumar

{"title":"A novel efficient data storage and data auditing in cloud environment using enhanced child drawing development optimization strategy.","authors":"Aruna Kari Balakrishnan, Arunachalaperumal Chellaperumal, Sudha Lakshmanan, Sureka Vijayakumar","doi":"10.1080/0954898X.2024.2443622","DOIUrl":"https://doi.org/10.1080/0954898X.2024.2443622","url":null,"abstract":"The optimization on the cloud-based data structures is carried out using Adaptive Level and Skill Rate-based Child Drawing Development Optimization algorithm (ALSR-CDDO). Also, the overall cost required in computing and communicating is reduced by optimally selecting these data structures by the ALSR-CDDO algorithm. The storage of the data in the cloud platform is performed using the Divide and Conquer Table (D&CT). The location table and the information table are generated using the D&CT method. The details, such as the file information, file ID, version number, and user ID, are all present in the information table. Every time data is deleted or updated, and its version number is modified. Whenever an update takes place using D&CT, the location table also gets upgraded. The information regarding the location of a file in the Cloud Service Provider (CSP) is given in the location table. Once the data is stored in the CSP, the auditing of the data is then performed on the stored data. Both dynamic and batch auditing are carried out on the stored data, even if it gets updated dynamically in the CSP. The security offered by the executed scheme is verified by contrasting it with other existing auditing schemes.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-41"},"PeriodicalIF":1.1,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized recommendation system to handle skin cancer at early stage based on hybrid model. 基于混合模型的皮肤癌早期治疗个性化推荐系统。

IF 1.1 3区计算机科学

Network-Computation in Neural Systems Pub Date : 2025-01-15 DOI: 10.1080/0954898X.2024.2449173

Siva Prasad Reddy K V, Meera Selvakumar

{"title":"Personalized recommendation system to handle skin cancer at early stage based on hybrid model.","authors":"Siva Prasad Reddy K V, Meera Selvakumar","doi":"10.1080/0954898X.2024.2449173","DOIUrl":"10.1080/0954898X.2024.2449173","url":null,"abstract":"Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM). Preprocessing, improved deep joint segmentation, feature extraction, and classification are the major steps to identify the stages of skin cancer. The input image is first preprocessed using the Gaussian filtering method. Improved deep joint segmentation is employed to segment the preprocessed image. A set of features including Median Binary Pattern (MBP), Gray Level Co-occurrence Matrix (GLCM), and Improved Local Direction Texture Pattern (ILDTP) are extracted in the next step. Finally, the hybrid classification includes Improved Bi-directional Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) used for the classification process, where the training will be carried out by the Integrated Bald Eagle and Average and Subtraction Optimizer (IBEASO) algorithm via optimizing the weights of the models.","PeriodicalId":54735,"journal":{"name":"Network-Computation in Neural Systems","volume":" ","pages":"1-40"},"PeriodicalIF":1.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0