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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 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-01-30 DOI: 10.1080/0954898X.2025.2453620
Kerlin Sara Wilson, K K Saravanan

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.

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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 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-01-20 DOI: 10.1080/0954898X.2025.2451388
Selvalakshmi B, Hemalatha K, Kumarganesh S, Vijayalakshmi P

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.

图像检索是在网络上以最小的搜索时间检索到所查询图像的相关图像的过程。传统的基于内容的图像检索(CBIR)系统的问题是,它们只能对彩色图像或灰度图像产生检索结果。此外,CBIR系统比较复杂,要产生有意义的检索结果需要耗费更多的时间。这些问题是通过在这项工作中提出的方法来克服的。本文采用深度学习架构对通用图像(GI)和医学图像(MI)进行检索。该系统由特征计算模块、检索卷积神经网络(RETCNN)模块和距离计算算法组成。距离计算算法用于计算查询图像与数据集中图像之间的距离,并产生检索结果。基于retcnn的CBIRS在GI分类上的平均准确率和召回率分别为98.98%和99.15%,在MI分类上的平均准确率和召回率分别为99.04%和98.89%。利用这些实验结果的显著性,使所提出的系统具有较高的图像检索率。
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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 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-01-17 DOI: 10.1080/0954898X.2024.2443622
Aruna Kari Balakrishnan, Arunachalaperumal Chellaperumal, Sudha Lakshmanan, Sureka Vijayakumar

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.

采用基于自适应水平和技能率的儿童绘画发展优化算法(ALSR-CDDO)对基于云的数据结构进行优化。此外,通过ALSR-CDDO算法对这些数据结构进行优化选择,降低了计算和通信所需的总成本。使用分治表(D&CT)在云平台中存储数据。位置表和信息表采用D&CT法生成。详细信息,如文件信息、文件ID、版本号和用户ID,都显示在信息表中。每次删除或更新数据时,都会修改其版本号。每当使用D&CT进行更新时,位置表也会得到升级。有关文件在云服务提供商(CSP)中的位置的信息在位置表中给出。一旦数据存储在CSP中,就会对存储的数据执行数据审计。对存储的数据执行动态和批处理审计,即使它在CSP中得到动态更新。通过将所执行的方案与其他现有的审计方案进行对比,验证其提供的安全性。
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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 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-01-15 DOI: 10.1080/0954898X.2024.2449173
Siva Prasad Reddy K V, Meera Selvakumar

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.

皮肤癌是最普遍和最有害的癌症之一,早期发现对于成功的治疗结果至关重要。然而,目前的皮肤癌检测方法往往存在局限性,例如依赖于临床医生的人工检查,诊断准确性不一致,以及缺乏基于患者特定数据的个性化建议。在我们的工作中,我们提出了一种基于混合模型(PRSSCHM)的早期皮肤癌个性化推荐系统。预处理、改进的深度关节分割、特征提取和分类是识别皮肤癌分期的主要步骤。首先使用高斯滤波方法对输入图像进行预处理。采用改进的深关节分割方法对预处理后的图像进行分割。下一步提取中值二值模式(MBP)、灰度共生矩阵(GLCM)和改进局部方向纹理模式(ILDTP)等特征。最后,混合分类包括用于分类过程的改进双向长短期记忆(Bi-LSTM)和深度信念网络(DBN),其中通过优化模型的权值,由综合秃鹰和平均减法优化器(IBEASO)算法进行训练。
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引用次数: 0
Robust text-dependent speaker verification system using gender aware Siamese-Triplet Deep Neural Network. 基于性别感知暹罗-三重深度神经网络的鲁棒文本依赖说话人验证系统。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-29 DOI: 10.1080/0954898X.2024.2438128
Sanghamitra V Arora

Speaker verification in text-dependent scenarios is critical for high-security applications but faces challenges such as voice quality variations, linguistic diversity, and gender-related pitch differences, which affect authentication accuracy. This paper introduces a Gender-Aware Siamese-Triplet Network-Deep Neural Network (ST-DNN) architecture to address these challenges. The Gender-Aware Network utilizes Convolutional 2D layers with ReLU activation for initial feature extraction, followed by multi-fusion dense skip connections and batch normalization to integrate features across different depths, enhancing discrimination between male and female speakers. A bottleneck layer compresses feature maps to capture gender-related characteristics effectively. For enhanced speaker verification, separate male and female ST-DNN models are used, each incorporating Individual, Siamese, and Triplet Networks. The Individual Network extracts unique utterance characteristics, the Siamese Network compares speech sample pairs for speaker identity, and the Triplet Network ensures closely grouped embeddings of samples from the same speaker, facilitating precise verification. Experimental results on RSR2015 and RedDots Challenge 2016 datasets demonstrate significant improvements, with reductions in Equal Error Rate (EER) ranging from 32.31% to 54.55% for males and 33.73% to 38.98% for females, and reductions in MinDCF from 53.47% to 86.36% and 39.46% to 71.19%, respectively, validating the efficacy of the ST-DNN in real-world applications.

文本依赖场景中的说话人验证对于高安全性应用至关重要,但面临语音质量变化、语言多样性和性别相关的音高差异等挑战,这些都会影响身份验证的准确性。本文介绍了一种性别感知连体-三重网络-深度神经网络(ST-DNN)架构来解决这些挑战。性别感知网络利用具有ReLU激活的卷积二维层进行初始特征提取,然后通过多融合密集跳跃连接和批处理归一化来整合不同深度的特征,增强对男性和女性说话者的区分。瓶颈层压缩特征映射以有效捕获与性别相关的特征。为了增强说话者验证,使用单独的男性和女性ST-DNN模型,每个模型都包含个人,连体和三重网络。个体网络提取独特的话语特征,连体网络比较语音样本对以确定说话者身份,而三重网络确保来自同一说话者的样本紧密分组嵌入,从而促进精确验证。在RSR2015和RedDots Challenge 2016数据集上的实验结果显示了显著的改进,男性的等效错误率(EER)从32.31%降低到54.55%,女性从33.73%降低到38.98%,MinDCF从53.47%降低到86.36%,从39.46%降低到71.19%,验证了ST-DNN在实际应用中的有效性。
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引用次数: 0
Investigation on the reliability calculation method of gravity dam based on CNN-LSTM and Monte Carlo method. 基于 CNN-LSTM 和 Monte Carlo 方法的重力坝可靠性计算方法研究。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-29 DOI: 10.1080/0954898X.2024.2447281
Ming-Wei Li, Jun-Qi Ren, Jing Geng, Hsin-Pou Huang, Wei-Chiang Hong

To improve the calculation accuracy of the Monte Carlo (MC) method and reduce the calculation time. Firstly, CNN and LSTM deep learning networks are introduced for designing nonlinear dynamic systems simulating dam stress. Then, spatial feature mining and sequence information extraction of nonlinear data of dam stress are carried out respectively, and a combined prediction model of dam stress depth (DS-FEM-CNN-LSTM) is proposed. Secondly, to solve the problem of a long time and heavy workload for the MC method to calculate a single sample point, the DOE test method is used to design the sample points. The weight factor and the distance to the failure surface are used as screening criteria. The reliability calculation method of the gravity dam (DS-FEM-CNN-LSTM-MC) is established. Finally, numerical results show that the proposed DS-FEM-CNN-LSTM-MC method performs better than the existing methods in terms of computational time consumption and accuracy.

为了提高蒙特卡罗(MC)方法的计算精度,减少计算时间。首先,引入CNN和LSTM深度学习网络,设计模拟大坝应力的非线性动力系统。然后,分别对坝体应力非线性数据进行空间特征挖掘和序列信息提取,提出了一种坝体应力深度组合预测模型(DS-FEM-CNN-LSTM)。其次,针对MC方法计算单个样本点耗时长、工作量大的问题,采用DOE测试方法对样本点进行设计。权重因子和到失效面的距离作为筛选标准。建立了重力坝的可靠度计算方法(DS-FEM-CNN-LSTM-MC)。最后,数值结果表明,所提出的DS-FEM-CNN-LSTM-MC方法在计算时间和精度方面都优于现有方法。
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引用次数: 0
Human activity recognition utilizing optimized attention induced Multihead Convolutional Neural Network with Mobile Net V1 from Mobile health data. 利用优化的注意诱导多头卷积神经网络和移动网络V1从移动健康数据中识别人类活动。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-17 DOI: 10.1080/0954898X.2024.2438967
R Anandha Praba, L Suganthi

Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance in intelligent home environments. In this manuscript, Human Activity Recognition utilizing optimized Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 from Mobile Health Data (HAR-AMCNN-MNV1) is proposed. The input data is collected through MHEALTH and UCI HAR datasets. Neural Spectrospatial Filtering (NSF) is used for avoiding accurate labelling and reduces errors. Afterwards, Variational Density Peak Clustering Algorithm (VDPCA) is used for segmenting the data. Feature Extraction and Classification is done by Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 (AMCNN-MNV1). AMCNN is used for extracting Hand-crafted features. AMCNN-MNV1 effectively classifies the human activities as Sitting and relaxing (Sit), Climbing stairs (CS), Walking (Walk), Standing still (Std), Waist bends forward (WBF), Frontal elevation of arms (FEA), Jogging (Jog), Knees bending (crouching) (KB), Cycling (Cycl), Lying down (Lay), Jump front & back (JFB) and Running (Run). Siberian Tiger Optimization Algorithm (STOA) is proposed to optimize the weight parameter of AMCNN-MNV1 classifier. The proposed method attains 21.19%, 23.45%, and 21.76% higher accuracy, 31.15%, 24.65% and 22.72% higher precision; 21.15%, 20.18%, and 21.28% higher recall evaluated to the existing methods.

人类活动识别(HAR)系统旨在持续监控人类行为,主要应用于智能家居环境中的娱乐和监控领域。本手稿提出了利用优化的注意力诱导多头卷积神经网络和移动网络 V1 从移动健康数据中进行人类活动识别(HAR-AMCNN-MNV1)。输入数据通过 MHEALTH 和 UCI HAR 数据集收集。神经频谱空间过滤(NSF)用于避免准确标记和减少误差。然后,使用变异密度峰聚类算法(VDPCA)对数据进行分割。特征提取和分类由带有移动网络 V1 的注意力诱导多头卷积神经网络(AMCNN-MNV1)完成。AMCNN 用于提取手工制作的特征。AMCNN-MNV1 能有效地将人类活动分类为:坐着休息 (Sit)、爬楼梯 (CS)、走路 (Walk)、站立不动 (Std)、腰部前屈 (WBF)、双臂前举 (FEA)、慢跑 (Jog)、膝盖弯曲(蹲下) (KB)、骑自行车 (Cycl)、躺下 (Lay)、前后跳跃 (JFB) 和跑步 (Run)。提出了西伯利亚虎优化算法(STOA)来优化 AMCNN-MNV1 分类器的权重参数。与现有方法相比,拟议方法的准确率分别提高了 21.19%、23.45% 和 21.76%,精确率分别提高了 31.15%、24.65% 和 22.72%,召回率分别提高了 21.15%、20.18% 和 21.28%。
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引用次数: 0
Multiagent DDOS attack detection model: Optimal trained hybrid classifier and entropy-based mitigation process. 多代理DDOS攻击检测模型:最优训练混合分类器和基于熵的缓解过程。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-17 DOI: 10.1080/0954898X.2024.2412674
Thiruselvan Palusamy, Balasubramanian Chelliah

This study proposes a novel multi-agent system designed to detect Distributed Denial of Service (DDoS) attacks, addressing the increasing need for robust cybersecurity measures. The hypothesis posits that a structured multi-agent approach can enhance detection accuracy and response efficiency in DDoS attack scenarios. The methodology involves a five-stage detection model: (1) Preprocessing using a modified double sigmoid normalization technique to eliminate duplicate data; (2) Feature Extraction where raw data and improved correlation-based features, mutual information, and statistical features are identified; (3) Dimensionality Reduction conducted by a reducer agent to streamline the feature set; (4) Classification utilizing Deep Belief Networks (DBN), Bi-LSTM, and Deep Maxout models, with their weights optimally tuned using the hybrid optimization algorithm, WUJSO; and (5) Decision Making by the decision agent to ascertain the presence of attacks, followed by mitigation through modified entropy-based techniques. The results demonstrate that the proposed method achieves a detection accuracy of 0.953 at a learning rate of 90%, significantly outperforming other methods, including Bi-GRU (0.857), DEEP-MAXOUT (0.910), Bi-LSTM (0.865), RNN (0.814), NN (0.894), and DBN (0.761). This research underscores the effectiveness of the multi-agent approach in enhancing DDoS attack detection and mitigation.

本研究提出了一种新型多代理系统,旨在检测分布式拒绝服务(DDoS)攻击,满足对稳健网络安全措施日益增长的需求。假设认为,结构化的多代理方法可以提高 DDoS 攻击场景中的检测准确性和响应效率。该方法包括一个五阶段检测模型:(1) 使用改进的双sigmoid归一化技术进行预处理,以消除重复数据;(2) 特征提取,确定原始数据和改进的基于相关性的特征、互信息和统计特征;(3) 由降维代理进行降维,以精简特征集;(4) 利用深度信念网络 (DBN)、Bi-LSTM 和深度 Maxout 模型进行分类,并使用混合优化算法 WUJSO 对其权重进行优化调整;以及 (5) 由决策代理做出决策,以确定是否存在攻击,然后通过修改后的基于熵的技术进行缓解。结果表明,在学习率为 90% 的情况下,所提出的方法达到了 0.953 的检测准确率,明显优于其他方法,包括 Bi-GRU (0.857)、DEEP-MAXOUT (0.910)、Bi-LSTM (0.865)、RNN (0.814)、NNN (0.894) 和 DBN (0.761)。这项研究强调了多代理方法在增强 DDoS 攻击检测和缓解方面的有效性。
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引用次数: 0
Design of a neural transformer for Spanish to Mexican Sign Language automatic translation/interpretation. 西班牙语到墨西哥语手语自动翻译/口译的神经转换器设计。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-11 DOI: 10.1080/0954898X.2024.2435495
Diana Vania Lara-Ortiz, Rita Q Fuentes Aguilar, Isaac Chairez

This paper uses a multi-head neural transformer to present the text-to-text translation/interpretation of Sign Language (SL) in the context of glosses (written SL). A Spanish to Mexican Sign Language (MSL) gloss dataset was built based on simple and compound sentences and the corresponding interpretation in MSL gloss. The interpretation process was achieved by implementing state-of-the-art tools in the natural language processing (NLP) field called neural transformers. We tried different architectures, varying the number of encoder-decoder layers and hyperparameters. The best of our models achieved 0.68 BLEU in the training phase and 0.33 in the validation phase. MSL glosses are crucial as they rule the grammatical order in which MSL has to be executed. All these quantitative and qualitative results confirm the potential applicability of neural transformers to create effective automatic translators for the Spanish language to MSL, with similar effectiveness shown by other automatic translators for other more likely languages.

本文使用一个多头神经转换器来呈现手语在文字背景下的文本到文本翻译/解释。在简单句和复合句的基础上建立了西班牙语到墨西哥语(MSL)注释数据集,并对MSL注释进行了相应的解释。解释过程是通过在自然语言处理(NLP)领域实施最先进的工具来实现的,称为神经转换器。我们尝试了不同的架构,改变了编码器-解码器层和超参数的数量。我们最好的模型在训练阶段达到0.68 BLEU,在验证阶段达到0.33。MSL注释是至关重要的,因为它们决定了MSL必须执行的语法顺序。所有这些定量和定性的结果都证实了神经转换器在创建有效的西班牙语到MSL的自动翻译方面的潜在适用性,其他更可能的语言的自动翻译也显示出类似的有效性。
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引用次数: 0
ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images. ViTBayesianNet:一种自适应深度贝叶斯网络辅助的阿尔茨海默病检测框架,基于视觉变换的残差密度网,用于MRI图像的特征提取。
IF 1.1 3区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-12-11 DOI: 10.1080/0954898X.2024.2435491
Revathi Mohan, Rajesh Arunachalam, Neha Verma, Shital Mali

One of the most familiar types of disease is Alzheimer's disease (AD) and it mainly impacts people over the age limit of 60. AD causes irreversible brain damage in humans. It is difficult to recognize the various stages of AD, hence advanced deep learning methods are suggested for recognizing AD in its initial stages. In this experiment, an effective deep model-based AD detection approach is introduced to provide effective treatment to the patient. Initially, an essential MRI is collected from the benchmark resources. After that, the gathered MRIs are provided as input to the feature extraction phase. Also, the important features in the input image are extracted by Vision Transformer-based Residual DenseNet (ViT-ResDenseNet). Later, the retrieved features are applied to the Alzheimer's detection stage. In this phase, AD is detected using an Adaptive Deep Bayesian Network (Ada-DBN). Additionally, the attributes of Ada-DBN are optimized with the help of Enhanced Golf Optimization Algorithm (EGOA). So, the implemented Alzheimer's detection model accomplishes relatively higher reliability than existing techniques. The numerical results of the suggested framework obtained an accuracy value of 96.35 which is greater than the 91.08, 91.95, and 93.95 attained by the EfficientNet-B2, TF- CNN, and ViT-GRU, respectively.

最常见的疾病之一是阿尔茨海默病(AD),它主要影响60岁以上的人群。阿尔茨海默病会对人类的大脑造成不可逆转的损伤。AD的各个阶段很难识别,因此建议采用先进的深度学习方法在AD的初始阶段进行识别。本实验引入了一种有效的基于深度模型的AD检测方法,为患者提供有效的治疗。最初,从基准资源中收集必要的MRI。之后,将收集到的mri作为特征提取阶段的输入。利用基于视觉变换的残差密度网(viti - resdensenet)提取输入图像中的重要特征。然后,将检索到的特征应用到阿尔茨海默病的检测阶段。在此阶段,使用自适应深度贝叶斯网络(Ada-DBN)检测AD。此外,利用增强高尔夫优化算法(Enhanced Golf Optimization Algorithm, EGOA)对Ada-DBN的属性进行了优化。因此,所实现的阿尔茨海默病检测模型比现有技术具有较高的可靠性。数值结果表明,该框架的准确率为96.35,高于EfficientNet-B2、TF- CNN和viti - gru的准确率91.08、91.95和93.95。
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引用次数: 0
期刊
Network-Computation in Neural Systems
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