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Hybrid deep model for brain age prediction in MRI with improved chi-square based selected features 基于改进卡方选择特征的MRI脑年龄预测混合深度模型
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-06-22 DOI: 10.3233/web-230060
Vishnupriya G.S, S. Rajakumari
Ageing and its related health conditions bring many challenges not only to individuals but also to society. Various MRI techniques are defined for the early detection of age-related diseases. Researchers continue the prediction with the involvement of different strategies. In that manner, this research intends to propose a new brain age prediction model under the processing of certain steps like preprocessing, feature extraction, feature selection, and prediction. The initial step is preprocessing, where improved median filtering is proposed to reduce the noise in the image. After this, feature extraction takes place, where shape-based features, statistical features, and texture features are extracted. Particularly, Improved LGTrP features are extracted. However, the curse of dimensionality becomes a serious issue in this aspect that shrinks the efficiency of the prediction level. According to the “curse of dimensionality,” the number of samples required to estimate any function accurately increases exponentially as the number of input variables increases. Hence, a feature selection model with improvement has been introduced in this paper termed an improved Chi-square. Finally, for prediction purposes, a Hybrid classifier is introduced by combining the models like Bi-GRU and DBN, respectively. In order to enhance the effectiveness of the hybrid method, Upgraded Blue Monkey Optimization with Improvised Evaluation (UBMOIE) is introduced as the training system by tuning the optimal weights in both classifiers. Finally, the performance of the suggested UBMIOE-based brain age prediction method was assessed over the other schemes to various metrics.
老龄化及其相关的健康状况不仅给个人也给社会带来了许多挑战。各种MRI技术被定义为年龄相关疾病的早期检测。研究人员继续使用不同的策略进行预测。因此,本研究拟提出一种经过预处理、特征提取、特征选择、预测等步骤处理的新的脑年龄预测模型。第一步是预处理,提出改进的中值滤波来降低图像中的噪声。在此之后,进行特征提取,提取基于形状的特征、统计特征和纹理特征。特别地,提取了改进的LGTrP特征。然而,在这方面,维数的诅咒成为一个严重的问题,降低了预测水平的效率。根据“维数诅咒”,准确估计任何函数所需的样本数量随着输入变量数量的增加呈指数增长。因此,本文提出了一种改进的特征选择模型,称为改进的卡方模型。最后,结合Bi-GRU和DBN模型,引入混合分类器进行预测。为了提高混合方法的有效性,通过对两个分类器的最优权值进行调整,引入了带临时评估的升级蓝猴优化(UBMOIE)作为训练系统。最后,对基于ubmioe的脑年龄预测方法的性能与其他方案进行了各种指标的评估。
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引用次数: 0
Internet of Things assisted Unmanned Aerial Vehicle for Pest Detection with Optimized Deep Learning Model 物联网辅助无人机害虫检测优化深度学习模型
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-06-22 DOI: 10.3233/web-230062
Vijayalakshmi G, Radhika Y
IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.
生态监测领域经常使用物联网技术和无人机。农业技术中的无人机和物联网可以相应地从地面的微观和宏观方面评估作物病虫害的发生情况。无人机可以使用光谱相机系统捕捉农场的图像,这些图像被用来检查农业害虫和疾病的存在。在本研究中,提出了一种基于基于算法交叉的黑寡妇优化卷积神经网络(ACBWO-CNN)的新型物联网辅助无人机害虫检测方法。采用云计算机制,利用无人机对作物生产过程中的害虫进行监测和发现。这种方法的需要是提供数据中心,因此除了处理若干图像外,还有必要的内存存储量。最初,无人机收集的输入图像被假定为通过物联网云服务器进行处理,从该服务器进行害虫识别。害虫检测单元的设计将分为三个主要阶段:(a)背景和前景分割,(b)特征提取和(c)分类。在前景和背景分割阶段,将利用k均值聚类对害虫图像进行分割。从分割后的图像中提取局部二值模式(LBP)和改进的局部向量模式(LVP)特征。利用这些特征,在分类阶段训练优化后的CNN分类器,用于农作物害虫的识别。由于最终的检测结果来自卷积神经网络(CNN);其权重通过ACBWO方法进行微调。因此,优化后的CNN输出将描绘出现场识别的害虫类型。并在几个指标上与现有方法进行了性能比较。
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引用次数: 0
Firefly-Aquila optimized Deep Q network for handoff management in context aware video streaming-based heterogeneous wireless networks 萤火虫- aquila优化了基于上下文感知视频流的异构无线网络中的Deep Q网络切换管理
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-06-06 DOI: 10.3233/web-220090
Uttam P. Waghmode, U. Kolekar
Handoff management is the method in which the mobile node maintains its connection active when it shifts from location to other. The devastating success of mobile devices as well as wireless communications is emphasizing the requirement for the expansion of mobility-aware facilities. Moreover, the mobility of devices requires services adapting their behavior to abrupt context variations and being conscious of handoffs, which make an intermittent discontinuities and unpredictable delays. Thus, the heterogeneity of wireless network devices confuses the situation, since a dissimilar treatment of handoffs and context-awareness is essential for every solution. Hence, this paper introduced the Deep Q network-based Firefly Aquila Optimizer (DQN-FAO) for performing the handoff management. In order to establish the handoff management, the process of selecting network is very important. Here, the network is selected based on the devised FAO algorithm, which is the consolidation of Aquila Optimizer (AO) and Firefly algorithm (FA) that considers the metrics, such as Jitter, Handoff latency, and Received Signal Strength Indicator (RSSI) as fitness function. Moreover, the handover decision is taken by the DQN, where the hyper-parameters are tuned by the devised FAO algorithm. According to the hand over decision taken, the context aware video streaming is happened by adjusting the bit rate of the videos using network bandwidth. Besides, the devised scheme attained the superior performance based on the call drop, energy consumption, handover delay, throughput, handoff latency, and PSNR of 0.5122, 7.086 J, 10.54 ms, 13.17 Mbps, 93.80 ms and 46.89 dB.
切换管理是移动节点在从一个位置转移到另一个位置时保持其连接活动的方法。移动设备和无线通信的巨大成功强调了扩展移动感知设施的需求。此外,设备的移动性要求服务调整其行为以适应突然的上下文变化,并意识到切换,这会导致间歇性的不连续和不可预测的延迟。因此,无线网络设备的异构性混淆了这种情况,因为对切换和上下文感知的不同处理对于每个解决方案都是必不可少的。因此,本文引入了基于Deep Q网络的萤火虫Aquila优化器(DQN-FAO)来执行切换管理。为了建立交接管理,网络的选择过程是非常重要的。在这里,网络的选择是基于设计的FAO算法,该算法是Aquila Optimizer (AO)和Firefly算法(FA)的整合,该算法将抖动、切换延迟和接收信号强度指标(RSSI)等指标作为适应度函数。此外,切换决策由DQN做出,其中超参数由设计的FAO算法进行调整。根据所做出的移交决策,利用网络带宽调整视频的比特率,实现上下文感知的视频流。此外,该方案在通话掉线、能耗、切换延迟、吞吐量、切换延迟、PSNR分别为0.5122、7.086 J、10.54 ms、13.17 Mbps、93.80 ms和46.89 dB等方面均取得了较好的性能。
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引用次数: 0
Identification of micro expressions in a video sequence by Euclidean distance of the facial contours 利用面部轮廓的欧几里得距离识别视频序列中的微表情
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-06-05 DOI: 10.3233/web-220010
S. Kherchaoui, A. Houacine
This paper presents an automatic facial micro-expression recognition system (FMER) from video sequence. Identification and classification are performed on basic expressions: happy, surprise, fear, disgust, sadness, anger, and neutral states. The system integrates three main steps. The first step consists in face detection and tracking over three consecutive frames. In the second step, the facial contour extraction is performed on each frame to build Euclidean distance maps. The last task corresponds to the classification which is achieved with two methods; the SVM and using convolutional neural networks. Experimental evaluation of the proposed system for facial micro-expression identification is performed on the well-known databases (Chon and Kanade and CASME II), with six and seven facial expressions for each classification method.
提出了一种基于视频序列的面部微表情自动识别系统(FMER)。对基本表情进行识别和分类:快乐、惊讶、恐惧、厌恶、悲伤、愤怒和中性状态。该系统集成了三个主要步骤。第一步是对连续三帧的人脸进行检测和跟踪。第二步,对每一帧进行人脸轮廓提取,构建欧几里得距离图。最后一个任务对应的分类是用两种方法实现的;支持向量机和卷积神经网络。在知名数据库(Chon and Kanade和CASME II)上对所提出的面部微表情识别系统进行了实验评估,每种分类方法分别有6种和7种面部表情。
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引用次数: 0
A secure authentication protocol for healthcare service in IoT with Q-net based secret key generation 基于Q-net密钥生成的物联网医疗保健服务安全认证协议
Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-29 DOI: 10.3233/web-220104
Rupali Mahajan, Smita Chavan, Deepika Amol Ajalkar, Balshetwar SV, Prajakta Ajay Khadkikar
The major intention of this research is to propose a secure authentication protocol for healthcare services in IoT based on a developed Q-Net-based secret key. Nine phases are included in the model. The sensor node, IoT device center, gateway node, and medical professional are the four entities involved in the key generation process. The designed model derived a mathematical model, which utilized hashing function, XOR, Chebyshev polynomial, passwords, encryption algorithm, secret keys, and other security operations for performing effective authentication. Here, the secret key is generated with the Deep Q-Net-based sub-key generation approach. The proposed method achieved the minimum computation time of 169xe9 ns, minimum memory usage is 71.38, and the obtained maximum detection rate is 0.957 for 64 key lengths. The secure authentication using the proposed method is accurate and improves the effectiveness of the system’s security.
本研究的主要目的是提出一种基于开发的q - net秘钥的物联网医疗保健服务安全认证协议。该模型包括九个阶段。传感器节点、物联网设备中心、网关节点和医疗专业人员是密钥生成过程中涉及的四个实体。所设计的模型推导了一个数学模型,利用哈希函数、异或、Chebyshev多项式、密码、加密算法、密钥等安全操作进行有效的身份验证。在这里,密钥是使用基于Deep q - net的子密钥生成方法生成的。该方法在64个密钥长度下的最小计算时间为169xe9 ns,最小内存占用为71.38,最大检测率为0.957。采用该方法进行的安全认证是准确的,提高了系统安全的有效性。
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引用次数: 0
Thinking space generation using context-enhanced knowledge fusion for systematic brain computing 基于上下文增强知识融合的思维空间生成
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-26 DOI: 10.3233/web-220089
Hongzhi Kuai, Xiao‐Rong Tao, Ning Zhong
The convergence of systems neuroscience and open science arouses great interest in the current brain big data era, highlighting the thinking capability of intelligent agents in handling multi-source knowledge, information and data across various levels of granularity. To realize such thinking-inspired brain computing during a brain investigation process, one of the major challenges is to find a holistic brain map that can model multi-dimensional variables of brain investigations across brain functions, experimental tasks, brain data and analytical methods synthetically. In this paper, we propose a context-enhanced graph learning method to fuse open knowledge from different sources, including: contextual information enrichment, structural knowledge fusion, and holistic graph learning. Such a method can enhance contextual learning of abstract concepts and relational learning between two concepts that have large gap from different dimensions. As a result, an extensible space, namely Thinking Space, is generated to represent holistic variables and their relations in a map, which currently contributes to the field of brain research for systematic brain computing. In the future, the Thinking Space coupled with the rapid development and spread of artificial intelligence generated content will be developed in more scenarios so as to promote global interactions of intelligence in the connected world.
在当前的大脑大数据时代,系统神经科学与开放科学的融合引起了人们的极大兴趣,突出了智能体处理多源知识、信息和数据的跨粒度思维能力。为了在脑研究过程中实现这种思维启发的脑计算,主要挑战之一是找到一个整体的脑图,可以综合模拟脑功能、实验任务、脑数据和分析方法等脑研究的多维变量。在本文中,我们提出了一种上下文增强的图学习方法来融合不同来源的开放知识,包括:上下文信息富集、结构知识融合和整体图学习。这种方法可以增强抽象概念的语境学习和两个概念之间在不同维度上存在较大差距的关系学习。因此,生成了一个可扩展的空间,即思维空间,用于在地图中表示整体变量及其关系,目前有助于大脑研究领域的系统脑计算。未来,随着人工智能生成内容的快速发展和传播,思维空间将在更多场景中得到发展,从而促进智能在互联世界中的全球互动。
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引用次数: 0
FLICM clustering with matrix factorization based course recommendation in an E-learning platform 基于矩阵分解聚类的网络学习平台课程推荐
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-25 DOI: 10.3233/web-220121
A. Madhavi, A. Nagesh, A. Govardhan
Technology-enabled learning has progressively grown for research areas with wide application of information and communication technologies for numerous standard-compliant Learning and Open Educational Resources. This provides formidable support to users for the selection of courses when they want to develop the course with available learning materials. But selecting a course via searching learning objects is an inherently complex operation having various repositories. In an E-learning Platform, many complexities arise due to various software tools and specification formats that hinder the success of the course. In this paper, many obstacles in the E-learning platform are eradicated by utilizing Fuzzy Local Information C-Means (FLICM) clustering with matrix factorization for the selection of courses. The dataset utilized in this work is E-Khool review data, from which an agglomerative matrix is generated. Here, the agglomerative matrix consists of the learner series matrix and course series matrix along with their binary matrix. After this process, course grouping is carried out by FLICM clustering with matrix factorization. Moreover, group course bilevel matching, followed by relevant learner retrieval and group user is done by Minkowski and Chebyshev distance. From this learner’s preferred course is retrieved and then a recommendation using matrix factorization is carried out. Finally, the course is recommended for the user based on maximum rating. Furthermore, the performance of developed FLICM_matrix factorization is achieved by performance metrics, like precision, recall, and f-measure with values 0.915, 0.850, and 0.882, accordingly.
随着信息和通信技术在众多符合标准的学习和开放教育资源中的广泛应用,技术支持的学习在研究领域逐步发展。当用户希望利用现有的学习材料开发课程时,这为他们选择课程提供了强大的支持。但是,通过搜索学习对象来选择课程本身就是一个复杂的操作,有各种各样的存储库。在电子学习平台中,由于各种软件工具和规范格式阻碍了课程的成功,因此出现了许多复杂性。本文利用模糊局部信息c均值(FLICM)聚类与矩阵分解相结合的方法进行课程选择,消除了电子学习平台中存在的诸多障碍。本工作中使用的数据集是e - kool评论数据,从中生成凝聚矩阵。在这里,集合矩阵由学习者级数矩阵和课程级数矩阵以及它们的二值矩阵组成。在此过程之后,通过矩阵分解的FLICM聚类对课程进行分组。通过Minkowski和Chebyshev距离进行小组课程双层匹配,然后进行相关学习者检索和小组用户。从学习者的首选课程中检索,然后使用矩阵分解进行推荐。最后,根据最大评分为用户推荐课程。此外,所开发的FLICM_matrix分解的性能通过精度、召回率和f-measure(分别为0.915、0.850和0.882)等性能指标来实现。
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引用次数: 0
Image compression based on vector quantization and optimized code-book design using Genetic Mating Influenced Slime Mould (GMISM) algorithm 基于矢量量化的图像压缩和基于遗传交配影响黏菌算法的优化码本设计
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-25 DOI: 10.3233/web-220050
Pratibha Chavan, B. Rani, M. Murugan, P. Chavan
Large amounts of storage are required to store the recent massive influx of fresh photographs that are uploaded to the internet. Many analysts created expert image compression techniques during the preceding decades to increase compression rates and visual quality. In this research work, a unique image compression technique is established for Vector Quantization (VQ) with the K-means Linde–Buzo–Gary (KLBG) model. As a contribution, the codebooks are optimized with the aid of hybrid optimization algorithm. The projected KLBG model included three major phases: an encoder for image compression, a channel for transitions of the compressed image, and a decoder for image reconstruction. In the encoder section, the image vector creation, optimal codebook generation, and indexing mechanism are carried out. The input image enters the encoder stage, wherein it’s split into immediate and non-overlapping blocks. The proposed GMISM model hybridizes the concepts of the Genetic Algorithm (GA) and Slime Mould Optimization (SMO), respectively. Once, the optimal codebook is generated successfully, the indexing of the every vector with index number from index table takes place. These index numbers are sent through the channel to the receiver. The index table, optimal codebook and reconstructed picture are all included in the decoder portion. The received index table decodes the received indexed numbers. The optimally produced codebook at the receiver is identical to the codebook at the transmitter. The matching code words are allocated to the received index numbers, and the code words are organized so that the reconstructed picture is the same size as the input image. Eventually, a comparative assessment is performed to evaluate the proposed model. Especially, the computation time of the proposed model is 69.11%, 27.64%, 62.07%, 87.67%, 35.73%, 62.35%, and 14.11% better than the extant CSA, BFU-ROA, PSO, ROA, LA, SMO, and GA algorithms, respectively.
为了存储最近大量上传至互联网的新鲜照片,需要大量的存储空间。在过去的几十年里,许多分析人员创造了专业的图像压缩技术,以提高压缩率和视觉质量。本研究利用K-means Linde-Buzo-Gary (KLBG)模型,建立了一种独特的矢量量化(VQ)图像压缩技术。作为一种贡献,利用混合优化算法对码本进行了优化。投影的KLBG模型包括三个主要阶段:用于图像压缩的编码器,用于压缩图像转换的通道,以及用于图像重建的解码器。在编码器部分,进行了图像矢量的创建、最优码本的生成和索引机制。输入图像进入编码器阶段,其中它被分割成直接和不重叠的块。提出的GMISM模型将遗传算法(GA)和黏菌优化(SMO)的概念相结合。一旦成功生成了最优码本,就对索引表中具有索引号的每个向量进行索引。这些索引号通过通道发送给接收方。解码器部分包括索引表、最优码本和重构图像。接收到的索引表对接收到的索引号进行解码。在接收端产生的最佳码本与在发送端产生的码本是相同的。将匹配的码字分配给接收到的索引号,并对码字进行组织,使重构图像与输入图像大小相同。最后,对所提出的模型进行了比较评估。与现有的CSA、BFU-ROA、PSO、ROA、LA、SMO和GA算法相比,该模型的计算时间分别提高了69.11%、27.64%、62.07%、87.67%、35.73%、62.35%和14.11%。
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引用次数: 0
Optimal hybrid classification model for event recommendation system 事件推荐系统的最优混合分类模型
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-19 DOI: 10.3233/web-220137
Nithya Bn, D. Geetha, Manish Kumar
There is a growing need for recommender systems and other ML-based systems as an abundance of data is now available across all industries. Various industries are currently using recommender systems in slightly different ways. These programs utilize algorithms to propose appropriate products to consumers based on their prior choices and interactions. Moreover, Systems for recommending events to users suggest pertinent happenings that they might find interesting. As opposed to an object recommender that suggests books or movies; event-based recommender systems typically require distinct algorithms. A developed event recommendation method is introduced which includes two stages: feature extraction and recommendation. In stage, I, a Set of features like personal willingness, community willingness, informative content, edge weight, and node interest degree are extracted. Stage II of the event recommendation system performs a hybrid classification by combining LSTM and CNN. In the LSTM classifier, optimal tuning is done by Improvised Cat and Mouse optimization (ICMO) algorithm. The results of the ICMO technique at an 80% training percentage have the maximum sensitivity value of 95.19%, whereas those of the existing approaches SSA, DINGO, BOA, and CMBO have values of 93.89%, 93.35%, 92.36%, and 92.24%. Finally, the best result is then determined by evaluating the whole performance.
由于现在所有行业都有丰富的数据,因此对推荐系统和其他基于ml的系统的需求越来越大。目前,不同行业使用推荐系统的方式略有不同。这些程序利用算法根据消费者之前的选择和互动向他们推荐合适的产品。此外,向用户推荐事件的系统会向用户推荐他们可能感兴趣的相关事件。与推荐书籍或电影的对象推荐相反;基于事件的推荐系统通常需要不同的算法。介绍了一种改进的事件推荐方法,该方法包括特征提取和推荐两个阶段。阶段1,提取个人意愿、社区意愿、信息量、边缘权重、节点兴趣度等特征。事件推荐系统的第二阶段将LSTM和CNN相结合进行混合分类。在LSTM分类器中,采用简易猫鼠优化(ICMO)算法进行最优调优。在训练率为80%时,ICMO技术的最大灵敏度值为95.19%,而现有的SSA、DINGO、BOA和CMBO方法的最大灵敏度值分别为93.89%、93.35%、92.36%和92.24%。最后,通过评估整体性能来确定最佳结果。
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引用次数: 0
Intelligence model for Alzheimer’s disease detection with optimal trained deep hybrid model 基于最优训练深度混合模型的阿尔茨海默病智能检测模型
IF 0.3 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2023-05-19 DOI: 10.3233/web-220129
Rajasree Rs, Brintha Rajakumari S
Alzheimer’s disease (AD), a neurodegenerative disorder, is the most common cause of dementia and continuing cognitive deficits. Since there are more cases each year, AD has grown to be a serious social and public health issue. Early detection of the diagnosis of Alzheimer’s and dementia disease is crucial, as is giving them the right care. The importance of early AD diagnosis has recently received a lot of attention. The patient cannot receive a timely diagnosis since the present methods of diagnosing AD take so long and are so expensive. That’s why we created a brand-new AD detection method that has four steps of operation: pre-processing, feature extraction, feature selection, and AD detection. During the pre-processing stage, the input data is pre-processed using an improved data normalization method. Following the pre-processing, these pre-processed data will go through a feature extraction procedure where features including statistical, enhanced entropy-based and mutual information-based features will be extracted. The appropriate features will be chosen from these extracted characteristics using the enhanced Chi-square technique. Based on the selected features, a hybrid model will be used in this study to detect AD. This hybrid model combines classifiers like Long Short Term Memory (LSTM) and Deep Maxout neural networks, and the weight parameters of LSTM and Deep Maxout will be optimized by the Self Updated Shuffled Shepherd Optimization Algorithm (SUSSOA). Our Proposed SUSSOA-based method’s statistical analysis of best values such as 57%, 53%, 28%, 25%, and 21% is higher than the other models like SSO, BMO, HGS, BRO, BES, and ISSO respectively.
阿尔茨海默病(AD)是一种神经退行性疾病,是痴呆症和持续认知缺陷的最常见原因。由于每年有更多的病例,阿尔茨海默病已经发展成为一个严重的社会和公共卫生问题。早期发现阿尔茨海默病和痴呆症的诊断至关重要,给予他们正确的护理也至关重要。阿尔茨海默病早期诊断的重要性最近得到了很多关注。由于目前诊断AD的方法耗时长、费用高,患者无法得到及时的诊断。这就是为什么我们创造了一种全新的AD检测方法,它有四个步骤:预处理、特征提取、特征选择和AD检测。在预处理阶段,使用改进的数据规范化方法对输入数据进行预处理。经过预处理后,这些预处理后的数据将进行特征提取,提取的特征包括统计特征、基于增强熵的特征和基于互信息的特征。将使用增强的卡方技术从这些提取的特征中选择适当的特征。基于所选择的特征,本研究将使用混合模型来检测AD。该混合模型结合了长短期记忆(LSTM)和深度Maxout神经网络等分类器,LSTM和深度Maxout的权重参数将通过自更新shuffle Shepherd优化算法(SUSSOA)进行优化。我们提出的基于sussoa的方法统计分析的最佳值分别为57%、53%、28%、25%和21%,高于SSO、BMO、HGS、BRO、BES和ISSO等其他模型。
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引用次数: 0
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