Prediction of Alzheimer's disease using hybrid machine learning technique

Kumar, Manmohan; Azath, H.; Velmurugan, A. K.; Padmanaban, K.; Subbiah, Murugan

doi:10.1063/5.0110283

Cited by 30 publications

(5 citation statements)

References 11 publications

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“…The proposed method shows a mathematical model of DT in (2). This function inputs a cipher content CC encrypted using the attribute set I.…”

Section: Decryption Timementioning

confidence: 99%

“…Cloud computing is a relatively emerging technology that provides users with on-demand network access to a shared computer resource that may be configured in various ways [1]. The surfacing of the cloud has significantly distorted the general insight into infrastructure, software delivery services, and development models [2]. It could merge many domains, such as networks, databases, operating systems, virtualization, resource scheduling, load balancing, concurrency control, network security, and transaction and memory management [3].…”

Section: Introductionmentioning

confidence: 99%

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Secure and efficient data storage with Rivest Shamir Adleman algorithm in cloud environment

Elumalai,

Muruganandam

2024

Bulletin EEI

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Cloud computing rapidly is a prerequisite and releases resources with minimal management effort. The surfacing of the cloud has significantly distorted the general insight into infrastructure, software services, and development models. In contrast to single-key encryption models based on public or private keys (PKs), hybrid encryption systems combine encryption methods using symmetric or asymmetric methods. Various hybrid algorithms fail to meet users’ expectations regarding data security and cannot prevent all security risks. The secure and efficient data storage and retrieval (SEDSR) algorithm was developed for scalable key management between the content owner, cloud user, and service providers in an un-trusted cloud environment. In the implementation, the SEDSR combines the Rivest Shamir Adleman (RSA) algorithm 4096 key length with a primary symmetric key method to provide adequate and compact security with optimal retrieval systems in the cloud. Based on the experimental evaluation, the SEDSR minimizes 1.7 seconds of encryption times (ET) and 1.5 seconds of decryption time (DT) and improves by 34% throughput (TRP) compared to existing parameters.

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“…The proposed method shows a mathematical model of DT in (2). This function inputs a cipher content CC encrypted using the attribute set I.…”

Section: Decryption Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Secure and efficient data storage with Rivest Shamir Adleman algorithm in cloud environment

Elumalai,

Muruganandam

2024

Bulletin EEI

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show abstract

“…Thus, the feature space is the input to the fully connected layer inputs. These input signals are weighted and summated (along with biases) to provide the neuron's activation [26]. Figure 4 shows the feed-forward neural network.…”

Section: Fully Connected Layermentioning

confidence: 99%

Efficient deep learning architecture for the classification of diseased plant leaves

Sadhasivam,

Kalaiselvi Geetha,

Gladson Maria Britto

2024

IJEECS

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<span>The classification of plant leaf diseases via machine learning and deep learning algorithms has a great deal of potential for enhancing agricultural operations by allowing the early and accurate diagnosis of diseases. These systems can potentially develop into useful instruments for environmentally responsible farming and increased food safety as technological advancements continue. In this work, an efficient deep learning architecture has been developed to classify the diseased plant leaves. A ten-layer architecture is designed, which includes 5-convolutional layers using different numbers of filters (32, 64, 128, 256, and 512) and for dimension reduction, five max-pooling layers are used. The PlantVillage dataset which consists of more than 50,000 plant leaf samples is used to analyze the proposed architecture's performance. The performances are evaluated across different training and testing configurations and different dropout configurations. When compared to well-known transfer learning methods using visual geometric group (VGG16), AlexNet, and GoogleNet architectures, the proposed architecture obtains a higher level of performance with 98.18% classification accuracy.</span>

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“…Bucholc et al, [52] proposed a two stages hybrid model based one classical machine learning algorithms. However, hybrid deep learning models improve the performance of AD detection [53], and they have been explored in other domains [54]. Robust hybrid deep learning models have already been successfully applied to AD progression detections [19].…”

Section: Related Workmentioning

confidence: 99%

Genetic algorithm-based hybrid deep learning model for explainable Alzheimer’s disease prediction using temporal multimodal cognitive data

Saleh,

ElRashidy,

Abd Elaziz

et al. 2024

Int J Data Sci Anal

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Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test

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Prediction of Alzheimer's disease using hybrid machine learning technique

Cited by 30 publications

References 11 publications

Secure and efficient data storage with Rivest Shamir Adleman algorithm in cloud environment

Secure and efficient data storage with Rivest Shamir Adleman algorithm in cloud environment

Efficient deep learning architecture for the classification of diseased plant leaves

Genetic algorithm-based hybrid deep learning model for explainable Alzheimer’s disease prediction using temporal multimodal cognitive data

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