Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Alenezi, Fayadh; Santosh, K. C.

doi:10.1155/2021/6664569

Cited by 28 publications

(18 citation statements)

References 55 publications

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“…Malware is a global name for infections, worms, Trojans, spyware, adware, etc. In the end, "infection" is used to represent all of the aforementioned infections, although real are generally a small number of existing malware [15][16][17][18][19][20][21].…”

Section: Background Information Of the Automaticmentioning

confidence: 99%

Analysis of Malware Detection and Signature Generation Using a Novel Hybrid Approach

Dugyala¹,

Reddy²,

Viswanadhula³

et al. 2022

Mathematical Problems in Engineering

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In recent years, malware detection has become necessary to improve system performance and prevent programs from infecting your computer. Signature-based malware failed to detect most new organisms. This article presents the hybrid technique to automatically generate and classify malicious signatures. The hybrid method is called the ANFIS-SSA approach. The hybrid system includes the Adaptive Neuro Fuzzy Interference System (ANFIS) and the Salp Swarm Optimization (SSA). Based on this observation, we propose a hybrid approach to detect malware using malware terminology and its API calls to each other. We create the master signature for the entire malware category, not the malicious template. This signature can also identify unknown extended variants of this class. We show our approach in some common malware classes, which show that each extended version of the malware class is recognized by its original signature. The proposed method is integrated into the Matlab/Simulink operating system and is comparable to existing secure methods. SAFE creates an abstract model for the malicious code and converts it to an internal representation.

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Section: Background Information Of the Automaticmentioning

confidence: 99%

Analysis of Malware Detection and Signature Generation Using a Novel Hybrid Approach

Dugyala¹,

Reddy²,

Viswanadhula³

et al. 2022

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a result, it is difficult to establish an early diagnosis of Alzheimer's. Recently, several deep learning and machine learning-based techniques to enhance picture quality have been presented (Khan et al, 2019 ; Alenezi and Santosh, 2021 ). Additionally, the feature extraction- and classification-based approaches can be regularly employed to create prediction models for applications based on intelligent and expert systems (Mehmood et al, 2021 ; Shams et al, 2021 ; El-Hasnony et al, 2022 ).…”

Section: Related Workmentioning

confidence: 99%

A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images

et al. 2023

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Alzheimer's is an acute degenerative disease affecting the elderly population all over the world. The detection of disease at an early stage in the absence of a large-scale annotated dataset is crucial to the clinical treatment for the prevention and early detection of Alzheimer's disease (AD). In this study, we propose a transfer learning base approach to classify various stages of AD. The proposed model can distinguish between normal control (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD. In this regard, we apply tissue segmentation to extract the gray matter from the MRI scans obtained from the Alzheimer's Disease National Initiative (ADNI) database. We utilize this gray matter to tune the pre-trained VGG architecture while freezing the features of the ImageNet database. It is achieved through the addition of a layer with step-wise freezing of the existing blocks in the network. It not only assists transfer learning but also contributes to learning new features efficiently. Extensive experiments are conducted and results demonstrate the superiority of the proposed approach.

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“…Blockchain is a technology that supports and provides a proper solution that will help to keep the recording. Secure and broadcast of medical information that maintains every patient's privacy to access their medical reports [12][13][14][15][16][17][18][19][20][21][22]. It enables an auditable path and creates transparency for individuals using their encryption key and unique credentials, and allows third parties to provide a grant to access individual medical records.…”

Section: Blockchain In Healthcarementioning

confidence: 99%

Feature Extraction Approach for Speaker Verification to Support Healthcare System Using Blockchain Security for Data Privacy

Upadhyay¹,

Kumar²,

Kumar³

et al. 2022

Computational and Mathematical Methods in Medicine

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Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People’s mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human’s various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system’s constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients’ facts which will cause a problem in sharing information over a network. So, this research paper’s approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.

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Geometric Regularized Hopfield Neural Network for Medical Image Enhancement

Cited by 28 publications

References 55 publications

Analysis of Malware Detection and Signature Generation Using a Novel Hybrid Approach

Analysis of Malware Detection and Signature Generation Using a Novel Hybrid Approach

A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images

Feature Extraction Approach for Speaker Verification to Support Healthcare System Using Blockchain Security for Data Privacy

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