The effects of serotonin inhibitors on bone metabolism: literature review

Privacy and security in the medical field are major aspects to consider in the current era. This is due to the huge necessity for data among providers, payers and patients, respectively. Recently, more researchers are making their contributions in this field under different aspects. But, there need more enhancements concerning security. In this circumstance, this paper intends to propose a new IoT-dependent health care privacy preservation model with the impact of the machine learning algorithm. Here, the information or data from IoT devices is subjected to the proposed sanitization process via generating the optimal key. In this work, the utility of the machine learning model is the greatest gateway to generating optimal keys as it is already trained with the neural network. Moreover, identifying the optimal key is the greatest crisis, which is done by a new Improved Dragonfly Algorithm algorithm. Thereby, the sanitization process works, and finally, the sanitized data are uploaded to IoT. The data restoration is the inverse process of sanitization, which gives the original data. Finally, the performance of the proposed work is validated over state-of-the-art models in terms of sanitization and restoration analysis.

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Optimized Deep CNN with Deviation Relevance-based LBP for Skin Cancer Detection: Hybrid Metaheuristic Enabled Feature Selection

Enturi

Suhasini

Satyala³

2023

Int. J. Image Grap.

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Segmentation of skin lesions is a significant and demanding task in dermoscopy images. This paper proposes a new skin cancer recognition scheme, with: “Pre-processing, Segmentation, Feature extraction, Optimal Feature Selection and Classification”. Here, pre-processing is done with certain processes. The pre-processed images are segmented via the “Otsu Thresholding model”. The third phase is feature extraction, where Deviation Relevance-based “Local Binary Pattern (DRLBP), Gray-Level Co-Occurrence Matrix (GLCM) features and Gray Level Run-Length Matrix (GLRM) features” are extracted. From these extracted features, the optimal features are chosen via Particle Updated WOA (PU-WOA) model. Subsequently, classification occurs via Optimized DCNN and NN to classify the skin lesion. To make the classification more precise, the DCNN is optimized by the introduced algorithm. The result has shown a higher accuracy of 0.998737, when compared with other extant models like IPSO, IWOA, PSO+CNN, WOA+CNN and CNN schemes.

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The effects of serotonin inhibitors on bone metabolism: literature review

Cited by 4 publications

References 22 publications

Impact of musical rhythm on blood, physiological and welfare parameters in stabled horses

Impact of musical rhythm on blood, physiological and welfare parameters in stabled horses

Role of Machine Learning on Key Extraction for Data Privacy Preservation of Health Care Sectors in IoT Environment

Optimized Deep CNN with Deviation Relevance-based LBP for Skin Cancer Detection: Hybrid Metaheuristic Enabled Feature Selection

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