Protein data in the identification and stage prediction of bronchopulmonary dysplasia on preterm infants: a machine learning study

Jeyananthan, Pratheeba; Bandara, K. M. D. D.; Nayanajith, Y. G. A.

doi:10.1007/s41870-023-01571-6

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2024

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Cited by 5 publications

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Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm

Marhoon,

Basil,

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Int. j. inf. tecnol.

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Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm

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Int. j. inf. tecnol.

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Machine learning in the identification of phenotypes of multiple sclerosis patients

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Sentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach

Abimbola,

Tan,

De La Cal Marín

2024

Int. j. inf. tecnol.

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Historical information in the Canadian Maritime Judiciary increases with time because of the need to archive data to be utilized in case references and for later application when determining verdicts for similar cases. However, such data are typically stored in multiple systems, making its reachability technical. Utilizing technologies like deep learning and sentiment analysis provides chances to facilitate faster access to court records. Such practice enhances impartial verdicts, minimizing workloads for court employees, and decreases the time used in legal proceedings for claims during maritime contracts such as shipping disputes between parties. This paper seeks to develop a sentiment analysis framework that uses deep learning, distributed learning, and machine learning to improve access to statutes, laws, and cases used by maritime judges in making judgments to back their claims. The suggested approach uses deep learning models, including convolutional neural networks (CNNs), deep neural networks, long short-term memory (LSTM), and recurrent neural networks. It extracts court records having crucial sentiments or statements for maritime court verdicts. The suggested approach has been used successfully during sentiment analysis by emphasizing feature selection from a legal repository. The LSTM + CNN model has shown promising results in obtaining sentiments and records from multiple devices and sufficiently proposing practical guidance to judicial personnel regarding the regulations applicable to various situations.

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Protein data in the identification and stage prediction of bronchopulmonary dysplasia on preterm infants: a machine learning study

Cited by 5 publications

References 24 publications

Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm

Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm

Machine learning in the identification of phenotypes of multiple sclerosis patients

Sentiment analysis of Canadian maritime case law: a sentiment case law and deep learning approach

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