Requirement of artificial intelligence technology awareness for thoracic surgeons

Darbari, Anshuman; Kumar, Krishan; Darbari, Shubhankar; Patil, Prashant L

doi:10.1186/s43057-021-00053-4

Cited by 19 publications

(7 citation statements)

References 29 publications

(22 reference statements)

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“…Arti cial intelligence technology has been widely applied in di erent elds that is technological innovation [1], intelligent military hardware facilities [2], commercial applications [3], surgical medicine [4] and oral therapy [5]. In view of the existing problems of hardware and software facilities in the eld of higher education, arti cial intelligence technology was used to extract indicators of related problems in higher education [6].…”

Section: Introductionmentioning

confidence: 99%

Clustering and Analysis of Verbs in English Language Based on Artificial Intelligence

Zheng

Zong-jie

2022

Mathematical Problems in Engineering

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There are related problems in the clustering and analysis of verbs in the English language. In order to further improve the application of verbs in the English language based on the theory of artificial intelligence, the clustering analysis of English verbs is carried out by using artificial intelligence technology and through the use of discrete matrix and evaluation index optimization, so as to obtain the optimized artificial intelligence model. The model can provide a good computational process for the verb clustering analysis of the English language, and the experimental data are used to verify the model. Relevant studies show that the influence of parameter change on the discrete change of the matrix can be divided into three cases: the curve shows a gentle change trend with the increase of distance; the increase of the distance makes the discrete data of the experiment increase approximately linear. The relationship between distance and discrete data is nonlinear. The change curve of MS has obvious multi-segment linear characteristics, and the linear slope of the first stage is the lowest, indicating that the change effect of the curve is the least obvious in this stage. Based on the clustering analysis of English language verbs using artificial intelligence technology, it can be seen that different factors vary in an obvious range at the initial stage and have good corresponding fluctuation characteristics. With the increase of samples, the corresponding curve gradually tends to be flat. Finally, the accuracy of the model is verified by experimental data. This research can provide a theoretical basis for the application and analysis of artificial intelligence in other fields and provide support for clustering and analysis of verbs in the English language.

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Section: Introductionmentioning

confidence: 99%

Clustering and Analysis of Verbs in English Language Based on Artificial Intelligence

Zheng

Zong-jie

2022

Mathematical Problems in Engineering

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Section: Discussionmentioning

confidence: 99%

“…Deep neural networks from artificial intelligence technologies are essential in detecting various diseases that cause severe complications in humans, such as cancer, lung diseases, and Alzheimer's [12]. Extensive data are generally needed for deep neural networks with complex structures and high parameters to be successful [43].…”

Section: Discussionmentioning

confidence: 99%

MediNet: transfer learning approach with MediNet medical visual database

Reis

Turk

Khoshelham

et al. 2023

Multimed Tools Appl

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The rapid development of machine learning has increased interest in the use of deep learning methods in medical research. Deep learning in the medical field is used in disease detection and classification problems in the clinical decision-making process. Large amounts of labeled datasets are often required to train deep neural networks; however, in the medical field, the lack of a sufficient number of images in datasets and the difficulties encountered during data collection are among the main problems. In this study, we propose MediNet, a new 10-class visual dataset consisting of Rontgen (X-ray), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Histopathological images such as calcaneal normal, calcaneal tumor, colon benign colon adenocarcinoma, brain normal, brain tumor, breast benign, breast malignant, chest normal, chest pneumonia. AlexNet, VGG19-BN, Inception V3, DenseNet 121, ResNet 101, EfficientNet B0, Nested-LSTM + CNN, and proposed RdiNet deep learning algorithms are used in the transfer learning for pre-training and classification application. Transfer learning aims to apply previously learned knowledge in a new task. Seven algorithms were trained with the MediNet dataset, and the models obtained from these algorithms, namely feature vectors, were recorded. Pre-training models were used for classification studies on chest X-ray images, diabetic retinopathy, and Covid-19 datasets with the transfer learning technique. In performance measurement, an accuracy of 94.84% was obtained in the traditional classification study for the InceptionV3 model in the classification study performed on the Chest X-Ray Images dataset, and the accuracy was increased 98.71% after the transfer learning technique was applied. In the Covid-19 dataset, the classification success of the DenseNet121 model before pre-trained was 88%, while the performance after the transfer application with MediNet was 92%. In the Diabetic retinopathy dataset, the classification success of the Nested-LSTM + CNN model before pre-trained was 79.35%, while the classification success was 81.52% after the transfer application with MediNet. The comparison of results obtained from experimental studies observed that the proposed method produced more successful results. Graphical abstract

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“…In [31] Hidden Markov Model(HMM) was utilized to detect the depression of mind. The thoracic surgery can be assisted with AI which was suggested in [32]. In [33][34] plant disease was simulated by AI.…”

Section: Related Workmentioning

confidence: 99%

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

Haque

Hoque²,

Paul

2023

Preprint

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The present world is badly affected by the novel coronavirus (COVID-19). Using medical kits to identify coronavirus-affected persons is very slow. What happens in the next, nobody knows. The world is facing an erratic problem and doesn't know what will happen in near future. This paper is trying to make a prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data from 258 regions, their latitude and longitude, and the number of death in 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect in extracting highly essential features for time series data (TSD) analysis. There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.

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Requirement of artificial intelligence technology awareness for thoracic surgeons

Cited by 19 publications

References 29 publications

Clustering and Analysis of Verbs in English Language Based on Artificial Intelligence

Clustering and Analysis of Verbs in English Language Based on Artificial Intelligence

MediNet: transfer learning approach with MediNet medical visual database

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

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