scholarly journals Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Munenori Uemura ◽  
Morimasa Tomikawa ◽  
Tiejun Miao ◽  
Ryota Souzaki ◽  
Satoshi Ieiri ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Training Dataset ◽  
Artificial Intelligence System ◽  
Correct Judgment ◽  
Skill Levels ◽  
Intelligence System ◽  
The Neural Network ◽  
Expert And Novice ◽  
Novice Surgeons

This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct judgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.

Research on innovation and entrepreneurship based on artificial intelligence system and neural network algorithm

2020 ◽  
pp. 1-12
Author(s):  
Haixia Chen
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Bat Algorithm ◽  
Artificial Intelligence System ◽  
New Era ◽  
Intelligence System ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Innovation And Entrepreneurship

Innovation and entrepreneurship are an important support for social and economic development in the new era, and it is also the key to the cultivation of practical talents in universities. In order to mine the effective information of innovation and entrepreneurship data, based on the neural network algorithm, this paper combines the bat algorithm to construct a data processing model to obtain an artificial intelligence innovation and entrepreneurship system with data analysis capabilities. Moreover, this study combines with actual needs to improve the algorithm, effectively eliminate the noise existing in the data, eliminate the interference of invalid data on the judgment ability of the system model, and choose the best denoising algorithm through comparison and verification of various algorithms. In order to verify the model proposed in this paper, the data is input into this research model by collecting data in a college survey, so as to verify and analyze the performance of the model. The research results show that the artificial intelligence system proposed in this paper has good performance and has certain practical value.

scholarly journals Features of Signature Verification By a Person as a Primary Criteria for Developing an Artificial Intelligence System

2020 ◽  
Vol 17 (4) ◽  
pp. 514-522
Author(s):  
D. V. Bakhteev
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Artificial Intelligence System ◽  
Experimental Conditions ◽  
Intelligence System ◽  
Initial Stage ◽  
Correct Determination ◽  
Artificial Neural ◽  
Applied Artificial Intelligence

The modern capabilities of computers have returned interest in artificial intelligence technologies. A particular area of application of these technologies is pattern recognition, which can be applied to the traditional forensic task – identification of signs of forgery (imitation) of a signature. The results of forgery are differentiated into three types: auto-forgery, simple and skilled forgeries. Only skilled forgeries are considered in this study. The online and offline approaches to the study of signatures and other handwriting material are described. The developed artificial intelligence system based on an artificial neural network refers to the offline type of signature recognition – that is, it is focused on working exclusively with the consequences of the signature – its graphic image. The content and principles of the formation of a hypothesis for the development of an artificial intelligence system are described with a combination of humanitarian (legal) knowledge and natural-technical knowledge. At the initial stage of the study, in order to develop an experimental-applied artificial intelligence system based on an artificial neural network focused on identifying forged signatures, 127 people were questioned in order to identify a person's ability to detect fake signatures. It was found that under experimental conditions the probability of a correct determination of the originality or forgery of the presented signature for the respondent is on average 69.29 %. Accordingly, this value can be used as a threshold for determining the effectiveness of the developed artificial intelligence system. In the process of preparing the dataset (an array for training and verification of its results) of the system in terms of fraudulent signatures, some forensically significant features were revealed, associated with the psychological and anatomical features of the person performing the forgery, both known to criminalistics and new ones. It is emphasized that the joint development of artificial intelligence systems by the methods of computer science and criminalistics can generate additional results that may be useful outside the scope of the research tasks.

scholarly journals Using Artificial Intelligence System for Pattern Identification Based on Features Extracted from Gel Electrophoresis Images

2020 ◽  
Vol 11 (2) ◽  
pp. 1944-1952
Author(s):  
Sarmad M. Hadi ◽  
Al-Faiz M Z ◽  
Ali A. Ibrahim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Gel Electrophoresis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Artificial Intelligence System ◽  
Average Standard Deviation ◽  
Learning Time ◽  
Intelligence System ◽  
Error Back Propagation

Artificial intelligence has many branches of image processing-based applications in terms of classification and identification, error back-propagation neural network is a great match for such applications as long as linear vector quantization (LVQ) and pattern recognition is another great match for recognition of digital images based on their features. The dataset used in this paper are gel electrophoresis images where 6 features had been extracted from the images and used as input to a neural network for learning and then checked for recognition purposed and the system managed to recognize all the 6 images. Six features had been used: average, standard deviation, smoothness, skewness, uniformity, and entropy. A tiny error rate where allowed in the recognition program to cover the variation of the dataset and the test data (gel-electrophoresis images). The proposed system had successfully managed to identify all of the learned data in both LVQ and error-back-propagation. Error-back-propagation proved itself as a great tool in terms of learning time compared with LVQ, which was very slow in terms of learning time and recognition.

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