Lens Opacities Classification System III-based artificial intelligence program for automatic cataract grading

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Qiang Lu ◽  
Ling Wei ◽  
Wenwen He ◽  
Keke Zhang ◽  
Jinrui Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Classification System ◽  
Lens Opacities ◽  
Artificial Intelligence Program

An artificial intelligence-enabled industry classification and its interpretation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daejin Kim ◽  
Hyoung-Goo Kang ◽  
Kyounghun Bae ◽  
Seongmin Jeon
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification System ◽  
Standard Industrial Classification ◽  
American Industry ◽  
Content Type ◽  
Global Industry ◽  
Machine Learning Methods ◽  
Industry Classification ◽  
Traditional Industry

PurposeTo overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).Design/methodology/approachThe authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.FindingsUsing the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.Originality/valueThe authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

scholarly journals Initial User Experience With an Artificial Intelligence Program for the Preliminary Design of Centrifugal Compressors

1987 ◽  
Author(s):  
Carol J. Russo ◽  
Dennis J. Nicklaus ◽  
Siu S. Tong
Keyword(s):  
Artificial Intelligence ◽  
Design Tool ◽  
Parameter Study ◽  
Balance Performance ◽  
New Approach ◽  
Trade Offs ◽  
Component Design ◽  
Small Set ◽  
Development Risks ◽  
Artificial Intelligence Program

A new approach is evaluated for the design of turbomachinery components using existing analysis codes coupled to a generic Artificial Intelligence (AI) software framework called ENGINEOUS. This AI framework uses intelligent search techniques with a small set of basic component design rules to iterate to an optimized solution and to quantify parameter trade-offs. Initial experience with ENGINEOUS indicates that it is a powerful design tool which quickly identifies non-obvious solutions balanced for conflicting multiple goals in a small number of iterations which vary linearly with the number of variables. The solution path and driving logic are easily visible to the designer and a parameter study option can rapidly quantify potential design trade-offs which together allow a critique of the selected design to balance performance against development risks. Because this AI design approach fosters intelligent interface with the designer and is generic, the potential application areas and productivity benefits appear enormous.

scholarly journals Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0248809
Author(s):  
Anna Lind ◽  
Ehsan Akbarian ◽  
Simon Olsson ◽  
Hans Nåsell ◽  
Olof Sköldenberg ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knee Joint ◽  
Classification System ◽  
Plain Radiograph ◽  
Treatment Strategies ◽  
University Hospital ◽  
Complication Rates ◽  
Radiographic Images ◽  
Femur Fractures

Background Fractures around the knee joint are inherently complex in terms of treatment; complication rates are high, and they are difficult to diagnose on a plain radiograph. An automated way of classifying radiographic images could improve diagnostic accuracy and would enable production of uniformly classified records of fractures to be used in researching treatment strategies for different fracture types. Recently deep learning, a form of artificial intelligence (AI), has shown promising results for interpreting radiographs. In this study, we aim to evaluate how well an AI can classify knee fractures according to the detailed 2018 AO-OTA fracture classification system. Methods We selected 6003 radiograph exams taken at Danderyd University Hospital between the years 2002–2016, and manually categorized them according to the AO/OTA classification system and by custom classifiers. We then trained a ResNet-based neural network on this data. We evaluated the performance against a test set of 600 exams. Two senior orthopedic surgeons had reviewed these exams independently where we settled exams with disagreement through a consensus session. Results We captured a total of 49 nested fracture classes. Weighted mean AUC was 0.87 for proximal tibia fractures, 0.89 for patella fractures and 0.89 for distal femur fractures. Almost ¾ of AUC estimates were above 0.8, out of which more than half reached an AUC of 0.9 or above indicating excellent performance. Conclusion Our study shows that neural networks can be used not only for fracture identification but also for more detailed classification of fractures around the knee joint.

scholarly journals Recycled Clothing Classification System Using Intelligent IoT and Deep Learning with AlexNet

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sun-Kuk Noh
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Object Recognition ◽  
Environmental Pollution ◽  
Classification System ◽  
Raw Material ◽  
Raspberry Pi ◽  
Utilization Rate ◽  
Automation System ◽  
Labor Costs

Recently, Internet of Things (IoT) and artificial intelligence (AI), led by machine learning and deep learning, have emerged as key technologies of the Fourth Industrial Revolution (4IR). In particular, object recognition technology using deep learning is currently being used in various fields, and thanks to the strong performance and potential of deep learning, many research groups and Information Technology (IT) companies are currently investing heavily in deep learning. The textile industry involves a lot of human resources in all processes, such as raw material collection, dyeing, processing, and sewing, and the wastage of resources and energy and increase in environmental pollution are caused by the short-term waste of clothing produced during these processes. Environmental pollution can be reduced to a great extent through the use of recycled clothing. In Korea, the utilization rate of recycled clothing is increasing, the amount of used clothing is high with the annual consumption being at $56.2 billion, but it is not properly utilized because of the manual recycling clothing collection system. It has several problems such as a closed workplace environment, workers’ health, rising labor costs, and low processing speed that make it difficult to apply the existing clothing recognition technology, classified by deformation and overlapping of clothing shapes, when transporting recycled clothing to the conveyor belt. In this study, I propose a recycled clothing classification system with IoT and AI using object recognition technology to the problems. The IoT device consists of Raspberry pi and a camera, and AI uses the transfer-learned AlexNet to classify different types of clothing. As a result of this study, it was confirmed that the types of recycled clothing using artificial intelligence could be predicted and accurate classification work could be performed instead of the experience and know-how of working workers in the clothing classification worksite, which is a closed space. This will lead to the innovative direction of the recycling clothing classification work that was performed by people in the existing working worker. In other words, it is expected that standardization of necessary processes, utilization of artificial intelligence, application of automation system, various cost reduction, and work efficiency improvement will be achieved.

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