Analysis of E-commerce Customer Consumption Model Based on Artificial Intelligence and Computer Aided

2019 ◽  
pp. 1915-1922
Author(s):  
Jiang Leilei ◽  
Dong Ke
Keyword(s):  
Artificial Intelligence ◽  
Model Based ◽  
Computer Aided ◽  
Consumption Model

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Recognition of teaching method effects based on grid model simplification and artificial intelligence

2020 ◽  
pp. 1-11
Author(s):  
Guo Yunfeng ◽  
Li Jing
Keyword(s):  
Artificial Intelligence ◽  
Teaching Method ◽  
Evaluation Model ◽  
Geometric Constraints ◽  
Sorting Algorithm ◽  
Grid Model ◽  
Model Based ◽  
Refinement Method ◽  
Method Effects ◽  
Structure Simplification

In order to improve the effect of the teaching method evaluation model, based on the grid model, this paper constructs an artificial intelligence model based on the grid model. Moreover, this paper proposes a hexahedral grid structure simplification method based on weighted sorting, which comprehensively sorts the elimination order of candidate base complexes in the grid with three sets of sorting items of width, deformation and price improvement. At the same time, for the elimination order of basic complex strings, this paper also proposes a corresponding priority sorting algorithm. In addition, this paper proposes a smoothing regularization method based on the local parameterization method of the improved SLIM algorithm, which uses the regularized unit as the reference unit in the local mapping in the SLIM algorithm. Furthermore, this paper proposes an adaptive refinement method that maintains the uniformity of the grid and reduces the surface error, which can better slow down the occurrence of geometric constraints caused by insufficient number of elements in the process of grid simplification. Finally, this paper designs experiments to study the performance of the model. The research results show that the model constructed in this paper is effective.

scholarly journals Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

2021 ◽  
Vol 13 (11) ◽  
pp. 6038
Author(s):  
Sergio Alonso ◽  
Rosana Montes ◽  
Daniel Molina ◽  
Iván Palomares ◽  
Eugenio Martínez-Cámara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Sustainable Development Goals ◽  
Modern Society ◽  
Agenda 2030 ◽  
Model Based ◽  
Development Goals ◽  
Sustainability Challenges ◽  
New Research ◽  
Decision Making Model

The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.

scholarly journals Application of artificial intelligence in pancreaticobiliary diseases

2021 ◽  
Vol 14 ◽  
pp. 263177452199305
Author(s):  
Hemant Goyal ◽  
Rupinder Mann ◽  
Zainab Gandhi ◽  
Abhilash Perisetti ◽  
Zhongheng Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Response To Treatment ◽  
Endoscopic Retrograde ◽  
Artificial Intelligence Techniques ◽  
Computer Aided ◽  
Rapid Pace ◽  
Cystic Pancreatic Lesions ◽  
Aided Diagnosis

The role of artificial intelligence and its applications has been increasing at a rapid pace in the field of gastroenterology. The application of artificial intelligence in gastroenterology ranges from colon cancer screening and characterization of dysplastic and neoplastic polyps to the endoscopic ultrasonographic evaluation of pancreatic diseases. Artificial intelligence has been found to be useful in the evaluation and enhancement of the quality measure for endoscopic retrograde cholangiopancreatography. Similarly, artificial intelligence techniques like artificial neural networks and faster region-based convolution network are showing promising results in early and accurate diagnosis of pancreatic cancer and its differentiation from chronic pancreatitis. Other artificial intelligence techniques like radiomics-based computer-aided diagnosis systems could help to differentiate between various types of cystic pancreatic lesions. Artificial intelligence and computer-aided systems also showing promising results in the diagnosis of cholangiocarcinoma and the prediction of choledocholithiasis. In this review, we discuss the role of artificial intelligence in establishing diagnosis, prognosis, predicting response to treatment, and guiding therapeutics in the pancreaticobiliary system.

Semantic Interoperability of GD&T Data Through ISO 10303 Step AP242

2016 ◽  
Author(s):  
Adarsh Venkiteswaran ◽  
Sayed Mohammad Hejazi ◽  
Deepanjan Biswas ◽  
Jami J. Shah ◽  
Joseph K. Davidson
Keyword(s):  
Data Exchange ◽  
Smart Manufacturing ◽  
Context Model ◽  
Product Model ◽  
Shape Information ◽  
Computer Aided Process Planning ◽  
Model Based ◽  
Computer Aided ◽  
Iso 10303 ◽  
Manufacturing Applications

Industries are continuously trying to improve the time to market through automation and optimization of existing product development processes. Large companies vow to save significant time and resources through seamless communication of data between design, manufacturing, supply chain and quality assurance teams. In this context, Model Based Definition/Engineering (MBD) / (MBE) has gained popularity, particularly in its effort to replace traditional engineering drawings and documentations with a unified digital product model in a multi-disciplinary environment. Widely used 3D data exchange models (STEP AP 203, 214) contains mere shape information, which does not provide much value for reuse in downstream manufacturing applications. However, the latest STEP AP 242 (ISO 10303-242) “Managed model based 3D engineering” aims to support smart manufacturing by capturing semantic Product Manufacturing Information (PMI) within the 3D model and also helping with long-term archival. As a primary, for interoperability of Geometric Dimensions & Tolerances (GD&T) through AP 242, CAx Implementor Forum has published a set of recommended practices for the implementation of a translator. In line with these recommendations, this paper discusses the implementation of an AP 203 to AP 242 translator by attaching semantic GD&T available in an in-house Constraint Tolerance Graph (CTF) file. Further, semantic GD&T data can be automatically consumed by downstream applications such as Computer Aided Process Planning (CAPP), Computer Aided Inspection (CAI), Computer Aided Tolerance Systems (CATS) and Coordinate Measuring Machines (CMM). Also, this paper will briefly touch base on the important elements that will constitute a comprehensive product data model for model-based interoperability.

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