scholarly journals MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

2020 ◽  
Author(s):  
Qifeng Bai ◽  
Shuoyan Tan ◽  
Tingyang Xu ◽  
Huanxiang Liu ◽  
Junzhou Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Drug Design ◽  
Membrane Protein ◽  
Learning Model ◽  
Glucagon Receptor ◽  
Protein Targets ◽  
Lipinski’S Rule ◽  
Classical Algorithm ◽  
Deep Learning Model

Abstract Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski’s rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

scholarly journals Advanced Deep Learning Model

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Learning Model ◽  
Intelligence Analysis ◽  
Basic Parameters ◽  
Deep Learning Model ◽  
Learning Sets

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.

scholarly journals Invoices Classification Using Deep Features Based on SME Perspectives

2021 ◽  
Author(s):  
Nurul Akmar Azman ◽  
Azlinah Mohamed ◽  
Amsyar Mohmad Jamil
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Model ◽  
Discussion Section ◽  
Validation Data ◽  
Record Keeping ◽  
Small And Medium Enterprise ◽  
Lower Productivity ◽  
Medium Enterprise ◽  
Deep Learning Model

Abstract Automation is seen as a potential alternative in improving productivity in the twenty-first century. Invoicing is the essential foundation of accounting record keeping and serves as a critical foundation for law enforcement inspections by auditing agencies and tax authorities. With the rise of artificial intelligence, automated record keeping systems are becoming more widespread in major organizations, allowing them to do tasks in real time and with no effort as well as a decision-making tool. Despite the system's benefits, many small and medium-sized businesses, particularly in Malaysia, are hesitant to implement it. Invoices are mostly processed manually that prone to human errors and lower productivity of the company. Artificial intelligence will further improve automated invoice handling making it simpler and efficient for all levels of businesses especially the small and medium enterprise This study presents a deep learning approach on record keeping focusing on invoices recognition by detecting invoice image classification. The deep learning model used in this research including the classic architecture of Convolutional Neural Network and its other variation such as VGG-16, VGG-19 and ResNet-50. Besides that, the constrains and expectation of the system to be implemented in small and medium enterprise in Malaysia are also presented in the interview scores. The research highlighted a comparison result between deep learning model and the perspective of SME presented in the discussion section. ResNet-50 shows a significant value in both training and validation accuracy compared to the other models with 95.90% accuracy in training and 74.24% accuracy for validation data. Future work will look at the suggested other deep learning method and intelligence features to be implemented for a more efficient invoices recognition and for small and medium enterprise.

scholarly journals Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mai Ngoc Anh ◽  
Duong Xuan Bien
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Voice Recognition ◽  
Learning Model ◽  
Redundant Manipulator ◽  
Efficient Operation ◽  
Inverse Kinematics Problem ◽  
Deep Learning Model

This study presents the construction of a Vietnamese voice recognition module and inverse kinematics control of a redundant manipulator by using artificial intelligence algorithms. The first deep learning model is built to recognize and convert voice information into input signals of the inverse kinematics problem of a 6-degrees-of-freedom robotic manipulator. The inverse kinematics problem is solved based on the construction and training. The second deep learning model is built using the data determined from the mathematical model of the system’s geometrical structure, the limits of joint variables, and the workspace. The deep learning models are built in the PYTHON language. The efficient operation of the built deep learning networks demonstrates the reliability of the artificial intelligence algorithms and the applicability of the Vietnamese voice recognition module for various tasks.

scholarly journals Patent Analysis Using Vector Space Model and Deep Learning Model : A Case of Artificial Intelligence Industry Technology

2021 ◽  
Author(s):  
Yongmin Yoo ◽  
Dongjin Lim ◽  
Kyungsun Kim
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Vector Space ◽  
Rapid Development ◽  
Vector Space Model ◽  
Learning Model ◽  
Patent Data ◽  
Space Model ◽  
Artificial Intelligence Technology ◽  
Deep Learning Model

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

scholarly journals To perform road signs recognition for autonomous vehicles using cascaded deep learning pipeline

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Riadh Ayachi ◽  
Yahia ElFahem Said ◽  
Mohamed Atri
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
High Performance ◽  
Autonomous Vehicle ◽  
Sensory Information ◽  
Learning Model ◽  
The Road ◽  
Road Signs ◽  
Deep Learning Model

Autonomous vehicle is a vehicle that can guide itself without human conduction. It is capable of sensing its environment and moving with little or no human input. This kind of vehicle has become a concrete reality and may pave the way for future systems where computers take over the art of driving. Advanced artificial intelligence control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and relevant road signs. In this paper, we introduce an intelligent road signs classifier to help autonomous vehicles to recognize and understand road signs. The road signs classifier will be based on an artificial intelligence technique. In particular, a deep learning model is used, Convolutional Neural Networks (CNN). CNN is a widely used Deep Learning model to solve pattern recognition problems like image classification and object detection. CNN has been successfully used to solve computer vision problems because of its methodology in processing images which is similar to the human brain decision making. The evaluation of the proposed pipeline is proved using two different datasets. The proposed CNNs achieved high performance in road sign classification with a validation accuracy of 99.8% and a testing accuracy of 99.6%. The proposed method can be easily implemented for real-time application.

scholarly journals Research on Model Compression for Embedded Platform through Quantization and Pruning

2021 ◽  
Vol 2078 (1) ◽  
pp. 012047
Author(s):  
Xiao Hu ◽  
Hao Wen
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Model ◽  
Model Simplification ◽  
Accuracy Rate ◽  
Embedded Platform ◽  
Remarkable Improvement ◽  
Model Compression ◽  
Artificial Intelligence Technology ◽  
Deep Learning Model

Abstract So far, artificial intelligence has gone through decades of development. Although artificial intelligence technology is not yet mature, it has already been applied in many walks of life. With the explosion of IoT technology in 2019, artificial intelligence has ushered in a new climax. It can be said that the development of IoT technology has led to the development of artificial intelligence once again. But the traditional deep learning model is very complex and redundant. The hardware environment of IoT can not afford the time and resources cost by the model which runs on the GPU originally, so model compression without decreasing accuracy rate so much is applicable in this situation. In this paper, we experimented with using two tricks for model compression: Pruning and Quantization. By utilizing these methods, we got a remarkable improvement in model simplification while retaining a relatively close accuracy.

Artificial Intelligence In Source Discrimination of Mine Water: A Deep Learning Algorithm For Water Source Discrimination

2021 ◽  
Author(s):  
Zhenwei Yang ◽  
Junchao Yue ◽  
Hang Lü ◽  
Xinyi Wang
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Mine Water ◽  
Water Inrush ◽  
Learning Model ◽  
Karst Water ◽  
Data Set ◽  
Source Discrimination ◽  
Hydrochemical Data ◽  
Deep Learning Model

Abstract With increasing coal mining depth, the source of mine water inrush becomes increasingly complex. The problem of distinguishing the source of mine water in mines and tunnels has been addressed by studying the hydrochemical components of the Pingdingshan Coalfield and applying the artificial intelligence (AI) method to discriminate the source of the mine water. 496 data of mine water have been collected. Six ions of mine water are used as the input data set: Na++K+, Ca2+, Mg2+, Cl-, SO2- 4, and HCO- 3. The type of mine water in the Pingdingshan coalfield is classified into surface water, Quaternary pore water, Carboniderous limestone karst water, Permian sandstone water, and Cambrian limestone karst water. Each type of water is encoded with the number 0 to 4. The one-hot code method is used to encode the numbers, which is the output set. On the basis of hydrochemical data processing, a deep learning model was designed to train the hydrochemical data. Ten new samples of mine water were tested to determine the precision of the model. Nine samples of mine water were predicted correctly. The deep learning model presented here provides significant guidance for the discrimination of mine water.

scholarly journals Can artificial intelligence predict the need for oxygen therapy in early stage COVID-19 pneumonia?

2020 ◽  
Author(s):  
Hirofumi Obinata ◽  
Peiying Ruan ◽  
Hitoshi Mori ◽  
Wentao Zhu ◽  
Hisashi Sasaki ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Clinical Features ◽  
Oxygen Therapy ◽  
Learning Model ◽  
Ct Images ◽  
Learning Models ◽  
Deep Learning Model ◽  
Machine Learning Models

Abstract This study investigated the utility of artificial intelligence in predicting disease progression. We analysed 194 patients with COVID-19 confirmed by reverse transcription polymerase chain reaction. Among them, 31 patients had oxygen therapy administered after admission. To assess the utility of artificial intelligence in the prediction of disease progression, we used three machine learning models employing clinical features (patient’s background, laboratory data, and symptoms), one deep learning model employing computed tomography (CT) images, and one multimodal deep learning model employing a combination of clinical features and CT images. We also evaluated the predictive values of these models and analysed the important features required to predict worsening in cases of COVID-19. The multimodal deep learning model had the highest accuracy. The CT image was an important feature of multimodal deep learning model. The area under the curve of all machine learning models employing clinical features and the deep learning model employing CT images exceeded 90%, and sensitivity of these models exceeded 95%. C-reactive protein and lactate dehydrogenase were important features of machine learning models. Our machine learning model, while slightly less accurate than the multimodal model, still provides a valuable medical triage tool for patients in the early stages of COVID-19.

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