scholarly journals Detection and Recognition of Text From Natural Camera Image using Deep Convolutional Network

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1043-1047
Keyword(s):  
Amino Acids ◽  
Life Science ◽  
Convolutional Network ◽  
Physiochemical Properties ◽  
Camera Image ◽  
Antigenic Properties ◽  
Deep Convolutional Network ◽  
Detection And Recognition

Amino acids are little bio-particles with different properties. The capacity to ascertain the physiochemical properties of proteins is pivotal in many research regions, for example, tranquilize plan, protein displaying and basic bioinformatics. The physiochemical properties of the protein decides its collaboration with different atoms and subsequently its capacity. Foreseeing the physiochemical properties of protein and translating its capacity is of extraordinary significance in the field of medication and life science. The point of this work is to create python based programming with graphical UI for anticipating the physiochemical and antigenic properties of protein. Thus the instrument was named as ASAP-Analysis of protein succession and antigenicity expectation. ASAP predicts the antigenicity of the protein succession from its amino corrosive arrangement, in light of Chou Fasman turns and antigenic file. ASAP computes different physiochemical properties that is required for invitro tests. ASAP utilizes standardization esteems that expansion the affectability of the apparatus.

scholarly journals Designing LLCL Filter and LCL Filter using Single Phase Multicell AC-DC Converter

2019 ◽  
Vol 8 (6S) ◽  
pp. 643-649
Keyword(s):  
Amino Acids ◽  
Single Phase ◽  
Life Science ◽  
Lcl Filter ◽  
Physiochemical Properties ◽  
Antigenic Properties

Amino acids are little bio-particles with different properties. The capacity to ascertain the physiochemical properties of proteins is pivotal in many research regions, for example, tranquilize plan, protein displaying and basic bioinformatics. The physiochemical properties of the protein decides its collaboration with different atoms and subsequently its capacity. Foreseeing the physiochemical properties of protein and translating its capacity is of extraordinary significance in the field of medication and life science. The point of this work is to create python based programming with graphical UI for anticipating the physiochemical and antigenic properties of protein. Thus the instrument was named as ASAP-Analysis of protein succession and antigenicity expectation. ASAP predicts the antigenicity of the protein succession from its amino corrosive arrangement, in light of Chou Fasman turns and antigenic file. ASAP computes different physiochemical properties that are required for invitro tests. ASAP utilizes standardization esteems that expansion the affectability of the apparatus

scholarly journals Prediction and Calculation of Physiochemical Properties using Structural Bioinformatics and Asap Tools

2019 ◽  
Vol 8 (9S2) ◽  
pp. 298-300
Keyword(s):  
Amino Acids ◽  
Life Science ◽  
Structural Bioinformatics ◽  
Physiochemical Properties ◽  
Antigenic Properties

Amino acids are little bio-particles with different properties. The capacity to ascertain the physiochemical properties of proteins is pivotal in many research regions, for example, tranquilize plan, protein displaying and basic bioinformatics. The physiochemical properties of the protein decides its collaboration with different atoms and subsequently its capacity. Foreseeing the physiochemical properties of protein and translating its capacity is of extraordinary significance in the field of medication and life science. The point of this work is to create python based programming with graphical UI for anticipating the physiochemical and antigenic properties of protein. Thus the instrument was named as ASAP-Analysis of protein succession and antigenicity expectation. ASAP predicts the antigenicity of the protein succession from its amino corrosive arrangement, in light of Chou Fasman turns and antigenic file. ASAP computes different physiochemical properties that is required for invitro tests. ASAP utilizes standardization esteems that expansion the affectability of the apparatus.

scholarly journals Detection of Huntington’s Disease in Human DNA Sequence using Numerical Encoding Method and Machine Learning based Classifier

2019 ◽  
Vol 8 (4) ◽  
pp. 7426-7432
Keyword(s):  
Machine Learning ◽  
Amino Acids ◽  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Dna Sequence ◽  
Life Science ◽  
Physiochemical Properties ◽  
Human Dna ◽  
Antigenic Properties ◽  
Encoding Method

Amino acids are little bio-particles with different properties. The capacity to ascertain the physiochemical properties of proteins is pivotal in many research regions, for example, tranquilize plan, protein displaying and basic bioinformatics. The physiochemical properties of the protein decides its collaboration with different atoms and subsequently its capacity. Foreseeing the physiochemical properties of protein and translating its capacity is of extraordinary significance in the field of medication and life science. The point of this work is to create python based programming with graphical UI for anticipating the physiochemical and antigenic properties of protein. Thus the instrument was named as ASAP-Analysis of protein succession and antigenicity expectation. ASAP predicts the antigenicity of the protein succession from its amino corrosive arrangement, in light of Chou Fasman turns and antigenic file. ASAP computes different physiochemical properties that is required for invitro tests. ASAP utilizes standardization esteems that expansion the affectability of the apparatus.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

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