SMILES to Smell: Decoding the Structure–Odor Relationship of Chemical Compounds Using the Deep Neural Network Approach

2021 ◽  
Vol 61 (2) ◽  
pp. 676-688
Author(s):  
Anju Sharma ◽  
Rajnish Kumar ◽  
Shabnam Ranjta ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Chemical Compounds ◽  
Network Approach ◽  
Neural Network Approach ◽  
Relationship Of

scholarly journals An optimized Deep Neural Network Approach for Vehicular Traffic Noise Trend Modelling

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed Abdulkareem Ahmed ◽  
Biswajeet Pradhan ◽  
Subrata Chakraborty ◽  
Abdullah Alamri ◽  
Chang-Wook Lee
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Traffic Noise ◽  
Vehicular Traffic ◽  
Network Approach ◽  
Neural Network Approach

Validating the impact of accounting disclosures on stock market: A deep neural network approach

2021 ◽  
Vol 170 ◽  
pp. 120903
Author(s):  
Prajwal Eachempati ◽  
Praveen Ranjan Srivastava ◽  
Ajay Kumar ◽  
Kim Hua Tan ◽  
Shivam Gupta
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Deep Neural Network ◽  
Network Approach ◽  
Neural Network Approach ◽  
Accounting Disclosures ◽  
The Impact

State-of-Charge Estimation of Li-Ion Battery in Electric Vehicles: A Deep Neural Network Approach

2020 ◽  
Vol 56 (5) ◽  
pp. 5565-5574
Author(s):  
Dickshon N. T. How ◽  
Mahammad A. Hannan ◽  
Molla S. Hossain Lipu ◽  
Khairul S. M. Sahari ◽  
Pin Jern Ker ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicles ◽  
Deep Neural Network ◽  
State Of Charge ◽  
Li Ion Battery ◽  
Network Approach ◽  
Neural Network Approach ◽  
Li Ion ◽  
State Of Charge Estimation

scholarly journals Neural Network for Predicting Thermal Conductivity of Knit Materials

2008 ◽  
Vol 3 (4) ◽  
pp. 155892500800300 ◽  
Author(s):  
Faten Fayala ◽  
Hamza Alibi ◽  
Sofien Benltoufa ◽  
Abdelmajid Jemni
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Mathematical Modelling ◽  
Physical Characteristic ◽  
Air Permeability ◽  
Network Approach ◽  
Conductivity Data ◽  
Neural Network Approach ◽  
Knitted Structure ◽  
Relationship Of

The major aim of comfort research is to find the comfort temperature for an individual or group. This subjective property can be evaluated by means of thermal conductivity as a physical characteristic of fabric. This phenomenon depends on many fabric parameters and it is difficult to study the effect of ones without changing the others. In addition, the non-linear relationship of fabric parameters and thermal conductivity handicap mathematical modelling. So a neural network approach was used to predict the thermal conductivity of knitting structure as a function of porosity, air permeability, weight and fiber conductivity. Data on thermal conductivity are measured by experiments carried out on jersey knitted structure.

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