List of Titles

Authors: Khandelwal, Manoj , Mastorakis, Nikos
Date: 2014
Type: Text , Conference paper
Relation: Advances in neural networks, fuzzy systems and artifical intelligence : Proceedings of the 13th International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '14) p. 100-105
Full Text: false
Reviewed:
Description: The present paper mainly with deals the prediction of safe charge of explosive used per delay (Qmax) using artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 120 blast vibration data sets were monitored at different vulnerable and strategic locations in and around a major coal producing opencast coal mines in India. 100 blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 20 new randomly selected data sets were used to compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of determination (R2) between measured and predicted values of Qmax. It was found that coefficient of determination between measured and predicted Qmax by ANN was 0.894, whereas it was ranging from 0.023 to 0.417 by different conventional predictor equations.

Authors: Khandelwal, Manoj
Date: 2014
Type: Text , Conference paper
Relation: 6th International Conference on Agents and Artificial Intelligence, ICAART 2014 p. 366-371
Full Text: false
Reviewed:
Description: The present paper mainly deals with the prediction of maximum explosive charge used per delay (QMAX) using artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). 150 blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. 124 blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. Rest 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R) and mean absolute error (MAE) between calculated and predicted values of QMAX.

Authors: Mirzaei, Bahareh , Jafarabad, Mohammad , Karimi, Hossein , Khastavaneh, Mohammad , Parveh, Abdolkarim , Dehno, Shahin , Khandelwal, Manoj , Safari, Amirali
Date: 2015
Type: Text , Conference proceedings , Conference paper
Relation: 3rd IEEE International Conference on Progress in Informatics and Computing, PIC 2015; Nanjing, China; 18th-20th December 2015; Published in Proceedings of 2015 IEEE Interntational Conference on Progress in Informatics and Computing, PIC 2015 p. 71-76
Full Text: false
Reviewed:
Description: One of the main reasons of text complexity is usage of uncommon vocabulary. We can overcome this issue by suggesting simpler words as replacements. This paper analyzed texts from Simple English Wikipedia, Wikipedia and IEEE articles. After running the tests, the results indicated that removing Complex words and replacing them with simpler phrases is the fastest practical algorithm to simplify a scientific complex text. Result of our data analysis tests and checking inputs against a database of 2000 words show that IEEE articles use about 15% uncommon words. © 2015 IEEE.
Description: Proceedings of 2015 IEEE International Conference on Progress in Informatics and Computing, PIC 2015

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