Conference Papers

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Modeling of phenol degradation in spouted bed contactor using artificial neural network (ANN)
(Walter de Gruyter GmbH, 2008) Dabhade, M.A.; Saidutta, M.B.; Murthy, D.V.R.
Presence of phenol and phenolic compounds in various wastewaters and its harmful effects has led to the use of different treatment methods. Work on biological methods shows the use of different microorganisms and different bioreactors so as to improve the removal efficiency economically. The present work deals with the use of N. hydrocarbonoxydans (NCIM 2386), an actinomycetes, for the degradation of phenol. N. hydrocarbonoxydans was immobilized on GAC and used in a spouted bed contactor for effective contact of microorganisms and the substrate. The contactor performance was studied by varying flow rates, influent concentrations and the solids loading in the contactor. The effect of these variables on phenol degradation was investigated and modeling study was carried out using the artificial neural network (ANN). A feed forward neural network with back propagation was used for the model development. The experiments were planned as per the face centered cube design (FCCD) and used for training of the model, whereas data from four other experimental runs were used for testing and validation of the model. The network was optimized for the number of neurons based on the mean square error. The ANN model with three layers with three input neurons, eight neurons in hidden layers and one output neuron was found to predict effectively the effluent concentration for the given operating conditions in the spouted bed contactor. The mean square error was found to be 9.318e-12 for this ANN model. Also the experimental data was used to develop second order nonlinear empirical model obtained using multiple regression (MR) and the results compared with ANN using correlation coefficient (R2), average absolute error (AAE) and root mean square error (RMSE). Results show that R2, AAE and RMSE values of MR model were 0.9363, 2.085 % and 2.338 % respectively, while in case of ANN model these values were 0.9995, 0.59 % and 1.263 % respectively. This shows that ANN model prediction is better than multiple regression model prediction. Copyright Â© 2008 The Berkeley Electronic Press. All rights reserved.
A Novel Machine Learning Approach for Bug Prediction
(Elsevier B.V., 2016) Puranik, S.; Deshpande, P.; Chandrasekaran, K.
With the growing complexities of the software, the number of potential bugs is also increasing rapidly. These bugs hinder the rapid software development cycle. Bugs, if left unresolved, might cause problems in the long run. Also, without any prior knowledge about the location and the number of bugs, managers may not be able to allocate resources in an efficient way. In order to overcome this problem, researchers have devised numerous bug prediction approaches so far. The problem with the existing models is that the researchers have not been able to arrive at an optimized set of metrics. So, in this paper, we make an attempt to select the minimal number of best performing metrics, thereby keeping the model both simple and accurate at the same time. Most of the bug prediction models use regression for prediction and since regression is a technique to best approximate the training data set, the approximations don't always fit well with the test data set. Keeping this in mind, we propose an algorithm to predict the bug proneness index using marginal R square values. Though regressions are performed as intermediary steps in this algorithm, the underlying logic is different in nature when compared with the models using regressions alone. Â© 2016 The Authors. Published by Elsevier B.V.
Development of psychoacoustics index for motorcycle exhaust noise by multiple regression method
(Elsevier Ltd, 2023) Anas, D.; Joladarashi, S.; Kadoli, R.; Chavan, P.; Bhangale, R.
Exhaust sound is an essential characteristic of a motorcycle when it comes to the interest of young motorcycle riders. It has become such an essential characteristic that riders, after looking into the performance, style, and ergonomics, look into the exhaust sound produced by the muffler. It has thus become critical for motorcycle manufacturers to tune their mufflers in such a way that they are appealing to the customers while adhering to sound emission regulations. As per the current trend of Indian motorcycle rider's scenario, more attention is given to sound attributes like beat feeling, sportiness, and pleasantness. This research paper aims to develop a psychoacoustic model that can rate the exhaust sound of motorcycles targeting the Indian customer's preferences and interests with regard to the engine exhaust sound. Presently, a study has been carried out on beat feeling, sportiness, and pleasantness, a perceivable attribute of sound in the family of motorcycles. Motorcycle buyers often perceive the exhaust sound of motorcycles in idling conditions at some distance away from the muffler, which formed the basis while recording exhaust sounds. Initially, exhaust sounds were recorded, which led to the calculation of psychometrics as objective variables. The calculated objective variables were given as input to the multiple regression model. A jury panel consisting of experienced NVH professionals and riders evaluated these sounds on a 7-point evaluation scale. These subjective ratings were given as the target variable to the multiple regression model. The obtained model was later validated with subjective data of motorcycles, including those at the prototype stage. Thus, a model has been established to aid exhaust designers in comparing their motorcycle at different prototype stages with the competitor's vehicle, allowing them to make an early decision with respect to the muffler design modifications, thereby reducing the time-consuming and expensive jury tests. Â© 2022

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