Investigation of air conditioning temperature variation by modifying the structure of passenger car using computational fluid dynamics

Krishnaswamy, Haribabu; Muthukrishnan, Sivaprakash; Sathish, T.; Arockiaraj, Godwin Antony; Venkatraman, Vijayan

doi:10.2298/tsci190409397k

Cited by 200 publications

(59 citation statements)

References 8 publications

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“…Samuel, Acharya, and Rao 2020;M. S. Samuel et al 2019;Mehta et al 2019;Sharma et al 2019;Malli Sureshbabu et al 2019;Krishnaswamy et al 2020;Muthukrishnan et al 2020;Gheena and Ezhilarasan 2019;Vignesh et al 2019;Ke et al 2019;Vijayakumar Jain et al 2019;Jose, Ajitha, and Subbaiyan 2020). Now the growing trend in this area motivated us to pursue this project.…”

Section: Research Articlementioning

confidence: 99%

Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students

Harini¹,

Rekha²

2021

alinteri

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Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students.

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Section: Research Articlementioning

confidence: 99%

Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students

Harini¹,

Rekha²

2021

alinteri

View full text Add to dashboard Cite

show abstract

“…R. Samuel, Acharya, and Rao 2020;M. S. Samuel et al 2019;Mehta et al 2019;Sharma et al 2019;Malli Sureshbabu et al 2019;Krishnaswamy et al 2020;Muthukrishnan et al 2020;Gheena and Ezhilarasan 2019;Vignesh et al 2019;Ke et al 2019;Vijayakumar Jain et al 2019;Jose, Ajitha, and Subbaiyan 2020). Now the growing trend in this area motivated us to pursue this project.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Analysis of Road Accidents Severity Using Novel Support Vector Machines Over Artificial Neural Networks with Improved Accuracy Rate

Kumar¹,

Jeyavathana²

2021

revistageintec

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Aim: To perform an efficient analysis of road accidents severity prediction using Support Vector Machine and Artificial Neural Network algorithms. Materials and Methods: Road accident severity prediction is performed by Support Vector Machine algorithm (N=10) and Artificial Neural Network algorithm (N=10) using Traffic-crashes dataset. Results and Discussion: The road accident severity prediction using Support Vector Machine algorithm (Accuracy 83.6%) and with Artificial Neural Network algorithm (Accuracy 73.3%). There is no significant difference between the groups. Conclusion: Within the limits of this study, SVM algorithm has significantly better accuracy than ANN algorithm.

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Section: Introductionmentioning

confidence: 99%

Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm

Reddy¹

2021

revistageintec

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Aim: The main motive of this study is to perform Adaptive Multi Rate Wideband (AMR-WB) Speech Steganography in network security to produce the stego speech with less loss of quality while increasing embedding capacities. Materials and Methods: TIMIT Acoustic-Phonetic Continuous Speech Corpus dataset consists of about 16000 speech samples out of which 1000 samples are taken and 80% pretest power for analyzing the speech steganography. AMR-WB Speech steganography is performed by Diameter Neighbor codebook partition algorithm (Group 1) and Neighbor Index Division codebook division algorithm (Group 2). Results: The AMR-WB speech steganography using DN codebook partition obtained average quality rate of 2.8893 and NID codebook division algorithm obtained average quality rate of 2.4196 in the range of 300bps embedding capacity. Conclusion: The outcomes of this study proves that the decrease in quality in NID is twice more than the DN based steganography while increasing the embedding capacities.

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Investigation of air conditioning temperature variation by modifying the structure of passenger car using computational fluid dynamics

Cited by 200 publications

References 8 publications

Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students

Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students

An Efficient Analysis of Road Accidents Severity Using Novel Support Vector Machines Over Artificial Neural Networks with Improved Accuracy Rate

Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm

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