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Renuka Devi, K.
- Ultrasonic Investigation on Surfactants in the Presence of Builders and Fillers
Abstract Views :148 |
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Authors
Affiliations
1 Cauvery College for Women, Trichy, Tamil Nadu, IN
2 St. Joseph’s College, Trichy, Tamil Nadu, IN
3 Govt., Arts College for Women, Pudukkottai, Tamil Nadu, IN
1 Cauvery College for Women, Trichy, Tamil Nadu, IN
2 St. Joseph’s College, Trichy, Tamil Nadu, IN
3 Govt., Arts College for Women, Pudukkottai, Tamil Nadu, IN
Source
Journal of Pure and Applied Ultrasonics, Vol 34, No 1 (2012), Pagination: 22-24Abstract
Surfactants and builders are the two most important ingredients in laundry, household and personal-care cleaning products. They play a key role in washing processes. The enzyme Lipase is coupled with three fillers in the presence of aqueous surfactants builder solutions to study the efficiency of the detergent in the removal of dye colour through acoustical investigations. Ultrasonic velocities (U) and densities (ρ) are measured for different solutions at 303 K using thermostatically controlled water bath whose temperature was maintained to an accuracy of ±0.01°C. Acoustical Parameters such as adiabatic compressibility (β), free length (Lf), acoustic impedance (Z) and surface properties like surface tension (γ), surface area (Y) and molar surface energy (E) are determined.Keywords
Surfactants, Builders, Fillers, Enzyme-Lipase, Acoustical Parameters.Full Text
- Internal Pressure and Free Volume of Polymer Solutions of Poly Vinyl Acetate
Abstract Views :121 |
PDF Views:0
Authors
V. Lalitha
1,
K. Renuka Devi
2
Affiliations
1 Seethalakshmi Ramaswamy College, Tiruchirappalli, Tamil Nadu, IN
2 Govt. Arts College (W), Pudukkottai, Tamil Nadu, IN
1 Seethalakshmi Ramaswamy College, Tiruchirappalli, Tamil Nadu, IN
2 Govt. Arts College (W), Pudukkottai, Tamil Nadu, IN
Source
Journal of Pure and Applied Ultrasonics, Vol 28, No 2-4 (2006), Pagination: 157-161Abstract
Ultrasonic studies have been widely used to know more about the properties of the polymer solutions. Ultrasonic velocity, density and viscosity of polymer solutions of Poly vinyl acetate were measured at different temperatures ranging from 35°C to 55°C. The internal pressure (πi) and free volume (Vf) of the solutions are computed. At a given concentration, the variation of πi and Vf due to a change in temperature can be represented by the relation, πi Vfx=k where k is a constant and x is the slope of the straight lines. In all the systems, the above equation was checked thus signifying the importance of internal pressure and free volume in fixing the polymer solutions thermodynamically.Full Text
- Acoustical Behaviour of Binary Solutions of Tartaric Acid
Abstract Views :192 |
PDF Views:0
Authors
V. Lalitha
1,
K. Renuka Devi
2
Affiliations
1 Seethalakshmi Ramaswamy College, Tiruchirappalli, Tamil Nadu, IN
2 Govt. Arts College (W), Pudukkottai, Tamil Nadu, IN
1 Seethalakshmi Ramaswamy College, Tiruchirappalli, Tamil Nadu, IN
2 Govt. Arts College (W), Pudukkottai, Tamil Nadu, IN
Source
Journal of Pure and Applied Ultrasonics, Vol 28, No 2-4 (2006), Pagination: 162-165Abstract
The nature of molecular interaction present in liquids and in various organic liquid mixtures can be studied by ultrasonic measurements. Ultrasonic velocities of solutions of Tartaric acid in water, methanol, ethanol, and propanol were measured at 15°C, 25°C, 35°C, 45°C and 55°C using an ultrasonic interferometer of frequency 2MHz. The ultrasonic velocity, density and viscosity of the solutions are used to calculate adiabatic compressibility, Rao's constant, Wada's constant and apparent molal compressibility. The obtained results indicate the presence of molecular interactions in the binary solutions studied.Full Text
- Diet Recommendation for Glycemic Patients using Improved Kmeans and Krill-Herd Optimization
Abstract Views :174 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 3 (2020), Pagination: 2096-2101Abstract
Maintaining nutrition for glycemic (diabetic) patients in order to retain the blood glucose level is one of the important activity to be followed. Stimulating the amount of carbohydrates, protein, vitamins, and minerals will result in a healthy diet. So, there is a necessity for recommendation of nutrition to those diabetic patients nowadays. Recommender Systems (RS) play a vital role in urging relevant suggestions to the users. To promote improvised and optimized results, Optimization technique plays a significant role in refining the parameters of chosen algorithm. To optimize and to upgrade the accuracy of recommendations, the system has been developed by implementing improved Krill-Herd algorithm. The system which clusters the profiles of diabetic patients using improved k-means clustering algorithm and results has been optimized using Improved Krill-Herd optimization algorithm. The performance will be analysed using different measures like Precision, Recall, F-measure, Accuracy, Matthews correlation, Fallout rate and Miss rate. The evaluation results show that the proposed system performs better and produces optimized results to the diabetic patients with minimum error rate.Keywords
Data Mining, Diabetes Patients, Recommender Systems, Clustering Algorithm, Improved K-Means, Krill Herd Optimization.
References
- Y. Koren and R.M. Bell, “Advances in Collaborative Filtering”, Springer, 2011.
- Kunal Shah, “Unsupervised Recommender Systems: An Overview of Different Approaches to Recommendations”, Proceedings of IEEE International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-4, 2017.
- Xiaofeng Li, “Improved Hybrid Collaborative Filtering Algorithm Based on K-Means”, Expert Systems with Applications, Vol. 842, pp. 928-934, 2019.
- Semeh Ben Salema and Sami Naouali, “A Fast and Effective Partitional Clustering Algorithm for Large Categorical Datasets using a K-means Based Approach”, Computers and Electrical Engineering, Vol. 37, No. 9, pp. 463-483, 2018.
- J. Hu, J. Liang, Y. Kuang and V. Honavar, “A User Similarity-Based Top-N Recommendation Approach for Mobile In-Application Advertising”, Expert Systems with Applications, Vol. 111, pp. 51-60, 2018.
- Anand Khandare and A.S. Alvi, “Clustering Algorithms: Experiment and Improvements”, Computing and Network Sustainability, Vol. 25, No. 12, pp. 263-271, 2017.
- Laith Mohammad Abualigah, Ahamad Tajudin Khader, Essam Said Hanandeh, and Amir H. Gandomi, “A Novel Hybridization Strategy for Krill-Herd Algorithm Applied to Clustering Techniques”, Applied Soft Computing, Vol. 60, pp. 423-435, 2017.
- Laith Mohammad Abualigah, Ahamad Tajudin Khader and Essam Said Hanandeh, “Hybrid Clustering Analysis using Improved Krill-Herd Algorithm”, Applied Intelligence, Vol. 48, No. 11, pp. 4047-4071, 2018.
- D. Dua and E. Karra Taniskidou, “UCI Machine Learning Repository”, Available at: http://archive.ics.uci.edu/ml, Accessed on 2017.
- Methods and Application of Food Composition Laboratory, Available at: http://www.ars.usda.gov/Services/docs.htm?docid=8964.
- R.V. Rao, D.P. Rai and J. Balic, “A Multi-Objective Algorithm for Optimization of Modern Machining Processes”, Engineering Applications of Artificial Intelligence, Vol. 61, pp. 103-125, 2017.
- Divya Pandove, “A Comprehensive Study on Clustering Approaches for Big Data Mining”, Proceedings of IEEE International Conference on Electronics and Communication System, pp. 1333-1338, 2015.
- Jonathan Jeffrey, “Optimizing Web Structures using Web Mining Techniques”, Springer, 2007.
- Daniel Kluver, M.D. Ekstrand and J.A. Konstan, “Rating-Based Collaborative Filtering: Algorithms and Evaluation”, Social Information Access, Vol. 42, No. 8, pp. 344-390, 2018.
- Gunnar Schroder, Maik Thiele and Wolfgang Lehner, “Setting Goals and Choosing Metrics for Recommender System Evaluations”, Proceedings of Workshop at 5th ACM Conference on Recommender Systems, pp. 53-61, 2011.
- M.V.B.T. Santhi, “Enhancing K-Means Clustering Algorithm”, International Journal on Computer Science and Technology, Vol. 2, No. 4, pp. 235-246, 2012.
- Huanjing Wang and Jaason Van Hulse, “A Comparative Study of Threshold-based Feature Selection Techniques”, Proceedings of IEEE International Conference on Granular Computing, pp. 499-504, 2010.
- X. Chen, X. Xu, J.Z. Huang and Y. Ye, “K Means: Automated Two-Level Variable Weighting Clustering Algorithm for Multiview Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 4, pp. 932-944, 2013.
- G.Y. Zhang, C.D. Wang and D. Huang, “K-Means: Two Level Weighted Collaborative K-Means for Multiview Clustering”, Knowledge Based Systems, Vol. 150, pp. 127-138, 2018.
- X.T. Zhang, “Multi-Task Multi-View Clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 4, pp. 3324-3338, 2016.
- L.M. Abualigah, A.T. Khader, E.S. Hanandeh and A.H. Gandomi, “A Novel Hybridization Strategy for Krill-Herd Algorithm Applied to Clustering Techniques”, Applications of Soft Computing, Vol. 60, pp. 423-435, 2017.
- J. Bhavithra and A. Saradha, “Personalized Web Page Recommendation using Case-Based Clustering and Weighted Association Rule Mining”, Cluster Computing, Vol. 22, No. 3, pp. 6991-7002, 2018.
- K. Renuka Devi, “Evaluation of Partitional and Hierarchical Clustering Techniques”, International Journal of Computer Science and Mobile Computing, Vol. 8, No. 11, pp. 48-54, 2019.
- K. Renuka Devi, J. Bhavithra and A. Saradha, “Personalized Nutrition Recommendation for Diabetic Patients using Improved K-Means and Krill-Herd Optimization”, International Journal of Scientific and Technology Research, Vol. 9, No. 3, pp. 1076-1083, 2020.