http://13.232.72.61:8080/jspui/handle/123456789/1892 2026-04-04T01:48:34Z 2026-04-04T01:48:34Z Sathish Kumar, K B http://13.232.72.61:8080/jspui/handle/123456789/8390 2024-07-31T10:44:40Z 2023-01-01T00:00:00Z

Title: Synthesis, characterization of Zinc oxide and assessment of electrical DC conductivity properties Authors: Sathish Kumar, K B Abstract: Polymer composites are widely used in electrical devices, sensor materials, electromagnetic interference (EMI) shielding, and other applications. Composites may be customized for any intended application by changing the ratios of the polymeric components. Polyaniline (PANI) is not as sensitive as metal oxides toward gasoline species, and its negative solubility in organic solvents limits its packages, however it's miles suitable as a matrix for instruction of engaging in polymer nanocomposites. Therefore, there has been increasing hobby of the researchers for the education of Nano composites based totally on PANI The fuel for the production of nanometal oxide comes from naturally occurring sources. The weight percentages of such generated metal oxides are altered during chemical polymerization with polyaniline. Composites of polyaniline cellulose and varying weight ratios of Zinc oxide (ZnO) are produced. In synthesised Polyaniline cellulose/ZnO composites, the formation of polymers and their interactions with metal oxides are investigated using Ultraviolet (UV)-vis, Fourier-transform infrared spectroscopy (FTIR), and X Ray Diffraction. The surface morphology of the composites is studied using Scanning electron Microscopy. The characteristics of the specified composites are investigated using electrical dc conductivity electrical conductivity.

2023-01-01T00:00:00Z K.Ramakrishna Reddy and Dr.G.N.K.Suresh Babu http://13.232.72.61:8080/jspui/handle/123456789/8389 2024-07-12T10:28:09Z 2022-04-01T00:00:00Z

Title: A Novel Functional Machine Learning Approaches on Prostate Cancer Authors: K.Ramakrishna Reddy and Dr.G.N.K.Suresh Babu Abstract: Cancer registries are collections of curated data about malignant tumor diseases. The amount of data processed by cancer registries increases every year, making manual registration more and more tedious.This research work finds Bayes Net classifier gives an optimal results. The Sequential Minimal Optimization of functional machine learning approach is having highest accuracy level which is 85% of accuracy level. The Sequential Minimal Optimization of functional machine learning approach is having highest precision level which is 0.85 of precision level. The least precision value is 0.80 of precision value which is having Quadratic Discriminant Analysis of functional machine learning classifier approach. The Sequential Minimal Optimization of functional machine learning approach is having highest recall level which is 0.85 of recall level. The least recall value is 0.79 which is produced by Quadratic Discriminant Analysis functional machine learning classification approach. The Sequential Minimal Optimization of functional machine learning approach is having highest F- Measure level which is 0.85 of F-Measure level. The Fisher’s Discriminant Analysis algorithm of functional machine learning classifier and Linear Discriminant Analysis classification algorithm of functional machine learning classifier are having same receiver operating characteristic curve value which is 0.90 of receiver operating characteristic curve value.The maximum precision recall curve value is 0.90 of precision recall curve value which is produced by Linear Discriminant Analysis of functional machine learning classifier. This system recommends that the Sequential Minimal Optimization of functional machine learning approach produces optimal results compare with other models.

2022-04-01T00:00:00Z K.Ramakrishna Reddy and Dr.G.N.K.Suresh Babu http://13.232.72.61:8080/jspui/handle/123456789/8388 2024-07-12T10:20:53Z 2022-02-01T00:00:00Z

Title: Statistical Machine Learning Classifications On Prostate Cancer Dataset Authors: K.Ramakrishna Reddy and Dr.G.N.K.Suresh Babu Abstract: Cancer is the second prominent cause of death worldwide. Per annum around 6, 50,000 death cases in this current situation due to Prostate cancer. Need to improve determination the causal factors of prostate cancer. In this research work considers a medical dataset containing clinical information on 100 prostate cancer patients by using the inductive learning algorithms. This research work finds Bayes Net classifier gives an optimal results. The Bayes classifier has highest accuracy level which is 84% of accuracy. The lowest accuracy level is 62% of accuracy which is produced by Naïve Bayes Multinomial Text classifier of Bayes classifier. The Bayes classifier has highest precision level which is 0.85 of precision level. The lowest precision level is 0.62 of precision level which is produced by Naïve Bayes Multinomial Text classifier of Bayes classifier. The Bayes classifier has highest recall level which is 0.84 of recall level. The lowest precision level is 0.62 of recall level which is produced by Naïve Bayes Multinomial Text classifier of Bayes classifier. The Bayes classifier has highest F-Measure level which is 0.84 of F-Measure level. The lowest F-Measure level is 0.76 of F-Measure level which is produced by Naïve Bayes Multinomial Text classifier of Bayes classifier. The Bayes classifier has highest ROC value level which is 0.93 of ROC level. The lowest ROC level is 0.46 of ROC level which is produced by Naïve Bayes Multinomial Text classifier of Bayes classifier. The Bayes classifier has highest PRC value level which is 0.92 of ROC level. The lowest ROC level is 0.51 of PRC level which is produced by Naïve Bayes Multinomial Text classifier of Bayes classifier.

2022-02-01T00:00:00Z Basavaraj, R. B. Malleshappa, J. Darshan, G. P. Prasad, B. Daruka Nagabhushana, H. http://13.232.72.61:8080/jspui/handle/123456789/2260 2019-05-18T09:23:10Z 2017-01-01T00:00:00Z

Title: Cationic Surfactant Assisted Sonochemical Synthesis of Nd3+ Doped Zn2SiO4 Nanostructures for Solid State Lighting Applications Authors: Basavaraj, R. B.; Malleshappa, J.; Darshan, G. P.; Prasad, B. Daruka; Nagabhushana, H. Abstract: .For the first time cationic surfactant assisted ultrasound synthesis route has been used for the preparation of pure and Nd3+ (0.5-9 mol %) doped Zn2SiO4 nanophosphors. The shape, size and morphology of the products were tuned by controlling the various experimental parameters. The final product was well characterized by sophisticated techniques viz. powder X-ray diffraction (PXRD), Ultraviolet visible spectroscopy (UV-Vis) and photoluminescence (PL). The powder X-ray diffraction patterns confirmed that the synthesized samples exhibit hexagonal phase without any impurity. The DRS spectra showed major peaks at 275, 360, 529, 586, 680, 742 and 806 nm due to the transitions of the 4f electrons of Nd3+ from the ground-state 4 I9/2 to 2 F5/2, 4 D3/2 + 4 D5/2 + 2 I11/2, 2 K13/2 + 4 G7/2 + 4 G9/2, 4 G5/2 + 2 G7/2, 4 F7/2 + 4 S3/2, 4 F5/2 + 2 H9/2 and 4 F3/2 respectively. The band energy gap (Eg) of the samples were estimated and found to be in the range 5.32 – 5.52 eV. Under 421 nm excitation, PL spectra exhibit strong near ultraviolet emission peaks at ~ 444 nm, 459 nm and 520 nm were attributed to 2 P3/2→ 4 I13/2, 2 P3/2→4 I15/2, 1 I6→3 H4, 2 P1/2→4 I9/2 and 4 G7/2→4 I9/2 transitions respectively. The photometric studies indicate that the synthesized Zn2SiO4: Nd3+ nanophosphors can be tuned from blue to pale green by varying the dopant concentration. The current synthesis route is rapid, environmentally benign, cost-effective and useful for industrial applications such as solid state lighting and display devices.

2017-01-01T00:00:00Z