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RISS 인기검색어
- 검색키워드
- 저자 : P. Kiran Kumar Reddy (검색결과 3 건)
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B., Kiran Kumar,Gyani, Jayadev,Y., Bhavani,P., Ganesh Reddy,T, Nagasai Anjani Kumar International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10
Nowadays software defect prediction (SDP) is most active research going on in software engineering. Early detection of defects lowers the cost of the software and also improves reliability. Machine learning techniques are widely used to create SDP models based on programming measures. The majority of defect prediction models in the literature have problems with class imbalance and high dimensionality. In this paper, we proposed Centroid and Nearest Neighbor based Class Imbalance Reduction (CNNCIR) technique that considers dataset distribution characteristics to generate symmetry between defective and non-defective records in imbalanced datasets. The proposed approach is compared with SMOTE (Synthetic Minority Oversampling Technique). The high-dimensionality problem is addressed using Ant Colony Optimization (ACO) technique by choosing relevant features. We used nine different classifiers to analyze six open-source software defect datasets from the PROMISE repository and seven performance measures are used to evaluate them. The results of the proposed CNNCIR method with ACO based feature selection reveals that it outperforms SMOTE in the majority of cases.
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G. Satheesh kumar,T. Kiran Reddy,B. Madhavi,P. Charan Teja,M. Subhosh Chandra,Yong Lark Choi 한국생명과학회 2010 생명과학회지 Vol.20 No.5
폐식용유에서 지방분해효소를 생산하는 세균을 분리하였고, PIBWIN 세균동정 방법으로 생리 생화학적 특성을 조사하여 확인한 결과 Pseudomonas sp. OME로 동정하였다. 여러 기질로 지방분해효소 생산을 조사한 결과 올리브유에서 6.1 U/㎖의 생산력을 나타내었다. 물리적 인자인 배양시간, 온도. pH 및 올리브유와 효모 추출액의 영양인자에 의한 지방분해효소 생산 조건을 조사 하였다. 효소의 분비는 배양시간. 올리브유 와 효모 추출액의 농도에 강한 영향을 받았으며, RSM을 이용한 최적화는 이들 인자를 가지고 조사하였다. RSM을 이용한 지방분해효소 생산은 배양시간. 올리브유와 효모 추출액의 농도가 48 hr, 0.3 g, 및 0.9 ㎖에서 최적 생산조건을 나타냈다. Lipolytic enzyme-producing bacteria were isolated from edible oil mill effluents on tributyrin agar medium. The shake-flask-scale studies yielded a promising isolate and it was identified as Pseudomonas sp. An OME using various microbiological observations such as cultural, microscopic, and biochemical tests was undertaken and confirmed using PIBWIN bacterial identification software. Lipolytic enzyme production was screened with oils such as sunflower, caster, coconut, tributyrin, and olive. Amongst these, olive oil showed an increased lipase production 6.1 U/㎖. In view of the highest lipolytic enzyme production with olive oil, further optimizations were carried out using olive oil as a carbon source. Lipolytic enzyme production was optimized by a conventional ‘one variable at a time’ approach and the significant factors were further analyzed statistically using response surface methodology (RSM). The effect of physical factors such as incubation time, temperature, initial medium pH, and nutritional factors such as concentration of olive oil and yeast extract were examined for lipase production. Lipolytic enzyme secretion was strongly affected by three variables (incubation time, concentration of yeast extract and olive oil). Therefore, the interaction of these three factors was further optimized using response surface methodology. The optimized conditions of lipase production using response surface methodology yielded a maximum of 9.62 U/㎖ with optimum conditions for incubation, yeast extract and olive oil concentrations were found to be 48 hr, 0.3 g. and 0.9 ㎖. respectively.
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