http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Sankhadeep (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
-
Sankhadeep Chatterjee,Sarbartha Sarkar,Sirshendu Hore,Nilanjan Dey,Amira S. Ashour,Fuqian Shi,Dac-Nhuong Le 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.4
Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multiobjective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLPFFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.
-
Chatterjee, Sankhadeep,Sarkar, Sarbartha,Hore, Sirshendu,Dey, Nilanjan,Ashour, Amira S.,Shi, Fuqian,Le, Dac-Nhuong Techno-Press 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.4
Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.
-
Neural-based prediction of structural failure of multistoried RC buildings
Sirshendu Hore,Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Amira S. Ashour,Dana Bălas-Timar,Valentina E. Balas 국제구조공학회 2016 Structural Engineering and Mechanics, An Int'l Jou Vol.58 No.3
Various vague and unstructured problems encountered the civil engineering/ designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.
-
Basu, Partha,Dutta, Sankhadeep,Begum, Rakiba,Mittal, Srabani,Dutta, Paromita Das,Bharti, Alok Chandra,Panda, Chinmay Kumar,Biswas, Jaydip,Dey, Bindu,Talwar, Gursaran Prashad,Das, Bhudev Chandra Asian Pacific Journal of Cancer Prevention 2013 Asian Pacific journal of cancer prevention Vol.14 No.10
Curcumin and curcumin containing polyherbal preparations have demonstrated anti-microbial and antiviral properties in pre-clinical studies. Till date no therapeutic intervention has been proved to be effective and safe in clearing established cervical human papillomavirus (HPV) infection. The present study evaluated the efficacy of Basant polyherbal vaginal cream (containing extracts of curcumin, reetha, amla and aloe vera) and of curcumin vaginal capsules to eliminate HPV infection from cervix. Women were screened by Pap smear and HPV DNA test by PCR. HPV positive women without high grade cervical neoplasias (N=287) were randomized to four intervention arms to be treated with vaginal Basant cream, vaginal placebo cream, curcumin vaginal capsules and placebo vaginal capsules respectively. All subjects were instructed to use one application of the assigned formulation daily for 30 consecutive days except during menstruation and recalled within seven days of the last application for repeat HPV test, cytology and colposcopy. HPV clearance rate in Basant arm (87.7%) was significantly higher than the combined placebo arms (73.3%). Curcumin caused higher rate of clearance (81.3%) than placebo though the difference was not statistically significant. Vaginal irritation and itching, mostly mild to moderate, was significantly higher after Basant application. No serious adverse events were noted.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
