|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Epidemiology of lung cancer and approaches for its prediction:a systematic review and analysis显示文摘Background: Owing to the use of tobacco and the consumption of alcohol and adulterated food, worldwide cancer incidence is increasing at an alarming and frightening rate. Since the last decade of the twentieth century, lung cancer has been the most common cancer type. This study aimed to determine the global status of lung cancer and to evaluate the use of computational methods in the early detection of lung cancer.Methods: We used lung cancer data from the United Kingdom(UK), the United States(US), India, and Egypt. For statistical analysis, we used incidence and mortality as well as survival rates to better understand the critical state of lung cancer.Results: In the UK and the US, we found a significant decrease in lung cancer mortalities in the period of 1990–2014, whereas, in India and Egypt, such a decrease was not much promising. Additionally, we observed that, in the UK and the US, the survival rates of women with lung cancer were higher than those of men. We observed that the data mining and evolutionary algorithms were efficient in lung cancer detection.Conclusions: Our findings provide an inclusive understanding of the incidences, mortalities, and survival rates of lung cancer in the UK, the US, India, and Egypt. The combined use of data mining and evolutionary algorithm can be efficient in lung cancer detection. | Ashutosh Kumar Dubey Umesh Gupta Sonal Jain | 2016 | Chinese Journal of Cancer2016，35，9： | 37 |
| 2 | A New Database on Human Capital Stock inDeveloping and Industrial Countries:Sources,Methodology,and Results显示文摘 | ERIC SWANSON ASHUTOSH DUBEY | 1995 | Journal of Development Economics1995，46，2： | 1 |
| 3 | A new database on human capital stock in developing and industrial countries:Sources,methodology,and results显示文摘 | Vikram Nehru Eric Swanson Ashutosh Dubey | 1995 | Journal of Development Economics1995，46，2： | 1 |
| 4 | A New Database on human capital stock jin developing country and industrial countries: sources, methodology and result显示文摘 | Nehru Vikram Eric Swanson Ashutosh Dubey | 1995 | Journal of Development Economics1995，，61： | 1 |
| 5 | A New Database on human capital stock in developing and industrial countries:Sources, Methodology, and Results显示文摘 | Vikram Nehru Eric Swanson and Ashutosh Dubey | 1995 | Journal of development economics1995，，46： | 1 |
| 6 | A New Database on Human Capital Stock in Developing and Industrial Countries: Sources, Methodology, and Resuhs显示文摘 | Vikram Nehru Eric Swanson Ashutosh Dubey | 1995 | Journal of Development Economics1995，46，： | 1 |
| 7 | Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms显示文摘This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and classification.In the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different computationalmeasures.During the second phase,the non-clustered data obtained from the first phase were preprocessed with TLBO.TLBO was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)algorithms.The objective function was determined by considering both minimization and maximization criteria.Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization.Five benchmark datasets were considered from theUniversity of California,Irvine(UCI)Machine Learning Repository for comparative study and experimentation.These are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease datasets.The combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of TLBOFCM.This approach is also capable of finding the dominating attributes.The findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant results.Evaluating different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and accuracy.TLBOKM/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data. | Ashutosh Kumar Dubey Umesh Gupta Sonal Jain | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 8 | Improving Linear Type Traits to Improve Production Sustainability and Longevity in Purebred Sahiwal Cattle显示文摘 | Ashutosh Dubey Sharad Mishra Vikas Khune Pavan K. Gupta Bhooshan K. Sahu Arvind K. Nandanwar | 2012 | Journal of Agricultural Science and Technology(A)2012，2，5： | 0 |
| 9 | Performance Estimation of Machine Learning Algorithms in the Factor Analysis of COVID-19 Dataset显示文摘Novel Coronavirus Disease(COVID-19)is a communicable disease that originated during December 2019,when China officially informed the World Health Organization(WHO)regarding the constellation of cases of the disease in the city of Wuhan.Subsequently,the disease started spreading to the rest of the world.Until this point in time,no specific vaccine or medicine is available for the prevention and cure of the disease.Several research works are being carried out in the fields of medicinal and pharmaceutical sciences aided by data analytics and machine learning in the direction of treatment and early detection of this viral disease.The present report describes the use of machine learning algorithms[Linear and Logistic Regression,Decision Tree(DT),K-Nearest Neighbor(KNN),Support Vector Machine(SVM),and SVM with Grid Search]for the prediction and classification in relation to COVID-19.The data used for experimentation was the COVID-19 dataset acquired from the Center for Systems Science and Engineering(CSSE),Johns Hopkins University(JHU).The assimilated results indicated that the risk period for the patients is 12–14 days,beyond which the probability of survival of the patient may increase.In addition,it was also indicated that the probability of death in COVID cases increases with age.The death probability was found to be higher in males as compared to females.SVM with Grid search methods demonstrated the highest accuracy of approximately 95%,followed by the decision tree algorithm with an accuracy of approximately 94%.The present study and analysis pave a way in the direction of attribute correlation,estimation of survival days,and the prediction of death probability.The findings of the present study clearly indicate that machine learning algorithms have strong capabilities of prediction and classification in relation to COVID-19 as well. | Ashutosh Kumar Dubey Sushil Narang Abhishek Kumar Satya Murthy Sasubilli Vicente García-Díaz | 2021 | Computers, Materials & Continua2021，，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员