Objective: The main objective of this study is to evaluate the prevalence of HIV infection among the clients attending ICTC, Gauhati Medical College & Hospital, Guwahati, for a period of seven years, i.e., from Ap...Objective: The main objective of this study is to evaluate the prevalence of HIV infection among the clients attending ICTC, Gauhati Medical College & Hospital, Guwahati, for a period of seven years, i.e., from April 2008 to March 2015. Material & Method: A total of 40,983 clients attended ICTC, Gauhati Medical College & Hospital from the year 2008 to 2015. Serum samples were collected after taking informed consent and pre-test counseling. In India for all ICTCs, NACO (National AIDS Control Organization), a national guidelines has been followed for HIV testing, reporting and release of results with post test counseling. Results: Of the total 40,983 clients tested for HIV infection, 1919 (4.68%) were found to be HIV seropositive. Seropositivity was higher in male clients i.e. 1314 (68.47%) than female i.e. 604 (31.47%) followed by transgender (TG), i.e., 1 (0.0005%). Heterosexual route of transmission was the major route seen in 1666 clients (86.81%). Maximum HIV seropositivity was in the age group of 45 - 49 years (43.62%). Conclusion: HIV prevalence of 4.68 % among the clients attending ICTC, Gauhati Medical College & Hospital, Guwahati, puts the spotlight on the HIV burden in this part of the country and suggests the need for the scaling up of focused prevention efforts in high-risk groups.展开更多
This article adopts three soft computing techniques including support vector machine(SVM), least square support vector machine(LSSVM) and relevance vector machine(RVM) for prediction of status of epimetemorphic rock s...This article adopts three soft computing techniques including support vector machine(SVM), least square support vector machine(LSSVM) and relevance vector machine(RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.展开更多
文摘Objective: The main objective of this study is to evaluate the prevalence of HIV infection among the clients attending ICTC, Gauhati Medical College & Hospital, Guwahati, for a period of seven years, i.e., from April 2008 to March 2015. Material & Method: A total of 40,983 clients attended ICTC, Gauhati Medical College & Hospital from the year 2008 to 2015. Serum samples were collected after taking informed consent and pre-test counseling. In India for all ICTCs, NACO (National AIDS Control Organization), a national guidelines has been followed for HIV testing, reporting and release of results with post test counseling. Results: Of the total 40,983 clients tested for HIV infection, 1919 (4.68%) were found to be HIV seropositive. Seropositivity was higher in male clients i.e. 1314 (68.47%) than female i.e. 604 (31.47%) followed by transgender (TG), i.e., 1 (0.0005%). Heterosexual route of transmission was the major route seen in 1666 clients (86.81%). Maximum HIV seropositivity was in the age group of 45 - 49 years (43.62%). Conclusion: HIV prevalence of 4.68 % among the clients attending ICTC, Gauhati Medical College & Hospital, Guwahati, puts the spotlight on the HIV burden in this part of the country and suggests the need for the scaling up of focused prevention efforts in high-risk groups.
文摘This article adopts three soft computing techniques including support vector machine(SVM), least square support vector machine(LSSVM) and relevance vector machine(RVM) for prediction of status of epimetemorphic rock slope. The input variables of SVM, LSSVM and RVM are bulk density, height, inclination, cohesion and internal friction angle. There are 53 datasets which have been used to develop the SVM, LSSVM and RVM models. The developed SVM, LSSVM and RVM give equations for prediction of status of epimetemorphic rock slope. The performance of SVM, LSSVM and RVM is 100%. A comparative study has been presented between the developed SVM, LSSVM and RVM. The results confirm that the developed SVM, LSSVM and RVM are effective tools for prediction of status of epimetemorphic rock slope.
