Ku Naim - Academia.edu (original) (raw)

Papers by Ku Naim

Research paper thumbnail of Prevalence of long gun use in Maryland firearm suicides

Injury Epidemiology, 2020

Background Firearms account for the majority of US suicides, largely due to lethality and accessi... more Background Firearms account for the majority of US suicides, largely due to lethality and accessibility. Under Federal and Maryland law, long guns are less regulated than handguns which is a concern for increased suicide risk. This study uses Maryland data to ascertain the impact of long guns on suicides in the state. We hypothesize that the prevalence of long gun use among firearm suicides will be increased in rural and young populations. Methods This is a cross sectional study using police and medical examiner narratives to identify firearm type involved in all 3931 Maryland gun suicides from 2003 to 2018. Proportions of firearm suicides utilizing long guns were calculated. Urban-rural differences were determined using the National Center for Health Statistics’ classification system. Logistic regression was used to calculate odds ratios of long gun to handgun suicides across the urban-rural spectrum, controlling for decedent demographics. Results From 2003 to 2018, 28.4% of Maryla...

Research paper thumbnail of Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets

Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015

It is necessary to represent the probabilities of fuzzy events based on a Bayesian knowledge. Ins... more It is necessary to represent the probabilities of fuzzy events based on a Bayesian knowledge. Inspired by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy sets with Bayesian logistic regression is introduced. This includes official models, elementary operations, basic properties and advanced application. The Vectorial Centroid method for interval type-2 fuzzy set takes a broad view by exampled labelled by a classical Vectorial Centroid defuzzification method for type-1 fuzzy sets. Rather than using type-1 fuzzy sets for implementing fuzzy events, type-2 fuzzy sets are recommended based on the involvement of uncertainty quantity. It also highlights the incorporation of fuzzy sets with Bayesian logistic regression allows the use of fuzzy attributes by considering the need of human intuition in data analysis. It is worth adding here that this proposed methodology then applied for BUPA liver-disorder dataset and validated theoretically and empirically.

Research paper thumbnail of Ranking of Interval Type-2 Fuzzy Numbers based on Centroid Point and Spread

Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015

A concept of interval type-2 fuzzy numbers is introduced in decision making analysis as this conc... more A concept of interval type-2 fuzzy numbers is introduced in decision making analysis as this concept is capable to effectively deal with the uncertainty in the information about a decision. It considers two types of uncertainty namely inter and intra personal uncertainties, in enhancing the representation of type-1 fuzzy numbers in the literature of fuzzy sets. As interval type-2 fuzzy numbers are crucial in decision making, this paper proposes a methodology for ranking interval type-2 fuzzy numbers. This methodology consists of two parts namely the interval type-2 fuzzy numbers reduction methodology as the first part and ranking of type-1 fuzzy numbers as the second part. In this study, established reduction methodology of interval type-2 fuzzy numbers into type-1 fuzzy numbers is extended to reduction into standardised generalised type-1 fuzzy numbers as the extension complements the capability of the methodology on dealing with both positive and negative data values. It is worth adding here that this methodology is analysed using thorough empirical comparison with some established ranking methods for consistency evaluation. This methodology is considered as a generic decision making procedure, especially when interval type-2 fuzzy numbers are applied to real decision making problems.

Research paper thumbnail of A reliability based consistent fuzzy preference relations – fuzzy similarity for risk assessment in oil and gas industry

In decision making, linguistic variables tend to be complex to handle but they make more sense th... more In decision making, linguistic variables tend to be complex to handle but they make more sense than classical fuzzy numbers. Fuzziness is not sufficient enough to deal with information and degree of reliability of information is critical. Znumbers is proposed to model the uncertainty produced by human judgment when eliciting information. Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in the uncertain information development. This issue has motivated the authors to propose fuzzy multi criteria group decision making methodology using z-numbers. The proposed methodology is demonstrated the capability to handle knowledge of human being and uncertain information for risk assessment in oil and gas industry. This assessment is due to periodic basis, which will give insights from the operational until the strategic level of decision making process that is capable of dealing wit...

Research paper thumbnail of Implementing Adaptive Vectorial Centroid in Bayesian Logistic Regression for Interval Type-2 Fuzzy Sets

Studies in Computational Intelligence, 2016

A prior distributions in standard Bayesian knowledge are assumed to be classical probability dist... more A prior distributions in standard Bayesian knowledge are assumed to be classical probability distribution. It is required to representable those probabilities of fuzzy events based on Bayesian knowledge. Propelled by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy set with Bayesian logistic regression is introduced. As opposed of utilising type-1 fuzzy set, type-2 fuzzy set is recommended based on the involvement of uncertainty quantity. It additionally highlights the association of fuzzy sets with Bayesian logistic regression permits the use of fuzzy attributes by considering the need of human intuition in data analysis. It may be worth including here that this proposed methodology then applied for BUPA liver-disorder dataset and validated theoretically and empirically.

Research paper thumbnail of Prevalence of long gun use in Maryland firearm suicides

Injury Epidemiology, 2020

Background Firearms account for the majority of US suicides, largely due to lethality and accessi... more Background Firearms account for the majority of US suicides, largely due to lethality and accessibility. Under Federal and Maryland law, long guns are less regulated than handguns which is a concern for increased suicide risk. This study uses Maryland data to ascertain the impact of long guns on suicides in the state. We hypothesize that the prevalence of long gun use among firearm suicides will be increased in rural and young populations. Methods This is a cross sectional study using police and medical examiner narratives to identify firearm type involved in all 3931 Maryland gun suicides from 2003 to 2018. Proportions of firearm suicides utilizing long guns were calculated. Urban-rural differences were determined using the National Center for Health Statistics’ classification system. Logistic regression was used to calculate odds ratios of long gun to handgun suicides across the urban-rural spectrum, controlling for decedent demographics. Results From 2003 to 2018, 28.4% of Maryla...

Research paper thumbnail of Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets

Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015

It is necessary to represent the probabilities of fuzzy events based on a Bayesian knowledge. Ins... more It is necessary to represent the probabilities of fuzzy events based on a Bayesian knowledge. Inspired by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy sets with Bayesian logistic regression is introduced. This includes official models, elementary operations, basic properties and advanced application. The Vectorial Centroid method for interval type-2 fuzzy set takes a broad view by exampled labelled by a classical Vectorial Centroid defuzzification method for type-1 fuzzy sets. Rather than using type-1 fuzzy sets for implementing fuzzy events, type-2 fuzzy sets are recommended based on the involvement of uncertainty quantity. It also highlights the incorporation of fuzzy sets with Bayesian logistic regression allows the use of fuzzy attributes by considering the need of human intuition in data analysis. It is worth adding here that this proposed methodology then applied for BUPA liver-disorder dataset and validated theoretically and empirically.

Research paper thumbnail of Ranking of Interval Type-2 Fuzzy Numbers based on Centroid Point and Spread

Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015

A concept of interval type-2 fuzzy numbers is introduced in decision making analysis as this conc... more A concept of interval type-2 fuzzy numbers is introduced in decision making analysis as this concept is capable to effectively deal with the uncertainty in the information about a decision. It considers two types of uncertainty namely inter and intra personal uncertainties, in enhancing the representation of type-1 fuzzy numbers in the literature of fuzzy sets. As interval type-2 fuzzy numbers are crucial in decision making, this paper proposes a methodology for ranking interval type-2 fuzzy numbers. This methodology consists of two parts namely the interval type-2 fuzzy numbers reduction methodology as the first part and ranking of type-1 fuzzy numbers as the second part. In this study, established reduction methodology of interval type-2 fuzzy numbers into type-1 fuzzy numbers is extended to reduction into standardised generalised type-1 fuzzy numbers as the extension complements the capability of the methodology on dealing with both positive and negative data values. It is worth adding here that this methodology is analysed using thorough empirical comparison with some established ranking methods for consistency evaluation. This methodology is considered as a generic decision making procedure, especially when interval type-2 fuzzy numbers are applied to real decision making problems.

Research paper thumbnail of A reliability based consistent fuzzy preference relations – fuzzy similarity for risk assessment in oil and gas industry

In decision making, linguistic variables tend to be complex to handle but they make more sense th... more In decision making, linguistic variables tend to be complex to handle but they make more sense than classical fuzzy numbers. Fuzziness is not sufficient enough to deal with information and degree of reliability of information is critical. Znumbers is proposed to model the uncertainty produced by human judgment when eliciting information. Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in the uncertain information development. This issue has motivated the authors to propose fuzzy multi criteria group decision making methodology using z-numbers. The proposed methodology is demonstrated the capability to handle knowledge of human being and uncertain information for risk assessment in oil and gas industry. This assessment is due to periodic basis, which will give insights from the operational until the strategic level of decision making process that is capable of dealing wit...

Research paper thumbnail of Implementing Adaptive Vectorial Centroid in Bayesian Logistic Regression for Interval Type-2 Fuzzy Sets

Studies in Computational Intelligence, 2016

A prior distributions in standard Bayesian knowledge are assumed to be classical probability dist... more A prior distributions in standard Bayesian knowledge are assumed to be classical probability distribution. It is required to representable those probabilities of fuzzy events based on Bayesian knowledge. Propelled by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy set with Bayesian logistic regression is introduced. As opposed of utilising type-1 fuzzy set, type-2 fuzzy set is recommended based on the involvement of uncertainty quantity. It additionally highlights the association of fuzzy sets with Bayesian logistic regression permits the use of fuzzy attributes by considering the need of human intuition in data analysis. It may be worth including here that this proposed methodology then applied for BUPA liver-disorder dataset and validated theoretically and empirically.