Ds Hooda - Academia.edu (original) (raw)

Papers by Ds Hooda

Research in Statistics, 2023

Missing data exists in almost every science dataset. Generally, these missing data need to be est... more Missing data exists in almost every science dataset. Generally, these missing data need to be estimated or collected again from an application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess highly desirable noise tolerance and robustness properties. But to collect data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Open Access Journal of Astronomy, 2025

In the present paper a brief account of ancient astronomy covering the Pre-Siddhanta period and t... more In the present paper a brief account of ancient astronomy covering the Pre-Siddhanta period and the Siddhanta Jyotish period is given. Five Siddhanta books supposed to be written by the sages are described and some other books on astronomy written by outstanding mathematicians and astronomers are also discussed. The historical development of Ancient Mathematics regarding various manuscripts is described with examples. In particular, the development of decimal systems and numerals with zero symbols are discussed in detail. Some concluding remarks are also given with a list of references in the end.

In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matri... more In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matrices were defined with examples. The lattice of fuzzy matrices was explained and characterized by a theorem. The fuzzy binary relation matrix and its inverse were defined with examples. Its application in a medical diagnosis model was studied and illustrated with an example. Finally, a conclusion and references were provided.

In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matri... more In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matrices were defined with examples. The lattice of fuzzy matrices was explained and characterized by a theorem. The fuzzy binary relation matrix and its inverse were defined with examples. Its application in a medical diagnosis model was studied and illustrated with an example. Finally, a conclusion and references were provided.

Information theory as a separate subject is about 70 years old. Since information is energy, therefore, it is measured, managed, regulated and controlled for the sake of the welfare of humanity. In this chapter, a new ‘useful’ R-norm relative information measure is introduced and characterized..., 2024

Information theory as a separate subject is about 70 years old. Since information is energy, ther... more Information theory as a separate subject is about 70 years old. Since information is energy, therefore, it is measured, managed, regulated and controlled for the sake of the welfare of humanity. In this chapter, a new ‘useful’ R-norm relative information measure is introduced and characterized axiomatically. Its inequalities with particular cases are
described. This new information measure has also been applied to study the status of the
companies with regard to their loss and profit and that has been illustrated by considering
an example of empirical data and drawing figures. Ad joint of the relative information
measure is defined and illustrated with an example and its application in the share market
is also studied with examples. The ‘Useful’ R-norm relative information measures of degree β and its ad joint are defined and studied in this communication and can further be generalized parametrically and applied in planning, forecasting, agriculture, etc.

Estimation of missing data in intuitionistic fuzzy soft matrix (IFSM) with application in MCDM, 2024

The theory of fuzzy soft as well as intuitionistic soft set generally depends on complete informa... more The theory of fuzzy soft as well as intuitionistic soft set generally depends on complete information. But there are situations in which the complete information is not available due to various different reasons like data erroneous, in sufficiency of data, lacking of evidence and illegibility of data. Missing data restrict fuzzy soft matrix and intuitionistic fuzzy soft matrix and that causes more ambiguous uncertainty in the process of decision making. Thus, estimation of incomplete information data plays an important role in multi criteria decision making process which has been developed recently. Here, a novel method of missing data estimation in intuitionistic fuzzy soft matrix is described. Firstl, we estimate the missing values using the information given in data. After

Oriental Journal of Physical Sciences, 2023

During last few decades Statistics has penetrated into almost all sciences like agriculture, biol... more During last few decades Statistics has penetrated into almost all sciences like agriculture, biology, business, social, engineering, medical sciences, etc. Its wide and varied applications have lead to the growth of many branches, such as Industrial Statistics, Biometrics, Biostatistics and Agricultural Statistics. In addition to the above, some new branches have also emerged as distinct entities or subjects with a bulk of statistical techniques specific to their application areas for example computational

research in statistics, 2023

Missing data exists in almost every dataset in science. Generally, these missing data needs to be... more Missing data exists in almost every dataset in science. Generally, these missing data needs to be estimated or should be collected again for application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess high desirable properties of noise tolerance and robustness. But to collect data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Research in Statistics, 2023

Missing data exists in almost every dataset in science. Generally, these missing data needs to be... more Missing data exists in almost every dataset in science. Generally, these missing data needs to be estimated or should be collected again for application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess high desirable properties of noise tolerance and robustness. But to collect data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Article, 2023

During last few decades Statistics has penetrated into almost all sciences like agriculture, biol... more During last few decades Statistics has penetrated into almost all sciences like agriculture, biology, business, social, engineering, medical sciences, etc. Its wide and varied applications have lead to the growth of many branches, such as Industrial Statistics, Biometrics, Biostatistics and Agricultural Statistics. In addition to the above, some new branches have also emerged as distinct entities or subjects with a bulk of statistical techniques specific to their application areas for example computational

Missing data exist in almost every dataset in science. Generally, these missing data needs to be ... more Missing data exist in almost every dataset in science. Generally, these missing data needs to be estimated or should be collected again for application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess high desirable properties of noise tolerance and robustness. However, collecting data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Acta Scientific Computer Sciences, 2023

In the literature of information theory and fuzzy set doctrine, there exist various prominent mea... more In the literature of information theory and fuzzy set doctrine, there exist various prominent measures of divergence, each possesses own merits, demerits and disciplines of applications. Basically, divergence measure is a tool used to quantify the discrimination between two objects. Particularly, the idea of divergence measure for fuzzy sets is significant since it has applications in different disciplines such as process control, decision making, image segmentation, medical diagnosis, pattern recognition and so on. In this paper, some new fuzzy divergence measures which are generalizations of probabilistic divergence measures are introduced. Then, an approach, which is based on divergence measure for fuzzy sets, is proposed for solving multi-criteria decision-making (MCDM) problems within fuzzy atmosphere. Finally, an example is provided in order to demonstrate the practicality and effectiveness of the proposed method.

Keywords: Divergence Measure, Entropy, Fuzzy Set, MCDM.

ACta Scientific Computer Sciences , 2023

In the literature of information theory and fuzzy set doctrine, there exist various prominent mea... more In the literature of information theory and fuzzy set doctrine, there exist various prominent measures of divergence, each possesses own merits, demerits and disciplines of applications. Basically, divergence measure is a tool used to quantify the discrimination between two objects. Particularly, the idea of divergence measure for fuzzy sets is significant since it has applications in different disciplines such as process control, decision making, image segmentation, medical diagnosis, pattern recognition and so on. In this paper, some new fuzzy divergence measures which are generalizations of probabilistic divergence measures are introduced. Then, an approach, which is based on divergence measure for fuzzy sets, is proposed for solving multi-criteria decision-making (MCDM) problems within fuzzy atmosphere. Finally, an example is provided in order to demonstrate the practicality and effectiveness of the proposed method.

Keywords: Divergence Measure, Entropy, Fuzzy Set, MCDM.

ON SOFT AND FUZZY SOFT RELATIONS WITH THEIR APPLICATIONS

In our day to day life we face many problems which are abstract or vague in nature. These problem... more In our day to day life we face many problems which are abstract or vague in nature. These problems cannot be solved only by using simple mathematical tools. To deal with such kind of problems a new technique popularly known as Fuzzy Set Theory was discovered. Fuzzy set is the generalization ofOn crisp set and is used in almost every field of life including Medical Sciences, Business, Administration, Social Science and Operation Research. Later on, a new concept of parameterization of power set of the universal set was introduced. Consequently, Fuzzy Soft Set Theory was defined by embedding Fuzzy Set and Soft Set. In the present communication fuzzy binary relation is described and its applications are studied. The concepts of soft and fuzzy soft relations are also defined with their applications in decision making problems.

Research in Statistics, 2023

Missing data exists in almost every science dataset. Generally, these missing data need to be est... more Missing data exists in almost every science dataset. Generally, these missing data need to be estimated or collected again from an application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess highly desirable noise tolerance and robustness properties. But to collect data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Open Access Journal of Astronomy, 2025

In the present paper a brief account of ancient astronomy covering the Pre-Siddhanta period and t... more In the present paper a brief account of ancient astronomy covering the Pre-Siddhanta period and the Siddhanta Jyotish period is given. Five Siddhanta books supposed to be written by the sages are described and some other books on astronomy written by outstanding mathematicians and astronomers are also discussed. The historical development of Ancient Mathematics regarding various manuscripts is described with examples. In particular, the development of decimal systems and numerals with zero symbols are discussed in detail. Some concluding remarks are also given with a list of references in the end.

In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matri... more In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matrices were defined with examples. The lattice of fuzzy matrices was explained and characterized by a theorem. The fuzzy binary relation matrix and its inverse were defined with examples. Its application in a medical diagnosis model was studied and illustrated with an example. Finally, a conclusion and references were provided.

In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matri... more In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matrices were defined with examples. The lattice of fuzzy matrices was explained and characterized by a theorem. The fuzzy binary relation matrix and its inverse were defined with examples. Its application in a medical diagnosis model was studied and illustrated with an example. Finally, a conclusion and references were provided.

Information theory as a separate subject is about 70 years old. Since information is energy, therefore, it is measured, managed, regulated and controlled for the sake of the welfare of humanity. In this chapter, a new ‘useful’ R-norm relative information measure is introduced and characterized..., 2024

Information theory as a separate subject is about 70 years old. Since information is energy, ther... more Information theory as a separate subject is about 70 years old. Since information is energy, therefore, it is measured, managed, regulated and controlled for the sake of the welfare of humanity. In this chapter, a new ‘useful’ R-norm relative information measure is introduced and characterized axiomatically. Its inequalities with particular cases are
described. This new information measure has also been applied to study the status of the
companies with regard to their loss and profit and that has been illustrated by considering
an example of empirical data and drawing figures. Ad joint of the relative information
measure is defined and illustrated with an example and its application in the share market
is also studied with examples. The ‘Useful’ R-norm relative information measures of degree β and its ad joint are defined and studied in this communication and can further be generalized parametrically and applied in planning, forecasting, agriculture, etc.

Estimation of missing data in intuitionistic fuzzy soft matrix (IFSM) with application in MCDM, 2024

The theory of fuzzy soft as well as intuitionistic soft set generally depends on complete informa... more The theory of fuzzy soft as well as intuitionistic soft set generally depends on complete information. But there are situations in which the complete information is not available due to various different reasons like data erroneous, in sufficiency of data, lacking of evidence and illegibility of data. Missing data restrict fuzzy soft matrix and intuitionistic fuzzy soft matrix and that causes more ambiguous uncertainty in the process of decision making. Thus, estimation of incomplete information data plays an important role in multi criteria decision making process which has been developed recently. Here, a novel method of missing data estimation in intuitionistic fuzzy soft matrix is described. Firstl, we estimate the missing values using the information given in data. After

Oriental Journal of Physical Sciences, 2023

During last few decades Statistics has penetrated into almost all sciences like agriculture, biol... more During last few decades Statistics has penetrated into almost all sciences like agriculture, biology, business, social, engineering, medical sciences, etc. Its wide and varied applications have lead to the growth of many branches, such as Industrial Statistics, Biometrics, Biostatistics and Agricultural Statistics. In addition to the above, some new branches have also emerged as distinct entities or subjects with a bulk of statistical techniques specific to their application areas for example computational

research in statistics, 2023

Missing data exists in almost every dataset in science. Generally, these missing data needs to be... more Missing data exists in almost every dataset in science. Generally, these missing data needs to be estimated or should be collected again for application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess high desirable properties of noise tolerance and robustness. But to collect data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Research in Statistics, 2023

Missing data exists in almost every dataset in science. Generally, these missing data needs to be... more Missing data exists in almost every dataset in science. Generally, these missing data needs to be estimated or should be collected again for application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess high desirable properties of noise tolerance and robustness. But to collect data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Article, 2023

During last few decades Statistics has penetrated into almost all sciences like agriculture, biol... more During last few decades Statistics has penetrated into almost all sciences like agriculture, biology, business, social, engineering, medical sciences, etc. Its wide and varied applications have lead to the growth of many branches, such as Industrial Statistics, Biometrics, Biostatistics and Agricultural Statistics. In addition to the above, some new branches have also emerged as distinct entities or subjects with a bulk of statistical techniques specific to their application areas for example computational

Missing data exist in almost every dataset in science. Generally, these missing data needs to be ... more Missing data exist in almost every dataset in science. Generally, these missing data needs to be estimated or should be collected again for application point of view. These methods are a kind of treatment for uncertainty and vagueness existing in the data sets. On the other hand, methods based on rough fuzzy sets provide excellent tools for dealing with uncertainty as they possess high desirable properties of noise tolerance and robustness. However, collecting data in the same environmental and physical conditions is not possible. Also, it may be possible that in the estimated values there is some biasedness involved. Fortunately, recent advances in theoretical and computational statistics have led to more flexible techniques to deal with missing data problems. In the present paper, we defined an algorithmic method for the selection of robot using an incomplete rough fuzzy set without estimating the missing data by using available information. The technique is also illustrated by considering a numerical problem.

Acta Scientific Computer Sciences, 2023

In the literature of information theory and fuzzy set doctrine, there exist various prominent mea... more In the literature of information theory and fuzzy set doctrine, there exist various prominent measures of divergence, each possesses own merits, demerits and disciplines of applications. Basically, divergence measure is a tool used to quantify the discrimination between two objects. Particularly, the idea of divergence measure for fuzzy sets is significant since it has applications in different disciplines such as process control, decision making, image segmentation, medical diagnosis, pattern recognition and so on. In this paper, some new fuzzy divergence measures which are generalizations of probabilistic divergence measures are introduced. Then, an approach, which is based on divergence measure for fuzzy sets, is proposed for solving multi-criteria decision-making (MCDM) problems within fuzzy atmosphere. Finally, an example is provided in order to demonstrate the practicality and effectiveness of the proposed method.

Keywords: Divergence Measure, Entropy, Fuzzy Set, MCDM.

ACta Scientific Computer Sciences , 2023

In the literature of information theory and fuzzy set doctrine, there exist various prominent mea... more In the literature of information theory and fuzzy set doctrine, there exist various prominent measures of divergence, each possesses own merits, demerits and disciplines of applications. Basically, divergence measure is a tool used to quantify the discrimination between two objects. Particularly, the idea of divergence measure for fuzzy sets is significant since it has applications in different disciplines such as process control, decision making, image segmentation, medical diagnosis, pattern recognition and so on. In this paper, some new fuzzy divergence measures which are generalizations of probabilistic divergence measures are introduced. Then, an approach, which is based on divergence measure for fuzzy sets, is proposed for solving multi-criteria decision-making (MCDM) problems within fuzzy atmosphere. Finally, an example is provided in order to demonstrate the practicality and effectiveness of the proposed method.

Keywords: Divergence Measure, Entropy, Fuzzy Set, MCDM.

ON SOFT AND FUZZY SOFT RELATIONS WITH THEIR APPLICATIONS

In our day to day life we face many problems which are abstract or vague in nature. These problem... more In our day to day life we face many problems which are abstract or vague in nature. These problems cannot be solved only by using simple mathematical tools. To deal with such kind of problems a new technique popularly known as Fuzzy Set Theory was discovered. Fuzzy set is the generalization ofOn crisp set and is used in almost every field of life including Medical Sciences, Business, Administration, Social Science and Operation Research. Later on, a new concept of parameterization of power set of the universal set was introduced. Consequently, Fuzzy Soft Set Theory was defined by embedding Fuzzy Set and Soft Set. In the present communication fuzzy binary relation is described and its applications are studied. The concepts of soft and fuzzy soft relations are also defined with their applications in decision making problems.

Fuzzy Binary Relation Matrix and its Application in Medical Diagnosis, 2024

In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matri... more In the present chapter, a fuzzy matrix was introduced and different operations on two fuzzy matrices were defined with examples. The lattice of fuzzy matrices was explained and characterized by a theorem. The fuzzy binary relation matrix and its inverse were defined with examples. Its application in a medical diagnosis model was studied and illustrated with an example. Finally, a conclusion and references were provided.