Job shop scheduling (JSS) is a problem which involves an assignment of a set of tasks to the mach... more Job shop scheduling (JSS) is a problem which involves an assignment of a set of tasks to the machines in a predefined sequence in order to optimize one or more objectives considering job performance measures of the system. It's a multi-objective, multi-criteria optimization problem. A job shop environment consists of 'n' jobs. Each job has a given machine routine in which some machines could be given a "miss". The JSS could be considered as a queuing system that consists of machines and jobs where each job demands a specified sequence (routing) on the machines and involves certain amount of processing time. In the dynamic JSSP, the problem size is not fixed. The jobs arrive randomly and addition of each job increases problem size exponentially. The optimal resource scheduling/re-scheduling for a mechanical manufacturing shop in reasonable computational time is achieved using particle Swarm Optimization (PSO). PSO (Particle swarm optimization) is a meta-heuristi...
Astroinformatics is a new impact area in the world of astronomy, occasionally called the final fr... more Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems. Exponential growth in the data volume and increased complexity of the data augments difficult questions to the existing challenges. Classical problems in Astronomy are compounded by accumulation of astronomical volume of complex data, rendering the task of classification and interpretation incredibly laborious. The presence of noise in the data makes analysis and interpretation even more arduous. Machine learning algorithms and data analytic techniques provide the right platform for the challenges posed by these problems. A diverse range of open problem like star-galaxy separation, detection and classification of exoplanets, classification of supernovae is discussed. The focus of the paper is the applicability and efficacy of various machine l...
Scientometrics deals with analyzing and quantifying works in science, technology, and innovation.... more Scientometrics deals with analyzing and quantifying works in science, technology, and innovation. It is a study that focuses on quality rather than quantity. The journals are evaluated against several different metrics such as the impact of the journals, scientific citation, SJR, SNIP indicators as well as the indicators used in policy and management context. The practice of using journal metrics for evaluation involves handling a large volume of data to derive useful patterns and conclusions. These metrics play an important role in the measurement and evaluation of research performance. Due to the fact that most metrics are being manipulated and abused, it becomes essential to judge and evaluate a journal by using a single metric or a reduced set of significant metrics. We propose l1-norm Singular Value Decomposition(l1SVD) to efficiently solve this problem. We evaluate our method to study the emergence of a new journal, Astronomy and Computing, by comparing it with 46000 journals ...
In this chapter, authors have discussed few machine learning techniques and their application to ... more In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).
Option contracts can be valued by using the Black-Scholes equation, a partial differential equati... more Option contracts can be valued by using the Black-Scholes equation, a partial differential equation with initial conditions. An exact solution for European style options is known. The computation time and the error need to be minimized simultaneously. In this paper, the authors have solved the Black-Scholes equation by employing a reasonably accurate implicit method. Options with known analytic solutions have been evaluated. Furthermore, an overall second order accurate space and time discretization is proposed in this paper Keywords: Computational finance, implicit methods, finite differences, call/put options.
Astroinformatics is a new impact area in the world of astronomy, occasionally called the final fr... more Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems. Exponential growth in the data volume and increased complexity of the data augments difficult questions to the existing challenges. Classical problems in Astronomy are compounded by accumulation of astronomical volume of complex data, rendering the task of classification and interpretation incredibly laborious. The presence of noise in the data makes analysis and interpretation even more arduous. Machine learning algorithms and data analytic techniques provide the right platform for the challenges posed by these problems. A diverse range of open problem like star-galaxy separation, detection and classification of exoplanets, classification of supernovae is discussed. The focus of the paper is the applicability and efficacy of various machine l...
Job shop scheduling (JSS) is a problem which involves an assignment of a set of tasks to the mach... more Job shop scheduling (JSS) is a problem which involves an assignment of a set of tasks to the machines in a predefined sequence in order to optimize one or more objectives considering job performance measures of the system. It's a multi-objective, multi-criteria optimization problem. A job shop environment consists of 'n' jobs. Each job has a given machine routine in which some machines could be given a "miss". The JSS could be considered as a queuing system that consists of machines and jobs where each job demands a specified sequence (routing) on the machines and involves certain amount of processing time. In the dynamic JSSP, the problem size is not fixed. The jobs arrive randomly and addition of each job increases problem size exponentially. The optimal resource scheduling/re-scheduling for a mechanical manufacturing shop in reasonable computational time is achieved using particle Swarm Optimization (PSO). PSO (Particle swarm optimization) is a meta-heuristi...
Astroinformatics is a new impact area in the world of astronomy, occasionally called the final fr... more Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems. Exponential growth in the data volume and increased complexity of the data augments difficult questions to the existing challenges. Classical problems in Astronomy are compounded by accumulation of astronomical volume of complex data, rendering the task of classification and interpretation incredibly laborious. The presence of noise in the data makes analysis and interpretation even more arduous. Machine learning algorithms and data analytic techniques provide the right platform for the challenges posed by these problems. A diverse range of open problem like star-galaxy separation, detection and classification of exoplanets, classification of supernovae is discussed. The focus of the paper is the applicability and efficacy of various machine l...
Scientometrics deals with analyzing and quantifying works in science, technology, and innovation.... more Scientometrics deals with analyzing and quantifying works in science, technology, and innovation. It is a study that focuses on quality rather than quantity. The journals are evaluated against several different metrics such as the impact of the journals, scientific citation, SJR, SNIP indicators as well as the indicators used in policy and management context. The practice of using journal metrics for evaluation involves handling a large volume of data to derive useful patterns and conclusions. These metrics play an important role in the measurement and evaluation of research performance. Due to the fact that most metrics are being manipulated and abused, it becomes essential to judge and evaluate a journal by using a single metric or a reduced set of significant metrics. We propose l1-norm Singular Value Decomposition(l1SVD) to efficiently solve this problem. We evaluate our method to study the emergence of a new journal, Astronomy and Computing, by comparing it with 46000 journals ...
In this chapter, authors have discussed few machine learning techniques and their application to ... more In this chapter, authors have discussed few machine learning techniques and their application to perform the supernovae classification. Supernovae has various types, mainly categorized into two important types. Here, focus is given on the classification of Type-Ia supernova. Astronomers use Type-Ia supernovae as “standard candles” to measure distances in the Universe. Classification of supernovae is mainly a matter of concern for the astronomers in the absence of spectra. Through the application of different machine learning techniques on the data set authors have tried to check how well classification of supernovae can be performed using these techniques. Data set used is available at Riess et al. (2007) (astro-ph/0611572).
Option contracts can be valued by using the Black-Scholes equation, a partial differential equati... more Option contracts can be valued by using the Black-Scholes equation, a partial differential equation with initial conditions. An exact solution for European style options is known. The computation time and the error need to be minimized simultaneously. In this paper, the authors have solved the Black-Scholes equation by employing a reasonably accurate implicit method. Options with known analytic solutions have been evaluated. Furthermore, an overall second order accurate space and time discretization is proposed in this paper Keywords: Computational finance, implicit methods, finite differences, call/put options.
Astroinformatics is a new impact area in the world of astronomy, occasionally called the final fr... more Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems. Exponential growth in the data volume and increased complexity of the data augments difficult questions to the existing challenges. Classical problems in Astronomy are compounded by accumulation of astronomical volume of complex data, rendering the task of classification and interpretation incredibly laborious. The presence of noise in the data makes analysis and interpretation even more arduous. Machine learning algorithms and data analytic techniques provide the right platform for the challenges posed by these problems. A diverse range of open problem like star-galaxy separation, detection and classification of exoplanets, classification of supernovae is discussed. The focus of the paper is the applicability and efficacy of various machine l...
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