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Acharya, Sushil; Sadananda, R.

doi:10.1023/a:1009603129257

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…[44] develop Software Self-Organizing Map (SSOM) that is based on SOM, to classify software modules. [45] proposed a Software Reuse Methodology based on SOM to promote UNIX commands software components reuse. [36] discussed the applicability of SOM in analysis and visualization of software modules, based on its metrics values, to detect software quality measures.…”

Section: Self-organizing Map (Som)mentioning

confidence: 99%

Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

Hudaib¹,

Huneiti²,

Othman³

2016

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Due to rapid development in software industry, it was necessary to reduce time and efforts in the software development process. Software Reusability is an important measure that can be applied to improve software development and software quality. Reusability reduces time, effort, errors, and hence the overall cost of the development process. Reusability prediction models are established in the early stage of the system development cycle to support an early reusability assessment. In Object-Oriented systems, Reusability of software components (classes) can be obtained by investigating its metrics values. Analyzing software metric values can help to avoid developing components from scratch. In this paper, we use Chidamber and Kemerer (CK) metrics suite in order to identify the reuse level of object-oriented classes. Self-Organizing Map (SOM) was used to cluster datasets of CK metrics values that were extracted from three different java-based systems. The goal was to find the relationship between CK metrics values and the reusability level of the class. The reusability level of the class was classified into three main categorizes (High Reusable, Medium Reusable and Low Reusable). The clustering was based on metrics threshold values that were used to achieve the experiments. The proposed methodology succeeds in classifying classes to their reusability level (High Reusable, Medium Reusable and Low Reusable). The experiments show how SOM can be applied on software CK metrics with different sizes of SOM grids to provide different levels of metrics details. The results show that Depth of Inheritance Tree (DIT) and Number of Children (NOC) metrics dominated the clustering process, so these two metrics were discarded from the experiments to achieve a successful clustering. The most efficient SOM topology [2 × 2] grid size is used to predict the reusability of classes.

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Section: Self-organizing Map (Som)mentioning

confidence: 99%

Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

Hudaib¹,

Huneiti²,

Othman³

2016

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“…Marma's are the specific locations on the human body, which when traumatized, will either surely cause death or permanent deformity with extreme pain [5,6]. Marmas are the juncture of Mamsa (muscular tissue), Sira (blood vessels), Snayu (nerve or tendons or ligament), Asthi (bone tissue) and Sandhi (joints).…”

Section: Introductionmentioning

confidence: 99%

Comprehend of Patho-physiological Changes and Consequential Prognosis in Marmabhighata (Injury to Marma)

Banjare¹,

Meher²,

Baghel³

et al. 2019

IJSRBS

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Marma" literary communicates the sense of vital parts of the body. The science of Marma is an unique approach of Ayurveda and established to help the practice of surgery. There are total 107 Marma"s in the human body. These are classified into five types according to location, structures involved, effect and consequences or prognosis of the trauma over there. Any type of Marmabhighata (injury to Marma) is likely to produce subjective manifestations governed by the extent and the nature of injuries to the Marmas. Tridosha in the body have their Panchabhautik origin. The Pancha mahabhautas are the main constituting component of Tridosha such that, the consequence of traumatised Marma has got direct bearing on the predominance of Tridoshas over there. Any trauma to these Marmas is likely to provoke both the Shareerik and Manasik dosha which thereby affects the body (Sharira) and Mind (Mana). So, as the base (body and Mind) gets affected, the Jeevatma or Bhootatma itself ceases to exit. This changes in Marma after injury such as deformities and delayed healing mainly depends upon the consequences that occur due to patho-physiological changes over Marma.

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Discovering Common Features in Software Code Using Self-Organizing Maps

Chan

Spracklen

2000

The State of the Art in Computational Intelligence

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Untitled

Cited by 4 publications

References 7 publications

Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

Software Reusability Classification and Predication Using Self-Organizing Map (SOM)

Comprehend of Patho-physiological Changes and Consequential Prognosis in Marmabhighata (Injury to Marma)

Discovering Common Features in Software Code Using Self-Organizing Maps

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