Asian Journal of Research in Computer Science <p style="text-align: justify;"><strong>Asian Journal of Research in Computer Science (ISSN: 2581-8260 )&nbsp;</strong>aims to publish high-quality papers in all areas of 'computer science, information technology,&nbsp;and related subjects'. The journal also encourages the submission of useful reports of negative results. This is a quality controlled,&nbsp;OPEN&nbsp;peer-reviewed, open access INTERNATIONAL journal.</p> SCIENCEDOMAIN international en-US Asian Journal of Research in Computer Science 2581-8260 Integrated Distribution Network Design through Simulation <p>Simulation is important to validate quick-response scenarios, providing an extent on how technology can effectively upgrade a process. Thus, computer simulation is a crucial aspect for supply chain management. The present paper analyses a distribution problem which involves inventory supply planning. Then, a simulation model was developed to evaluate its current performance and to provide a better operation scheme. The advances of this work extend on the modelling and simulation of distribution networks that must comply with retailers’ demands at end points.</p> Adriana Martínez- Osorio Santiago-Omar Caballero- Morales Diana Sánchez- Partida Patricia Cano- Olivos ##submission.copyrightStatement## 2020-08-11 2020-08-11 1 15 10.9734/ajrcos/2020/v6i230153 Sign Language Digit Recognition Using Different Convolutional Neural Network Model <p>An enormous number of world populations in current time are unique in that sense that they have no broad language because of the absence of their hearing capability. The people with hearing impairment have their own language called Sign Language however it is hard for understanding to general individuals [1]. Sign digits are additionally a significant piece of gesture based communication. So a machine interpreter is important to permit them to speak with general individuals. For making their language justifiable to general individual’s computer vision based arrangements are notable these days. In this exploration of work we target to develop a model based on CNN to deal with the recognition of Sign Language digits. A dataset of 10 classes is used to train (70%), validation (20%) and test (10%) of the network. We consider three different models of CNN network to train and test the accuracy of sign digit. Among the three model transfer learning based pre-trained CNN performs better with test accuracy of 92%.</p> Md. Bipul Hossain Apurba Adhikary Sultana Jahan Soheli ##submission.copyrightStatement## 2020-08-11 2020-08-11 16 24 10.9734/ajrcos/2020/v6i230154