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> en-US (Asian Journal of Research in Computer Science) (Asian Journal of Research in Computer Science) Mon, 11 May 2020 11:34:27 +0000 OJS 60 Forecasting of Campus Placement for Students Using Ensemble Voting Classifier <p>Campus placement is a measure of students’ performance in a course. A forecasting method is proposed in this paper to predict possible campus placement of any institution. Data mining and knowledge discovery processes on academic career of students are applied. Supervised machine learning technique based classifiers are used for achieving this process. It uses an ensemble approach based voting classifier for choosing best classifier models to achieve better result over other classifiers. Experimental results have indicated 86.05% accuracy of ensemble based approach which is significantly better over other classifiers.</p> Shawni Dutta, Samir Kumar Bandyopadhyay ##submission.copyrightStatement## Mon, 11 May 2020 00:00:00 +0000 Determination of Customers’ Arrival and Service Patterns for a Restaurant Food Serving Process <p>Restaurant industry has become one of the most profitable industries in the world where incessant long waiting time may not only lead to customers’ dissatisfactions but also facilitate loosing of customers to other competitors. In this paper, in order to determine customers’ arrival patterns and service patterns which are critical factors in determining customers’ queue length and waiting time for a given restaurant, the food serving process employed at a named International Institute Restaurant (IIR), Ibadan, Nigeria, was used as a case study. Data were collected on customers’ number, customers’ inter arrival time and service time from Monday to Friday for a week. The data were analyzed statistically using the ARENA Input Analyzer to determine the arrival patterns and service patterns of customers within five working days of the week (Monday, Tuesday, Wednesday, Thursday and Friday). The results of the data analysis revealed that the arrival times of customers who patronized the IIR on Monday and Tuesday followed a Beta distribution. Furthermore, the arrival times of customers patronizing the IIR on Wednesday and Thursday followed a Weibull distribution while that of Friday assumed an Erlang distribution. Besides, the results of the data analysis revealed that the service times at IIR on Monday and Tuesday followed a Lognormal distribution. Beta, Lognormal and Weibull distributions were recorded in respect of service times characterizing the IIR on Wednesday, Thursday and Friday, respectively.</p> Latifat A. Odeniyi, Rafiu A. Ganiyu, Elijah O. Omidiora, Stephen O. Olabiyisi ##submission.copyrightStatement## Thu, 28 May 2020 00:00:00 +0000 CNN-LSTM Model for Verifying Predictions of Covid-19 Cases <p>COVID-19 disease came to earth in December 2019 in Wuhan. It is increasing exponentially throughout the world and affected an enormous number of human beings. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. Clinical Doctors have been working on it 24 hours in the entire world. These doctors are testing whether the particular human has been affected with the disease using testing kit and other related process. Researchers have been working day-night for developing vaccine for the disease. Since the rate of affected people is so high, it is difficult for clinical doctors to check such a large number of coronavirus detected humans within reasonable time.</p> <p>This paper attempts to use Machine Learning Approach to build up model which will help clinical doctors for verification of disease within short period of time and also the paper attempts to predict growth of the disease in near future in the world. Two models were used for achieving this purpose- One is based on Convolutional Neural Network model where as another one consists of Convolutional Neural Network and Recurrent Neural Network. These two models are evaluated and compared for verifying the predicted result with respect to the original one. Experimental results indicate that the combined CNN-LSTM approach outperforms well over the other model.</p> Shawni Dutta, Samir Kumar Bandyopadhyay, Tai-Hoon Kim ##submission.copyrightStatement## Fri, 29 May 2020 00:00:00 +0000