Browsing by Author "Sharif, Omar"

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Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19
(Daffodil International University, 2021-09-16) Sharif, Omar; Islam, Md Rafiqul; Hasan, Md Zobaer; Kabir, Muhammad Ashad; Hasan, Md Emran; AlQahtani, Salman A.; Xu, Guandong
The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher’s Exact test to investigate the association between the infected groups of COVID-19 and demographical variables. Second, it exploits the ANOVA test to examine significant difference in the mean infected number of COVID-19 cases across the population density, literacy rate, and regions/divisions in Bangladesh. Third, this research predicts the number of infected cases in the epidemic peak region of Bangladesh for the year 2021. As a result, from the Fisher’s Exact test, we find a very strong significant association between the population density groups and infected groups of COVID-19. And, from the ANOVA test, we observe a significant difference in the mean infected number of COVID-19 cases across the five different population density groups. Besides, the prediction model shows that the cumulative number of infected cases would be raised to around 500,000 in the most densely region of Bangladesh, Dhaka division.
Effect of Total Population, Population Density and Weighted Population Density on the Spread of COVID-19 in Malaysia
(PLOS ONE Publications, 2023-09-27) Wong, Hui Shan; Hasan, Md Zobaer; Sharif, Omar; RahmanI, Azizur
Since November 2019, most countries across the globe have suffered from the disastrous consequences of the Covid-19 pandemic which redefined every aspect of human life. Given the inevitable spread and transmission of the virus, it is critical to acknowledge the factors that catalyse transmission of the disease. This research investigates the relation of the external demographic parameters such as total population, population density and weighted population density on the spread of Covid-19 in Malaysia. Pearson correlation and simple linear regression were utilized to identify the relation between the population-related variables and the spread of Covid-19 in Malaysia using data from 15th March 2020 to 31st March 2021. As a result, a strong positive significant correlation between the total population and Covid-19 cases was found. However, a weak positive relationship was found between the density variable (population density and weighted population density) and the spread of Covid-19. Our findings suggest that the transmission of Covid-19 during lockdown (Movement Control Order, MCO) in Malaysia was more readily explained by the demographic variable population size, than population density or weighted population density. Thus, this study could be helpful in intervention planning and managing future virus outbreaks in Malaysia.
Effectiveness Analysis of Different POS Tagging Techniques for Bangla Language
(Springer, 2022-01-01) Jahara, Fatima; Barua, Adrita; Iqbal, MD. Asif; Das, Avishek; Sharif, Omar; Hoque, Mohammed Moshiul; Sarker, Iqbal H.
Parts-of-speech (POS) tagging plays an important role in the field of natural language processing (NLP), such as—retrieval of information, machine translation, spelling check, language processing, sentiment analysis, and so on. Many works have been done for Bangla part-of-speech (POS) tagging using machine learning but the result does not enough. It is a matter of fact that not even a single effective research work has been conducted for Bangla POS tagging using deep learning due to a lack of data scarcity. Considering that our context is the Bangla POS tagging employing both machine learning and deep learning approach. In our research, we have compared some well-known supervised POS tagging approaches (Brill, HMM, unigram, bigram, trigram, and recurrent neural network) for Bangla languages. The supervised POS tagging technique requires a large number of data set to tag accurately. That is why we have used a large number of data set for POS tagging of Bangla languages, which will accept a raw Bangla text to produce a Bangla POS tagged output that can be directly used for other NLP applications. After the comparison, we have found the best tagging approach in terms of performance. Bangla is an inflectional language. That is why it is a very much tough job for grammatical categories of Bangla language. But our proposed model works well for Bangla languages.
Productivity and Efficiency Analysis Using DEA
(Management Science Letters, 2019) Sharif, Omar; Hasan, Md Zobaer; Kurniasari, Florentina; Hermawan, Atang; Gunardi, Ardi
This study evaluates the technical efficiency, productivity change of financial companies listed in the Malaysian stock exchange (Bursa Malaysia) and examines the effects of productivity change on efficiency over the period 2007–2016. Moreover, this study also concentrates on the ranking of financial companies according to their efficiency scores. Data Envelopment Analysis (DEA) is uti-lized on a Malmquist Productivity Index in order to calculate the financial companies’ efficiency scores. The results of this study show that some firms were fully efficient. The results implied that these companies were in optimal control of their inputs or resources to generate the maximum outputs. Also, the results indicate a tremendous productivity gain was mostly because of a positive shift in frontier technology and positive shift in technical efficiency. This study is significant because it helps to identify the efficient companies from the financial sector in Malaysia based on multiple inputs and outputs by using the DEA model. Common misspecification problems observed that instability of efficiency scores over productivity.
Productivity and efficiency analysis using DEA: Evidence from financial companies listed in bursa Malaysia
(2019) Sharif, Omar; Hasan, Md Zobaer; Kurniasari, Florentina; Hermawan, Atang; Gunardi, Ardi
This study evaluates the technical efficiency, productivity change of financial companies listed in the Malaysian stock exchange (Bursa Malaysia) and examines the effects of productivity change on efficiency over the period 2007–2016. Moreover, this study also concentrates on the ranking of financial companies according to their efficiency scores. Data Envelopment Analysis (DEA) is uti-lized on a Malmquist Productivity Index in order to calculate the financial companies’ efficiency scores. The results of this study show that some firms were fully efficient. The results implied that these companies were in optimal control of their inputs or resources to generate the maximum outputs. Also, the results indicate a tremendous productivity gain was mostly because of a positive shift in frontier technology and positive shift in technical efficiency. This study is significant because it helps to identify the efficient companies from the financial sector in Malaysia based on multiple inputs and outputs by using the DEA model. Common misspecification problems observed that instability of efficiency scores over productivity.
Review Analysis of Ride-Sharing Applications Using Machine Learning Approaches Bangladesh Perspective
(CRC Press, 2023-01-01) Islam, Taminul; Kundu, Arindom; Lima, Rishalatun Jannat; Hena, Most Hasna; Sharif, Omar; Rahman, Azizur; Hasan, Md Zobaer
Technology and ride-sharing services have become more accessible and convenient as a result of the growth of the Internet. Passengers increasingly focus on digital reviews to help them make purchasing decisions. Online reviews are incredibly inaccurate, as we have seen time and time again. False reviews were created to deceive customers for commercial purposes. A misleading review might have major repercussions for any organization. Organization is focused on providing good feedback to attract passengers and grow the market. It is possible that a bad review of an app would reduce interest in it. These false reviews endanger the reputation of a product. Because of this, it is critical to have a system in place for detecting fraudulent reviews. This research aims to improve the performance of machine learning models that classify fake reviews. This research aims to contribute to the authenticity of reviews using contemporary techniques and the data from ride-sharing apps. This contribution is vital and significant in a country where ride-sharing apps are becoming more convenient and useful. We created a fresh dataset using different apps-based reviews from the current Bangladesh ride-sharing users’ review section. In this work Decision tree, Random Forest, Gradient Boosting, AdaBoost, and Bi-LSTM machine learning approaches were implemented to get the best performance on our dataset. After creating and running the model, Bidirectional Long Short-Term Memory (Bi-LSTM) achieved 85% best accuracy and 85.0 F1 score with training data rather than other machine learning algorithms.